The Curious Wavefunction

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Computational chemist doing drug design and discovery. Specific interests include virtual screening, docking, free energy calculations and molecular dynamics. General interests include biochemistry, pharmacology, structural biology, organic chemistry and physics.

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  • September 17, 2010
  • 01:37 PM
  • 1,260 views

'SAR by C13 NMR'

by The Curious Wavefunction in The Curious Wavefunction

The biggest utility of NMR spectroscopy in drug discovery is in assessing three things; whether a particular ligand binds to a protein, what site on the protein it binds, and what parts of the ligand interact with the protein. Over the last few years a powerful technique named ‘SAR by NMR’ has emerged which is now widely used in ligand screening. In this technique, changes in the resonances of ligand and protein protons are observed to pinpoint the ligand binding site and corresponding residues. Generally when a ligand binds to a protein, both its and the protein’s rotational correlation time decreases; the result is a broadening of signals in the spectrum which can be used to detect ligand binding. One of the most effective methods in this general area is Saturation Transfer Difference (STD) spectroscopy. As the name indicates, it hinges on the transfer of magnetization between protein and ligand; the resulting decrease in intensity of ligand signals can provide valuable information about proximity of ligand protons with specific protein residues.But these kinds of techniques suffer from some drawbacks. One straightforward drawback is that signals from protein and ligand may simply overlap. Secondly, the broadening may be so much as to virtually make the signals disappear. Thirdly from a practical perspective, it is hard to get sufficient amounts of N15-labeled protein (usually obtained by growing bacteria on a N15-rich source and then purifying the proteins of interest).To circumvent some of these problems, a team at Abbott Laboratories has come up with a neat and relatively simple method which they call ‘labeled ligand displacement’. The method involves synthesizing a protein-binding probe that has been selectively labeled with C13. Protein binding broadens and diminishes the signals of this probe. However, when a high-affinity ligand is then added, it displaces the probe and we get recovery of the C13 signals. The authors illustrate this paradigm with several proteins of pharmaceutical interest, including heat-shock protein and carbonic anhydrase.The method is relatively simple. For one thing, using a commercially available C13-labeled building block for synthesizing a ligand is easier than obtaining a N15-labeled protein. The biggest merit of the method though is the fact that it hinges on C13 signals very specific to the probe; thus there is no complicating overlap of signals. And finally, the ligand seems to be general enough to be applied to any protein. Only time will tell how much it is utilized, but for now it seems like a neat addition to the arsenal of NMR methods for studying protein-ligand interactions.Swann, S., Song, D., Sun, C., Hajduk, P., & Petros, A. (2010). Labeled Ligand Displacement: Extending NMR-Based Screening of Protein Targets ACS Medicinal Chemistry Letters, 1 (6), 295-299 DOI: 10.1021/ml1000849... Read more »

Swann, S., Song, D., Sun, C., Hajduk, P., & Petros, A. (2010) Labeled Ligand Displacement: Extending NMR-Based Screening of Protein Targets. ACS Medicinal Chemistry Letters, 1(6), 295-299. DOI: 10.1021/ml1000849  

  • April 14, 2009
  • 04:36 PM
  • 1,166 views

Much ado about protein dynamics

by The Curious Wavefunction in The Curious Wavefunction

Let me alert you, in case you haven't noticed, to the latest issue of Science which is a special issue on protein dynamics. There is much of merit here, but this article is especially relevant to drug discovery. It talks about the interaction of small molecules and how it reshapes the energy landscape of protein conformational motion. One of the most useful ways of thinking about small molecule-protein interactions is to visualize a protein that fluctuates between several conformational states which are in equilibrium. A small molecule can inhibit the protein by preferentially stabilizing one of these states. The article illustrates this concept with several examples, most notably inhibition of kinases. Many kinases exist in an inactive and active state and kinase inhibitors stabilize and block one of these states. Such conformational trapping can also reduce mobility of the protein. The article also describes how certain kinase inhibitors such as imatinib and nilotinib trap the kinase in the inactive state while others such as dasatinib trap it in an active state. Although all three of these are classified as ATP-competitive inhibitors, dasatinib blocks the Abl-Bcr kinase by effecting an allosteric movement of a particular loop. Allosteric inhibitors of kinases are of value since they won't target the highly conserved ATP-binding site, thus reducing problems with selectivity. But allosteric targeting is difficult since many times it involves targeting shallow, poorly defined sites including those involved in protein-protein interactions. HTS campaigns aimed at disrupting P-P interactions usually give very poor results. However, recent tools and especially NMR with labeled residues may improve the detection of weakly binding molecules that may be missed in assays (where the limit is usually 30 µM). HSQC spectra are generally taken of the protein, with and without the inhibitor, and changes in residue resonances can give an indication of conformational changes.In any case, this article and the others are worth reading. Basically it seems that the remodeling of energy landscapes of proteins by either small molecules or other signals is a concept acquiring central traction. Such a concept could essentially tie together the dual problems of protein folding and inhibiting proteins with small molecules.Reference:Lee, G., & Craik, C. (2009). Trapping Moving Targets with Small Molecules Science, 324 (5924), 213-215 DOI: 10.1126/science.1169378... Read more »

Lee, G., & Craik, C. (2009) Trapping Moving Targets with Small Molecules. Science, 324(5924), 213-215. DOI: 10.1126/science.1169378  

  • April 2, 2010
  • 02:44 PM
  • 1,145 views

Will virtual screening ever work?

by The Curious Wavefunction in The Curious Wavefunction

Virtual screening (VS), wherein a large number of compounds are screened, either by docking against a protein target of interest or by similarity searching against a known active, is one of the most popular computational techniques in drug discovery. The goal of VS is to complement high-throughput screening (HTS) and the ideal goal is to at least partly substitute HTS in finding new hits.But this goal is still far from being achieved. VS still has to make a significant contribution in the discovery of a major drug and typical hit rates range from a few tenths of a percent to perhaps a percent or two. VS has been intensively investigated for more than a decade. What do we know about its limitations, and where do we go from here?Gisbert Schneider of ETH Zurich has some thoughts on VS in a recent review. Success in VS ultimately boils down to understanding the detailed structure and dynamics of protein-ligand complexes, a goal that we are still miles away from. We still struggle to realistically include entropy in any calculation, and we are still not completely clear about the role that buried water molecules play in dictating ligand binding. Plus we cannot yet take allosteric binding properly into account, let alone more complex interactions like protein-protein interactions. Thus, maybe, as pointed out in a past post and article, the correct question to ask would be the "anti-question", namely, why does VS work at all in spite of this supposedly woeful lack of understanding?First of all it is important to know what VS can do well and what it can't. As the article notes, VS is still best for negative selection, that is for weeding out inactive molecules which are bad binders. One of the goals of VS is also to duplicate the correct protein-bound x-ray conformation of the ligand, and in this endeavor (termed pose prediction) VS seems to be succeeding much better than in the ultimate goal which is to rank ligand binding to a protein in order of free energy of binding. As the article notes, the true binding interaction energy landscape for a protein might be more of a plateau; thus there may be a variety of protein-ligand contacts corresponding to a 'good' solution, rather than a global optimum. Plus, one may end up modeling details that are not very relevant to the gist of the ligand binding event; in such a case productive contacts can be preserved with no great sacrifice of qualitative prediction.Nonetheless, tiny details can sometimes radically shift the balance. No wonder that VS has been heavily dependent on the target rather than on the computational algorithm. Nature continues to throw up surprises as protein entropy, hydrophobic interactions and subtle behavior of water molecules continue to be uncovered as powerful forces operating for a particular protein-ligand complex.In the end, modeling the dynamic behavior of macromolecules is an absolute must for lending general utility to VS campaigns. In the absence of adequate modeling of entropy, it may be wise from a practical viewpoint to aim for ligand chemotypes whose binding is dominated more by enthalpic effects. It's interesting to note a past set of studies which I had highlighted which suggested that it's really the enthalpy rather than entropy which is rendered favorable in a drug discovery project as one proceeds from hit to lead.Finally, the author makes an appeal to fields spread far and wide to come up with ideas that could be applied in VS and related approaches. It is likely that while incremental improvements will continue to be made in the field through better understanding of protein-ligand interactions, only a novel idea would revolutionize the field. Thus insights could possibly come from unlikely quarters, including complexity theory, non linear dynamics, other aspects of physics and even engineering and architecture. How this might happen is not at all clear, but it definitely calls for more multidisciplinary work and for more scientists from diverse fields to become interested in the problem. After all VS is fundamentally an optimization problem, one of locating the optimal ligand energetic minimum in a multidimensional landscape of protein, ligand, ions and solvent. I can't see why any mathematician, physicist or engineer worth his or her salt won't find it exciting.Schneider, G. (2010). Virtual screening: an endless staircase? Nature Reviews Drug Discovery, 9 (4), 273-276 DOI: 10.1038/nrd3139... Read more »

Schneider, G. (2010) Virtual screening: an endless staircase?. Nature Reviews Drug Discovery, 9(4), 273-276. DOI: 10.1038/nrd3139  

  • February 24, 2010
  • 04:24 PM
  • 1,102 views

Intramolecular hydrogen bonds in medicinal chemistry

by The Curious Wavefunction in The Curious Wavefunction

In the latest issue of J. Med. Chem., researchers from Roche in Basel have a nice analysis of intramolecular hydrogen bonds in druglike molecules. An internal hydrogen bond can intuitively confer an important property on a drug; it can make the drug more lipophilic by shielding the hydrogen bonding groups from solvent and render it more lipophilic. Thus, intramolecular h-bonding has emerged as a useful strategy in improving membrane permeability.The authors look at both the CSD and the PDB and do a reasonably exhaustive analysis of HB motifs of all ligands in these two important databases. They find that internal hydrogen bonds in six membered rings are most common, followed by five and then 7 and 8 membered rings. The HBs in five membered rings are around the edge of definitions for hydrogen bond formation; this indicates the difficulty in defining a HB based on strict geometric criteria. The strongest HBs in six membered rings are, not surprisingly, those between NH and C=O groups, followed by NH and sp2 N groups. In fact nitrogen acceptors for HBs seem to be almost as common as carbonyl acceptors. The authors also find that particularly strong HBs exist for donating groups that are part of a resonance substructure such as an amide linkage (resonance assisted hydrogen bonds). Percentages of hydrogen bonded forms for various structural motifs are noted which could be useful in deliberately designing in such HBs.After looking at various features of these HBs in several ring sizes including length, angle and torsional dependence, the authors also analyze the effects of internal HBs on membrane permeability. For this they synthesize four pairs of eight model compounds in which each pair consists of two compounds, one able to form a HB and the other one unable to (usually where the donor H is replaced with a methyl). They then calculate parameters like PAMPA permeability, logD and clogP which can be indicators of lipophilicity and permeability. They discover that because of the fragment-based rules used in calculating clogP values, computer programs cannot often predict the increase in lipophilicity resulting from internal HBs. This is a valuable finding that could be translated into a correction applied by computer programs calculating clogP.The authors find that although internal hydrogen bonds do seem to improve logD, the relationship is not completely straightforward. The hydrogen bonding compounds can exist in closed (h-bonded) and open conformations. They find that only if the open form is a relatively low energy conformation can the molecule readily adopt the closed conformation with a hydrogen bond. They indicate how quantum chemical calculations can be useful for qualitatively rationalizing such energy differences; in one case for instance, the open form was too high in energy according to such calculations and therefore the other form was not easily populated. Because of the sharp dependence of equilibrium populations on free energy differences, I would think that the open form should not be more than about 1.8 kcal/mol higher in energy compared to the closed form (when the population of the former would be about 5%).This overview should be useful in designing specific internal hydrogen bonds for use in drug design programs.Kuhn, B., Mohr, P., & Stahl, M. (2010). Intramolecular Hydrogen Bonding in Medicinal Chemistry Journal of Medicinal Chemistry DOI: 10.1021/jm100087s... Read more »

Kuhn, B., Mohr, P., & Stahl, M. (2010) Intramolecular Hydrogen Bonding in Medicinal Chemistry. Journal of Medicinal Chemistry, 2147483647. DOI: 10.1021/jm100087s  

  • October 27, 2010
  • 02:30 PM
  • 1,060 views

Functional selectivity: Nature's Bach concerto

by The Curious Wavefunction in The Curious Wavefunction

One of the great things about Bach’s organ music is how changes of a single note in a whole pattern can have rather dramatic effects on the sound. A unique and potentially very important similar phenomenon has been discovered recently in the area of GPCR research.The understanding of the basic process by which GPCRs transmit signals from the cell exterior to the interior has seen remarkable advances in the last three decades, but much still remains to be deciphered. Our knowledge of signaling responses until now hinged on the action of agonists and antagonists. Central to this knowledge was the concept of ‘intrinsic efficacy’; according to this concept, there was no difference between two full agonists for instance, and both of them would produce the same response irrespective of the situation.But this understanding failed to explain some observations. For instance, a full agonist would function as a partial agonist and even as an inverse agonist under different circumstances. Several such observations, especially in the context of GPCRs involved in neurotransmission, have forced a re-evaluation of the concept of intrinsic efficacy and led to an integrated formulation of a fascinating concept called ‘functional selectivity’.So what is functional selectivity? It is the phenomenon by which the same kind of ligand (agonist, antagonist etc.) can modulate different signaling pathways activated by a single GPCR, leading to different physiological responses. Functional selectivity thus opens up a whole new method of modifying GPCR signaling in complex ways. It comprises a new layer of complexity and control that biological systems enforce at the molecular level to engage in complex signaling and homeostasis. Functional selectivity can allow the ‘tuning’ of ligands on a continuum scale of properties, from agonism to inverse agonism. In addition it can tightly regulate the strength of the particular property. It is what allows GPCRs to function as rheostats rather than as binary switches and allows them to exercise a fine layer of biological control and discrimination.Functional selectivity is not just of academic interest. It can have clinical significance. Probably most tantalizingly, it may be one of the holy grails of pharmacology that allows us to separate the beneficial and harmful effects of a drug, leading to Paul Ehrlich’s ‘magic bullet’. Until now, side-effects have been predominantly thought to result from the lack of subtype-specificity of drugs. For instance, morphine’s side effects are thought to result from its activation of the μ-opioid receptor. But functional selectivity could provide a totally new avenue for explaining and possibly mitigating side-effects of drugs. For instance, consider the dopamine receptor agonist ropinirole, used in the treatment of Parkinson’s disease. There are several D-receptor agonists and just like them ropinirole interacts with several receptor subtypes. But unlike many of these, ropinirole does not demonstrate the dangerous side-effect named valvulopathy, a weakening of the heart valves that makes them stiff and inflamed. This can be a potentially life-threatening condition that seems to be facilitated by several dopamine agonists, but not ropinirole. The cause seems to be becoming clear only now; ropinirole is a functionally selective ligand that activates a certain pattern of second messenger pathways that is different from those activated by other agonists. Somehow this pattern of pathways is responsible for reduced valvulopathy.Let’s go back to the organ/piano analogy to gauge the significance of such control. The sound produced by a piano depends on two variables- the exact identities of the keys pressed, and the intensity (how hard or softly you press them). The second variable can be as important as the first since a pressing a key particularly hard can drown out other notes and influence the very nature of the sound. The analogy to functional selectivity would be in looking at the keys themselves as different signaling pathways and the intensity of the notes as the strength of the pathways. Now, if one ligand binding to a single GPCR is able to activate a specific combination of these pathways, each with its own strengths, think of the permutations and combinations you could get from a set of even a dozen pathways- an astonishing number. Thus, functional selectivity could be the key that unlocks the puzzle of how one ligand can put into motion such a complex set of signaling events and physiological responses. One ligand- one receptor- several pathways with differing strengths. An added variable is the concentration of certain second messengers in a particular environment or cell type, which could add even more combinations. This picture could go a long way toward explaining how we can get such complex signaling in the brain from just a few ligands like dopamine, serotonin and histamine. And as described above, it also provides a fascinating direction - along with control of subtype selectivity (a much more well known and accepted cause) - for developing therapies that demonstrate all the good stuff without the bad stuff.The basic foundation of functional selectivity is as tantalizing. Whatever the reasons for the phenomenon, the proximal cause for it has to concern the stabilization of different protein conformations by the same kind of ligands. Unravel these protein conformations and you would make significant inroads into unraveling functional selectivity. If you come to think of it, this principle is not too different from the current model of conformational selection used in explaining the action of agonists and antagonists in general, which involves the stabilization of certain conformations by specific molecules.Nature never ceases to amaze. As we plumb its mysteries further, it reveals deeper, more subtle and finer layers of control and discrimination that allows it to generate profound complexity starting from some relatively simple events like the binding of a disarmingly simple molecule like adrenaline to a protein. And combined with the action of several proteins, the concerto turns into a symphony. We have been privileged to be in the audience.Mailman, R., & Murthy, V. (2010). Ligand functional selectivity advances our understanding of drug mechanisms and drug discovery Neuropsychopharmacology, 35 (1), 345-346 DOI: 10.1038/npp.2009.117Kelly, E., Bailey, C., & Henderson, G. (2009). Agonist-selective mechanisms of GPCR desensitization British Journal of Pharmacology, 153 (S1) DOI: 10.1038/sj.bjp.0707604... Read more »

Mailman, R., & Murthy, V. (2010) Ligand functional selectivity advances our understanding of drug mechanisms and drug discovery. Neuropsychopharmacology, 35(1), 345-346. DOI: 10.1038/npp.2009.117  

Kelly, E., Bailey, C., & Henderson, G. (2009) Agonist-selective mechanisms of GPCR desensitization. British Journal of Pharmacology, 153(S1). DOI: 10.1038/sj.bjp.0707604  

  • June 17, 2009
  • 10:04 PM
  • 1,035 views

So what exactly are force fields good for?

by The Curious Wavefunction in The Curious Wavefunction

Sue Storm tries hard to use her favorite force field to counter the 1 kcal/mol barrierEvery once in a while there is a study asking what method X (X = docking, free energy calculations, molecular dynamics, force fields etc.) is good for. Such studies can be useful to take stock of a particular paradigm. So the question that Jonathan Goodman and his group ask in this paper is "Are force fields good for reproducing non-bonded interactions, especially hydrogen bonding, pi-stacking and dispersion?". He and his group compare very high-level quantum chemical ab initio data with data obtained from the most commonly used force fields, namely MM2*, MM3*, MMFFs, OPLS-2005 etc. The ab initio data used is from Pavel Hobza who has almost consummately published on these methods. The question is; how well do the force fields do compared to the gold standard? The answer is necessarily incomplete and complex and again raises many interesting questions about the enigmatic role of hydrogen bonding in chemical and biological systems.The complexes studied include purely pi-stacked complexes, purely hydrogen bonded complexes and mixed complexes where both interactions play roles. Typical examples include alcohol-amide complexes, water oligomers and of course, the classic stacked and hydrogen bonded DNA nucleoside bases. The parameters that the authors looked at were geometries and energies, both of optimized complexes as well as crystal structures.The results are perhaps not too surprising; the more recent OPLS-2005 and MMFFs are probably the best in reproducing known geometries and energies while MM2* and MM3* don't perform that well in general. As noted in some other studies, at least some of the results for MMFFs and OPLS compare with those obtained with high-level ab initio calculations, thus indicating the value of these cost-effective methods for geometry optimization and energy determination (let's ignore for a moment that solvation models in ab initio methods make even these less than perfect). What is more important though is that all the force fields are generally not good for reproducing hydrogen bonded systems compared to systems where dispersion, stacking etc. are the key players. This is partly an indication of the tricky events including long-range solvation which play an important role in h-bond formation. But what is interesting is that the methods underestimate the energetics of hydrogen bonds. While I am a little puzzled by this, one of the explanations that comes to my mind regarding this curious fact is that in real systems, h-bonding is a cooperative interaction. An h-bond can pay for loss of entropy, thus making the overall free energy of the next h-bond more favourable. Of course force fields don't calculate free energy, but to a first approximation we can probably assume that the enthalpy and free energy are similar for these simple systems. To be honest, because of the complex nature of long-range dispersion interactions I would have assumed that the force fields would be worse in modeling these. I frankly don't understand why they work better for such interactions but it's an interesting observation.But now for some general thoughts; it's always worth remembering that for molecules like proteins which are stabilized by h-bonds, the h-bonds when formed are simply swapped for similar bonds with water, thus making a relatively insubstantial contribution to protein stability. It is the large number of such interactions that can tip the balance for a protein, but the real driving force is now universally recognized as the hydrophobic effect and the burial of non-polar groups. Calculations such as those above indicate that because of the fine-tuning of h-bonds that proteins often use to achieve stability, force fields have some way to go in predicting tiny energy differences. It is still a great challenge to model the sub-angstrom geometry optimization of h-bonds that biopolymers achieve. But force fields are hardly unique in not being able to do this; so are other methods which are still trying to break the 1 kcal/mol barrier. Ironically in this study, the mean unsigned error when the hydrogen-bonded complexes are included is about 1 kcal/mol.So are force fields good for anything at all? The short answer is yes, exemplified by the massive number of publications that regularly use force fields as well as the substantial number of people in academia and industry studying them. Obviously people think they are important, otherwise so many common programs doing everything from protein folding to drug-protein interactions would not have relied on them. I have had reasonable experience with force fields and have always kept in mind a couple of things about them that are worth reiterating:1. Force fields are usually good at reproducing geometries, and best for reproducing sterics.2. Force fields are usually not so good at reproducing energies since energy estimation is a function of the special parameterization and convergence criteria unique to every force field (As the Zen master says, "What the answer is depends on what question you ask"). However, relative conformational energies using a single force field for instance may be useful.3. As a corollary, force fields can be pretty poor for dealing with molecules having a large number of polar functional groups. While this means that peptides are hard to model, modeling of peptides has also been mitigated by the fact that unlike small molecules, the chemistry to be parameterized is limited.3. Many times the real problem is not with force fields per se but with the accompanying implicit solvation models. Admirable effort has been expended in developing these models but to be honest we still don't understand enough about that enigmatic solvent named water to do a satisfactory job. We are just scratching the surface when it comes to modeling things like solvent entropy for instance.If you are following the field's developments, you also see an engaging and ongoing debate that pits the "science first" camp against the "parameterization first" camp. The science first camp disapproves of the other camp's efforts to improve their force fields simply by adding more parameters and optimizing against experiment; to them it is much more important to meticulously improve the methodology by incorporating as much real science as possible. The parameterization first camp argues that statistical methods have their honored place in the annals of science and that getting results fast and efficiently is important for application-oriented scientists like drug discovery people. I believe that as in other matters, both sides are right. It is an uncomfortable feeling when you don't truly understand the science behind a method and yet the method works, but at the same time it is important to have a well-parameterized and tested model that could help you in a practical sense, even if incompletely understood.As with everything else, finally it is an astute application of force fields that takes into account their strengths and limitations which will lead to productive results. One of the most interesting things about doing science involves weighing the pros and cons of methods, techniques and algorithms and deciding what judicious combination would provide the best answer and why. It may not always work, but it could keep us from getting seduced by the dark side of the force (field)Paton, R., & Goodman, J. (2009). Hydrogen Bonding and π-Stacking: How Reliable are Force Fields? A Critical Evaluation of Force Field Descriptions of Nonbonded Interactions Journal of Chemical Information and Modeling, 49 (4), 944-955 DOI: 10.1021/ci900009f... Read more »

Paton, R., & Goodman, J. (2009) Hydrogen Bonding and π-Stacking: How Reliable are Force Fields? A Critical Evaluation of Force Field Descriptions of Nonbonded Interactions. Journal of Chemical Information and Modeling, 49(4), 944-955. DOI: 10.1021/ci900009f  

  • February 1, 2009
  • 10:24 AM
  • 1,034 views

A Drug Design Class Act: ∆S or ∆H?

by The Curious Wavefunction in The Curious Wavefunction

One of the most important relations in all of chemistry is the free energy relation ∆G = ∆H - T∆S. Tuning the potency of a ligand binding to a protein involves a fine balance between optimizing both entropy and enthalpy. In his review on the role of these two variables, Johns Hopkins's Ernesto Freire makes a very interesting observation; that for a given series of progressively improved set of drugs binding to a given protein, it's the enthalpy that becomes more and more favourable while the entropy pretty much starts out being favourable. Thus, it's enthalpy optimization that's the harder part in drug design.Freire illustrates his observation by looking at two sets of famous drugs- HIV protease inhibitors and cholesterol-lowering statins. For example if we look at the progression of the protease inhibitors over time starting from about 1995 when we had good but not highly-potent drugs to about 2006 when we had greatly improved drugs, we essentially find the striking fact that while ∆S for drug binding was favourable to begin with in 1995, it's really a great improvement in ∆H that has made the drugs more potent through the years.Thus Freire concludes that it's improving enthalpy of binding and not entropy that's the hard part in improving potency. The argument is fairly straightforward; favourable entropy depends on desolvation of the drug to get rid of water molecules, as well as displacement of water molecules from the active site by the drug. Both these events are more or less favourable for most drugs as most drugs are reasonably hydrophobic. Thus, high hydrophobicity can by itself confer favourable entropy. But tuning enthalpy is much harder for two reasons; first of all because as Freire notes, it is not possible yet to engineer hydrogen bonds to the tenths of angstroms needed for optimum energetic gain. Secondly, even a slight sub-optimal feature in hydrogen bonding can tip the scales because remember, the hydrogen bonds that the ligand forms with the protein are simply replacing strong hydrogen bonds that were previously formed with the surrounding water. Thus the new hydrogen bonds may not be as strong, and indeed may even be unfavourable with respect to the previous ones. From past discussions, recall that a mere 1.4 kcal/change in free energy from unfavourable hydrogen bonding can lead to a 10-fold loss in binding affinity ("Life is a 3 kcal/mol denizen"). Clearly one has to be careful while designing ligands to form hydrogen bonds. It's perhaps not surprising that hydrophobic effect-induced binding is the main stabilizing factor in both protein folding and ligand design; both man and nature apparently find it much easier to get binding affinity from hydrophobic interactions than polar ones.These facts are borne out when we see the change in these two thermodynamic variables in the optimization of HIV protease inhibitors over time where, while ∆S is favourable to begin with, ∆H is actually positive and unfavourable at the beginning. Let's also not forget that potency is only the beginning for a ligand that needs to traverse many obstacles in order to be converted into a drug, including ADME-T optimization. Any one of these further modifications can modify the one or both of the thermodynamic variables and change the initially optimized potency.However, since optimizing enthalpy is both more challenging and more important, Freire prescribes experimentally measuring the two variables as much as possible for every stage of the drug design process starting as early as possible. This can be best accomplished through Isothermal Titration Calorimetry (ITC) which can also shed light on enthalpy-entropy compensation, an important process in the binding of ligands.Thermodynamic optimization is a tightrope walking act, a prelude to the ultimate juggling game that is drug design. Not only are you trying to balance several variables at once, but some of them are trying to actually pull you down. But simple strategies can quell at least some of these nefarious efforts and reward you with a fine ligand, if not drug.Reference:E Freire (2008). Do enthalpy and entropy distinguish first in class from best in class? Drug Discovery Today, 13 (19-20), 869-874 DOI: 10.1016/j.drudis.2008.07.005... Read more »

E Freire. (2008) Do enthalpy and entropy distinguish first in class from best in class?. Drug Discovery Today, 13(19-20), 869-874. DOI: 10.1016/j.drudis.2008.07.005  

  • March 29, 2010
  • 10:03 AM
  • 1,013 views

How useful is cheminformatics in drug discovery?

by The Curious Wavefunction in The Curious Wavefunction

Just like bioinformatics, cheminformatics has come into its own an independent framework and tool for drug design. As a measure of the field's independence and importance, consider that at least five journals primarily dedicated to it have emerged in the last couple of years, and 15000 articles on it have been published since 2003.But how useful is it in drug discovery? The answer, just like for other approaches and technologies, is that it depends. For calculating and analyzing some properties it is more useful than for others. A group at Abbott summarizes the current knowledge of cheminformatics approaches as applied to various parameters.To do this the group utilizes a useful classification made (in)famous by ex-Secretary of Defense Donald Rumsfeld (Rumsfeld et al., J. Improb. Res. 2002). This is the classification of knowledge and facts into 'known knowns', 'unknown knowns', 'known unknowns' and 'unknown unknowns'.The 'known known' category of properties calculated by cheminformatics consists of those that we think we have a very accurate handle on and that are easy to calculate. These include molecular weight, substructure (SS) searches, and ligand efficiency. Molecular weight can be easily calculated, and a variety of studies have indicated that high MW generally impacts drug discovery negatively. Thus the general thrust is on keeping your compounds small. Ligand efficiency is calculated as the free energy of binding per heavy atom. Medicinal chemists are more familiar with IC50 than free energy. Since IC50s are usually easy to measure and the number of heavy atoms are of course known, ligand efficiency would be a 'known known'. However the caveat is that IC50 is not the same as in vivo activity, so biochemical potency in terms of ligand efficiency might be a very different kettle of fish. Lastly, substructure searches can be carried out easily by many computer programs. Such searches are usually used when a compound having a substructure similar to one that is known is sought. The problem with SS searches arises when the decision on which SS to search becomes subjective. SS is also valuably used when certain SSs are to be avoided. In general SS searching belongs with the 'known known' category because of its ease, but subjective interpretations can render this more fuzzy.In the 'unknown knowns' category lie an interesting set of properties; those which we think we know how to calculate but which sometimes look deceptively simple and are often subject to overconfidence in calculation. The first property in this category is one of the most important ones in drug design; logP, which is regarded to be a measure of the lipophilicity of the compound, a key parameter dictating absorption, bioavailability, and partitioning of drugs between membranes and body fluids. Several programs can calculate logP. However, as the article notes, a recent study of no less than 30 such programs located a mean error in logP calculation of 1 log unit, which means that some programs would do much worse. Thus calculation of logP, just like other computational techniques, crucially depends on the method used to calculate it. The caveat is that absolute cutoffs for logP values in library design of compounds searches might mislead, but many programs seem to do a fairly good job in producing trends. Solubility is another parameter that is notoriously difficult to calculate, especially since its calculation hinges on calculation of pKa values. pKa values in turn again are very method-dependent, and trouble arises especially for charged compounds, which include most drugs. Medicinal chemists are understandably suspicious of theoretical solubility prediction, but as for logP, such calculations may at least be used for quick estimation of qualitative trends. Another parameter in this category is plasma protein binding. Although we know a fair amount about the effect of this parameter on drug ADME, good luck trying to calculate this. Lastly, in vivo ADME is a minefield of complications. I personally would not have placed this in the 'unknown knowns' category, but at least some of the properties in this category can be calculated on the basis of specialized fragment-based models.What about 'known unknowns'? This includes polar surface area (PSA)'. PSA is a known unknown because we know that is not exactly a real, measurable quantity. However it is still a useful parameter since PSA has been shown to relate to membrane permeability and hence is especially useful for guessing blood-brain barrier penetration for CNS drugs. A set of rules similar to the Lipinski rules says that compounds with a PSA of less than 120 A^2 are more likely to penetrate membranes. As for other properties though, calculation of PSA depends on method and usually involves calculating the 3D structure of a molecule and then calculating the PSA by assigning some kind of a 'surface area' associated with polar atoms. As the article notes, one can get wildly different results depending on which atoms are assigned as 'polar'. Plus compounds containing sulfur or phosphorus can result in big discrepancies. Clearly PSA is a useful parameter, but we have to go some way in calculating it reliably. Finally, similarity searching is all the rage these days and promises a windfall of potential discoveries. In its simplest incarnation, similarity searching aims to discover compounds similar in some structural metric to a given compound, with the assumption that similarity in structure would correspond to similarity in function. But similarity, just like beauty, is notoriously in the eye of the beholder. As the authors quip from a quaint piece of literature-related controversy:Deciding whether two molecules are similar is much like trying to decide whether something is beautiful. There are no concrete definitions, and most chemists take an “I know it when I see it” attitude (attributed to United States Justice Potter Stewart, concurring opinion in Jacobellis v. Ohio 378 U.S. 184 (1964), regarding possible obscenity in The Lovers)." As again illustrated, different similarity searching methods give very different hits. One of the most successful metrics for measuring similarities has turned out to be the 'Tanimoto coefficient', but other metrics are also rampant. Thus it is quite remarkable that similarity is already being used widely in every endeavor from virtual screening to finding new protein targets for old drugs based on drug similarity. One of the most valuable application of similarity is in finding bioisosteres (chemically similar fragments), something which medicinal chemists strive for all the time. A recent review of similarity-based methods in JMC summarizes the utility of such techniques. Nonetheless, similarity based methods are still in the 'known unknowns' because we don't have an objective handle on what constitutes similarity, and we may never have such a handle even if such methods are widely and successfully adopted.Finally we come to the dreaded 'unknown unknowns'. As the authors, can we even list properties which we have no idea about? We can take a shot. This category includes flight of fancy which may never be achieved but which are worth striving for. One such holy grail is the large-scale, high-throughput computation of binding free energies. The binding free energy for a protein ligand complex includes contributions from an enormous numbers of complicated factors, but the mere attempt to calculate all these factors have valuably increased our understanding of biological systems. Thus we should continue in this endeavor. Another fancy endeavor which is the talk of the town these days is systems biology, where construction of biological networks and applications of graph theory are supposed to shed valuable light on molecular interactions. Such approaches may well be successful, but they always run the risk of becoming too abstract and divorced from reality to be truly understandable. QSAR models can suffer from the same shortcomings.In the end, only a robust collaboration between informaticians, computer scientists, medicinal chemists and biologists can make sense of the jungle of data uncovered by cheminformatics approaches. It is key for one group of scientists to keep reality checks on others. In the end it's all about reality checks. And it is necessary if we are to successfully rescue the 'unknown unknowns' into other territory, and if we are to avoid the fiasco that the previous classification of these categories led to.Muchmore, S., Edmunds, J., Stewart, K., & Hajduk, P. (2010). Cheminformatic Tools for Medicinal Chemists Journal of Medicinal Chemistry DOI: 10.1021/jm100164z... Read more »

Muchmore, S., Edmunds, J., Stewart, K., & Hajduk, P. (2010) Cheminformatic Tools for Medicinal Chemists. Journal of Medicinal Chemistry, 2147483647. DOI: 10.1021/jm100164z  

  • July 12, 2010
  • 10:05 AM
  • 990 views

Computational modeling of GPCRs: What are the challenges?

by The Curious Wavefunction in The Curious Wavefunction

GPCRs are extremely important proteins both for pure and applied science research, but they are also very difficult to crystallize and hence structural information on them has been sparse. Naturally in such a case, computational modeling can be expected to be of great value of providing insight into GPCR structure and function. However, even though progress has been impressive, such modeling still has to overcome many challenges. A recent review lists some of them.Firstly, in the absence of crystal structure, homology modeling wherein a sequence for an unknown structure is 'threaded' through that of a known one is well-established as a valuable technique. However the technique is tricky. First and foremost one has to get the right sequence alignment between the target and the template. As the article notes, recent studies have suggested that using multiple structures for alignment instead of a single one provides better results. Particularly noteworthy is this detailed study. Once a homology model has been obtained, it must be meticulously examined, both for internal consistency (bad contacts, incorrect hydrogen bonding interactions etc.) and for its agreement with experiment. Data from cross-linking studies and mutagenesis can be used to achieve this. A recent promising development has been termed 'ligand-supported homology modeling'. In this process, topographical protein-ligand interaction data from mutagenesis and other studies is used to limit the number of homology models. Such data-driven homology modeling is becoming increasingly popular.Once a good homology model has been obtained, many things can be done with it. Molecular dynamics (MD) simulations provide a very valuable avenue for exploring protein motion and be used to detect structural features not obvious in static models. A recent MD simulation of the beta-adrenergic receptor helped to resolve discrepancies between biochemical and structural observations. MD simulations can be used to investigate protein dynamics and to refine the models. Several challenges present themselves during this procedure. Firstly, while helices in GPCRs can be well-modeled, loops (of which there are six- three intracellular and three extracellular) are much harder to model because of their higher flexibility and because they are often ill-resolved in crystal structures. Unfortunately, it's these loops which are important ligand-interacting elements, so getting them right is key. Recently developed algorithms for loop-refinement based on either first-principles energy minimization or by statistical modeling based on a database of known loop conformations have been used in getting loops right. Also, state-of-the-art long MD simulations spanning several microseconds can be used to model large-scale structural changes in GPCRs.There are still immense challenges still to be overcome in understanding GPCRs. One of the biggest concerns the cycling between several inactive and active states (and not just one active and one inactive state) that present often conflicting features that can be subject to varying interpretation. For instance, for class A GPCRs (which is the largest class), it has been well-established that activated states involve the breakage of the "ionic lock", a salt bridge between arginines and glutamates on transmembrane helices 6 and 3. Breaking this lock allows TM6 to shift away from TM3 and towards TM5, a hallmark of GPCR activation. Yet the MD study on the beta2 cited above indicated that even an inactive state may feature breakage of this lock.In the GPCR jungle, strange shape-shifting creatures appear and clutch gems of insight in their palms. It is only fitting that we throw the kitchen sink at them to unravel their secrets, and computational techniques can only be a valuable arrow in this quiver.Yarnitzky T, Levit A, & Niv MY (2010). Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Current opinion in drug discovery & development, 13 (3), 317-25 PMID: 20443165... Read more »

Yarnitzky T, Levit A, & Niv MY. (2010) Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Current opinion in drug discovery , 13(3), 317-25. PMID: 20443165  

  • March 16, 2009
  • 12:18 PM
  • 983 views

So salt bridges are not stable in water? Shocking

by The Curious Wavefunction in The Curious Wavefunction

Three salt bridges seen in this protein in the xtal structure were not observed by detailed NMR experiments in water. Here's the abstract: NMR investigations have been carried out on the B1 domain of protein G. This protein has six lysine residues, of which three are consistently found to form surface-exposed salt bridges in crystal structures, while the other three are not. The Nζ and Hζ chemical shifts of all six lysines are similar and are not affected significantly by pH titration of the carboxylate groups in the protein, except for a relatively small titration of K39 Nζ. Deuterium isotope effects on nitrogen and proton are of the size expected for a simple hydrated amine (a result supported by density functional theory calculations), and also do not titrate with the carboxylates. The line shapes of the J-coupled 15N signals suggest rapid internal reorientation of all NH3+ groups. pKa values have been measured for all charged side chains except Glu50 and do not show the perturbations expected for salt bridge formation, except that E35 has a Hill coefficient of 0.84. The main differential effect seen is that the lysines that are involved in salt bridges in the crystal display faster exchange of the amine protons with the solvent, an effect attributed to general base catalysis by the carboxylates. This explanation is supported by varying buffer composition, which demonstrates reduced electrostatic shielding at low concentration. In conclusion, the study demonstrates that the six surface-exposed lysines in protein G are not involved in significant salt bridge interactions, even though such interactions are found consistently in crystal structures. However, the intrahelical E35−K39 (i,i+4) interaction is partially present. The title was meant in half-jest of course and I don't mean to disparage such studies. But I think it just goes to show the kind of difficult, tedious and careful work that has to be often carried out in science even to reach "obvious" conclusions. An an aside though, this conclusion was not at all obvious for a fair amount of time. There was a vigorous debate in the 90s kicked off by Bruce Tidor's paper arguing that salt bridges are not really that energetically important in protein stabilization, especially on surfaces. People who believed in the intense power of the holy electrostatic attraction did not really believe this. While the debate still continues, to my knowledge the general consensus is now on the side of the original Tidor proposition; salt bridges mostly provide only a marginal energetic gain (1-2 kcal/mol) to protein stability. This has been shown to be so primarily because of the loss in solvation and especially long-range solvation that formation of a salt-bridge incurs. Well, let the "obvious" research continue.References:1. Tomlinson, J., Ullah, S., Hansen, P., & Williamson, M. (2009). Characterization of Salt Bridges to Lysines in the Protein G B1 Domain Journal of the American Chemical Society DOI: 10.1021/ja808223p2. Z.S. Hendsch and B. Tidor. Do salt bridges stabilize proteins? A continuum electrostatic analysis. Protein Sci. 3: 211-226 (1994)... Read more »

Tomlinson, J., Ullah, S., Hansen, P., & Williamson, M. (2009) Characterization of Salt Bridges to Lysines in the Protein G B1 Domain. Journal of the American Chemical Society, 2147483647. DOI: 10.1021/ja808223p  

  • September 3, 2008
  • 09:59 AM
  • 956 views

Gernot Frenking is not happy...not at all

by The Curious Wavefunction in The Curious Wavefunction

Stable is simply "able" with a "st"Wow. This is a first for me. Three of the heavyweights in theoretical and computational chemistry have published a set of prescriptions in Angewandte Chemie for theoretical chemists claiming to have discovered new, "stable" molecules. In response, Gernot Frenking who is a well-known chemist himself has not just published a piercing and trenchant critique in reply to this article, but they actually seem to have reproduced the text of his referee's comments as a reply. This is a lively and extremely readable debate.In an article asking for more "realism" from theory, the three heavyweights- Roald Hoffmann, Paul von Schleyer and Henry Shaefer III- have basically come up with a roster of suggestions in response to what they see as the rather flippant declarations by theoretical chemists of molecules as "stable". One of the annoying things about theoreticians is that they regularly analyze molecules and proclaim them as stable. Experimentalists then have to sweat it out for years to actually try to make these molecules. Frequently such molecules are stable under rather extreme conditions, for example in gas phase at 4 degrees kelvin. To address the animosity that experimentalists feel against such carefree theoretical predictions, the three chemists have come up with suggestions for publication.They make some interesting points about criteria that should be satisfied when declaring molecules as stable. In fact they think that one must do away with the word "stable" and replace it by the words "viable" and "fleeting". For example for "viable" molecules, one has to be clear about the difference between thermodynamic and kinetic stability. Molecules described as viable by theoreticians must have half lives of about a day, must be isolable in condensed phases at room temperature and pressure, and must not react easily with oxygen, nitrogen and ozone (?). Molecules with more than +1 positive or negative charge must also be included with "realistic" counterions. Molecules must even be stable under conditions of some humidity. The authors then also make suggestions about reporting accuracy and precision, and about the well-known fact that theoretically reported precision cannot be more than experimentally measured precision.If theoreticians think these suggestions are asking for too much, they have a friend in Gernot Frenking.Frenking batters these suggestions down by basically launching two criticisms:1. The suggestions are too obvious and well-known to be published in Angewandte Chemie2. The suggestions are heavily biased towards experimentalists' preferencesAs Frenking puts it, he expected to walk into a "gourmet restaurant", and was served a "thin soup" instead. Ouch.I have to say that while the suggestions made by the three prominent scientists are quite sound, Frenking's points are also well-taken. He lambasts the suggestions that realistic counterions should be included in the calculation of a molecule with multiple charges; there are already molecules with multiple charges predicted to be theoretically stable which were then isolated by experiment. Ionic molecules with charges more than + or -1 are easily isolated in condensed phases. And one of the central questions Frenking asks is; why does a molecule need to be so experimentally stable in order to justify the publication of its theoretical existence. After all there are many molecules present in interstellar space which cannot be isolated under average Joe lab conditions. Under these circumstances, Frenking is of the opinion that the distinction between "viable" and "fleeting" is "eyewash" (it's the European way of euphemism)I resoundingly agree especially with this contention, harsh as it sounds. Why should experimentalists get an easy pass? The whole point of theory is to push the boundaries of what's experimentally possible. To suggest that one should only publish a theoretical prediction if it can easily be verified by experiment is to do disservice to the frontiers of science. While I can understand the angst that an experimentalist may feel when he sees an unusual molecule stable only under extreme conditions declared by a theoretician as "stable", that's exactly the challenge experimentalists should be up to, to devise conditions under which they can observe these short-lived molecules. If they do this they are the ones who carry the day. Since stability as is well-known is a relative term anyway, why insist on calling something "stable" only if it satisfies the everyday lab conditions of the experimentalist. I believe that it is precisely by testing the extreme frontiers of stability that chemistry progresses. And this can be done only by making things hard for experimentalists, not easy. Theoreticians pushing experimentalists and vice versa is how science itself progresses, and there is no reason for either one of them to quit questioning the boundaries of the others' domain.There are other points and criticisms worth reading, include other referee comments which endorse the article and are also quite interesting. In the end however, I cannot answer Frenking's central question; should this article have been published in Angewandte Chemie? We should leave it for readers to judge.Roald Hoffmann, Paul von Ragué Schleyer, Henry F. Schaefer III (2008). Predicting Molecules - More Realism, Please! Angewandte Chemie International Edition, 47 (38), 7164-7167 DOI: 10.1002/anie.200801206Gernot Frenking (2008). No Important Suggestions Angewandte Chemie International Edition, 47 (38), 7168-7169 DOI: 10.1002/anie.200802500... Read more »

Roald Hoffmann, Paul von Ragué Schleyer, & Henry F. Schaefer III. (2008) Predicting Molecules - More Realism, Please!. Angewandte Chemie International Edition, 47(38), 7164-7167. DOI: 10.1002/anie.200801206  

Gernot Frenking. (2008) No Important Suggestions. Angewandte Chemie International Edition, 47(38), 7168-7169. DOI: 10.1002/anie.200802500  

  • June 14, 2010
  • 05:11 PM
  • 949 views

The origin of life cannot escape basic organic chemistry

by The Curious Wavefunction in The Curious Wavefunction

One of the key challenges facing any theories of the molecular origins of life concerns the synthesis, stability polymerization and self-assembly of early life's molecular components. If you cannot explain the chemical origin of these components, you cannot really explain the origin of life. In case of life as we know it, this boils down to explaining the origin of the building blocks of living organisms, namely nucleotides and amino acids.The simplest principles and quirks of chemistry could have had an influence on how life could have evolved. A neat paper in ACS Chemical Biology offers a potential explanation based on basic organic chemistry for why a certain class of phosphorylated nucleotides formed in preference to others, even though 'conventional' organic chemistry would dictate the opposite.An anhydroarabinonucleoside has been postulated as an important potential precursor to further nucleotide synthesis. A key step is the phosphorylation of this nucleoside to yield an activated cyclic nucleoside phosphate. Having an activated molecule makes all the difference since activation primes the molecule to be attacked by further nucleophiles, thus triggering polymerization and growth.However, the phosphorylation of the arabinose nucleoside raises a fundamental question (hopefully) familiar to sophomore organic chemistry students. Why does phosphorylation take place preferentially on the secondary 3'-OH while sterically, as every student of organic chemistry knows, it should be preferred much more on the primary 5'-OH?To tackle this question, the authors get a crystal structure of the nucleoside in question. This x-ray structure shows an unusually short distance between the 2'-OH oxygen and the C2 carbon (2.7 A). Energy optimization using quantum chemical techniques surprisingly does not get rid of the short distance. Because of this proximity, the 2'-OH can undergo an internal attack on this carbon to generate a reactive intermediate, whose ring can be opened in turn by a 3'-OH phosphate to form the activated phosphate product. Now, the 5'-OH also gets phosphorylated; it's just that it cannot attack the C3 carbon of the activated intermediate the way the 2'-OH can because it's not in proximity to this carbon the way the 2'-OH is.The authors explain the short distance between the 2'-OH and the C3 carbon by postulating an interaction between the lone pair of the 2'-OH oxygen and the pi* orbital of the C2=N bond. This kind of interaction is quite familiar to organic chemists; it is invoked in the famous Burgi-Dunitz trajectory that enables nucleophilic attack on carbonyl carbons. Indeed, the authors perform a theoretical analysis that shows the angle of attack for the 2'-OH to be about a 100 degrees, close enough to the Burgi-Dunitz trajectory.This is a classic case of there being two competing pathways in chemistry, one of which is preferred to the other because of a subsequent low-energy route that can be traversed. It's a common theme in chemistry and biochemistry and illustrates how otherwise counter-intuitive reactions can be accelerated by putting them at the top of the right energy cliffs. No matter how complex life may be, it still cannot get around the basic laws of organic chemistry. Score one for thermodynamics.Choudhary, A., Kamer, K., Powner, M., Sutherland, J., & Raines, R. (2010). A Stereoelectronic Effect in Prebiotic Nucleotide Synthesis ACS Chemical Biology DOI: 10.1021/cb100093g... Read more »

Choudhary, A., Kamer, K., Powner, M., Sutherland, J., & Raines, R. (2010) A Stereoelectronic Effect in Prebiotic Nucleotide Synthesis. ACS Chemical Biology, 2147483647. DOI: 10.1021/cb100093g  

  • January 24, 2009
  • 10:51 PM
  • 899 views

Strain Energies in Ligand Binding: Round Two- Fight!

by The Curious Wavefunction in The Curious Wavefunction

Or why to be wary of ligands in the PDB, force field energies, and anybody who tells you not to be wary of these twoOne of the longstanding questions in protein-ligand binding has been; what is the energy penalty that a protein has to pay in order to bind a ligand? Another question is; what is the strain energy that a protein pays in order to bind the protein? Contrary to what one might initially think, the two questions are not the same. Strain energy is the price paid to twist the conformation of the ligand into the binding conformation. Free energy of binding is the energy that the protein has to pay in addition to the strain energy in order to bind the ligand.A few years ago, this question shot into the limelight because of a publication in J. Med. Chem. by Perola et al. from Vertex. The authors did a meticulous study of hundreds of ligands in their protein-bound complexes, some from the PDB and others proprietary. They used force fields to estimate the difference between the energy of the bound conformation of the ligands and the nearest local energy minimum conformation; the strain energy penalty. For most ligands, they obtained strain energies ranging from 2-5 kcal/mol. But what raised eyebrows was that for a rather significant minority of ligands, the strain energies seemed to be more than 10 kcal/mol, and for some they seemed to be up to 20 kcal/mol.These are extremely high numbers. To understand why this is so, consider a fact that I have frequently emphasized on this blog; the concentration of a particular conformation in solution is virtually negligible if the free energy difference between it and a stable conformation is about only 3 kcal/mol. For a conformation to pay that much of an energy penalty in order to transform itself into the bound concentration would already be a stretch, considering its low concentration. For a conformation to pay an energy penalty of 20 kcal/mol does not make sense at all in this light, since such a conformation should be non-existent. Plus, think about the fact that hydrogen bonds usually contribute about 5 kcal/mol and that energy at room temperature is itself about 20 kcal/mol- significantly greater than the rotational barriers in most molecules- and this number for the strain energy penalty starts looking extremely high indeed. Where exactly would it come from?Perola's paper generated a lot of buzz- a good thing. It was discussed by speakers at a conference in March last year that I attended. Now, a paper in J. Comp. Chem. seems to clear up the air a little. In a nutshell, the authors conclude that the strain energies they have measured seldom, if ever, surpass 2 kcal/mol. Needless to say, this is a huge difference compared to the earlier studies.Why such a startling difference? It seems that as always, the answer strongly depends on the method and the data. First of all, the PDB is not as flawless as people assume it is. Most people who are crystallizing protein-ligand complexes are first and foremost interested in the structure of the protein. They often do a poor job of fitting ligands to the electron density; Gerard Kleywegt of the University of Uppsala has done some marvelous work on detecting errors in PDB ligands, and his review on this should be a must-read for all scientists even marginally connected with crystallography. Because of poor fits, conformations of ligand in the electron densities in the PDB can be completely unrealistic and at the very least, brutally strained. Amides can be cis or non-planar, and more rarely planar aromatic rings can be deformed. There can be severe steric clashes which are not easily apparent. Quite naturally, such conformation when refined would lead to huge drops in energy. Therein lies the first source of the unrealistically large energy differences.The second factor has to do with the vagaries and inadequacies of force fields, often unknown to crystallographers but known to experienced computational chemists. Force fields are quite poor at determining energies and their results are especially skewed by an overemphasis on electrostatic interactions which the force-fields are ill-equipped to damp. Now consider what happens when a ligand in a PDB that has a positively and negatively charged group in it. If you relax it to the nearest local energy minimum, these two groups would instantly snap together and form an ionic bond. This would lead to a huge overstabilization of the conformation, thus again giving the illusion of a large strain energy difference between the PDB conformation and the local minimum. Finally, the devil is in the details. The earlier study used a constraint called the flat-bottom potential in optimizing the PDB ligands in their bound state. However the flat-bottom potential, which extracts no penalties for atomic movement within a certain short distance and suddenly ramps up the penalty, is not physically realistic. A better methods might be to use a harmonic potential which continuously and smoothy extracts a penalty proportional to atomic displacement.The present study takes all these factors into account and also substitutes the force field results with some well-established quantum chemical energy determinations at the B3LYP/6-31G* level. They use this method to calculate the energies of bound and local energy minimum conformations. Secondly, they use a well-established continuum solvation model (PCM) as incorporated in the latest version of the Gaussian program to incorporate damping effects due to solvation. Thirdly as indicated above, they use the harmonic potential for optimization. Fourthly and most importantly, for the cases where the strain energy seems unusually high (and even there they set the bar quite high- anything greater than 2 kcal/mol), the authors closely investigate the relevant PDB entries and find that indeed, the ligands were not fit well into the electron density and had unrealistically strained conformations.Once they tackled these problems, the strain energies all fell down to between 0.5 and 2 kcal/mol, which seems to be a realistic penalty than a conformation with a respectable concentration in solution could pay. There is now a second question; what is the maximum strain energy penalty that a ligand can pay to be transformed into the bound conformation? The authors are working on this question, and we will await their answer.But this study reiterates two important lessons that should be remembered by anyone dealing with structure at all times:1. Don't trust the PDB2. Don't trust force field energiesBetter still, as old Fox Mulder said, trust no one.References:Keith T. Butler, F. Javier Luque, Xavier Barril (2009). Toward accurate relative energy predictions of the bioactive conformation of drugs Journal of Computational Chemistry, 30 (4), 601-610 DOI: 10.1002/jcc.21087Emanuele Perola, Paul S. Charifson (2004). Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding Journal of Medicinal Chemistry, 47 (10), 2499-2510 DOI: 10.1021/jm030563wA Davis, S Stgallay, G Kleywegt (2008). Limitations and lessons in the use of X-ray structural information in drug design Drug Discovery Today, 13 (19-20), 831-841 DOI: 10.1016/j.drudis.2008.06.006... Read more »

Keith T. Butler, F. Javier Luque, & Xavier Barril. (2009) Toward accurate relative energy predictions of the bioactive conformation of drugs. Journal of Computational Chemistry, 30(4), 601-610. DOI: 10.1002/jcc.21087  

Emanuele Perola, & Paul S. Charifson. (2004) Conformational Analysis of Drug-Like Molecules Bound to Proteins:  An Extensive Study of Ligand Reorganization upon Binding. Journal of Medicinal Chemistry, 47(10), 2499-2510. DOI: 10.1021/jm030563w  

A DAVIS, S STGALLAY, & G KLEYWEGT. (2008) Limitations and lessons in the use of X-ray structural information in drug design. Drug Discovery Today, 13(19-20), 831-841. DOI: 10.1016/j.drudis.2008.06.006  

  • April 15, 2009
  • 01:03 PM
  • 898 views

The rest is all noise: errors in R values and the greatness of Carl Friedrich Gauss reiterated

by The Curious Wavefunction in The Curious Wavefunction

One of the questions seldom asked when building a model or assessing experimental data is "What's the error in that?". Unless we know the errors in the measurement of a variable, fitting predicted to experimental values may be a flawed endeavor. For instance when one expects a linear relationship between calculated and experimental values and does not see it, it could either mean that there is a flaw in the underlying expectation or calculation (commonly deduced) or that there is a problem with the errors in the measurements (not always discussed).Unfortunately it's not easy to find out the distribution of errors in experimental values. The law of large numbers and central limit theorem often thwart us here; most of the times the values are not adequate enough to get a handle on the type of error. But in the absence of such concrete error estimation, nature has provided us with a wonderful measure to handle error; assume that the errors are normally distributed. The Gaussian or normal distribution of quantities in nature is an observation and assumption that is remarkably consistent and spectacularly universal in its application. You can apply it to people's heights, car accidents, length of nails, frequency of sex, number of photos emitted by a source and virtually any other variable. While the Gaussian distribution is not always followed (and strictly speaking it applies only when the central limit theorem holds), I personally regard it to be as much of a view into the "mind of God" as anything else.In any case, it's thus important to calculate the distribution of errors in a dataset, Gaussian or otherwise. In biological assays where compounds are tested, this becomes especially key. An illustration of the importance in error estimation in these common assays is provided by this recent analysis of model performance by Philip Hajduk and Steve Muchmore's group at Abbott. Essentially what they do is to estimate the standard deviations or errors in a set of in-house measurements on compound activities and look at the effect of those errors on the resulting R values during comparison of calculated activities with these experimental ones. The R value or correlation coefficient is a time-tested and standard measure of fit between two datasets. The authors apply the error they have obtained in the form of "Gaussian noise" to a hypothetical set of calculated vs predicted activity plots with 4, 10 and 20 points. They find that after applying the error, the R-value itself adopts a Gaussian distribution that varies from 0.7 to 0.9 in case of the 20 point measurement. This immediately tells us that any such measurement in the real world that gives us, say, a R value of 0.95 is suspicious since the probability of such a value arising is very low (0.1%), given the errors in its distribution.You know what should come next. The authors apply this methodology and look at several cases of calculated R values for various calculated vs measured biological activities in leading journals during 2006-2007. As they themselves say,It is our opinion that the majority of R-values obtained from this (small) literature sample are unsubstantiated given the properties of the underlying data. Following this analysis they then apply similar noise to measurements for High-Throughput Screening (HTS) and Lead Optimization (LO). Unlike HTS, LO usually deals with molecules sequentially synthesized by medicinal chemists that are separated by small changes in activity. To investigate the effect of such errors, enrichment factors (EFs) are calculated for both scenarios. The EF denotes the percentage of active molecules found or "enriched" in the top fraction of screened molecules relative to random screening, with a higher EF corresponding to better performance. The observation for HTS is that small errors give large EFs, but what is interesting is that even large errors in measurement can give modest enrichment, thus obscuring the presence of such error. For LO the dependence of enrichment on error is less, reflecting the relatively small changes in activity engendered by structure optimization. The take home message from all this is of course that one needs to be aware of errors and needs to apply those errors in quantifying measures of model assessment. God is in the details and in this case his name is Carl Friedrich Gauss, who must be constantly beaming from his Hanover grave.Reference:Brown, S., Muchmore, S., & Hajduk, P. (2009). Healthy skepticism: assessing realistic model performance Drug Discovery Today, 14 (7-8), 420-427 DOI: 10.1016/j.drudis.2009.01.012... Read more »

Brown, S., Muchmore, S., & Hajduk, P. (2009) Healthy skepticism: assessing realistic model performance. Drug Discovery Today, 14(7-8), 420-427. DOI: 10.1016/j.drudis.2009.01.012  

  • April 29, 2010
  • 01:29 PM
  • 876 views

Steering library bias toward adenosine A2A receptor ligand discovery

by The Curious Wavefunction in The Curious Wavefunction

The A2A adenosine receptor is an important GPCR, well-known for binding caffeine. Adenosine receptors are emerging as relevant drug targets for a variety of disorders including Parkinson's disease, and there is interest in discovering new ligands that bind to them. Among adenosine receptor subtypes, the A2A receptor is one of the few GPCRs whose crystal structure is available. Thus the A2A is amenable to structure-based design efforts, and virtual screening is an especially attractive endeavor in this regard.In the "present report, a team of researchers from NIH and UCSF led by Brian Shoichet, John Irwin and Kenneth Jacobson use virtual screening to discover new ligands for the A2A. There are several points to note here. The authors use the ZINC library of drug like molecules to dock about a million and a half compounds into the binding pocket of the A2A crystal structure. They pick the best-scored 500 (0.035% of the total) ligands and investigate their fit in the binding site. Using criteria like electrostatic and VdW complementarity and novelty of chemotype, they finally select 20 of these 500 hits and test them in assays. Out of these 20, 7 inhibited binding by more than 40% at 20 μM concentration, thus constituting a hit rate of 35%. While the compounds formed the same kinds of interactions as some other A2A ligands, they were also relatively diverse in structure. The ligands were also tested in aggregation-based screens to determine that their activity was not a spurious artifact of aggregation-based inhibition.This is a pretty good hit rate. Generally virtual screening campaigns are lucky to have a hit rate of a few percent. Curiously, the authors also found a similarly high hit rate during a past VS campaign against the well-known β2 adrenergic receptor. What could be responsible for this high hit rate against GPCRs? The reasons are interesting. One reason could be that GPCRs are very well adapted to bind small molecules in compact pockets, enclosing them and forming many kinds of productive interactions. But more intriguingly, as the authors have noted earlier, there is "biogenic bias" in favor of certain target-specific chemotypes in commercial libraries that are screened, both during VS as well as HTS. This in turn reflects the biases of medicinal chemists in picking and synthesizing certain kinds of chemotypes based on the importance of drug targets and past successes in hitting these targets. GPCRs clearly are enormously important, and GPCR-friendly ligand chemotypes thus constitute a large part of screening libraries. These chemotypes are much more prevalent than those for kinases or ion channels for instance.This observation has both positive and negative implications. The positive implication is that one is likely to keep finding high hit rates for GPCRs using VS. However, the negative implication is that one is also going to be constrained by biogenic bias, and this might preclude finding more diverse and entirely novel subtypes. Thus, while VS campaigns for GPCRs might find a good number of hits, the novelty of these hits might not always be satisfying. One other quite intriguing point emerging in this study is that the kind of hits found (agonist, inverse agonist, antagonist etc.) reflects the ligand which the target structure used for VS is co-crystallized with. Thus the A2A houses an antagonist in the binding site, leading to a preponderance of antagonists in the top docking hits. Indeed, agonists ranked abysmally low in the list.GPCR ligand discovery is one of the most important goals in drug discovery. This and other similar studies demonstrate that, with all its caveats, VS can be productively used to mine for new GPCR drugs.Carlsson, J., Yoo, L., Gao, Z., Irwin, J., Shoichet, B., & Jacobson, K. (2010). Structure-Based Discovery of A Adenosine Receptor Ligands Journal of Medicinal Chemistry DOI: 10.1021/jm100240h... Read more »

Carlsson, J., Yoo, L., Gao, Z., Irwin, J., Shoichet, B., & Jacobson, K. (2010) Structure-Based Discovery of A Adenosine Receptor Ligands . Journal of Medicinal Chemistry, 2147483647. DOI: 10.1021/jm100240h  

  • March 16, 2010
  • 10:09 AM
  • 863 views

Remote control of peptide screw sense

by The Curious Wavefunction in The Curious Wavefunction

As is well-known, peptides helices can be right or left handed. Many details of structure, amino acid identity and orientation can control this screw sense, and sometimes the controlling factors can be quite subtle. In a JACS communication, Jonathan Clayden (yes, the co-author of the amazing organic chemistry textbook) and his group uncover a surprising factor that controls the helical screw sense and also incorporate a neat "reporter group" to monitor the screw sense.But this reporter group is nothing fancy and is simply a gycine installed in the middle of a long sequence of amino acids which consists of alpha-aminoisobutyric acid or Aib. Aib is simply alanine with an extra methyl at the alpha carbon. It is well known to impart helical propensities to peptides and has been used several times as a helical 'lock'.In this case the Gly is in the center of a 20 amino acid peptide where all other residues are Aib. The peptide is clearly helical, but what's the screw sense? That's where the power of NMR spectroscopy comes in. The two protons in Gly are diastereotopic which means that in principle they could have different chemical shifts and signals in the NMR spectrum. In practice though, rapid interconversion between the left and right handed helices leads to an average and gives a single signal in the spectrum.However if interconversion between the two screw senses could be 'biased' by making the equilibrium constant favor one of them, then one could presumably observe two separate signals for the two Gly protons even if the transition is fast on the NMR time scale. To accomplish this, Clayden et al. do something peculiar; they incorporate a L-Phe residue at the N-terminal of the helix. This group, even if far away from the central Gly, somehow seems to remotely interact differently with each of the two Gly protons. The incorporation of this terminal group leads to a considerable splitting in the signals of the two protons (up to 100 ppm), easily distinguishing them apart. Also for some reason, N-terminal groups seem to work better than C-terminal groups.The reasons for the transmission of this effect over no less than 27 bonds are not clear, but they probably have something to do with the subtle change in conformational behavior that dictate helix folding. The authors even observe small differences for amide vs ester bonds as capping groups. Finally, they obtain an x-ray structure of this helix which turns out to be a 3/10 helix and confirm their observations.These days there is a drive to 'tether' certain parts of oligopeptides to lock the resulting conformation in a helical form. Sometimes, even constraining end groups covalently (by metathesis for instance) seems to ensure a critical 'nucleation' structure that then zips up the rest of the helix. The exact percentage of the helix in solution could be a matter for discussion, but this study seems to indicate similiar end-group influenced conformational organization. I thought it was neat and points to further challenges and questions in our understanding of the deceptively simple question, "Why are helices stable in solution"?Solà, J., Helliwell, M., & Clayden, J. (2010). N- versus C-Terminal Control over the Screw-Sense Preference of the Configurationally Achiral, Conformationally Helical Peptide Motif Aib-Gly-AibJournal of the American Chemical Society DOI: 10.1021/ja100662d... Read more »

Solà, J., Helliwell, M., & Clayden, J. (2010) N- versus C-Terminal Control over the Screw-Sense Preference of the Configurationally Achiral, Conformationally Helical Peptide Motif Aib GlyAib . Journal of the American Chemical Society, 2147483647. DOI: 10.1021/ja100662d  

  • March 23, 2009
  • 10:55 AM
  • 860 views

Meta-substitution: challenging a classic textbook paradigm

by The Curious Wavefunction in The Curious Wavefunction

With my graduate school circus hopefully about to fold up tent, I will leave you with the abstract for this recent interesting Science paper which challenges a classic sophomore organic chemistry notion; that electron donating groups on benzene direct para and ortho substitution in electrophilic aromatic substitution reactions. By using a clever copper catalyst the authors manage to coax an aryl group to neatly substitute meta to an amido substituent, thus effecting a valuable C-H bond arylation. "For over a century, chemical transformations of benzene derivatives have been guided by the high selectivity for electrophilic attack at the ortho/para positions in electron-rich substrates and at the meta position in electron-deficient molecules. We have developed a copper-catalyzed arylation reaction that, in contrast, selectively substitutes phenyl electrophiles at the aromatic carbon–hydrogen sites meta to an amido substituent. This previously elusive class of transformation is applicable to a broad range of aromatic compounds." I also want to state that I remember many in my sophomore organic class misunderstanding the facts about the effects of e-withdrawing and donating substituents. For some reason they used to think that electron donating groups activate ortho and para positions and electron withdrawing groups activate meta positions. But that's completely incorrect. The correct statement is one which I still remember from a then classic organic chemistry textbook (which sadly went out of print). Electron donating groups on benzene activate all positions; it's just that they activate ortho and para positions more than meta. Similarly, electron withdrawing groups on benzene deactivate all positions; it's just that they deactivate para and ortho more than meta. Thus the effect of any group, whether electron donating or electron withdrawing, is greatest at the ortho and para positions Reference:Phipps, R., & Gaunt, M. (2009). A Meta-Selective Copper-Catalyzed C-H Bond Arylation Science, 323 (5921), 1593-1597 DOI: 10.1126/science.1169975... Read more »

Phipps, R., & Gaunt, M. (2009) A Meta-Selective Copper-Catalyzed C-H Bond Arylation. Science, 323(5921), 1593-1597. DOI: 10.1126/science.1169975  

  • February 11, 2009
  • 01:12 PM
  • 839 views

Another entertaining exchange

by The Curious Wavefunction in The Curious Wavefunction

You may remember that Manfred Christl who is emerging as a kind of vigilante of incorrect chemical structure determinations had debunked a previous claim and shown it to be a manifestation of a 100 year old reaction. There he goes again about the structure of some alleged cyclic allenes. But this time accompanied by a firm rebuttal; it's not often that you see the strong word "incorrect" in a scientific publication.Are strained amides, where delocalization of the nitrogen electron lone pairs is not possible, still amides? Hell, yes!References:Manfred Christl, Bernd Engels (2009). Stable Five-Membered-Ring Allenes with Second-Row Elements Only: Not Allenes, But Zwitterions Angewandte Chemie International Edition, 48 (9), 1538-1539 DOI: 10.1002/anie.200803476Vincent Lavallo, C. Adam Dyker, Bruno Donnadieu, Guy Bertrand (2009). Are Allenes with Zwitterionic Character Still Allenes? Of Course! Angewandte Chemie International Edition, 48 (9), 1540-1542 DOI: 10.1002/anie.200804909... Read more »

Manfred Christl, & Bernd Engels. (2009) Stable Five-Membered-Ring Allenes with Second-Row Elements Only: Not Allenes, But Zwitterions. Angewandte Chemie International Edition, 48(9), 1538-1539. DOI: 10.1002/anie.200803476  

Vincent Lavallo, C. Adam Dyker, Bruno Donnadieu, & Guy Bertrand. (2009) Are Allenes with Zwitterionic Character Still Allenes? Of Course!. Angewandte Chemie International Edition, 48(9), 1540-1542. DOI: 10.1002/anie.200804909  

  • September 11, 2010
  • 09:56 AM
  • 828 views

Why modeling GPCRs is (still) hard

by The Curious Wavefunction in The Curious Wavefunction

Well, it's hard for several reasons which I have discussed in previous posts, but here's one reason demonstrated by a recent paper. In this paper they crystallized the ß2 adrenergic receptor with an antagonist. Previously, in the landmark publication of the ß2 structure in 2007, the protein had been crystallized with an inverse agonist. Recall that an inverse agonist inhibits the basal activity of the GPCR whereas an antagonist stabilizes both active and inactive states but does not affect the basal activity. In this case they crystallized the ß2 with an antagonist and compared the resulting structure to that of the agonist-GPCR complex. And they saw...nothing in particular. The protein backbone and side-chain locations are very similar for the antagonist (compound 3) and inverse agonist (compound 2) shown in the figure below. As we can see in the figure, about the only side-chain that shows any movement is the tyrosine on the left (Y316). No wonder that cross-docking the two ligands (that is, docking one ligand into the other ligand's protein conformation) gave very accurate ligand orientations; this was essentially a softball problem for a docking program since the antagonist was being docked into a protein conformation that was virtually identical to its own. But of course, we know that antagonists and agonists affect GPCR function quite differently. As this study shows, clearly the action is not taking place in the ligand-binding pocket where things aren't really moving. So where is the real action? It's naturally taking place on the intracellular side, where the GPCR interacts with a medley of other proteins. And as the paper accurately notes, the difference between antagonist and inverse agonist binding is probably also reflected in the protein dynamics corresponding to the two ligands. Good luck modeling that. That's the whole deal with modeling GPCRs. Simply modeling the ligand-binding pocket is not going to help us understand the differences between the binding of various ligands; one has to model multiprotein interactions and subtle effects on dynamics that are relayed through the helices. The program Desmond which I described in a earlier post is a powerful MD program, but even MD is going to really turn heads when it can take account of multiprotein interactions, and such interactions happen on a time-scale much longer than what even Desmond can access. We have a long way to go before we can do all this. But please, don't stop.Wacker, D., Fenalti, G., Brown, M., Katritch, V., Abagyan, R., Cherezov, V., & Stevens, R. (2010). Conserved Binding Mode of Human β-2 Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-ray Crystallography Journal of the American Chemical Society, 132 (33), 11443-11445 DOI: 10.1021/ja105108q... Read more »

Wacker, D., Fenalti, G., Brown, M., Katritch, V., Abagyan, R., Cherezov, V., & Stevens, R. (2010) Conserved Binding Mode of Human β Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-ray Crystallography . Journal of the American Chemical Society, 132(33), 11443-11445. DOI: 10.1021/ja105108q  

  • February 5, 2010
  • 02:56 PM
  • 825 views

Scaling further GPCR summits

by The Curious Wavefunction in The Curious Wavefunction

There's a nice review on GPCRs and their continuing challenges in the British Journal of Pharmacology this month. The authors focus on both structural and functional challenges in the characterization of this most important class of signaling proteins. As is well-known, drugs targeting GPCRs generate the highest revenue among all drugs. And given their basic roles in signal transduction, GPCRs are also clearly very important from an academic standpoint. Yet there is a wall of obstacles confronting us.For starters there are the well-known problems with crystallization plaguing all membrane proteins like GPCRs. Until now only four GPCRs- rhodopsin, beta1 and beta2 adrenergic receptors and A2a adenosine receptor- have been crystallized, and the publication of each structure was considered a breakthrough. As the review mentions, the proteins are unstable outside the membrane and conditions for stabilization and crystallization are frequently incompatible; for instance stabilization is often effected by long-chain detergents while the opposite is true for crystallization. To circumvent these problems clever strategies have been adopted and immense trial and error and hard work were required. The rhodopsin and adrenergic receptors were crystallized by point mutations and special techniques; in one case an antibody was tethered to the protein and in another case a fusion protein was attached to stabilize the domain.It's when we enter the dense jungle of GPCR biology that crystallization problems almost start sounding trivial. GPCRs couple to a variety of ligands including well-known biogenic amines (like adrenaline and serotonin), peptides, proteins and nucleotides. Where is starts to become complex is in the kind of response these ligands elicit, which could be full agonism, partial agonism, inverse agonism and full antagonism.What structural features distinguish these different responses from each other? This is a key question in GPCR biology. But not only can ligands be agonists or antagonists but they can act in different ways on the same GPCR, activating different pathways. The case of partial agonists is especially interesting and more protein-partial agonist structures would be quite valuable. The traditional model of protein binding assumes two dominant states, inactive and active. Agonists stabilize the active state, antagonists stabilize both states, and inverse agonists stabilize the inactive state. But, as the authors say, the traditional model is slowly undergoing a revision:The concept of a receptor existing in a simple pair of active and inactive states (R and R*) is no longer sufficient to explain the observations of pharmacology. Agonists vary considerably in their efficacy and how this relates to the bound conformational states is unclear. A partial agonist with 50% efficacy could fully activate 50% of the receptors or could activate 100% of the receptor by 50%. Alternatively, a partial agonist might stabilize a different form of the receptor to a full agonist state and this different conformation might activate the G protein with a lower efficiency. The study of rhodopsin suggests that activation of the receptor involves the release of key structural constraints within the E/DRY and NPxxY regions. Energy provided by agonist binding must be sufficient to break these constraints and stabilize the new active conformation. In the case of rhodopsin, whether this transition is complete or partial depends on the chemical nature of the ligand (Fritze et al., 2003). The retinal analogue 9-demethyl-retinal is a partial agonist of rhodopsin which only poorly activates G protein in response to light. Spin-labeling studies (Knierim et al., 2008) suggest that in the presence of this ligand, only a small proportion of receptors are in the active conformation equivalent to all-trans-retinal. However, this can also result in a new state that is not formed with the full agonist. Therefore, rhodopsin studies suggest that that partial agonism may result in either a reduced number of fully active receptors or conformations which are not capable of fully engaging the signal transduction process. Structures of other GPCRs in complex with partial agonists are required to determine their effects on conformation. An example makes the hideous complexity clear. The mu-opioid receptor is activated by several ligands including morphine, etorphine and fentanyl. However, morphine acts only as a partial agonist in effecting a phosphorylation endpoint whereas the other two act as full agonists. But it gets more interesting. While morphine effects phosphorylation of the kinase ERK through activation of PKC (protein kinase C), etorphine also activates ERK but by activation of beta-arrestin. Thus the same endpoint can be effected through different pathways. And it doesn't even stop there. Morphine causes the phosphorylated ERK to stay in the cytoplasm while etorphine causes the ERK to translocate to the nucleus. Not done yet; in addition, morphine can reverse its role and act as a full agonist on the adenylyl cyclase pathway.Thus, the same ligand adopts different roles when activating different pathways. To begin with it's not even clear which pathway is activated under what circumstances. And the problem is only accentuated by the participation of different G proteins in inducing different responses.Another dense layer of complexity is added by the fact that GPCRs have been found to dimerize and oligomerize. Crystallography can often be misleading in studying these dimers since there are several documented reports of dimers being formed as misleading artifacts of the crystallization conditions. Apart from the stated problems, there are even more differences in further downstream signaling and receptor internalization induced by oligomerization. It's clearly a jungle out there. No wonder the design of drugs targeting GPCRs needs a measure of faith. For instance consider the various drugs targeting CNS proteins. CNS drug discovery has long been considered a black box for a good reason. Once a drug enters the brain, one can imagine it not only targeting a diverse subset of GPCRs (and even other classes of proteins) but, given the above complexities, also acting separately as agonist and antagonist at the various receptors. We clearly have a long way to go before we can prospectively design a CNS drug that will do all this on cue.It would be a tall order trying to explain all these differences simply through structural modifications induced by the ligands. Yet whatever signal is eventually transmitted to the G proteins must begin with a crucial structural movement. It seems that elucidating the differences in helix and loop movements induced by partial and full agonists, inverse agonists and antagonists is a tantalizing part of the GPCR puzzle.Since crystal structure data on GPCR is lacking, modeling approaches especially based on homology modeling have proved especially fruitful. Earlier attempts were all based on the single rhodopsin template. Since then the higher resolution adrenergic and adenosine receptor structures have provided significant insight. But here again numerous caveats abound. Modeling the helices is relatively easy since all GPCRs share the same general 7TM helix topology which is highly conserved, but modeling the fine differences between helices that lead to structural changes upon ligand binding is harder. And most difficult and important of all is modeling the extracellular loops which actually bind the ligands. Subtle changes in loop movement, salt-bridge breakage, hydrophobic effects and interaction of loops with helices is difficult to model. Often a change in conformation of a single residue can be enough to throw the modeling off balance. Nonetheless, the paucity of structural data means that modeling when done right will continue to be valuable. In the absence of structural data, computational ligand-based approaches which search for ligands similar to known compounds could be useful.We have made a lot of progress in understanding the structure and function of these key proteins. But investigations seem to have unearthed more questions than answers. Which is always good for science since then it can have more choice fodder for contemplation.Congreve, M., & Marshall, F. (2009). The impact of GPCR structures on pharmacology and structure-based drug design British Journal of Pharmacology DOI: 10.1111/j.1476-5381.2009.00476.x... Read more »

Congreve, M., & Marshall, F. (2009) The impact of GPCR structures on pharmacology and structure-based drug design. British Journal of Pharmacology. DOI: 10.1111/j.1476-5381.2009.00476.x  

Zheng, H., Loh, H., & Law, P. (2010) Agonist-selective signaling of G protein-coupled receptor: Mechanisms and implications. IUBMB Life. DOI: 10.1002/iub.293  

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