The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design.

The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.

The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states.

The intramolecular reorganization energy (ΔEReorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔEReorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔEReorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔEReorg is technically difficult. Typically, ΔEReorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔHReorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔHReorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔHReorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔHReorg values. Such counterintuitive larger ΔHReorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.

Fragment-Derived Selective Inhibitors of Dual-Specificity Kinases DYRK1A and DYRK1B.

The serine/threonine kinase DYRK1A has been implicated in regulation of a variety of cellular processes associated with cancer progression, including cell cycle control, DNA damage repair, protection from apoptosis, cell differentiation, and metastasis. In addition, elevated-level DYRK1A activity has been associated with increased severity of symptoms in Down's syndrome. A selective inhibitor of DYRK1A could therefore be of therapeutic benefit. We have used fragment and structure-based discovery methods to identify a highly selective, well-tolerated, brain-penetrant DYRK1A inhibitor which showed in vivo activity in a tumor model. The inhibitor provides a useful tool compound for further exploration of the effect of DYRK1A inhibition in models of disease.

Structure-Guided Discovery of Potent and Selective DYRK1A Inhibitors.

The kinase DYRK1A is an attractive target for drug discovery programs due to its implication in multiple diseases. Through a fragment screen, we identified a simple biaryl compound that is bound to the DYRK1A ATP site with very high efficiency, although with limited selectivity. Structure-guided optimization cycles enabled us to convert this fragment hit into potent and selective DYRK1A inhibitors. Exploiting the structural differences in DYRK1A and its close homologue DYRK2, we were able to fine-tune the selectivity of our inhibitors. Our best compounds potently inhibited DYRK1A in the cell culture and in vivo and demonstrated drug-like properties. The inhibition of DYRK1A in vivo translated into dose-dependent tumor growth inhibition in a model of ovarian carcinoma.

A dynamic view of DNA structure within the nucleosome: Biological implications.

Most of eukaryotic cellular DNA is packed in nucleosome core particles (NCPs), in which the DNA (DNANCP) is wrapped around histones. The influence of this organization on the intrinsic local dynamics of DNA is largely unknown, in particular because capturing such information from experiments remains notoriously challenging. Given the importance of dynamical properties in DNA functions, we addressed this issue using CHARMM36 MD simulations of a nucleosome containing the NCP positioning 601 sequence and four related free dodecamers. Comparison between DNANCP and free DNA reveals a limited impact of the dense DNA-histone interface on correlated motions of dinucleotide constituents and on fluctuations of inter base pair parameters. A characteristic feature intimately associated with the DNANCP super-helical path is a set of structural periodicities that includes a marked alternation of regions enriched in backbone BI and BII conformers. This observation led to uncover a convincing correspondence between the sequence effect on BI/BII propensities in both DNANCP and free DNA, strengthening the idea that the histone preference for particular DNA sequences relies on those intrinsic structural properties. These results offer for the first time a detailed view of the DNA dynamical behavior within NCP. They show in particular that the DNANCP dynamics is substantial enough to preserve the ability to structurally adjust to external proteins, for instance remodelers. Also, fresh structural arguments highlight the relevance of relationships between DNA sequence and structural properties for NCP formation. Overall, our work offers a more rational framework to approach the functional, biological roles of NCP.

Modelling the binding mode of macrocycles: Docking and conformational sampling.

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding.

There has been an explosion of structural information for pharmaceutical compounds bound to biological targets, but the conformations and dynamics of compounds free in solution are poorly characterized, if at all. Yet, knowledge of the unbound state is essential to understand the fundamentals of molecular recognition, including the much debated conformational intramolecular reorganization energy of a compound upon binding (ΔEReorg). Also, dependable observation of the unbound compounds is important for ligand-based drug discovery, e.g. with pharmacophore modelling. Here, these questions are addressed with long (⩾0.5µs) state-of-the-art molecular dynamics (MD) simulations of 26 compounds (including 7 approved drugs) unbound in explicit solvent. These compounds were selected to be chemically diverse, with a range of flexibility, and good quality bioactive X-ray structures. The MD-simulated free compounds are compared to their bioactive structure and conformers generated with ad hoc sampling in vacuo or with implicit generalized Born (GB) aqueous solvation models. The GB conformational models clearly depart from those obtained in explicit solvent, and suffer from conformational collapse almost as severe as in vacuo. Thus, the global energy minima in vacuo or with GB are not suitable representations of the unbound state, which can instead be extensively sampled by MD simulations. Many, but not all, MD-simulated compounds displayed some structural similarity to their bioactive structure, supporting the notion of conformational pre-organization for binding. The ligand-protein complexes were also simulated in explicit solvent, to estimate ΔEReorg as an enthalpic difference ΔHReorg between the intramolecular energies in the bound and unbound states. This fresh approach yielded ΔHReorg values⩽6kcal/mol for 18 out of 26 compounds. For three particularly polar compounds 15⩽ΔHReorg⩽20kcal/mol, supporting the notion that ΔHReorg can be substantial. Those large ΔHReorg values correspond to a redistribution of electrostatic interactions upon binding. Overall, the study illustrates how MD simulations offer a promising avenue to characterize the unbound state of medicinal compounds.

The intrinsic mechanics of B-DNA in solution characterized by NMR.

Experimental characterization of the structural couplings in free B-DNA in solution has been elusive, because of subtle effects that are challenging to tackle. Here, the exploitation of the NMR measurements collected on four dodecamers containing a substantial set of dinucleotide sequences provides new, consistent correlations revealing the DNA intrinsic mechanics. The difference between two successive residual dipolar couplings (ΔRDCs) involving C6/8-H6/8, C3'-H3' and C4'-H4' vectors are correlated to the(31)P chemical shifts (δP), which reflect the populations of the BI and BII backbone states. The δPs are also correlated to the internucleotide distances (Dinter) involving H6/8, H2' and H2″ protons. Calculations of NMR quantities on high resolution X-ray structures and controlled models of DNA enable to interpret these couplings: the studied ΔRDCs depend mostly on roll, while Dinterare mainly sensitive to twist or slide. Overall, these relations demonstrate how δP measurements inform on key inter base parameters, in addition to probe the BI↔BII backbone equilibrium, and shed new light into coordinated motions of phosphate groups and bases in free B-DNA in solution. Inspection of the 5' and 3' ends of the dodecamers also supplies new information on the fraying events, otherwise neglected.

Simulations Meet Experiment to Reveal New Insights into DNA Intrinsic Mechanics.

The accurate prediction of the structure and dynamics of DNA remains a major challenge in computational biology due to the dearth of precise experimental information on DNA free in solution and limitations in the DNA force-fields underpinning the simulations. A new generation of force-fields has been developed to better represent the sequence-dependent B-DNA intrinsic mechanics, in particular with respect to the BI ↔ BII backbone equilibrium, which is essential to understand the B-DNA properties. Here, the performance of MD simulations with the newly updated force-fields Parmbsc0εζOLI and CHARMM36 was tested against a large ensemble of recent NMR data collected on four DNA dodecamers involved in nucleosome positioning. We find impressive progress towards a coherent, realistic representation of B-DNA in solution, despite residual shortcomings. This improved representation allows new and deeper interpretation of the experimental observables, including regarding the behavior of facing phosphate groups in complementary dinucleotides, and their modulation by the sequence. It also provides the opportunity to extensively revisit and refine the coupling between backbone states and inter base pair parameters, which emerges as a common theme across all the complementary dinucleotides. In sum, the global agreement between simulations and experiment reveals new aspects of intrinsic DNA mechanics, a key component of DNA-protein recognition.

Identification of novel, in vivo active Chk1 inhibitors utilizing structure guided drug design.

Chk1 kinase is a critical component of the DNA damage response checkpoint especially in cancer cells and targeting Chk1 is a potential therapeutic opportunity for potentiating the anti-tumor activity of DNA damaging chemotherapy drugs. Fragment elaboration by structure guided design was utilized to identify and develop a novel series of Chk1 inhibitors culminating in the identification of V158411, a potent ATP-competitive inhibitor of the Chk1 and Chk2 kinases. V158411 abrogated gemcitabine and camptothecin induced cell cycle checkpoints, resulting in the expected modulation of cell cycle proteins and increased cell death in cancer cells. V158411 potentiated the cytotoxicity of gemcitabine, cisplatin, SN38 and camptothecin in a variety of p53 deficient human tumor cell lines in vitro, p53 proficient cells were unaffected. In nude mice, V158411 showed minimal toxicity as a single agent and in combination with irinotecan. In tumor bearing animals, V158411 was detected at high levels in the tumor with a long elimination half-life; no pharmacologically significant in vivo drug-drug interactions with irinotecan were identified through analysis of the pharmacokinetic profiles. V158411 potentiated the anti-tumor activity of irinotecan in a variety of human colon tumor xenograft models without additional systemic toxicity. These results demonstrate the opportunity for combining V158411 with standard of care chemotherapeutic agents to potentiate the therapeutic efficacy of these agents without increasing their toxicity to normal cells. Thus, V158411 would warrant further clinical evaluation.

Motions and entropies in proteins as seen in NMR relaxation experiments and molecular dynamics simulations.

Molecular dynamics simulations of E. coli glutaredoxin1 in water have been performed to relate the dynamical parameters and entropy obtained in NMR relaxation experiments, with results extracted from simulated trajectory data. NMR relaxation is the most widely used experimental method to obtain data on dynamics of proteins, but it is limited to relatively short timescales and to motions of backbone amides or in some cases (13)C-H vectors. By relating the experimental data to the all-atom picture obtained in molecular dynamics simulations, valuable insights on the interpretation of the experiment can be gained. We have estimated the internal dynamics and their timescales by calculating the generalized order parameters (O) for different time windows. We then calculate the quasiharmonic entropy (S) and compare it to the entropy calculated from the NMR-derived generalized order parameter of the amide vectors. Special emphasis is put on characterizing dynamics that are not expressed through the motions of the amide group. The NMR and MD methods suffer from complementary limitations, with NMR being restricted to local vectors and dynamics on a timescale determined by the rotational diffusion of the solute, while in simulations, it may be difficult to obtain sufficient sampling to ensure convergence of the results. We also evaluate the amount of sampling obtained with molecular dynamics simulations and how it is affected by the length of individual simulations, by clustering of the sampled conformations. We find that two structural turns act as hinges, allowing the α helix between them to undergo large, long timescale motions that cannot be detected in the time window of the NMR dipolar relaxation experiments. We also show that the entropy obtained from the amide vector does not account for correlated motions of adjacent residues. Finally, we show that the sampling in a total of 100 ns molecular dynamics simulation can be increased by around 50%, by dividing the trajectory into 10 replicas with different starting velocities.

NMR studies of DNA support the role of pre-existing minor groove variations in nucleosome indirect readout.

We investigated how the intrinsic sequence-dependent properties probed via the phosphate linkages (BI ↔ BII equilibrium) influence the preferred shape of free DNA, and how this affects the nucleosome formation. First, this exploits NMR solution studies of four B-DNA dodecamers that together cover 39 base pairs of the 5' half of the sequence 601, of special interest for nucleosome formation. The results validate our previous prediction of a systematic, general sequence effect on the intrinsic backbone BII propensities. NMR provides new evidence that the backbone behavior is intimately coupled to the minor groove width. Second, application of the backbone behavior predictions to the full sequence 601 and other relevant sequences demonstrates that alternation of intrinsic low and high BII propensities, coupled to intrinsic narrow and wide minor grooves, largely coincides with the sinusoidal variations of the DNA minor groove width observed in crystallographic structures of the nucleosome. This correspondence is much poorer with low affinity sequences. Overall, the results indicate that nucleosome formation involves an indirect readout process implicating pre-existing DNA minor groove conformations. It also illustrates how the prediction of the intrinsic structural DNA behavior offers a powerful framework to gain explanatory insight on how proteins read DNA.

rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids.

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrödinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net/

Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles.

Understanding the -C-X1-X2-C- motif in the active site of the thioredoxin superfamily: E. coli DsbA and its mutants as a model system.

E. coli DsbA is an intensively studied enzyme of the thioredoxin superfamily of thiol-disulfide oxidoreductases. DsbA catalyzes the disulfide bond formation and folding of proteins in the bacterial periplasm. DsbA and its mutants have highlighted the strong and puzzling influence of the -C-X1-X2-C- active site variants, found across the thioredoxin superfamily, on the ionization and redox properties of this site. However, the interpretation of these observations remains wanting, largely due to a dearth of structural information. Here, molecular dynamics simulations are used to provide extensive information on the structure and dynamics of reduced -C30-X31-X32-C33- motifs in wild type DsbA and 13 of its mutants. These simulations are combined with calculations of the pK of H32 and of the very low pK of the catalytic cysteine C30. In wild type DsbA, the titrations of C30 and H32 are shown to be coupled; the protonation states and dynamics of H32 are examined. The thiolate of C30 is stabilized by hydrogen bonds with the protein. Modulation of these hydrogen bonds by alteration of residue X32 has the greatest impact on the pK of C30, which rationalizes its higher pK in thioredoxin and tryparedoxin. Because of structural constrains, residue X31 has only an indirect and weak influence on the pK of C30. The dynamics of C30 is clearly related to its stabilizing interactions and pK value. Although relatively small differences between pKs were not reproduced in the calculations, the major trends are explained, adding new insights to our understanding of enzymes in this family.

Quantum mechanics/molecular mechanics modeling of regioselectivity of drug metabolism in cytochrome P450 2C9.

Cytochrome P450 enzymes (P450s) are important in drug metabolism and have been linked to adverse drug reactions. P450s display broad substrate reactivity, and prediction of metabolites is complex. QM/MM studies of P450 reactivity have provided insight into important details of the reaction mechanisms and have the potential to make predictions of metabolite formation. Here we present a comprehensive study of the oxidation of three widely used pharmaceutical compounds (S-ibuprofen, diclofenac, and S-warfarin) by one of the major drug-metabolizing P450 isoforms, CYP2C9. The reaction barriers to substrate oxidation by the iron-oxo species (Compound I) have been calculated at the B3LYP-D/CHARMM27 level for different possible metabolism sites for each drug, on multiple pathways. In the cases of ibuprofen and warfarin, the process with the lowest activation energy is consistent with the experimentally preferred metabolite. For diclofenac, the pathway leading to the experimentally observed metabolite is not the one with the lowest activation energy. This apparent inconsistency with experiment might be explained by the two very different binding modes involved in oxidation at the two competing positions. The carboxylate of diclofenac interacts strongly with the CYP2C9 Arg108 side chain in the transition state for formation of the observed metabolite-but not in that for the competing pathway. We compare reaction barriers calculated both in the presence and in the absence of the protein and observe a marked improvement in selectivity prediction ability upon inclusion of the protein for all of the substrates studied. The barriers calculated with the protein are generally higher than those calculated in the gas phase. This suggests that active-site residues surrounding the substrate play an important role in controlling selectivity in CYP2C9. The results show that inclusion of sampling (particularly) and dispersion effects is important in making accurate predictions of drug metabolism selectivity of P450s using QM/MM methods.

Simulating DNA by molecular dynamics: aims, methods, and validation.

The structure and dynamics of the B-DNA double helix involves subtle sequence-dependent effects which are decisive for its function, but difficult to characterize. These structural and dynamic effects can be addressed by simulations of DNA sequences in explicit solvent. Here, we present and discuss the state-of-art of B-DNA molecular dynamics simulations with the major force fields in use today. We explain why a critical analysis of the MD trajectories is required to assess their reliability, and estimate the value and limitations of these models. Overall, simulations of DNA bear great promise towards deciphering the structural and physical subtleties of this biopolymer, where much remains to be understood.

Understanding the pK(a) of redox cysteines: the key role of hydrogen bonding.

Many cellular functions involve cysteine chemistry via thiol-disulfide exchange pathways. The nucleophilic cysteines of the enzymes involved are activated as thiolate. A thiolate is much more reactive than a neutral thiol. Therefore, determining and understanding the pK(a)s of functional cysteines are important aspects of biochemistry and molecular biology with direct implications for redox signaling. Here, we describe the experimental and theoretical methods to determine cysteine pK(a) values, and we examine the factors that control these pK(a)s. Drawing largely on experience gained with the thioredoxin superfamily, we examine the roles of solvation, charge-charge, helix macrodipole, and hydrogen bonding interactions as pK(a)-modulating factors. The contributions of these factors in influencing cysteine pK(a)s and the associated chemistry, including the relevance for the reaction kinetics and thermodynamics, are discussed. This analysis highlights the critical role of direct hydrogen bonding to the cysteine sulfur as a key factor modulating the equilibrium between thiol S-H and thiolate S(-). This role is easily understood intuitively and provides a framework for biochemical functional insights.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery.

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.