Ligand solvation in molecular docking

Accounting for solvent in structure-based drug design

Water plays a crucial role in the mediation of protein-ligand interactions, as underscored by the fact that most X-ray crystal structures (of sufficient resolution) of protein-ligand complexes possess water molecules at the protein-ligand interface. In this chapter, the accuracy and reliability of ordered waters observed in crystal structures is discussed. Additionally, the thermodynamic aspects of the inclusion of water in ligand binding to proteins is described, with the goal of providing practical guidelines for dealing with ordered water molecules during structure-guided lead optimization.

Structure-based functional design of drugs: from target to lead compound

Proteomic and genomic discoveries have identified vast numbers of new drug targets for investigation. In the quest to discover drugs that modulate the function of these targets, identification of small-molecule drug leads is one of the earliest steps. Structure-based drug design has emerged as a valuable, inexpensive, and rapid computational resource that identifies lead compounds that are complementary to the structure of the target. Leads identified through this process are biologically evaluated and "hit compounds" with affinity and activity are further optimized. This chapter introduces the process of structure-based drug design, including preparation of the ligand database, preparation of the target structure, docking and scoring, and evaluation.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Statistical potential for modeling and ranking of protein-ligand interactions

Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF(1)) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 Å from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScore(CSD) and ITScore/SE and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp) and the LigScore Web server (http://salilab.org/ligscore/).

Identification of novel inhibitors of tropomyosin-related kinase A through the structure-based virtual screening with homology-modeled protein structure

Tropomyosin-related kinase A (TrkA) is a promising target for the development of cancer and pain therapeutics. Here, we report the first successful example of the use of a structure-based virtual screening to identify novel TrkA inhibitors. The accuracy of the virtual screening was improved by introducing an accurate solvation free energy term into the original AutoDock scoring function. We applied a drug design protocol involving homology modeling, docking analysis of a large chemical library, and enzyme inhibition assays to identify six structurally diverse TrkA inhibitors with K(d) values ranging from 3 to 40 μM. The significant potencies and good physicochemical properties of these drug candidates strongly support their consideration in a development effort that would involve structure-activity relationship (SAR) studies to optimize the inhibitory activities. We also addressed the structural and energetic features associated with binding of the newly identified inhibitors in the ATP-binding site of TrkA. The results indicate that any structural modifications introduced for the purpose of enhancing the activity of TrkA inhibitors should maximize the attractive interactions within the ATP-binding site and simultaneously minimize the desolvation cost for complexation.

Molecular docking: a powerful approach for structure-based drug discovery

Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.

Computational perspectives into plasmepsins structure-function relationship: implications to inhibitors design

The development of efficient and selective antimalariais remains a challenge for the pharmaceutical industry. The aspartic proteases plasmepsins, whose inhibition leads to parasite death, are classified as targets for the design of potent drugs. Combinatorial synthesis is currently being used to generate inhibitor libraries for these enzymes, and together with computational methodologies have been demonstrated capable for the selection of lead compounds. The high structural flexibility of plasmepsins, revealed by their X-ray structures and molecular dynamics simulations, made even more complicated the prediction of putative binding modes, and therefore, the use of common computational tools, like docking and free-energy calculations. In this review, we revised the computational strategies utilized so far, for the structure-function relationship studies concerning the plasmepsin family, with special focus on the recent advances in the improvement of the linear interaction estimation (LIE) method, which is one of the most successful methodologies in the evaluation of plasmepsin-inhibitor binding affinity.

Discrepancy in the near-solute electric dipole moment calculated from the electric field

The electric dipole moment p(r) was computed as the integral of the permanent dipole moment of the solvent molecule μ(r) weighted by the orientational probability distribution Ω(r;O) over all orientations, where O is the orientation of the solvent molecule at r. The relationship between Ω(r;O) and the potential of the mean torque was derived; p(r) is proportional to the electric field E(r) under the following assumptions: (1) the van der Waals (vdW) interaction is independent of the orientation of the solvent molecule at r; (2) the solvent molecule and its electrical effect are modeled as a point dipole moment; (3) the solvent molecule at r is in a region far from the solute; and (4) μE(r) ≪ k(B) T, where k(B) is Boltzmann's constant and T is absolute temperature. The errors caused by calculating near-solute Ω(r) and p(r) from E(r) are unclear. The results show that Ω(r) is inconsistent with the value calculated from E(r) for water molecules in the first and second shells of solute with charge state Q = ±1 e, and a large variation in solvent molecular polarizability γ(mol) (r), which appeared in the first valley of 4πr(2) E(r) for |Q| < 1 e. Nonetheless, p(r) is consistent with the values calculated from E(r) for |Q| ≤ 1 e. The implication is that the assumptions for calculating p(r) can be ignored in the calculation of the solvation free energy of biomolecules, as they pertain to protein folding and protein-protein/ligand interactions.

Identification of novel inhibitors of mitogen-activated protein kinase phosphatase-1 with structure-based virtual screening

Mitogen-activated protein kinase phosphatase-1 (MKP-1) has proved to be an attractive target for the development of therapeutics for the treatment of cancer. We report the first example for a successful application of the structure-based virtual screening to identify the novel inhibitors of MKP-1. It is shown that the efficiency of virtual screening can be enhanced significantly by the incorporation of a new solvation energy term in the scoring function. The newly found inhibitors have desirable physicochemical properties as a drug candidate and reveal a moderate potency with IC(50) values ranging from 20 to 50 μM. Therefore, they deserve a consideration for further development by structure-activity relationship studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the inhibitors in the active site of MKP-1 are discussed in detail.

Structure-based virtual screening approach to the discovery of phosphoinositide 3-kinase alpha inhibitors

Phosphoinositide 3-kinase alpha (PI3Kα) has proved to be an attractive target for the development of therapeutics for the treatment of cancer. Herein we report a successful application of the structure-based virtual screening to identify the novel inhibitors of PI3Kα. These inhibitors have desirable physicochemical properties as a drug candidate and reveal a moderate potency with IC(50) values ranging from 20 to 40 μM. Therefore, they deserve a consideration for further development by structure-activity relationship (SAR) studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the newly identified inhibitors in the ATP-binding site of PI3Kα are addressed in detail.

Rapid context-dependent ligand desolvation in molecular docking

In structure-based screens for new ligands, a molecular docking algorithm must rapidly score many molecules in multiple configurations, accounting for both the ligand's interactions with receptor and its competing interactions with solvent. Here we explore a context-dependent ligand desolvation scoring term for molecular docking. We relate the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation and then use this fraction to scale an atom-by-atom decomposition of the full transfer free energy. The fractional desolvation is precomputed on a scoring grid by numerically integrating over the volume of receptor proximal to a ligand atom, weighted by distance. To test this method's performance, we dock ligands versus property-matched decoys over 40 DUD targets. Context-dependent desolvation better enriches ligands compared to both the raw full transfer free energy penalty and compared to ignoring desolvation altogether, though the improvement is modest. More compellingly, the new method improves docking performance across receptor types. Thus, whereas entirely ignoring desolvation works best for charged sites and overpenalizing with full desolvation works well for neutral sites, the physically more correct context-dependent ligand desolvation is competitive across both types of targets. The method also reliably discriminates ligands from highly charged molecules, where ignoring desolvation performs poorly. Since this context-dependent ligand desolvation may be precalculated, it improves docking reliability with minimal cost to calculation time and may be readily incorporated into any physics-based docking program.

Computer-aided drug discovery and development

Computer-aided approaches have been widely used in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. To identify and design small molecules as clinically effective therapeutics, various computational methods have been evaluated as promising strategies, depending on the purpose and systems of interest. Both ligand and structure-based drug design approaches are powerful technologies, which can be applied to virtual screening for lead identification and optimization. Here, we review the progress in this field and summarize the application of some new technologies we developed. These state-of-the-art tools have been used for the discovery and development of active agents for various diseases, in particular for cancer therapies. The described protocols are appropriate for all drug discovery stages, but expertise is still needed to perform the studies based on the targets of interest.

Comparative study of predictive computational models for nanoparticle-induced cytotoxicity

With the increasing use of nanomaterials incorporated into consumer products, there is a need for developing approaches to establish "quantitative structure-activity relationships" (QSARs). These relationships could be used to predict various biological responses after exposure to nanomaterials for the purposes of risk analysis. This risk analysis is applicable to manufacturers of nanomaterials in an effort to determine potential hazards. Because metal oxide materials are some of the most widely applicable and studied nanoparticle types for incorporation into cosmetics, food packaging, and paints and coatings, we focused on comparing different approaches for establishing QSARs for this class of materials. Metal oxide nanoparticles are believed, by some, to cause alterations in cellular function due to their size and/or surface area. Others have said that these nanomaterials, because of the oxidized state of the metal, do not induce stress in biological tests systems. This controversy highlights the need to systematically develop structure-activity relationships (i.e., the relationship between physicochemical features to the cellular responses) and tools for predicting potential biological effects after a metal oxide nanomaterial exposure. Here, we attempt to identify a set of properties of two specific metal oxide nanomaterials-TiO(2) and ZnO-that could be used to characterize and predict the induced cellular membrane damage of immortalized human lung epithelial cells. We adopt a mathematical modeling approach that uses the engineered nanomaterial size characterized as a dry nanopowder and the nanomaterial behavior in ultrapure water, phosphate buffer, and cell culture media to predict nanomaterial-induced cellular membrane damage (via lactate dehydrogenase release). Results of these studies provide insights on how engineered nanomaterial features influence cellular responses and thereby outline possible approaches for developing and applying predictive computational models for biological responses caused by exposure to nanomaterials.

Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions

The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking. The commonly-used assessment criteria and publicly available protein-ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

Multipole electrostatics in hydration free energy calculations

Hydration free energy (HFE) is generally used for evaluating molecular solubility, which is an important property for pharmaceutical and chemical engineering processes. Accurately predicting HFE is also recognized as one fundamental capability of molecular mechanics force field. Here, we present a systematic investigation on HFE calculations with AMOEBA polarizable force field at various parameterization and simulation conditions. The HFEs of seven small organic molecules have been obtained alchemically using the Bennett Acceptance Ratio method. We have compared two approaches to derive the atomic multipoles from quantum mechanical calculations: one directly from the new distributed multipole analysis and the other involving fitting to the electrostatic potential around the molecules. Wave functions solved at the MP2 level with four basis sets (6-311G*, 6-311++G(2d,2p), cc-pVTZ, and aug-cc-pVTZ) are used to derive the atomic multipoles. HFEs from all four basis sets show a reasonable agreement with experimental data (root mean square error 0.63 kcal/mol for aug-cc-pVTZ). We conclude that aug-cc-pVTZ gives the best performance when used with AMOEBA, and 6-311++G(2d,2p) is comparable but more efficient for larger systems. The results suggest that the inclusion of diffuse basis functions is important for capturing intermolecular interactions. The effect of long-range correction to van der Waals interaction on the hydration free energies is about 0.1 kcal/mol when the cutoff is 12Å, and increases linearly with the number of atoms in the solute/ligand. In addition, we also discussed the results from a hybrid approach that combines polarizable solute with fixed-charge water in the HFE calculation.

Discovery of the inhibitors of tumor necrosis factor alpha with structure-based virtual screening

Tumor necrosis factor alpha (TNF-α) has been considered as one of the attractive drug targets for allergic diseases including asthma. We have been able to identify five novel TNF-α inhibitors with a drug-design protocol involving the structure-based virtual screening and in vitro cell-based assay for antagonistic activity. Because the newly discovered inhibitors are structurally diverse and have the desirable physicochemical properties as a drug candidate, they deserve a further investigation as anti-asthmatic drugs. The interactions of the identified inhibitors in the binding site of TNF-α dimer are addressed in detail to understand the mechanisms for the stabilization of the inactive dimeric form of TNF-α.

The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities

The Binding Energy Distribution Analysis Method (BEDAM) for the computation of receptor-ligand standard binding free energies with implicit solvation is presented. The method is based on a well established statistical mechanics theory of molecular association. It is shown that, in the context of implicit solvation, the theory is homologous to the test particle method of solvation thermodynamics with the solute-solvent potential represented by the effective binding energy of the protein-ligand complex. Accordingly, in BEDAM the binding constant is computed by means of a weighted integral of the probability distribution of the binding energy obtained in the canonical ensemble in which the ligand is positioned in the binding site but the receptor and the ligand interact only with the solvent continuum. It is shown that the binding energy distribution encodes all of the physical effects of binding. The balance between binding enthalpy and entropy is seen in our formalism as a balance between favorable and unfavorable binding modes which are coupled through the normalization of the binding energy distribution function. An efficient computational protocol for the binding energy distribution based on the AGBNP2 implicit solvent model, parallel Hamiltonian replica exchange sampling and histogram reweighting is developed. Applications of the method to a set of known binders and non-binders of the L99A and L99A/M102Q mutants of T4 lysozyme receptor are illustrated. The method is able to discriminate without error binders from non-binders, and the computed standard binding free energies of the binders are found to be in good agreement with experimental measurements. Analysis of the results reveals that the binding affinities of these systems reflect the contributions from multiple conformations spanning a wide range of binding energies.

A structure-based virtual screening approach toward the discovery of histone deacetylase inhibitors: identification of promising zinc-chelating groups

The inhibitors of histone deacetylases (HDACs) have drawn a great deal of attention due to their promising potential as small-molecule therapeutics for the treatment of cancer. By means of virtual screening with docking simulations under consideration of the effects of ligand solvation, we were able to identify six novel HDAC inhibitors with IC(50) values ranging from 1 to 100 muM. These newly identified inhibitors are structurally diverse and have various chelating groups for the active site zinc ion, including N-[1,3,4]thiadiazol-2-yl sulfonamide, N-thiazol-2-yl sulfonamide, and hydroxamic acid moieties. The former two groups are included in many drugs in current clinical use and have not yet been reported as HDAC inhibitors. Therefore, they can be considered as new inhibitor scaffolds for the development of anticancer drugs by structure-activity relationship studies to improve the inhibitory activities against HDACs. Interactions with the HDAC1 active site residues responsible for stabilizing these new inhibitors are addressed in detail.

Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone.

Structure-based discovery of A2A adenosine receptor ligands

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A_2A adenosine receptor, based on complementarity to its recently determined structure. The A_2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A_2A versus the related A₁ and A₃ subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.

Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field

The accurate prediction of protein-ligand binding free energies is a primary objective in computer-aided drug design. The solvation free energy of a small molecule provides a surrogate to the desolvation of the ligand in the thermodynamic process of protein-ligand binding. Here, we use explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules. We also compare the performance of absolute solvation free energies obtained using the OPLS_2005 force field with two other commonly used small molecule force fields-general AMBER force field (GAFF) with AM1-BCC charges and CHARMm-MSI with CHelpG charges. Using the OPLS_2005 force field, we obtain high correlation with experimental solvation free energies (R(2) = 0.94) and low average unsigned errors for a majority of the functional groups compared to AM1-BCC/GAFF or CHelpG/CHARMm-MSI. However, OPLS_2005 has errors of over 1.3 kcal/mol for certain classes of polar compounds. We show that predictions on these compound classes can be improved by using a semiempirical charge assignment method with an implicit bond charge correction.

Structure-based virtual screening approach to the discovery of novel inhibitors of factor-inhibiting HIF-1: identification of new chelating groups for the active-site ferrous ion

The inhibitors of factor-inhibiting HIF-1 (FIH1) have been shown to be useful as therapeutics for the treatment of anemia. We have been able to identify eight novel FIH1 inhibitors with IC(50) values ranging from 30 to 80microM by means of the virtual screening with docking simulations under consideration of the effects of ligand solvation in the scoring function. The newly identified inhibitors are structurally diverse and have various chelating groups for the active-site ferrous ion including sulfonamide, carboxylate, N-benzo[1,2,5]oxadiazol-4-yl amide, and 2-[1,2,4]triazolo[3,4-b]][1,3,4]thiadiazol-3-yl-quinoline moieties. Each of these four structural classes has not been reported as FIH1 inhibitor, and therefore can be considered for further development by structure-activity relationship or denovo design methods. The interactions with the amino acid residues responsible for the stabilizations of the inhibitors in the active site are addressed in detail.

Structure-based virtual screening approach to identify novel classes of Cdc25B phosphatase inhibitors

Discovery of Cdc25B phosphatase inhibitors has been actively pursued with the aim to develop anticancer agents. We have been able to identify eight novel Cdc25B inhibitors by means of a computer-aided drug design protocol involving the virtual screening with docking simulations under consideration of the effects of ligand solvation in the binding free energy function. Structural features relevant to the interactions of the newly identified inhibitors with the active-site residues of Cdc25B are also discussed in detail.

Binding of small-molecule ligands to proteins: "what you see" is not always "what you get"

We review insights from computational studies of affinities of ligands binding to proteins. The power of structural biology is in translating knowledge of protein structures into insights about their forces, binding, and mechanisms. However, the complementary power of computer modeling is in showing "the rest of the story" (i.e., how motions and ensembles and alternative conformers and the entropies and forces that cannot be seen in single molecular structures also contribute to binding affinities). Upon binding to a protein, a ligand can bind in multiple orientations; the protein or ligand can be deformed by the binding event; waters, ions, or cofactors can have unexpected involvement; and conformational or solvation entropies can sometimes play large and otherwise unpredictable roles. Computer modeling is helping to elucidate these factors.

Structure-based discovery of beta2-adrenergic receptor ligands

Aminergic G protein-coupled receptors (GPCRs) have been a major focus of pharmaceutical research for many years. Due partly to the lack of reliable receptor structures, drug discovery efforts have been largely ligand-based. The recently determined X-ray structure of the beta(2)-adrenergic receptor offers an opportunity to investigate the advantages and limitations inherent in a structure-based approach to ligand discovery against this and related GPCR targets. Approximately 1 million commercially available, "lead-like" molecules were docked against the beta(2)-adrenergic receptor structure. On testing of 25 high-ranking molecules, 6 were active with binding affinities <4 microM, with the best molecule binding with a K(i) of 9 nM (95% confidence interval 7-10 nM). Five of these molecules were inverse agonists. The high hit rate, the high affinity of the most potent molecule, the discovery of unprecedented chemotypes among the new inhibitors, and the apparent bias toward inverse agonists among the docking hits, have implications for structure-based approaches against GPCRs that recognize small organic molecules.

Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

BACKGROUND

Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm.

RESULTS

The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1.

CONCLUSION

On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.

Computations of Absolute Solvation Free Energies of Small Molecules Using Explicit and Implicit Solvent Model

Accurate determination of absolute solvation free energy plays a critical role in numerous areas of biomolecular modeling and drug discovery. A quantitative representation of ligand and receptor desolvation, in particular, is an essential component of current docking and scoring methods. Furthermore, the partitioning of a drug between aqueous and nonpolar solvents is one of the important factors considered in pharmacokinetics. In this study, the absolute hydration free energy for a set of 239 neutral ligands spanning diverse chemical functional groups commonly found in drugs and drug-like candidates is calculated using the molecular dynamics free energy perturbation method (FEP/MD) with explicit water molecules, and compared to experimental data as well as its counterparts obtained using implicit solvent models. The hydration free energies are calculated from explicit solvent simulations using a staged FEP procedure permitting a separation of the total free energy into polar and nonpolar contributions. The nonpolar component is further decomposed into attractive (dispersive) and repulsive (cavity) components using the Weeks-Chandler-Anderson (WCA) separation scheme. To increase the computational efficiency, all of the FEP/MD simulations are generated using a mixed explicit/implicit solvent scheme with a relatively small number of explicit TIP3P water molecules, in which the influence of the remaining bulk is incorporated via the spherical solvent boundary potential (SSBP). The performances of two fixed-charge force fields designed for small organic molecules, the General Amber force field (GAFF), and the all-atom CHARMm-MSI, are compared. Because of the crucial role of electrostatics in solvation free energy, the results from various commonly used charge generation models based on the semiempirical (AM1-BCC) and QM calculations [charge fitting using ChelpG and RESP] are compared. In addition, the solvation free energies of the test set are also calculated using Poisson-Boltzmann (PB) and Generalized Born model of solvation (GB), which are two widely used continuum electrostatic implicit solvent models. The protocol for running the absolute solvation free energy calculations used throughout is automated as much as possible, with minimum user intervention, so that it can be used in large-scale analysis and force field optimization.

Molecular docking and ligand specificity in fragment-based inhibitor discovery

Fragment screens have successfully identified new scaffolds in drug discovery, often with relatively high hit rates (5%) using small screening libraries (1,000-10,000 compounds). This raises two questions: would other noteworthy chemotypes be found were one to screen all commercially available fragments (>300,000), and does the success rate imply low specificity of fragments? We used molecular docking to screen large libraries of fragments against CTX-M beta-lactamase. We identified ten millimolar-range inhibitors from the 69 compounds tested. The docking poses corresponded closely to the crystallographic structures subsequently determined. Notably, these initial low-affinity hits showed little specificity between CTX-M and an unrelated beta-lactamase, AmpC, which is unusual among beta-lactamase inhibitors. This is consistent with the idea that the high hit rates among fragments correlate to a low initial specificity. As the inhibitors were progressed, both specificity and affinity rose together, yielding to our knowledge the first micromolar-range noncovalent inhibitors against a class A beta-lactamase.

Computations of standard binding free energies with molecular dynamics simulations

An increasing number of studies have reported computations of the standard (absolute) binding free energy of small ligands to proteins using molecular dynamics (MD) simulations and explicit solvent molecules that are in good agreement with experiments. This encouraging progress suggests that physics-based approaches hold the promise of making important contributions to the process of drug discovery and optimization in the near future. Two types of approaches are principally used to compute binding free energies with MD simulations. The most widely known is the alchemical double decoupling method, in which the interaction of the ligand with its surroundings are progressively switched off. It is also possible to use a potential of mean force (PMF) method, in which the ligand is physically separated from the protein receptor. For both of these computational approaches, restraining potentials may be activated and released during the simulation for sampling efficiently the changes in translational, rotational, and conformational freedom of the ligand and protein upon binding. Because such restraining potentials add bias to the simulations, it is important that their effects be rigorously removed to yield a binding free energy that is properly unbiased with respect to the standard state. A review of recent results is presented, and differences in computational methods are discussed. Examples of computations with T4-lysozyme mutants, FKBP12, SH2 domain, and cytochrome P450 are discussed and compared. Remaining difficulties and challenges are highlighted.

Identification of novel inhibitors of extracellular signal-regulated kinase 2 based on the structure-based virtual screening

Extracellular signal-regulated kinase 2 (ERK2) has become an attractive target for the development of therapeutics for the treatment of cancer. We have been able to identify eight new inhibitors of ERK2 by means of a drug design protocol involving the virtual screening with docking simulations and in vitro enzyme assay. The newly discovered inhibitors can be categorized into three structural classes and reveal a significant potency with IC(50) values ranging from 1 to 30 microM. Therefore, all of the three inhibitor scaffolds deserve further development by structure-activity relationship or de novo design methods. Structural features relevant to the stabilizations of the newly identified inhibitors in the ATP-binding site of ERK2 are discussed in detail.

Discovery of novel Cdc25 phosphatase inhibitors with micromolar activity based on the structure-based virtual screening

Cdc25 phosphatases have been considered as attractive drug targets for anticancer therapy because of the correlation of their overexpression with a wide variety of cancers. We have been able to identify five novel Cdc25 phosphatase inhibitors with micromolar activity by means of a computer-aided drug design protocol involving the homology modeling of Cdc25A and the virtual screening with the automated AutoDock program implementing the effects of ligand solvation in the scoring function. Because the newly discovered inhibitors are structurally diverse and reveal a significant potency with IC 50 values lower than 10 microM, they can be considered for further development by structure-activity relationship studies or de novo design methods. The differences in binding modes of the identified inhibitors in the active sites of Cdc25A and B are discussed in detail.

Toward the virtual screening of Cdc25A phosphatase inhibitors with the homology modeled protein structure

Cdc25 phosphatases have been considered as attractive drug targets for anticancer therapy due to the correlation of their overexpression with a wide variety of cancers. As a method for the discovery of novel inhibitors of Cdc25 phosphatases, we have evaluated the computer-aided drug design protocol involving the homology modeling of Cdc25A and virtual screening with the two docking tools: FlexX and the modified AutoDock program implementing the effects of ligand solvation in the scoring function. The homology modeling with the X-ray crystal structure of Cdc25B as a template provides a high-quality structure of Cdc25A that enables the structure-based inhibitor design. Of the two docking programs under consideration, AutoDock is found to be more accurate than FlexX in terms of scoring putative ligands. A detailed binding mode analysis of the known inhibitors shows that they can be stabilized in the active site of Cdc25A through the simultaneous establishment of the multiple hydrogen bonds and the hydrophobic interactions. The present study demonstrates the usefulness of the modified AutoDock program as a docking tool for virtual screening of new Cdc25 phosphatase inhibitors as well as for binding mode analysis to elucidate the activities of known inhibitors.

Discovery and biological evaluation of novel alpha-glucosidase inhibitors with in vivo antidiabetic effect

Discovery of alpha-glucosidase inhibitors has been actively pursued with the aim to develop therapeutics for the treatment of diabetes and the other carbohydrate-mediated diseases. We have identified four novel alpha-glucosidase inhibitors by means of a drug design protocol involving the structure-based virtual screening under consideration of the effects of ligand solvation in the scoring function and in vitro enzyme assay. Because the newly identified inhibitors reveal in vivo antidiabetic activity as well as a significant potency with more than 70% inhibition of the catalytic activity of alpha-glucosidase at 50 microM, all of them seem to deserve further development to discover new drugs for diabetes. Structural features relevant to the interactions of the newly identified inhibitors with the active site residues of alpha-glucosidase are discussed in detail.

A statistical rescoring scheme for protein-ligand docking: Consideration of entropic effect

Computational prediction of protein-ligand binding modes provides useful information on the relationship between structure and activity needed for drug design. A statistical rescoring method that incorporates entropic effect is proposed to improve the accuracy of binding mode prediction. A probability function for two sampled conformations to belong to the same broad basin in the potential energy surface is introduced to estimate the contribution of the state represented by a sampled conformation to the configurational integral. The rescoring function is reduced to the colony energy introduced by Xiang et al. (Proc Natl Acad Sci USA 2002;99:7432-7437) when a particular functional form for the probability function is used. The scheme is applied to rescore protein-ligand complex conformations generated by AutoDock. It is demonstrated that this simple rescoring improves prediction accuracy substantially when tested on 163 protein-ligand complexes with known experimental structures. For example, the percentage of complexes for which predicted ligand conformations are within 1 A root-mean-square deviation from the native conformations is doubled from about 20% to more than 40%. Rescoring with 11 different scoring functions including AutoDock scoring functions were also tested using the ensemble of conformations generated by Wang et al. (J Med Chem 2003;46:2287-2303). Comparison with other methods that use clustering and estimation of conformational entropy is provided. Examination of the docked poses reveals that the rescoring corrects the predictions in which ligands are tightly fit into the binding pockets and have low energies, but have too little room for conformational freedom and thus have low entropy.