Evaluation of library ranking efficacy in virtual screening

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Computer-aided design of 1,4-naphthoquinone-based inhibitors targeting cruzain and rhodesain cysteine proteases

Chagas disease and Human African Trypanosomiasis (HAT) are caused by Trypanosoma cruzi and T. brucei parasites, respectively. Cruzain (CRZ) and Rhodesain (RhD) are cysteine proteases that share 70% of identity and play vital functions in these parasites. These macromolecules represent promising targets for designing new inhibitors. In this context, 26 CRZ and 5 RhD 3D-structures were evaluated by molecular redocking to identify the most accurate one to be utilized as a target. Posteriorly, a virtual screening of a library containing 120 small natural and nature-based compounds was performed on both of them. In total, 14 naphthoquinone-based analogs were identified, synthesized, and biologically evaluated. In total, five compounds were active against RhD, being three of them also active on CRZ. A derivative of 1,4-naphthoquinonepyridin-2-ylsulfonamide was found to be the most active molecule, exhibiting IC₅₀ values of 6.3 and 1.8 µM for CRZ and RhD, respectively. Dynamic simulations at 100 ns demonstrated good stability and do not alter the targets' structures. MM-PBSA calculations revealed that it presents a higher affinity for RhD (-25.3 Kcal mol^-1) than CRZ, in which van der Waals interactions were more relevant. A mechanistic hypothesis (via C3-Michael-addition reaction) involving a covalent mode of inhibition for this compound towards RhD was investigated by covalent molecular docking and DFT B3LYP/6-31 + G* calculations, exhibiting a low activation energy (ΔG^‡) and providing a stable product (ΔG), with values of 7.78 and - 39.72 Kcal mol^-1, respectively; similar to data found in the literature. Nevertheless, a reversibility assay by dilution revealed that JN-11 is a time-dependent and reversible inhibitor. Finally, this study applies modern computer-aided techniques to identify promising inhibitors from a well-known chemical class of natural products. Then, this work could inspire other future studies in the field, being useful for designing potent naphthoquinones as RhD inhibitors.

Docking structurally similar analogues: Dealing with the false-positive

Analogue design forms one of the mainstays of new drug discovery. A fast-follow on approach is commonly used by modern day drug discoverers on the quest of the best in class. Monitoring close structural analogues of the pioneering drug by an algorithm such as docking is fraught with the risk of returning false positives. In this paper, we present the case of two near-pharmacophoric analogues of the JAK3 inhibitor Tasocitinib which give positive docking prediction despite being inactive. Post-processing the docked poses with MM/GBSA and parallel computation of electrostatic potential maps point towards a potential weakening of one of the crucial hydrogen bonds (hinge) within the ATP-binding pocket of JAK3. Perturbing the bound ligands by molecular dynamics (MD) simulations show the complexes to be unstable with the analogues losing their original hold on the protein within 1.2 ns. A short post MD simulation dramatically improves the prediction value of docking runs, especially when dealing with 'me-too' analogues.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Discovery of a 3,4,5-trisubstituted-1,2,4-triazole agonist with high affinity and selectivity at the somatostatin subtype-4 (sst4) receptor

A series of compounds containing a 1,2,4-triazole moiety were synthesized, targeting the somatostatin receptor subtype-4 (sst4). Compounds were developed in which the Phe6/Phe7/Phe11, Trp8, and Lys9 mimetic groups were interchanged at positions 3, 4, and 5 of the 1,2,4-triazole ring. The 1,2,4-triazoles containing an 2-(imidazol-4-yl)ethyl substituent at position-3 demonstrated moderate binding affinity at sst4. 1,2,4-Triazoles containing an (indol-3-yl)methyl substituent at position-5 lacked affinity at sst4. The 1,2,4-triazoles containing an aminopropyl group at position-4 showed enhanced binding affinity compared to the 3-position. One compound with an 3-(imidazol-4-yl)propyl group at position-4 (compound 44) imparted high affinity and selectivity at sst4 (sst2A = >10 000 nM; sst4 = 19 nM), acting as an agonist (EC50 = 6.8 nM). Docking 44 into a model-built structure of sst4 pointed to differences in its binding versus the other low-affinity compounds and was also in line with one of the two previously reported binding modes. A virtual screening (VS) experiment, employing two separate docking algorithms, was able to score 44 among the top-ranked poses. In summary, compound 44 represents a novel and promising lead structure towards the development of a clinically viable sst4 agonist for the treatment of conditions ranging from Alzheimer's disease to chronic pain.

Facile Syntheses and Molecular-Docking of Novel Substituted 3,4-Dimethyl-1H-pyrrole-2-carboxamide/carbohydrazide Analogues with Antimicrobial and Antifungal Properties

The article describes the use of facile one-pot, high-yielding reactions to synthesize substituted 3,4-dimethyl-1H-pyrrole-2-carboxamides 3a–m and carbohydrazide analogues 5a–l as potential antifungal and antimicrobial agents. The structural identity and purity of the synthesized compounds were assigned based on appropriate spectroscopic techniques. Synthesized compounds were assessed in vitro for antifungal and antibacterial activity. The compounds 5h, 5i and 5j were found to be the most potent against Aspergillusfumigatus, with MIC values of 0.039 mg/mL. The compound 5f bearing a 2, 6-dichloro group on the phenyl ring was found to be the most active broad spectrum antibacterial agent with a MIC value of 0.039 mg/mL. The mode of action of the most promising antifungal compounds (one representative from each series; 3j and 5h) was established by their molecular docking with the active site of sterol 14α-demethylase. Molecular docking studies revealed a highly spontaneous binding ability of the tested compounds in the access channel away from catalytic heme iron of the enzyme, which suggested that the tested compounds inhibit this enzyme and would avoid heme iron-related deleterious side effects observed with many existing antifungal compounds.

Docking and Virtual Screening in Drug Discovery

Stages in a typical drug discovery organization include target selection, hit identification, lead optimization, preclinical and clinical studies. Hit identification and lead optimization are very much intertwined with computational modeling. Structure-based virtual screening (VS) has been a staple for more than a decade now in drug discovery with its underlying computational technique, docking, extensively studied. Depending on the objective, the parameters for VS may change, but the overall protocol is very straightforward. The idea behind VS is that a library of small compounds are docked into the binding pocket of a protein (e.g., receptor, enzyme), a number of solutions per molecule, among the top-ranked, are being returned, and a choice is made on the fraction of compounds to be moved forward for testing toward hit identification. The underlying principle of VS is that it differentiates between active and inactive compounds, thus reducing the number of molecules moving forward and possibly offering a complementary tool to high-throughput screening (HTS). Best practices in library selection, target preparation and refinement, criteria in selecting the most appropriate docking/scoring scheme, and a step-wise approach in performing Glide VS are discussed.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

Molecular Shape Analysis-Guided Virtual Screening Platform for Adenosine Kinase Inhibitors

We propose a new application of molecular shape descriptors in hierarchical selection during virtual screening (VS). Here, a structure-based pharmacophore and docking-guided VS protocol have been evolved to identify inhibitors against adenosine kinase (AK). The knowledge gained on the shape requirements has been extrapolated in classifying active and inactive molecules against this target. This classification enabled us to pick the appropriate ligand conformation in the binding site. We have suggested a set of hierarchical filters for VS, from a simple molecular shape analysis (MSA) descriptor-based recursive models to docking scores. This approach permits a systematic study to understand the importance of spatial requirements and limitations for inhibitors against AK. Finally, the guidelines on how to select compounds for AK to achieve success have been highlighted. The utility of this approach has been suggested by giving an example of database screening for plausible active compounds.

Analysis of Enoyl-Acyl Carrier Protein Reductase Structure and Interactions Yields an Efficient Virtual Screening Approach and Suggests a Potential Allosteric Site

Enoyl-acyl carrier protein reductases have an important role in fatty acid biosynthesis and are considered essential for bacterial and protozoal survival. Here, we perform a computational assessment of enoyl-acyl carrier protein reductase structures, providing insights for inhibitor design that we incorporate into a virtual screening approach. Firstly, we analyse 80 crystal structures of 16 different enoyl-acyl carrier protein reductases for their active site characteristics and druggability, finding these sites contain a readily druggable pocket, of varying size and shape. Interestingly, a high affinity, potentially allosteric site was identified for pfFabl. Analysis of the ligand-protein interactions of four enoyl-acyl carrier protein reductases from different micro-organisms (InhA, pfFabl, saFabl and ecFabl), involving 59 available crystal structures, found three commonly shared interactions; constraining these interactions in docking improved enrichment of enoyl-acyl carrier protein reductase virtual screens, by up to 60% in the top 3% of the ranked library. This docking protocol also improved pose prediction, decreasing the root-mean-square deviation to crystallographic pose by up to 75% on average. The binding site analysis and knowledge-based docking protocol presented here can potentially assist in the structure-based design of new enoyl-acyl carrier protein reductase inhibitors.

MoDock: A multi-objective strategy improves the accuracy for molecular docking

BACKGROUND

As a main method of structure-based virtual screening, molecular docking is the most widely used in practice. However, the non-ideal efficacy of scoring functions is thought as the biggest barrier which hinders the improvement of the molecular docking method.

RESULTS

A new multi-objective strategy for molecular docking, named as MoDock, is presented to further improve the docking accuracy with available scoring functions. Instead of simple combination of multiple objectives with fixed weight factors, an aggregate function is adopted to approximate the real solution of the original multi-objective and multi-constraint problem, which will simultaneously smooth the energy surface of the combined scoring functions. Then, method of centers and genetic algorithm are used to find the optimal solution. Tests of MoDock against the GOLD test data set reveal the multi-objective strategy improves the docking accuracy over the individual scoring functions. Meanwhile, a 70% ratio of the good docking solutions with the RMSD value below 1.0 Å outperforms other 6 commonly used docking programs, even with a flexible receptor docking program included.

CONCLUSIONS

The results show MoDock is an effective strategy to overcome the deviations brought by single scoring function, and improves the prediction power of molecular docking.

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches.

Validation of a homology model of Mycobacterium tuberculosis DXS: rationalization of observed activities of thiamine derivatives as potent inhibitors of two orthologues of DXS

We present the a homology model ofM. tuberculosisDXS that we validated by identifying thiamine and thiamine diphosphate analogues as potent inhibitors of DXS.

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein-ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening.

Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set

Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein-ligand complexes. This data set was selected out of 8302 protein-ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualified complexes were clustered by 90% similarity in protein sequences. Three representative complexes were chosen from each cluster to control sample redundancy. The final outcome, namely, the PDBbind core set (version 2013), consists of 195 protein-ligand complexes in 65 clusters with binding constants spanning nearly 10 orders of magnitude. In this data set, 82% of the ligand molecules are "druglike" and 78% of the protein molecules are validated or potential drug targets. Correlation between binding constants and several key properties of ligands are discussed. Methods and results of the scoring function evaluation will be described in a companion work in this issue (doi: 10.1021/ci500081m ).

Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results

Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein-ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein-ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of "scoring power" (binding affinity prediction), "ranking power" (relative ranking prediction), "docking power" (binding pose prediction), and "screening power" (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score(HM), ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein-ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein-ligand complexes also need attention.

Improving the scoring of protein-ligand binding affinity by including the effects of structural water and electronic polarization

Docking programs that use scoring functions to estimate binding affinities of small molecules to biological targets are widely applied in drug design and drug screening with partial success. But accurate and efficient scoring functions for protein-ligand binding affinity still present a grand challenge to computational chemists. In this study, the polarized protein-specific charge model (PPC) is incorporated into the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method to rescore the binding poses of some protein-ligand complexes, for which docking programs, such as Autodock, could not predict their binding modes correctly. Different sampling techniques (single minimized conformation and multiple molecular dynamics (MD) snapshots) are used to test the performance of MM/PBSA combined with the PPC model. Our results show the availability and effectiveness of this approach in correctly ranking the binding poses. More importantly, the bridging water molecules are found to play an important role in correctly determining the protein-ligand binding modes. Explicitly including these bridging water molecules in MM/PBSA calculations improves the prediction accuracy significantly. Our study sheds light on the importance of both bridging water molecules and the electronic polarization in the development of more reliable scoring functions for predicting molecular docking and protein-ligand binding affinity.

Improved estimation of protein-ligand binding free energy by using the ligand-entropy and mobility of water molecules

We previously developed the direct interaction approximation (DIA) method to estimate the protein-ligand binding free energy (DG). The DIA method estimates the DG value based on the direct van der Waals and electrostatic interaction energies between the protein and the ligand. In the current study, the effect of the entropy of the ligand was introduced with protein dynamic properties by molecular dynamics simulations, and the interaction between each residue of the protein and the ligand was also weighted considering the hydration of each residue. The molecular dynamics simulation of the apo target protein gave the hydration effect of each residue, under the assumption that the residues, which strongly bind the water molecules, are important in the protein-ligand binding. These two effects improved the reliability of the DIA method. In fact, the parameters used in the DIA became independent of the target protein. The averaged error of DG estimation was 1.3 kcal/mol and the correlation coefficient between the experimental DG value and the calculated DG value was 0.75.

Statistical estimation of the protein-ligand binding free energy based on direct protein-ligand interaction obtained by molecular dynamics simulation

We have developed a method for estimating protein-ligand binding free energy (DG) based on the direct protein-ligand interaction obtained by a molecular dynamics simulation. Using this method, we estimated the DG value statistically by the average values of the van der Waals and electrostatic interactions between each amino acid of the target protein and the ligand molecule. In addition, we introduced fluctuations in the accessible surface area (ASA) and dihedral angles of the protein-ligand complex system as the entropy terms of the DG estimation. The present method included the fluctuation term of structural change of the protein and the effective dielectric constant. We applied this method to 34 protein-ligand complex structures. As a result, the correlation coefficient between the experimental and calculated DG values was 0.81, and the average error of DG was 1.2 kcal/mol with the use of the fixed parameters. These results were obtained from a 2 nsec molecular dynamics simulation.

How well do the substrates KISS the enzyme? Molecular docking program selection for feruloyl esterases

Molecular docking is the most commonly used technique in the modern drug discovery process where computational approaches involving docking algorithms are used to dock small molecules into macromolecular target structures. Over the recent years several evaluation studies have been reported by independent scientists comparing the performance of the docking programs by using default ‘black box’ protocols supplied by the software companies. Such studies have to be considered carefully as the docking programs can be tweaked towards optimum performance by selecting the parameters suitable for the target of interest. In this study we address the problem of selecting an appropriate docking and scoring function combination (88 docking algorithm-scoring functions) for substrate specificity predictions for feruloyl esterases, an industrially relevant enzyme family. We also propose the ‘Key Interaction Score System’ (KISS), a more biochemically meaningful measure for evaluation of docking programs based on pose prediction accuracy.

Leveraging structural information for the discovery of new drugs: computational methods

Escalating problems with drug resistance continue to compromise the effectiveness of commercial antibiotics, necessitating the search for novel classes of antimicrobial agents. To circumvent problems with resistance, a multitarget single-pharmacophore approach has been employed to discover inhibitors that possess balanced activity against multiple target enzymes. In this chapter, we examine the application of computational techniques, in particular, structure-based drug design approaches, to design new dual-targeting antibacterial agents against bacterial topoisomerases.

A structure-based approach to understanding somatostatin receptor-4 agonism (sst4)

It has been reported that somatostatin receptor subtypes 4 and 5 would be high-impact templates for homology modeling if their 3D structures became available. We have generated a homology model of the somatostatin receptor subtype 4 (sst4), using the newest active state β(2) adrenoreceptor crystal structure, and subsequently docked a variety of agonists into the model-built receptor to elucidate the binding modes of reported agonists. Using experimental restraints, we were able to explain observed activity profiles. We propose two binding modes that can consistently explain findings for high-affinity agonists and reason why certain structures display low affinities for the receptor.

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.

Identification of alternative binding sites for inhibitors of HIV-1 ribonuclease H through comparative analysis of virtual enrichment studies

The ribonuclease H (RNase H) domain on the p66 monomer of HIV-1 reverse transcriptase enzyme has become a target for inhibition. The active site is one potential binding site, but other RNase H sites can accommodate inhibitors. Using a combination of experimental and computational studies, potential new binding sites and binding modes have been identified. Libraries of compounds were screened with an experimental assay to identify actives without knowledge of the binding site. The compounds were computationally docked at putative binding sites. Based on positive enrichment of natural-product actives relative to the database of compounds, we propose that many inhibitors bind to an alternative, potentially allosteric, site centered on Q507 of p66. For a series of hydrazone compounds, a small amount of positive enrichment was obtained when active compounds were bound by induced-fit docking at the interface between the DNA:RNA substrate and the RNase H domain near residue Q500.

Using free energy of binding calculations to improve the accuracy of virtual screening predictions

Virtual screening of small molecule databases against macromolecular targets was used to identify binding ligands and predict their lowest energy bound conformation (i.e., pose). AutoDock4-generated poses were rescored using mean-field pathway decoupling free energy of binding calculations and evaluated if these calculations improved virtual screening discrimination between bound and nonbound ligands. Two small molecule databases were used to evaluate the effectiveness of the rescoring algorithm in correctly identifying binders of L99A T4 lysozyme. Self-dock calculations of a database containing compounds with known binding free energies and cocrystal structures largely reproduced experimental measurements, although the mean difference between calculated and experimental binding free energies increased as the predicted bound poses diverged from the experimental poses. In addition, free energy rescoring was more accurate than AutoDock4 scores in discriminating between known binders and nonbinders, suggesting free energy rescoring could be a useful approach to reduce false positive predictions in virtual screening experiments.

Estimating binding affinities by docking/scoring methods using variable protonation states

To investigate the effects of multiple protonation states on protein-ligand recognition, we generated alternative protonation states for selected titratable groups of ligands and receptors. The selection of states was based on the predicted pK(a) of the unbound receptor and ligand and the proximity of titratable groups of the receptor to the binding site. Various ligand tautomer states were also considered. An independent docking calculation was run for each state. Several protocols were examined: using an ensemble of all generated states of ligand and receptor, using only the most probable state of the unbound ligand/receptor, and using only the state giving the most favorable docking score. The accuracies of these approaches were compared, using a set of 176 protein-ligand complexes (15 receptors) for which crystal structures and measured binding affinities are available. The best agreement with experiment was obtained when ligand poses from experimental crystal structures were used. For 9 of 15 receptors, using an ensemble of all generated protonation states of the ligand and receptor gave the best correlation between calculated and measured affinities.

Design, synthesis, and structure-activity relationship exploration of 1-substituted 4-aroyl-3-hydroxy-5-phenyl-1H-pyrrol-2(5H)-one analogues as inhibitors of the annexin A2-S100A10 protein interaction

S100 proteins are small adaptors that regulate the activity of partner proteins by virtue of direct protein interactions. Here, we describe the first small molecule blockers of the interaction between S100A10 and annexin A2. Molecular docking yielded candidate blockers that were screened for competition of the binding of an annexin A2 peptide to S100A10. Several inhibitory clusters were identified with some containing compounds with potency in the lower micromolar range. We chose 3-hydroxy-1-(2-hydroxypropyl)-5-(4-isopropylphenyl)-4-(4-methylbenzoyl)-1H-pyrrol-2(5H)-one (1a) as a starting point for structure-activity studies. These confirmed the hypothetical binding mode from the virtual screen for this series of molecules. Selected compounds disrupted the physiological complex of annexin A2 and S100A10, both in a broken cell preparation and inside MDA-MB-231 breast cancer cells. Thus, this class of compounds has promising properties as inhibitors of the interaction between annexin A2 and S100A10 and may help to elucidate the cellular function of this protein interaction.

Structure-based library design in efficient discovery of novel inhibitors

Structure-based library design employs both structure-based drug design (SBDD) and combinatorial library design. Combinatorial library design concepts have evolved over the past decade, and this chapter covers several novel aspects of structure-based library design together with successful case studies in the anti-viral drug design HCV target area. Discussions include reagent selections, diversity library designs, virtual screening, scoring/ranking, and post-docking pose filtering, in addition to the considerations of chemistry synthesis. Validation criteria for a successful design include an X-ray co-crystal complex structure, in vitro biological data, and the number of compounds to be made, and these are addressed in this chapter as well.

Computer-assisted combinatorial design of bicyclic thymidine analogs as inhibitors of Mycobacterium tuberculosis thymidine monophosphate kinase

Thymidine monophosphate kinase (TMPK(mt)) is an essential enzyme for nucleotide metabolism in Mycobacterium tuberculosis, and thus an attractive target for novel antituberculosis agents. In this work, we have explored the chemical space around the 2',3'-bicyclic thymidine nucleus by designing and in silico screening of a virtual focused library selected via structure based methods to identify more potent analogs endowed with favorable ADME-related properties. In all the library members we have exchanged the ribose ring of the template with a cyclopentane moiety that is less prone to enzymatic degradation. In addition, we have replaced the six-membered 2',3'-ring by a number of five-membered and six-membered heterocyclic rings containing alternative proton donor and acceptor groups, to exploit the interaction with the carboxylate groups of Asp9 and Asp163 as well as with several cationic residues present in the vicinity of the TMPK(mt) binding site. The three-dimensional structure of the TMPK(mt) complexed with 5-hydroxymethyl-dUMP, an analog of dTMP, was employed to develop a QSAR model, to parameterize a scoring function specific for the TMPK(mt) target and to select analogues which display the highest predicted binding to the target. As a result, we identified a small highly focused combinatorial subset of bicyclic thymidine analogues as virtual hits that are predicted to inhibit the mycobacterial TMPK in the submicromolar concentration range and to display favorable ADME-related properties.

Comparison of several molecular docking programs: pose prediction and virtual screening accuracy

Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.

Comparative assessment of scoring functions on a diverse test set

Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.

Validation of molecular docking programs for virtual screening against dihydropteroate synthase

Dihydropteroate synthase (DHPS) is the target of the sulfonamide class of antibiotics and has been a validated antibacterial drug target for nearly 70 years. The sulfonamides target the p-aminobenzoic acid (pABA) binding site of DHPS and interfere with folate biosynthesis and ultimately prevent bacterial replication. However, widespread bacterial resistance to these drugs has severely limited their effectiveness. This study explores the second and more highly conserved pterin binding site of DHPS as an alternative approach to developing novel antibiotics that avoid resistance. In this study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, and nine scoring functions, were evaluated for their ability to rank-order potential lead compounds for an extensive virtual screening study of the pterin binding site of B. anthracis DHPS. Their performance in ligand docking and scoring was judged by their ability to reproduce a known inhibitor conformation and to efficiently detect known active compounds seeded into three separate decoy sets. Two other metrics were used to assess performance; enrichment at 1% and 2% and Receiver Operating Characteristic (ROC) curves. The effectiveness of postdocking relaxation prior to rescoring and consensus scoring were also evaluated. Finally, we have developed a straightforward statistical method of including the inhibition constants of the known active compounds when analyzing enrichment results to more accurately assess scoring performance, which we call the 'sum of the sum of log rank' or SSLR. Of the docking and scoring functions evaluated, Surflex with Surflex-Score and Glide with GlideScore were the best overall performers for use in virtual screening against the DHPS target, with neither combination showing statistically significant superiority over the other in enrichment studies or pose selection. Postdocking ligand relaxation and consensus scoring did not improve overall enrichment.

Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits

Several studies have suggested that disrupting interactions of the G protein betagamma subunits with downstream binding partners might be a valuable study for pharmaceutical development. Recently, small molecules have been found which bind to Gbetagamma with high apparent affinity in an enzyme-linked immunosorbent assay (ELISA), have demonstrated selective inhibition of interactions of Gbetagamma with downstream signaling partners, and have been shown to increase antinociceptive effects of morphine and inhibit inflammation in vivo. In this paper we examine several docking and scoring protocols for estimating binding affinities for a set of 830 ligands from the NCI diversity set to the Gbeta1gamma2 subunit and compared these with IC50s measured in a competition ELISA with a high-affinity peptidic ligand. The best-performing docking protocol used a consensus score and ensemble docking and resulted in a 6-fold enrichment of high-affinity compounds in the top-ranked 5% of the ligand data set.

Combining docking with pharmacophore filtering for improved virtual screening

BACKGROUND

Virtual screening is used to distinguish potential leads from inactive compounds in a database of chemical samples. One method for accomplishing this is by docking compounds into the structure of a receptor binding site in order to rank-order compounds by the quality of the interactions they form with the receptor. It is generally established that docking can be reasonably successful at generating good poses of a ligand in an active site. However, the scoring functions that are used with docking are typically not successful at correctly ranking ligands according to binding affinity or even distinguishing correct poses of a given ligand from incorrect ones.

RESULTS

We have developed a simple method for reducing the number of false positives in a virtual screen, meaning ligands which are scored highly by the docking program but do not bind well in reality. This method uses a docking program for pose generation without regard to scoring, followed by filtering with receptor-based pharmacophore searches. We applied it to three test-case targets: neuraminidase A, cyclin-dependent kinase 2, and the C1 domain of protein kinase C.

CONCLUSION

The pharmacophore filtering method can perform better than more traditional docking + scoring methods, and allows the advantages of both docking-based and pharmacophore-based approaches to virtual screening to be fully realized.

Virtual screening to identify lead inhibitors for bacterial NAD synthetase (NADs)

Virtual screening was employed to identify new drug-like inhibitors of NAD synthetase (NADs) as antibacterial agents. Four databases of commercially available compounds were docked against three subsites of the NADs active site using FlexX in conjunction with CScore. Over 200 commercial compounds were purchased and evaluated in enzyme inhibition and antibacterial assays. 18 compounds inhibited NADs at or below 100 microM (7.6% hit rate), and two were selected for future SAR studies.

Berberine potently inhibits protein tyrosine phosphatase 1B: Investigation by docking simulation and experimental validation

Berberine was investigated as an inhibitor of human protein tyrosine phosphatase 1B (h-PTP 1B) in an attempt to explain its anti-hyperglycemic activitiy. The investigation included simulated docking experiments to fit berberine within the binding pocket of h-PTP 1B. Berberine was found to readily fit within the binding pocket of h-PTP 1B in a low energy orientation characterized with optimal electrostatic attractive interactions bridging the isoquinolinium positively charged nitrogen atom of berberine and the negatively charged acidic residue of ASP 48 of h-PTP 1B. Experimentally, berberine was found to potently competitively inhibit recombinant h-PTP 1B in vitro (Ki value = 91.3 nM). Our findings strongly suggest that h-PTP 1B inhibition is at least one of the reasons for the reported anti-hyperglycemic activities of berberine.