Incorporating dynamics in E. coli dihydrofolate reductase enhances structure-based drug discovery

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Comparing pharmacophore models derived from crystallography and NMR ensembles

NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural aqueous environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.

Structural analysis of dihydrofolate reductases enables rationalization of antifolate binding affinities and suggests repurposing possibilities

Antifolates are competitive inhibitors of dihydrofolate reductase (DHFR), a conserved enzyme that is central to metabolism and widely targeted in pathogenic diseases, cancer and autoimmune disorders. Although most clinically used antifolates are known to be target specific, some display a fair degree of cross-reactivity with DHFRs from other species. A method that enables identification of determinants of affinity and specificity in target DHFRs from different species and provides guidelines for the design of antifolates is currently lacking. To address this, we first captured the potential druggable space of a DHFR in a substructure called the 'supersite' and classified supersites of DHFRs from 56 species into 16 'site-types' based on pairwise structural similarity. Analysis of supersites across these site-types revealed that DHFRs exhibit varying extents of dissimilarity at structurally equivalent positions in and around the binding site. We were able to explain the pattern of affinities towards chemically diverse antifolates exhibited by DHFRs of different site-types based on these structural differences. We then generated an antifolate-DHFR network by mapping known high-affinity antifolates to their respective supersites and used this to identify antifolates that can be repurposed based on similarity between supersites or antifolates. Thus, we identified 177 human-specific and 458 pathogen-specific antifolates, a large number of which are supported by available experimental data. Thus, in the light of the clinical importance of DHFR, we present a novel approach to identifying differences in the druggable space of DHFRs that can be utilized for rational design of antifolates.

Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase

The therapeutic challenges in the treatment of tuberculosis demand multidisciplinary approaches for the identification of potential drug targets as well as fast and accurate techniques to screen huge chemical libraries. Mycobacterial cyclopropane synthase (CmaA1) has been shown to be essential for the survival of the bacteria due to its critical role in the synthesis of mycolic acids. The present study proposes pharmacophore models based on the structure of CmaA1 taking into account its various states in the cyclopropanation process, and their dynamic nature as assessed using molecular dynamics (MD) simulations. The qualities of these pharmacophore models were validated by mapping 23 molecules that have been previously reported to exhibit inhibitory activities on CmaA1. Additionally, 1398 compounds that have been shown to be inactive for tuberculosis were collected from the ChEMBL database and were screened against the models for validation. The models were further validated by comparing the results from pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. The best models are suggested by validating all the models based on their screening abilities and by comparing with docking results. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

Exploiting conformational dynamics in drug discovery: design of C-terminal inhibitors of Hsp90 with improved activities

The interaction that occurs between molecules is a dynamic process that impacts both structural and conformational properties of the ligand and the ligand binding site. Herein, we investigate the dynamic cross-talk between a protein and the ligand as a source for new opportunities in ligand design. Analysis of the formation/disappearance of protein pockets produced in response to a first-generation inhibitor assisted in the identification of functional groups that could be introduced onto scaffolds to facilitate optimal binding, which allowed for increased binding with previously uncharacterized regions. MD simulations were used to elucidate primary changes that occur in the Hsp90 C-terminal binding pocket in the presence of first-generation ligands. This data was then used to design ligands that adapt to these receptor conformations, which provides access to an energy landscape that is not visible in a static model. The newly synthesized compounds demonstrated antiproliferative activity at ∼150 nM concentration. The method identified herein may be used to design chemical probes that provide additional information on structural variations of Hsp90 C-terminal binding site.

Novel phytochemical-antibiotic conjugates as multitarget inhibitors of Pseudomononas aeruginosa GyrB/ParE and DHFR

Background

There is a dearth of treatment options for community-acquired and nosocomial Pseudomonas infections due to several rapidly emerging multidrug resistant phenotypes, which show resistance even to combination therapy. As an alternative, developing selective promiscuous hybrid compounds for simultaneous modulation of multiple targets is highly appreciated because it is difficult for the pathogen to develop resistance when an inhibitor has activity against multiple targets.

Methods

In line with our previous work on phytochemical–antibiotic combination assays and knowledge-based methods, using a fragment combination approach we here report a novel drug design strategy of conjugating synergistic phytochemical–antibiotic combinations into a single hybrid entity for multi-inhibition of P. aeruginosa DNA gyrase subunit B (GyrB)/topoisomerase IV subunit B (ParE) and dihydrofolate reductase (DHFR) enzymes. The designed conjugates were evaluated for their multitarget specificity using various computational methods including docking and dynamic simulations, drug-likeness using molecular properties calculations, and pharmacophoric features by stereoelectronic property predictions.

Results

Evaluation of the designed hybrid compounds based on their physicochemical properties has indicated that they are promising drug candidates with drug-like pharmacotherapeutic profiles. In addition, the stereoelectronic properties such as HOMO (highest occupied molecular orbital), LUMO (lowest unoccupied molecular orbital), and MEP (molecular electrostatic potential) maps calculated by quantum chemical methods gave a good correlation with the common pharmacophoric features required for multitarget inhibition. Furthermore, docking and dynamics simulations revealed that the designed compounds have favorable binding affinity and stability in both the ATP-binding sites of GyrB/ParE and the folate-binding site of DHFR, by forming strong hydrogen bonds and hydrophobic interactions with key active site residues.

Conclusion

This new design concept of hybrid “phyto-drug” scaffolds, and their simultaneous perturbation of well-established antibacterial targets from two unrelated pathways, appears to be very promising and could serve as a prospective lead in multitarget drug discovery.

Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH)

A structure-based drug discovery method is described that incorporates target flexibility through the use of an ensemble of protein conformations. The approach was applied to fatty acid amide hydrolase (FAAH), a key deactivating enzyme in the endocannabinoid system. The resultant dynamic pharmacophore models are rapidly able to identify known FAAH inhibitors over drug-like decoys. Different sources of FAAH conformational ensembles were explored, with both snapshots from molecular dynamics simulations and a group of X-ray structures performing well. Results were compared to those from docking and pharmacophore models generated from a single X-ray structure. Increasing conformational sampling consistently improved the pharmacophore models, emphasizing the importance of incorporating target flexibility in structure-based drug design.

The role of long-range intermolecular interactions in discovery of new drugs

INTRODUCTION

Long-range intermolecular interactions (interactions at distances between 100 and 1000 Å) play an important role in the interaction between drugs and therapeutic targets, and design techniques based on this concept could significantly improve and accelerate new drug discovery. Understanding these long-range intermolecular interactions will also help further our understanding of the molecular mechanisms and the underlying basic biological processes.

AREAS COVERED

This article looks at the physical bases of long-range intermolecular interactions in biological systems with a brief review of the literature data to support this concept. The article also gives some examples of techniques used in drug discovery that were based on the long-range intermolecular interaction concept.

EXPERT OPINION

The electron-ion interaction potential (EIIP) and average quasivalence number (AQVN) concepts shed new light on the role of long-range intermolecular interactions in biological systems. Further research of physicochemical mechanisms underlying long-range interactions between biological molecules is necessary for a better understanding of the basic biological processes. The addition of the computer-aided design techniques based on the EIIP/AQVN concept to the research and development will lead not only to a significant reduction in cost but also to an acceleration in the development of new drugs.

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.

Insights in 17beta-HSD1 enzyme kinetics and ligand binding by dynamic motion investigation

BACKGROUND

Bisubstrate enzymes, such as 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), exist in solution as an ensemble of conformations. 17beta-HSD1 catalyzes the last step of the biosynthesis of estradiol and, thus, it is a potentially attractive target for breast cancer treatment.

METHODOLOGY/PRINCIPAL FINDINGS

To elucidate the conformational transitions of its catalytic cycle, a structural analysis of all available crystal structures was performed and representative conformations were assigned to each step of the putative kinetic mechanism. To cover most of the conformational space, all-atom molecular dynamic simulations were performed using the four crystallographic structures best describing apoform, opened, occluded and closed state of 17beta-HSD1 as starting structures. With three of them, binary and ternary complexes were built with NADPH and NADPH-estrone, respectively, while two were investigated as apoform. Free energy calculations were performed in order to judge more accurately which of the MD complexes describes a specific kinetic step.

CONCLUSIONS/SIGNIFICANCE

Remarkably, the analysis of the eight long range trajectories resulting from this multi-trajectory/-complex approach revealed an essential role played by the backbone and side chain motions, especially of the betaF alphaG'-loop, in cofactor and substrate binding. Thus, a selected-fit mechanism is suggested for 17beta-HSD1, where ligand-binding induced concerted motions of the FG-segment and the C-terminal part guide the enzyme along its preferred catalytic pathway. Overall, we could assign different enzyme conformations to the five steps of the random bi-bi kinetic cycle of 17beta-HSD1 and we could postulate a preferred pathway for it. This study lays the basis for more-targeted biochemical studies on 17beta-HSD1, as well as for the design of specific inhibitors of this enzyme. Moreover, it provides a useful guideline for other enzymes, also characterized by a rigid core and a flexible region directing their catalysis.

Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping

An increasing number of functional studies of proteins have shown that sequence and structural similarities alone may not be sufficient for reliable prediction of their interaction properties. This is particularly true for proteins recognizing specific antibodies, where the prediction of antibody-binding sites, called epitopes, has proven challenging. The antibody-binding properties of an antigen depend on its structure and related dynamics. Aiming to predict the antibody-binding regions of a protein, we investigate a new approach based on the integrated analysis of the dynamical and energetic properties of antigens, to identify nonoptimized, low-intensity energetic interaction networks in the protein structure isolated in solution. The method is based on the idea that recognition sites may correspond to localized regions with low-intensity energetic couplings with the rest of the protein, which allows them to undergo conformational changes, to be recognized by a binding partner, and to tolerate mutations with minimal energetic expense. Upon analyzing the results on isolated proteins and benchmarking against antibody complexes, it is found that the method successfully identifies binding sites located on the protein surface that are accessible to putative binding partners. The combination of dynamics and energetics can thus discriminate between epitopes and other substructures based only on physical properties. We discuss implications for vaccine design.

Virtual Screening with AutoDock: Theory and Practice

IMPORTANCE TO THE FIELD: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules. AREAS COVERED IN THIS REVIEW: We describe virtual screening methods that are available in the AutoDock suite of programs, and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery. WHAT THE READER WILL GAIN: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges. TAKE HOME MESSAGE: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.

Scoring ensembles of docked protein:ligand interactions for virtual lead optimization

Ensembles of protein structures to simulate protein flexibility are widely used throughout several applications including virtual lead optimization where they have been shown to improve ligand ranking. Yet, there is no established convention for weighting individual scores generated from ensemble members. To investigate the best method for weighting ensemble scores for proper ligand ranking, a series of dihydrofolate reductase inhibitors was docked to ensembles of Candida albicans dihydrofolate reductase (CaDHFR) structures created from a molecular dynamics (MD) simulation. From a single MD simulation, two ensemble collections were generated, one of which was subjected to a minimization procedure to create a group of structures of equal probability. As expected, ligand ranking accuracy was significantly improved when Boltzmann weighting was applied to the energies of the ensemble without structural minimization (60%), relative to that achieved with averaging (36%). However, accuracy was further improved (72%) by averaging docking scores across a minimized ensemble. To examine whether this accuracy results from structural variation in the single trajectory versus the possibility that error is minimized by averaging, a third collection of receptor structures was created in which each member was taken from an independent molecular dynamics simulation after minimization. Comparison of the docking accuracy results from the single trajectory (72%) to this third collection (61%) showed decreased accuracy, suggesting that ligands are more accurately oriented and assessed when docked to the minimized ensemble from a single MD trajectory, an effect that is more than simply error minimization. Averaging docking scores over a minimized ensemble of another target, influenza A neuraminidase, yielded a ligand ranking accuracy of 83%, representing a 24% improvement over other methods tested.

Non-peptidic thrombospondin-1 mimics as fibroblast growth factor-2 inhibitors: an integrated strategy for the development of new antiangiogenic compounds

Endogenous inhibitors of angiogenesis, such as thrombospondin-1 (TSP-1), are promising sources of therapeutic agents to treat angiogenesis-driven diseases, including cancer. TSP-1 regulates angiogenesis through different mechanisms, including binding and sequestration of the angiogenic factor fibroblast growth factor-2 (FGF-2), through a site located in the calcium binding type III repeats. We hypothesized that the FGF-2 binding sequence of TSP-1 might serve as a template for the development of inhibitors of angiogenesis. Using a peptide array approach followed by binding assays with synthetic peptides and recombinant proteins, we identified a FGF-2 binding sequence of TSP-1 in the 15-mer sequence DDDDDNDKIPDDRDN. Molecular dynamics simulations, taking the full flexibility of the ligand and receptor into account, and nuclear magnetic resonance identified the relevant residues and conformational determinants for the peptide-FGF interaction. This information was translated into a pharmacophore model used to screen the NCI2003 small molecule databases, leading to the identification of three small molecules that bound FGF-2 with affinity in the submicromolar range. The lead compounds inhibited FGF-2-induced endothelial cell proliferation in vitro and affected angiogenesis induced by FGF-2 in the chicken chorioallantoic membrane assay. These small molecules, therefore, represent promising leads for the development of antiangiogenic agents. Altogether, this study demonstrates that new biological insights obtained by integrated multidisciplinary approaches can be used to develop small molecule mimics of endogenous proteins as therapeutic agents.

In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking

Representing receptors as ensembles of protein conformations during docking is a powerful method to approximate protein flexibility and increase the accuracy of the resulting ranked list of compounds. Unfortunately, docking compounds against a large number of ensemble members can increase computational cost and time investment. In this article, we present an efficient method to evaluate and select the most contributive ensemble members prior to docking for targets with a conserved core of residues that bind a ligand moiety. We observed that ensemble members that preserve the geometry of the active site core are most likely to place ligands in the active site with a conserved orientation, generally rank ligands correctly and increase interactions with the receptor. A relative distance approach is used to quantify the preservation of the three-dimensional interatomic distances of the conserved ligand-binding atoms and prune large ensembles quickly. In this study, we investigate dihydrofolate reductase as an example of a protein with a conserved core; however, this method for accurately selecting relevant ensemble members a priori can be applied to any system with a conserved ligand-binding core, including HIV-1 protease, kinases, and acetylcholinesterase. Representing a drug target as a pruned ensemble during in silico screening should increase the accuracy and efficiency of high-throughput analyses of lead analogs.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

In pursuit of virtual lead optimization: the role of the receptor structure and ensembles in accurate docking

Accurate ranking during in silico lead optimization is critical to drive the generation of new ligands with higher affinity, yet it is especially difficult because of the subtle changes between analogs. In order to assess the role of the structure of the receptor in delivering accurate lead ranking results, we docked a set of forty related inhibitors to structures of one species of dihydrofolate reductase (DHFR) derived from crystallographic, NMR solution data, and homology models. In this study, the crystal structures yielded the superior results: the compounds were placed in the active site in the conserved orientation and the docking scores for 80% percent of the compounds clustered into the same bins as the measured affinity. Single receptor structures derived from NMR data or homology models did not serve as accurate docking receptors. To our knowledge, these are the first experiments that assess ranking of homologous lead compounds using a variety of receptor structures. We then extended the study to investigate whether ensembles, either computationally or experimentally derived, of all of the single starting structures aid, hinder or have no effect on the performance of the starting template. Impressively, when ensembles of receptor structures derived from NMR data or homology models were employed, docking accuracy improved to a level equal to that of the high resolution crystal structures. The same experiments using a second species of DHFR and set of ligands confirm the results. A comparison of the structures of the individual ensemble members to the starting structures shows that the effect of the ensembles can be ascribed to protein flexibility in addition to absorption of computational error.

Small molecule induction of MSH2-dependent cell death suggests a vital role of mismatch repair proteins in cell death

Avoidance of apoptosis is one of the hallmarks of cancer development and progression. Chemotherapeutic agents aim to initiate an apoptotic response, but often fail due to dysregulation. MSH proteins are capable of recognizing cisplatin damage in DNA and participate in the initiation of cell death. We have exploited this recognition and computationally simulated a MutS homolog (MSH) "death conformation". Screening and docking experiments based on this model determined that the MSH2-dependent cell-death pathway can be induced by a small molecule without DNA damage, reserpine. Reserpine was identified via virtual screening on structures obtained from molecular dynamics as a small molecule that selectively binds a protein "death" conformation. The virtual screening predicts that this small molecule binds in the absence of DNA. Cell biology confirmed that reserpine triggers the MSH2-dependent cell-death pathway. This result supports the hypothesis that the MSH2-dependent pathway is initiated by specific protein conformational changes triggered by binding to either DNA damage or small compound molecules. These findings have multiple implications for drug discovery and cell biology. Computational modeling may be used to identify and eventually design small molecules that selectively activate particular pathways through conformational control. Molecular dynamics simulations can be used to model the biologically relevant conformations and virtual screening can then be used to select for small molecules that bind specific conformations. The ability of a small molecule to induce the cell-death pathway suggests a broader role for MMR proteins in cellular events, such as cell-death pathways, than previously suspected.

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.

A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening

We examined the procedures to combine two different in silico drug-screening results to achieve a high hit ratio. When the 3D structure of the target protein and some active compounds are known, both structure-based and ligand-based in silico screening methods can be applied. In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method. To combine the predicted compound’s sets by these two screening methods, we examined the product of the sets (consensus set) and the sum of the sets. As a result, the consensus set achieved a higher hit ratio than the sum of the sets and than either individual predicted set. In addition, the current combination was shown to be robust enough for the structural diversities both in different crystal structure and in snapshot structures during molecular dynamics simulations.