Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.     

StructRank: a new approach for ligand-based virtual screening

Screening large libraries of chemical compounds against a biological target, typically a receptor or an enzyme, is a crucial step in the process of drug discovery. Virtual screening (VS) can be seen as a ranking problem which prefers as many actives as possible at the top of the ranking. As a standard, current Quantitative Structure-Activity Relationship (QSAR) models apply regression methods to predict the level of activity for each molecule and then sort them to establish the ranking. In this paper, we propose a top-k ranking algorithm (StructRank) based on Support Vector Machines to solve the early recognition problem directly. Empirically, we show that our ranking approach outperforms not only regression methods but another ranking approach recently proposed for QSAR ranking, RankSVM, in terms of actives found.     

Congestion game scheduling for virtual drug screening optimization

In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.     

On the value of homology models for virtual screening: discovering hCXCR3 antagonists by pharmacophore-based and structure-based approaches

Human chemokine receptor CXCR3 (hCXCR3) antagonists have potential therapeutic applications as antivirus, antitumor, and anti-inflammatory agents. A novel virtual screening protocol, which combines pharmacophore-based and structure-based approaches, was proposed. A three-dimensional QSAR pharmacophore model and a structure-based docking model were built to virtually screen for hCXCR3 antagonists. The hCXCR3 antagonist binding site was constructed by homology modeling and molecular dynamics (MD) simulation. By combining the structure-based and ligand-based screenings results, 95% of the compounds satisfied either pharmacophore or docking score criteria and would be chosen as hits if the union of the two searches was taken. The false negative rates were 15% for the pharmacophore model, 14% for the homology model, and 5% for the combined model. Therefore, the consistency of the pharmacophore model and the structural binding model is 219/273 = 80%. The hit rate for the virtual screening protocol is 273/286 = 95%. This work demonstrated that the quality of both the pharmacophore model and homology model can be measured by the consistency of the two models, and the false negatives in virtual screening can be reduced by combining two virtual screening approaches.     

Evaluation and application of multiple scoring functions for a virtual screening experiment

In order to identify novel chemical classes of factor Xa inhibitors, five scoring functions (FlexX, DOCK, GOLD, ChemScore and PMF) were engaged to evaluate the multiple docking poses generated by FlexX. The compound collection was composed of confirmed potent factor Xa inhibitors and a subset of the LeadQuest screening compound library. Except for PMF the other four scoring functions succeeded in reproducing the crystal complex (PDB code: 1FAX). During virtual screening the highest hit rate (80%) was demonstrated by FlexX at an energy cutoff of -40 kJ/mol, which is about 40-fold over random screening (2.06%). Limited results suggest that presenting more poses of a single molecule to the scoring functions could deteriorate their enrichment factors. A series of promising scaffolds with favorable binding scores was retrieved from LeadQuest. Consensus scoring by pair-wise intersection failed to enrich the hit rate yielded by single scorings (i.e. FlexX). We note that reported successes of consensus scoring in hit rate enrichment could be artificial because their comparisons were based on a selected subset of single scoring and a markedly reduced subset of double or triple scoring. The findings presented in this report are based upon a single biological system and support further studies.     

Geometry optimization using improved virtual orbitals: a complete active space numerical gradient approach

The improved virtual orbital-complete active space configuration interaction (IVO-CASCI) method is extended to enable geometry optimization and the calculation of vibrational frequencies for ground and excited states using numerical energy gradients. Applications consider the ground state geometries and vibrational frequencies of the Be2, LiF, H2S, and HCN molecules, as well as excited state properties for HCN, systems that are sufficiently complex to access the efficacy of the method. Comparisons with other standard approaches (self-consistent field, second order Moller-Plesset perturbation theory, complete active space self-consistent field, and coupled cluster singles and doubles methods) demonstrate that the numerical gradient version of the IVO-CASCI approach generally fares comparable to or better for all systems studied. The accurate estimates for the Be2 bond length and vibrational frequency are notable since many other computationally facile methods produce poor results.     

QUASI: a novel method for simultaneous superposition of multiple flexible ligands and virtual screening using partial similarity

The structure of many receptors is unknown, and only information about diverse ligands binding to them is available. A new method is presented for the superposition of such ligands, derivation of putative receptor site models and utilization of the models for screening of compound databases. In order to generate a receptor model, the similarity of all ligands is optimized simultaneously taking into account conformational flexibility and also the possibility that the ligands can bind to different regions of the site and only partially overlap. Ligand similarity is defined with respect to a receptor site model serving as a common reference frame. The receptor model is dynamic and coevolves with the ligand alignment until an optimal self-consistent superposition is achieved. When ligand conformational flexibility is permitted, different superposition models are possible and consistent with the data. Clustering of the superposition solutions is used to obtain diverse models. When the models are used to screen a database of compounds, high enrichments are obtained, comparable to those obtained in docking studies.     

Drug search for leishmaniasis: a virtual screening approach by grid computing

The trypanosomatid protozoa Leishmania is endemic in ~100 countries, with infections causing ~2 million new cases of leishmaniasis annually. Disease symptoms can include severe skin and mucosal ulcers, fever, anemia, splenomegaly, and death. Unfortunately, therapeutics approved to treat leishmaniasis are associated with potentially severe side effects, including death. Furthermore, drug-resistant Leishmania parasites have developed in most endemic countries. To address an urgent need for new, safe and inexpensive anti-leishmanial drugs, we utilized the IBM World Community Grid to complete computer-based drug discovery screens (Drug Search for Leishmaniasis) using unique leishmanial proteins and a database of 600,000 drug-like small molecules. Protein structures from different Leishmania species were selected for molecular dynamics (MD) simulations, and a series of conformational “snapshots” were chosen from each MD trajectory to simulate the protein’s flexibility. A Relaxed Complex Scheme methodology was used to screen ~2000 MD conformations against the small molecule database, producing >1 billion protein-ligand structures. For each protein target, a binding spectrum was calculated to identify compounds predicted to bind with highest average affinity to all protein conformations. Significantly, four different Leishmania protein targets were predicted to strongly bind small molecules, with the strongest binding interactions predicted to occur for dihydroorotate dehydrogenase (LmDHODH; PDB:3MJY). A number of predicted tight-binding LmDHODH inhibitors were tested in vitro and potent selective inhibitors of Leishmania panamensis were identified. These promising small molecules are suitable for further development using iterative structure-based optimization and in vitro/in vivo validation assays.     

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.     

A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models

Docking and pharmacophore screening tools were used to examine the binding of ligands in the active site of thymidine monophosphate kinase of Mycobacterium tuberculosis. Docking analysis of deoxythymidine monophosphate (dTMP) analogues suggests the role of hydrogen bonding and other weak interactions in enzyme selectivity. Water-mediated hydrogen-bond networks and a halogen-bond interaction seem to stabilize the molecular recognition. A pharmacophore model was developed using 20 dTMP analogues. The pharmacophoric features were complementary to the active site residues involved in the ligand recognition. On the basis of these studies, a composite screening model that combines the features from both the docking analysis and the pharmacophore model was developed. The composite model was validated by screening a database spiked with 47 known inhibitors. The model picked up 42 of these, giving an enrichment factor of 17. The validated model was used to successfully screen an in-house database of about 500,000 compounds. Subsequent screening with other filters gave 186 hit molecules.     

Exploration of enzyme-ligand interactions in CYP2D6 & 3A4 homology models and crystal structures using a novel computational approach

New crystal structures of human CYP2D6 and CYP3A4 have recently been reported, and in this study, we wanted to compare them with previously used homology models with respect to predictions of site of metabolism and ligand-enzyme interactions. The data set consisted of a family of synthetic opioid analgesics with the aim to cover both CYP2D6 and CYP3A4, as most of these compounds are metabolized by both isoforms. The program MetaSite was used for the site of metabolism predictions, and the results were validated by experimental assessment of the major metabolites formed with recombinant CYP450s. This was made on a selection of 14 compounds in the data set. The prediction rates for MetaSite were 79-100% except for the CYP3A4 homology model, which picked the correct site in half of the cases. Despite differences in orientation of some important amino acids in the active sites, the MetaSite-predicted sites were the same for the different structures, with the exception of the CYP3A4 homology model. Further exploration of interactions with ligands was done by docking substrates/inhibitors in the different structures with the docking program GLUE. To address the challenge in interpreting patterns of enzyme-ligand interactions for the large number of different docking poses, a new computational tool to handle the results from the dockings was developed, in which the output highlights the relative importance of amino acids in CYP450-substrate/inhibitor interactions. The method is based on calculations of the interaction energies for each pose with the surrounding amino acids. For the CYP3A4 structures, this method was compared with consensus principal component analysis (CPCA), a commonly used method for structural comparison to evaluate the usefulness of the new method. The results from the two methods were comparable with each other, and the highlighted amino acids resemble those that were identified to have a different orientation in the compared structures. The new method has clear advantages over CPCA in that it is far simpler to interpret and there is no need for protein alignment. The methodology enables structural comparison but also gives insights on important amino acid substrate/inhibitor interactions and can therefore be very useful when suggesting modifications of new chemical entities to improve their metabolic profiles.     

Supervised scoring models with docked ligand conformations for structure-based virtual screening

Protein-ligand docking programs have been used to efficiently discover novel ligands for target proteins from large-scale compound databases. However, better scoring methods are needed. Generally, scoring functions are optimized by means of various techniques that affect their fitness for reproducing X-ray structures and protein-ligand binding affinities. However, these scoring functions do not always work well for all target proteins. A scoring function should be optimized for a target protein to enhance enrichment for structure-based virtual screening. To address this problem, we propose the supervised scoring model (SSM), which takes into account the protein-ligand binding process using docked ligand conformations with supervised learning for optimizing scoring functions against a target protein. SSM employs a rough linear correlation between binding free energy and the root mean square deviation of a native ligand for predicting binding energy. We applied SSM to the FlexX scoring function, that is, F-Score, with five different target proteins: thymidine kinase (TK), estrogen receptor (ER), acetylcholine esterase (AChE), phosphodiesterase 5 (PDE5), and peroxisome proliferator-activated receptor gamma (PPARgamma). For these five proteins, SSM always enhanced enrichment better than F-Score, exhibiting superior performance that was particularly remarkable for TK, AChE, and PPARgamma. We also demonstrated that SSM is especially good at enhancing enrichments of the top ranks of screened compounds, which is useful in practical drug screening.     

Comparison of ligand-based and receptor-based virtual screening of HIV entry inhibitors for the CXCR4 and CCR5 receptors using 3D ligand shape matching and ligand-receptor docking

HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 co-receptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these co-receptors and, hence, ultimately block virus-cell fusion. This article describes a detailed comparison of the performance of receptor-based and ligand-based virtual screening approaches to find CXCR4 and CCR5 antagonists that could potentially serve as HIV entry inhibitors. Because no crystal structures for these proteins are available, homology models of CXCR4 and CCR5 have been built, using bovine rhodopsin as the template. For ligand-based virtual screening, several shape-based and property-based molecular comparison approaches have been compared, using high-affinity ligands as query molecules. These methods were compared by virtually screening a library assembled by us, consisting of 602 known CXCR4 and CCR5 inhibitors and some 4700 similar presumed inactive molecules. For each receptor, the library was queried using known binders, and the enrichment factors and diversity of the resulting virtual hit lists were analyzed. Overall, ligand-based shape-matching searches yielded higher enrichments than receptor-based docking, especially for CXCR4. The results obtained for CCR5 suggest the possibility that different active scaffolds bind in different ways within the CCR5 pocket.     

Library fingerprints: a novel approach to the screening of virtual libraries

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a na?ve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.     

Clustering and classifying diverse HIV entry inhibitors using a novel consensus shape-based virtual screening approach: further evidence for multiple binding sites within the CCR5 extracellular pocket

HIV entry inhibitors have emerged as a new generation of antiretroviral drugs that block viral fusion with the CXCR4 and CCR5 membrane coreceptors. Several small molecule antagonists for these coreceptors have been developed, some of which are currently in clinical trials. However, because no crystal structures for the coreceptor proteins are available, the binding modes of the known inhibitors within the coreceptor extracellular pockets need to be analyzed by means of site-directed mutagenesis and computational experiments. Previous studies have indicated that there is more than one binding site within the CCR5 extracellular pocket. This article investigates and develops this hypothesis using a novel spherical harmonic-based consensus shape clustering approach. The consensus shape approach is evaluated using retrospective virtual screening of CXCR4 and CCR5 inhibitors. Multiple combinations of CCR5 ligands in multiple trial superpositions are constructed to find consensus queries that give high virtual screening enrichments. Receiver-operator-characteristic performance analyses for both CXCR4 and CCR5 inhibitors show that the new consensus shape matching approach gives better virtual screening enrichments than existing shape matching and docking virtual screening techniques. The results obtained also provide strong evidence to support the notion that there are three main binding sites within the CCR5 extracellular cavity.