ElectroShape: fast molecular similarity calculations incorporating shape,chirality and electrostatics

We present ElectroShape, a novel ligand-based virtual screening method, that combines shape and electrostatic information into a single, unified framework. Building on the ultra-fast shape recognition (USR) approach for fast non-superpositional shape-based virtual screening, it extends the method by representing partial charge information as a fourth dimension. It also incorporates the chiral shape recognition (CSR) method, which distinguishes enantiomers. It has been validated using release 2 of the Directory of useful decoys (DUD), and shows a near doubling in enrichment ratio at 1% over USR and CSR, and improvements as measured by Receiver Operating Characteristic curves. These improvements persisted even after taking into account the chemotype redundancy in the sets of active ligands in DUD. During the course of its development, ElectroShape revealed a difference in the charge allocation of the DUD ligand and decoy sets, leading to several new versions of DUD being generated as a result. ElectroShape provides a significant addition to the family of ultra-fast ligand-based virtual screening methods, and its higher-dimensional shape recognition approach has great potential for extension and generalisation.     

Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring

Shape-based methods for aligning and scoring ligands have proven to be valuable in the field of computer-aided drug design. Here, we describe a new shape-based flexible ligand superposition and virtual screening method, Phase Shape, which is shown to rapidly produce accurate 3D ligand alignments and efficiently enrich actives in virtual screening. We describe the methodology, which is based on the principle of atom distribution triplets to rapidly define trial alignments, followed by refinement of top alignments to maximize the volume overlap. The method can be run in a shape-only mode or it can include atom types or pharmacophore feature encoding, the latter consistently producing the best results for database screening. We apply Phase Shape to flexibly align molecules that bind to the same target and show that the method consistently produces correct alignments when compared with crystal structures. We then illustrate the effectiveness of the method for identifying active compounds in virtual screening of eleven diverse targets. Multiple parameters are explored, including atom typing, query structure conformation, and the database conformer generation protocol. We show that Phase Shape performs well in database screening calculations when compared with other shape-based methods using a common set of actives and decoys from the literature.     

Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques

Computationally efficient structure-based virtual screening methods have recently been reported that seek to find effective means to utilize experimental structure information without employing detailed molecular docking calculations. These tools can be coupled with efficient experimental screening technologies to improve the probability of identifying hits and leads for drug discovery research. Commercial software ROCS (rapid overlay of chemical structures) from Open Eye Scientific is such an example, which is a shape-based virtual screening method using the 3D structure of a ligand, typically from a bound X-ray costructure, as the query. We report here the development of a new structure-based pharmacophore search method (called Shape4) for virtual screening. This method adopts a variant of the ROCS shape technology and expands its use to work with an empty crystal structure. It employs a rigorous computational geometry method and a deterministic geometric casting algorithm to derive the negative image (i.e., pseudoligand) of a target binding site. Once the negative image (or pseudoligand) is generated, an efficient shape comparison algorithm in the commercial OE SHAPE Toolkit is adopted to compare and match small organic molecules with the shape of the pseudoligand. We report the detailed computational protocol and its computational validation using known biologically active compounds extracted from the WOMBAT database. Models derived for five selected targets were used to perform the virtual screening experiments to obtain the enrichment data for various virtual screening methods. It was found that our approach afforded similar or better enrichment ratios than other related methods, often with better diversity among the top ranking computational hits.     

Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches

In continuation of our recent studies on the quality of conformational models generated with CATALYST and OMEGA we present a large-scale survey focusing on the impact of conformational model quality and several screening parameters on pharmacophore-based and shape-based virtual high throughput screening (vHTS). Therefore, we collected known active compounds of CDK2, p38 MAPK, PPAR-gamma, and factor Xa and built a set of druglike decoys using ilib:diverse. Subsequently, we generated 3D structures using CORINA and also calculated conformational models for all compounds using CAESAR, CATALYST FAST, and OMEGA. A widespread set of 103 structure-based pharmacophore models was developed with LigandScout for virtual screening with CATALYST. The performance of both database search modes (FAST and BEST flexible database search) as well as the fit value calculation procedures (FAST and BEST fit) available in CATALYST were analyzed in terms of their ability to discriminate between active and inactive compounds and in terms of efficiency. Moreover, these results are put in direct comparison to the performance of the shape-based virtual screening platform ROCS. Our results prove that high enrichment rates are not necessarily in conflict with efficient vHTS settings: In most of the experiments, we obtained the highest yield of actives in the hit list when parameter sets for the fastest search algorithm were used.     

Optimization of high throughput virtual screening by combining shape-matching and docking methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARgamma and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking.     

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.     

Minor groove binders and drugs targeting proteins cover complementary regions in chemical shape space

DNA minor groove binders (MGBs) are known to influence gene expression and are therefore widely studied to explore their therapeutic potential. We identified shape-based virtual screening with ROCS as a highly effective computational approach to enrich known MGBs in top-ranked molecules. Discovery of ten previously unknown MGBs by shape-based screening further confirmed the relevance of ligand shape for minor groove affinity. Based on experimental testing we propose three simple rules (at least two positive charges, four nitrogen atoms, and one aromatic ring) as filters to reach even better enrichment of true positives in ROCS hit lists. Interestingly, shape-based ranking of MGBs versus FDA-approved drugs again leads to high enrichment rates, indicating complementary coverage of chemical shape space and indicating minor groove affinity to be unfavorable for approval of drugs targeting proteins.     

Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.     

Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods

In recent years, many virtual screening (VS) tools have been developed that employ different molecular representations and have different speed and accuracy characteristics. In this paper, we compare ten popular ligand-based VS tools using the publicly available Directory of Useful Decoys (DUD) data set comprising over 100?000 compounds distributed across 40 protein targets. The DUD was developed initially to evaluate docking algorithms, but our results from an operational correlation analysis show that it is also well suited for comparing ligand-based VS tools. Although it is conventional wisdom that 3D molecular shape is an important determinant of biological activity, our results based on permutational significance tests of several commonly used VS metrics show that the 2D fingerprint-based methods generally give better VS performance than the 3D shape-based approaches for surprisingly many of the DUD targets. To help understand this finding, we have analyzed the nature of the scoring functions used and the composition of the DUD data set itself. We propose that to improve the VS performance of current 3D methods, it will be necessary to devise screening queries that can represent multiple possible conformations and which can exploit knowledge of known actives that span multiple scaffold families.     

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.     

Virtual high throughput screening in new lead identification

Drug discovery continues to be one of the greatest contemporary challenges and rational application of modelling approaches is the first important step to obtain lead compounds, which can be optimised further. Virtual high throughput screening (VHTS) is one of the efficient approaches to obtain lead structures for a given target. Strategic application of different screening filters like pharmacophore mapping, shape-based, ligand-based, molecular similarity etc., in combination with other drug design protocols provide invaluable insights in lead identification and optimization. Screening of large databases using these computational methods provides potential lead compounds, thus triggering a meaningful interplay between computations and experiments. In this review, we present a critical account on the relevance of molecular modelling approaches in general, lead optimization and virtual screening methods in particular for new lead identification. The importance of developing reliable scoring functions for non-bonded interactions has been highlighted, as it is an extremely important measure for the reliability of scoring function. The lead optimization and new lead design has also been illustrated with examples. The importance of employing a combination of general and target specific screening protocols has also been highlighted.     

Conditional probability: a new fusion method for merging disparate virtual screening results

This paper introduces a new consensus scoring approach for merging the results of different virtual screening methods based on conditional probabilities. The technique is experimentally evaluated using several ligand-based virtual screening methods and compared to two variations of the established Sum-rank fusion method where it performs as well or better than the Sum-rank methods. Our experiments confirm that consensus scoring increases the number of active compounds retrieved with respect to the best individual methods on average.     

Ligand based virtual screening using SVM on GPU

In silico methods play an essential role in modern drug discovery methods. Virtual screening, an in silico method, is used to filter out the chemical space on which actual wet lab experiments are need to be conducted. Ligand based virtual screening is a computational strategy using which one can build a model of the target protein based on the knowledge of the ligands that bind successfully to the target. This model is then used to predict if the new molecule is likely to bind to the target. Support vector machine, a supervised learning algorithm used for classification, can be utilized for virtual screening the ligand data. When used for virtual screening purpose, SVM could produce interesting results. But since we have a huge ligand data, the time taken for training the SVM model is quite high compared to other learning algorithms. By parallelizing these algorithms on multi-core processors, one can easily expedite these discoveries. In this paper, a GPU based ligand based virtual screening tool (GpuSVMScreen) which uses SVM have been proposed and bench-marked. This data parallel virtual screening tool provides high throughput by running in short time. The proposed GpuSVMScreen can successfully screen large number of molecules (billions) also. The source code of this tool is available at http://ccc.nitc.ac.in/project/GPUSVMSCREEN.     

Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors

The docking program LigandFit/Cerius(2) has been used to perform shape-based virtual screening of databases against the aspartic protease renin, a target of determined three-dimensional structure. The protein structure was used in the induced fit binding conformation that occurs when renin is bound to the highly active renin inhibitor 1 (IC(50) = 2 nM). The scoring was calculated using several different scoring functions in order to get insight into the predictability of the magnitude of binding interactions. A database of 1000 diverse and druglike compounds, comprised of 990 members of a virtual database generated by using the iLib diverse software and 10 known active renin inhibitors, was docked flexibly and scored to determine appropriate scoring functions. All seven scoring functions used (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, LUDI) were able to retrieve at least 50% of the active compounds within the first 20% (200 molecules) of the entire test database. A hit rate of 90% in the top 1.4% resulted using the quadruple consensus scoring of LigScore2, PLP1, PLP2, and JAIN. Additionally, a focused database was created with the iLib diverse software and used for the same procedure as the test database. Docking and scoring of the 990 focused compounds and the 10 known actives were performed. A hit rate of 100% in the top 8.4% resulted with use of the triple consensus scoring of PLP1, PLP2, and PMF. As expected, a ranking of the known active compounds within the focused database compared to the test database was observed. Adequate virtual screening conditions were derived empirically. They can be used for proximate docking and scoring application of compounds with putative renin inhibiting potency.     

A surface site interaction point methodology for macromolecules and huge molecular databases

Determining the position and magnitude of Surface Site Interaction Points (SSIP) is a useful technique for understanding intermolecular interactions. SSIPs have been used for the prediction of solvation properties and for virtual co‐crystal screening. To determine the SSIPs for a molecule, the Molecular Electrostatic Potential Surface (MEPS) is first calculated using ab initio methods such as Density Functional Theory. This leads to a high cost in terms of computation time and is not compatible with the analysis of huge molecular databases. Herein, we present a method for the fast estimation of SSIPs, which is based on the MEPS calculated from MMFF94 atomic partial charges. The results show that this method can be used to calculate SSIPs for large molecular databases with a much higher speed than the original ab initio methodology. © 2017 Wiley Periodicals, Inc.     

Virtual screening for ligands of the insect molting hormone receptor

Insect growth is regulated by the orchestrated event of ecdysteroids and their receptor proteins. Agonists/antagonists of ecdysteroid receptor are predicted to disrupt normal growth, providing good candidates of new insecticides. A database of over 2 million compounds was subjected to a shape-based virtual screening cascade to identify novel nonsteroidal hits similar to the known EcR ligand ponasterone A. Testing revealed micromolar hits against two strains of insect cells. Docking experiments against EcR were used to support the predicted binding mode of these ligands based on their overlay to ponasterone A.     

Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens

In this work, we calculated the pair wise chemical similarity for a subset of small molecules screened against the NCI60 cancer cell line panel. Four different compound similarity calculation methods were used: Brutus, GRIND, Daylight and UNITY. The chemical similarity scores of each method were related to the biological similarity data set. The same was done also for combinations of methods. In the end, we had an estimate of biological similarity for a given chemical similarity score or combinations thereof. The data from above was used to identify chemical similarity ranges where combining two or more methods (data fusion) led to synergy. The results were also applied in ligand-based virtual screening using the DUD data set. In respect to their ability to enrich biologically similar compound pairs, the ranking of the four methods in descending performance is UNITY, Daylight, Brutus and GRIND. Combining methods resulted always in positive synergy within a restricted range of chemical similarity scores. We observed no negative synergy. We also noted that combining three or four methods had only limited added advantage compared to combining just two. In the virtual screening, using the estimated biological similarity for ranking compounds produced more consistent results than using the methods in isolation.