Shape‐based virtual screening with volumetric aligned molecular shapes

Shape‐based virtual screening is an established and effective method for identifying small molecules that are similar in shape and function to a reference ligand. We describe a new method of shape‐based virtual screening, volumetric aligned molecular shapes (VAMS). VAMS uses efficient data structures to encode and search molecular shapes. We demonstrate that VAMS is an effective method for shape‐based virtual screening and that it can be successfully used as a prefilter to accelerate more computationally demanding search algorithms. Unique to VAMS is a novel minimum/maximum shape constraint query for precisely specifying the desired molecular shape. Shape constraint searches in VAMS are particularly efficient and millions of shapes can be searched in a fraction of a second. We compare the performance of VAMS with two other shape‐based virtual screening algorithms a benchmark of 102 protein targets consisting of more than 32 million molecular shapes and find that VAMS provides a competitive trade‐off between run‐time performance and virtual screening performance. © 2014 Wiley Periodicals, Inc.     

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org . © 2016 Wiley Periodicals, Inc.     

Comparison of topological, shape, and docking methods in virtual screening

Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D ligand similarity (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active and decoy compound sets were assembled from both the MDDR and the Merck compound databases. Averaged over multiple targets, ligand-based methods outperformed docking algorithms. This was true for 3D ligand-based methods only when chemical typing was included. Using mean enrichment factor as a performance metric, Glide appears to be the best docking method among the three with FRED a close second. Results for all virtual screening methods are database dependent and can vary greatly for particular targets.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

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.     

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.     

Receptor-based virtual ligand screening for the identification of novel CDC25 phosphatase inhibitors

CDC25 phosphatases play critical roles in cell cycle regulation and are attractive targets for anticancer therapies. Several small non-peptide molecules are known to inhibit CDC25, but many of them appear to form a covalent bond with the enzyme or act through oxidation of the thiolate group of the catalytic cysteine. Structure-based virtual ligand screening computations were performed with FRED, Surflex, and LigandFit, a compound collection of over 310,000 druglike molecules and the crystal structure of CDC25B in order to identify novel classes of ligands. In vitro experiments carried out on a selected list of 1500 molecules led to the discovery of 99 compounds able to inhibit CDC25B activity at 100 microM. Further docking computations were applied, allowing us to propose a binding mode for the most potent molecule (IC50 = 13 microM). Our best compounds represent promising new classes of CDC25 inhibitors that also exhibit antiproliferative properties.     

FRED and HYBRID docking performance on standardized datasets

The docking performance of the FRED and HYBRID programs are evaluated on two standardized datasets from the Docking and Scoring Symposium of the ACS Spring 2011 national meeting. The evaluation includes cognate docking and virtual screening performance. FRED docks 70?% of the structures to within 2?? in the cognate docking test. In the virtual screening test, FRED is found to have a mean AUC of 0.75. The HYBRID program uses a modified version of FRED's algorithm that uses both ligand- and structure-based information to dock molecules, which increases its mean AUC to 0.78. HYBRID can also implicitly account for protein flexibility by making use of multiple crystal structures. Using multiple crystal structures improves HYBRID's performance (mean AUC 0.80) with a negligible increase in docking time (~15?%).     

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.     

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.     

Shapelets: possibilities and limitations of shape-based virtual screening

Complementarity of molecular surfaces is crucial for molecular recognition. A method for representation of molecular shape is presented. We decompose the molecular surface into commensurate patches with defined shape by fitting hyperbolical paraboloids onto a triangulated isosurface of the Gaussian model of a molecule. As a result of this decomposition we obtain a 3D graph representation of the molecular shape, which can be used for complete and partial shape matching and isosteric group searching. To point out the possibilities and limitations of shape-only models, we challenged our method by three scenarios in a virtual screening contest: rigid body alignment, consensus shape filtering, and target-specific screening.     

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.     

Ligand and structure‐based models for the prediction of ligand‐receptor affinities and virtual screenings: Development and application to the β2‐adrenergic receptor

In this study, we evaluated the applicability of ligand‐based and structure‐based models to quantitative affinity predictions and virtual screenings for ligands of the β₂‐adrenergic receptor, a G protein‐coupled receptor (GPCR). We also devised and evaluated a number of consensus models obtained through partial least square regressions, to combine the strengths of the individual components. In all cases, the bioactive conformation of each ligand was derived from molecular docking at the crystal structure of the receptor. We identified the most effective models applicable to the different scenarios, in the presence or in the absence of a training set. For ranking the affinity of closely related analogs when a training set is available, a ligand‐based consensus model (LI‐CM) seems to be the best choice, while the structure‐based MM‐GBSA score seems the best alternative in the absence of a training set. For virtual screening purposes, the structure‐based MM‐GBSA score was found to be the method of choice. Consensus models consistently had performances superior or close to those of the best individual components, and were endowed with a significantly increased robustness. Given multiple models with no a priori knowledge of their predictive capabilities, constructing a consensus model ensures results very close to those that the best model alone would have yielded. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

Pharmacophore alignment search tool: influence of the third dimension on text-based similarity searching

Previously (Hähnke et al., J Comput Chem 2010, 31, 2810) we introduced the concept of nonlinear dimensionality reduction for canonization of two‐dimensional layouts of molecular graphs as foundation for text‐based similarity searching using our Pharmacophore Alignment Search Tool (PhAST), a ligand‐based virtual screening method. Here we apply these methods to three‐dimensional molecular conformations and investigate the impact of these additional degrees of freedom on virtual screening performance and assess differences in ranking behavior. Best‐performing variants of PhAST are compared with 16 state‐of‐the‐art screening methods with respect to significance estimates for differences in screening performance. We show that PhAST sorts new chemotypes on early ranks without sacrificing overall screening performance. We succeeded in combining PhAST with other virtual screening techniques by rank‐based data fusion, significantly improving screening capabilities. We also present a parameterization of double dynamic programming for the problem of small molecule comparison, which allows for the calculation of structural similarity between compounds based on one‐dimensional representations, opening the door to a holistic approach to molecule comparison based on textual representations. © 2011 Wiley Periodicals, Inc. J Comput Chem , 2011.     

A pseudo-ligand approach to virtual screening

A virtual screening method is presented that is grounded on a receptor-derived pharmacophore model termed "virtual ligand" or "pseudo-ligand". The model represents an idealized constellation of potential ligand sites that interact with residues of the binding pocket. For rapid virtual screening of compound libraries the potential pharmacophore points of the virtual ligand are encoded as an alignment-free correlation vector, avoiding spatial alignment of pharmacophore features between the pharmacophore query (i.e., the virtual ligand) and the candidate molecule. The method was successfully applied to retrieving factor Xa inhibitors from a Ugi three-component combinatorial library, and yielded high enrichment of actives in a retrospective search for cyclooxygenase-2 (COX-2) inhibitors. The approach provides a concept for "de-orphanizing" potential drug targets and identifying ligands for hitherto unexplored or allosteric binding pockets.     

Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein–ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.     

Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching

Shape similarity searching is a popular approach for ligand-based virtual screening on the basis of three-dimensional reference compounds. It is generally thought that well-defined experimentally determined binding modes of active reference compounds provide the best possible basis for shape searching. Herein, we show that experimental binding modes are not essential for successful shape similarity searching. Furthermore, we show that ensembles of analogs of X-ray ligands—in the absence of these ligands—further improve the search performance of single crystallographic reference compounds. This is even the case if ensembles of virtually generated analogs are used whose activity status is unknown. Taken together, the results of our study indicate that analog ensembles representing fuzzy reference states are effective starting points for shape similarity searching.