Virtual screening for compounds that mimic protein-protein interface epitopes

Modulation of protein-protein interactions (PPI) has emerged as a new concept in rational drug design. Here, we present a computational protocol for identifying potential PPI inhibitors. Relevant regions of interfaces (epitopes) are predicted for three-dimensional protein models and serve as queries for virtual compound screening. We present a computational screening protocol that incorporates two different pharmacophore models. One model is based on the mathematical concept of autocorrelation vectors and the other utilizes fuzzy labeled graphs. In a proof-of-concept study, we were able to identify serine protease inhibitors using a predicted trypsin epitope as query. Our virtual screening framework may be suited for rapid identification of PPI inhibitors and suggesting bioactive tool compounds.     

Virtual screening data fusion using both structure- and ligand-based methods

Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.     

Antibody-based methods for surfactant screening

This brief overview summarises the immunoassay-based results obtained in the course of two years of the European INCO-Copernicus project BIOTOOLS. The project is aimed at simplifying the procedures for detection of surface active compounds (SAC) using, among others, antibody-based methods, i.e., microtiter plate-based enzyme-linked immunosorbent assays (ELISA), polarisation fluoro immunoassays (PFIA), and enzyme flow injection immunoassays (FIIA). Thirty-three rabbits were immunised with five different sulphophenyl moieties and three p-hydroxyphenyl moieties conjugated to protein immunogens to produce analytical antibodies against linear alkylbenzene sulphonates (LAS) and nonylphenol (NP). Although most of the antibodies exhibited binding reaction in indirect ELISA, only a few showed the required assay sensitivity. The best antibodies for LAS exhibited a 50% binding inhibition at IC50 19.8 microg L(-1) in indirect ELISA. Similar inhibition was observed for direct ELISA using peroxidase tracers. Antibodies against NP allowed the establishment of an indirect assay operating in the mg L(-1) range. A rapid and simple protocol for the screening of NP and LAS using homogeneous PFIA is described. The assay time for 10 samples was 7 minutes, thus allowing fast detection of the selected SAC at the mg L(-1) level. A generic competitive FIIA system, using a protein G column for separation of free and antibody-bound beta-galactosidase (beta-Gal) tracer, was developed for the screening of LAS, NP, and nonylphenol decaethoxylate (NPEO10). The FIIA had a sample throughput (STP) of 5-10 samples per hour, with limits of detection (LOD) for LAS, NP, and NPEO10 of 19.5, 52, and 2.4 microg L(-1), respectively. The developed FIIAs were applied to spiked rain and surface water.     

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.     

Virtual fragment screening: exploration of MM-PBSA re-scoring

An NMR fragment screening dataset with known binders and decoys was used to evaluate the ability of docking and re-scoring methods to identify fragment binders. Re-scoring docked poses using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) implicit solvent model identifies additional active fragments relative to either docking or random fragment screening alone. Early enrichment, which is clearly most important in practice for selecting relatively small sets of compounds for experimental testing, is improved by MM-PBSA re-scoring. In addition, the value in MM-PBSA re-scoring of docked poses for virtual screening may be in lessening the effect of the variation in the protein complex structure used.     

Virtual target screening: validation using kinase inhibitors

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.     

Virtual screening using protein-ligand docking: avoiding artificial enrichment

This study addresses a number of topical issues around the use of protein-ligand docking in virtual screening. We show that, for the validation of such methods, it is key to use focused libraries (containing compounds with one-dimensional properties, similar to the actives), rather than "random" or "drug-like" libraries to test the actives against. We also show that, to obtain good enrichments, the docking program needs to produce reliable binding modes. We demonstrate how pharmacophores can be used to guide the dockings and improve enrichments, and we compare the performance of three consensus-ranking protocols against ranking based on individual scoring functions. Finally, we show that protein-ligand docking can be an effective aid in the screening for weak, fragment-like binders, which has rapidly become a popular strategy for hit identification. All results presented are based on carefully constructed virtual screening experiments against four targets, using the protein-ligand docking program GOLD.     

Rigorous integral screening for electron correlation methods

We derive rigorous multipole-based integral estimates (MBIE) in order to account for the distance dependence occurring in atomic-orbital (AO) formulations of electron correlation theory, where our focus is on AO-MP2 theory within a Laplace scheme. We find for the exact transformed integral products an extremely early onset of a linear-scaling behavior and a very small number of significant products. To preselect the significant integral products we adapt our MBIE method as rigorous upper bound. In this way it is possible to exploit the favorable scaling behavior observed and to reduce the scaling of estimated products asymptotically to linear, without sacrificing accuracy or reliability. By separating Coulomb- and exchange-type contractions only half-transformed integrals need to be computed. Furthermore, our scheme of rigorously preselecting transformed integral products via MBIE seems to offer particularly interesting perspectives for a direct formation of half- or fully transformed integrals by using multipole expansions and auxiliary basis sets.     

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.     

Virtual screening for binding of phenylalanine analogues to phenylalanyl-tRNA synthetase

Although incorporation of nonnatural amino acids provides a powerful means of controlling protein structure and function, experimental investigations of amino acid analogues for utilization by the protein biosynthetic machinery can be costly and time-consuming. In this paper, we describe a computational protocol (HierDock) for predicting the relative energies of binding of phenylalanine analogues to phenylalanyl-tRNA synthetase (PheRS). Starting with the crystal structure of Thermus thermophilus PheRS without bound ligand, HierDock predicts the binding site of phenylalanine (Phe) within 1.1 A of that revealed by the crystal structure of PheRS cocrystallized with Phe. The calculated binding energies of Phe analogues in PheRS, using HierDock, correlate well with the translational activities of the same analogues in Escherichia coli. HierDock identifies p-fluorophenylalanine and 3-thienylalanine as especially good substrates for PheRS, in agreement with experiment. These results suggest that the HierDock protocol may be useful for virtual screening of amino acid analogues prior to experiment.     

Virtual screening of some heterocyclic structures toward novel antibacterial agents

Infectious diseases and their treatment are among the most important issues in global health and economy. Moreover, increasing prevalence of antibiotic-resistant pathogenic bacteria necessitates the considerable need for discovering new drugs. Some heterocyclic structures with dihydropyridine (DHP) scaffold have been reported as antimicrobial agents. Herein, we report a structure-based virtual screening of a pool of 3,5-disubstituted DHPs and a post-analysis of virtual hits through in vitro antibacterial assessment. Four top-ranked DHP structures (6a–d) were found to interact with the relevant target active sites and exhibited superior stereoelectronic features within their enzyme inhibition. Selected compounds were synthesized and assessed for their antibacterial activity via microdilution method. Results of this study represented a significant application of multi-step virtual screening strategy in identifying privileged DHP structures as good starting points for further developments toward more potent antibacterial agents.     

Structure-based screening as applied to human FABP4: a highly efficient alternative to HTS for hit generation

The time-limiting step in HTS often is the development of an appropriate assay. In addition, hits from HTS fairly often turn out to be false positives and generally display unfavorable properties for further development. Here we describe an alternative process for hit generation, applied to the human adipocyte fatty acid binding protein FABP4. A small molecular ligand for FABP4 that blocks the binding of endogenous ligands may be developed into a drug for the treatment of type-2 diabetes. Using NMR spectroscopy, we screened FABP4 for low-affinity binders in a diversity library consisting of small soluble scaffolds, which yielded 52 initial hits in total. The potencies of these hits were ranked, and crystal structures of FABP4 complexes for two of the hits were obtained. The structural data were subsequently used to direct similarity searches for available analogues, as well as chemical synthesis of 12 novel analogues. In this way, a series of three selective FABP4 ligands with attractive pharmacochemical profiles and potencies of 10 microM or better was obtained.     

Practical outcomes of applying ensemble machine learning classifiers to High-Throughput Screening (HTS) data analysis and screening

Over the years numerous papers have presented the effectiveness of various machine learning methods in analyzing drug discovery biological screening data. The predictive performance of models developed using these methods has traditionally been evaluated by assessing performance of the developed models against a portion of the data randomly selected for holdout. It has been our experience that such assessments, while widely practiced, result in an optimistic assessment. This paper describes the development of a series of ensemble-based decision tree models, shares our experience at various stages in the model development process, and presents the impact of such models when they are applied to vendor offerings and the forecasted compounds are acquired and screened in the relevant assays. We have seen that well developed models can significantly increase the hit-rates observed in HTS campaigns.