SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization.

Identifying active compounds (hits) that bind to biological targets of pharmaceutical relevance is the cornerstone of drug design efforts. Structure based virtual screening, namely, the in silico evaluation of binding energies and geometries between a protein and its putative ligands, has emerged over the past few years as a promising approach in this field. The success of the method relies on the availability of reliable 3-dimensional (3D) structures of the target protein and its candidate ligands (the screening library), a reliable docking method that can fit the different ligands into the protein's binding site, and an accurate scoring function that can rank the resulting binding modes in accord with their binding affinities. This last requirement is arguably the most difficult to meet due to the complexity of the binding process. A potential solution to this so-called scoring problem is the usage of multiple scoring functions in an approach known as consensus scoring. Several consensus scoring methods were suggested in the literature and have generally demonstrated an improved ranking of screening libraries relative to individual scoring functions. Nevertheless, current consensus scoring strategies suffer from several shortcomings, in particular, strong dependence on the initial parameters and an incomplete treatment of inactive compounds. In this work we present a new consensus scoring algorithm (SeleX-Consensus Scoring abbreviated to SeleX-CS) specifically designed to address these limitations: (i) A subset of the initial set of the scoring functions is allowed to form the consensus score, and this subset is optimized via a Monte Carlo/Simulated Annealing procedure. (ii) Rank redundancy between the members of the screening library is removed. (iii) The method explicitly considers the presence of inactive compounds. The new algorithm was applied to the ranking of screening libraries targeting two G-protein coupled receptors (GPCR). Excellent enrichment factors were obtained in both cases: For the cannabinoid receptor 1 (CB1), SeleX-CS outperformed the best single score and afforded an enrichment factor of 41 at 1% of the screening library compared with the best single score value of 15 (GOLD_Fitness). For the chemokine receptor type 2 (CCR2) SeleX-CS afforded an enrichment factor of 72 (again at 1% of the screening library) once more outperforming any single score (enrichment factor of 20 by GSCORE). Moreover, SeleX-CS demonstrated success rates of 67% (CCR2) and 73% (CB1) when applied to ranking an external test set. In both cases, the new algorithm also afforded good derichment of inactive compounds (i.e., the ability to push inactive compounds to the bottom of the ranked library). The method was then extended to rank a lead optimization series targeting the Kv4.3 potassium ion channel, resulting in a Spearman's correlation coefficient, p = 0.63 (n = 40), between the SeleX-CS-based rank and the actual pKi values. These results suggest that SeleX-CS is a powerful method for ranking screening libraries in the lead discovery phase and also merits consideration as a lead optimization tool.

PREDICT modeling and in-silico screening for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.