Compounds for assying are selected as follows: Molecules that score high when docked in the RXRα protein ensemble that binds to the heterodimer partner of interest and at the same time score low for RXRα structures that bind to heterodimer partners of no interest, may be selected in order to achieve selectivity. Finally, a post-processing step is imposed to the top-scoring compounds by using Chembioserver  and FAF-Drugs2  filtering tools as well as pharmacological property prediction with the QikProp software . Workflow for the discovery of selective RXRα ligands based on a SBVS protocol of ED and counter-screening.
For decades, drug discovery was carried out using trial and error experimental techniques for screening large libraries of chemicals against a biological target. Recent advances in computer-aided drug design allow the tailored design of drugs for a target protein, shortening the development cycle of new drugs. The advent of Structure Based Virtual Screening has undoubtedly changed and improved the drug discovery process and has been established as one of the most promising in silico techniques for drug design. The focal point of this review is the detailed description of the SBVS steps in drug design, in combination with the presentation of recent advances and the introduction of various protocols that facilitate the identification of inhibitors with nM potency. Furthermore, we also propose herein two novel VS protocols that aim to enhance inhibitor selectivity for the target protein against close homologs. First, a SBVS workflow, which was utilized to discover novel inhibitors of the H1047R mutant form of PI3Kα is presented. In this workflow, MD simulations in aqueous solution were carried out for both WT and mutant PI3Ka proteins.
Binding site analysis in the kinase domain identified cavities in the vicinity of the H1047R mutation and the membrane binding regions. SBVS was performed in several binding pockets and top-ranked compounds in terms of predicted binding affinity were carefully post-processed to ensure the validity of docked poses, chemical diversity, desirable physicochemical properties and the absences of toxic or metabolically liable moieties. Finally, ten promising compounds were selected for in vitro assaying, four of which emerged as µM inhibitors of the H1047R mutant PI3Kα protein validating our approach . A second SBVS protocol is contributed for the identification of binders for the RXRα nuclear receptor that are selective depending on the protein’s heterodimer partner. A structural RXRα ensemble was created for this purpose by selecting different RXRα crystal structures based on (i) RMSD calculations, (ii) binding-site shape and volume, (iii) docking of a small database of known actives, and (iv) choosing representative structures from MD simulations. SBVS was then performed on three different subsets of RXRα arising from different heterodimer complexes of RXRα and/or its binding to different ligands. Candidate binders of RXRα were selected for purchase with an eye on their different orientation at the binding site of the various structures and different interactions with specific surrounding residues in order to maximize their selectivity potential. In vitro assying of these compounds is still pending experimental testing. Although SBVS is widely used nowadays by multiple academic and industrial research groups in the drug discovery process, it suffers from limitations that restrict its effectiveness (reviewed in Table 3 3 ). Significant breakthroughs are required in order to address fundamental challenges such as, for example, scoring, target flexibility and appropriately treating water molecules. Such challenges ultimately lead to the query: Is SBVS an indispensable tool for modern drug design? The significant reduction in time and cost compared to the high-throughput screening process, the continuous efforts in improving the efficiency of docking programs and scoring functions, and the plentiful successful case studies that have led to low nM leads are only a few representative examples showing that SBVS is here to stay. However, prospective users of the method should remember that SBVS is not as simple as running a computer program. Careful choices need to be made; appropriately selecting the structural ensemble for the screening exercise, cautious preparation of the biological target and the database to be used, treatment of water molecules in the cavity, and careful post-processing of SBVS results are of utmost importance that ultimately result in enhancing lead identification and selectivity rates. Virtual Screening Advantages Limitations Time and cost reduction of screening process of millions of small molecules, compared to HTS Many VS tools are applicable and successful to specific case studies (based on the training set) and not in general cases. There is no need for physically existing compounds to perform the screening process, unlike HTS. Compounds being identified by HTS are usually more bioactive than compounds identified by VS. Different approaches of VS have been created for lead discovery depending each time on the availability of experimental information (SBVS Ligand-Based VS, Fragment-Based VS,etc.) Weakness in perfect inclusion of receptor structural flexibility and of water in docking computations due to computational-cost and high complexity of its modeling Several successful examples of identifying low nM leads that show the intended biological activity Very potent leads ( i.e. A large number of docking programs and scoring functions Scoring is still challenging in predicting accurately the correct binding pose and ranking of the compounds due to the difficulty in parameterizing the complexity of the ligand-receptor binding interactions and the approximations in calculating desolvation and entropic terms. VS can use as input a desirable target structure complexed with a specific ligand even if there are no experimental data, through molecular modeling. Predicted protein structures from homology modeling and predicted protein-ligand complexes may result to increased rates of false positive/negative results. The authors confirm that this article content has no conflict of interest.
The initial part of this work was co-funded by the NSRF 2007-2013, the European Regional Development Fund and national resources, under the grant “Cooperation" [No. This work has been also supported by a Marie Curie Reintegration Grant (FP7-PEOPLE-2009-RG, No 256533) and an AACR Judah Folkman Fellowship for Cancer Research in Angiogenesis (08-40-18-COUR). EL was supported by an IKY graduate scholarship from funds of the “Education and lifelong learning” of the European Social Fund 2007-2013. Computational analyses presented herein were performed on resources of PRACE-PR and LinkSCEEM/Cy-tera, which are also gratefully acknowledged. Part of the computational work was performed at BRFAA using a cluster funded from European Economic Area Grant No. Fitting molecules to receptors using virtual reality. ChimeraX medical imaging in VR features added for NIAID. Color protein surface by NMR chemical shift perturbations with Chimera 1.13 with notes on pitfalls.
ChimeraX virtual reality example mutating a residue. Color protein surface by NMR chemical shift perturbations with Chimera 1.12. Virtual reality example data showing molecular systems with ChimeraX.