Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description

Spyrakis, Francesca; Cavasotto, Claudio N.

doi:10.1016/j.abb.2015.08.002

Cited by 104 publications

(100 citation statements)

References 288 publications

(319 reference statements)

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“…More problematic still is when a new bridging water mediates interactions between the ligand and the receptor. Because the energetics of bound water molecules have been challenging to calculate and bridging waters hard to anticipate, large-scale docking of chemical libraries has typically been conducted against artificially desolvated sites or has kept a handful of ordered water molecules that are treated as part of the site, based on structural precedence (5)(6)(7)(8).…”

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confidence: 99%

Testing inhomogeneous solvation theory in structure-based ligand discovery

Balius

Fischer

Stein

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Binding-site water is often displaced upon ligand recognition, but is commonly neglected in structure-based ligand discovery. Inhomogeneous solvation theory (IST) has become popular for treating this effect, but it has not been tested in controlled experiments at atomic resolution. To do so, we turned to a grid-based version of this method, GIST, readily implemented in molecular docking. Whereas the term only improves docking modestly in retrospective ligand enrichment, it could be added without disrupting performance. We thus turned to prospective docking of large libraries to investigate GIST's impact on ligand discovery, geometry, and water structure in a model cavity site well-suited to exploring these terms. Although top-ranked docked molecules with and without the GIST term often overlapped, many ligands were meaningfully prioritized or deprioritized; some of these were selected for testing. Experimentally, 13/14 molecules prioritized by GIST did bind, whereas none of the molecules that it deprioritized were observed to bind. Nine crystal complexes were determined. In six, the ligand geometry corresponded to that predicted by GIST, for one of these the pose without the GIST term was wrong, and three crystallographic poses differed from both predictions. Notably, in one structure, an ordered water molecule with a high GIST displacement penalty was observed to stay in place. Inclusion of this water-displacement term can substantially improve the hit rates and ligand geometries from docking screens, although the magnitude of its effects can be small and its impact in drug binding sites merits further controlled studies.water | inhomogeneous solvation theory | ligand discovery | structure-based drug design | docking T he treatment of receptor-bound water molecules, which are crucial for ligand recognition, is a widely recognized challenge in structure-based discovery (1-4). The more tightly bound a water in a site, the greater the penalty for its displacement upon ligand binding, ultimately leading to its retention and the adoption of ligand geometries that do not displace it. More problematic still is when a new bridging water mediates interactions between the ligand and the receptor. Because the energetics of bound water molecules have been challenging to calculate and bridging waters hard to anticipate, large-scale docking of chemical libraries has typically been conducted against artificially desolvated sites or has kept a handful of ordered water molecules that are treated as part of the site, based on structural precedence (5-8).Recently, several relatively fast approaches, pragmatic for early discovery, have been advanced to account for the differential displacement energies of bound water molecules (9-20), complementing more rigorous but computationally expensive approaches (18)(19)(20)(21)(22). Among the most popular of these has been inhomogeneous solvation theory (IST) (23)(24)(25). IST uses populations from molecular dynamics (MD) simulations on protein (solute) surfaces to calculate the cost of di...

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confidence: 99%

Testing inhomogeneous solvation theory in structure-based ligand discovery

Balius

Fischer

Stein

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…The ongoing expansion of public [40, 46–49] and proprietary [50] target-ligand binding data begins to enable “machine learning” prediction of target inhibition profiles [51–56]. Such methods, similar to the more common structure based methods [57, 58], generally do not allow precise (e.g. binding constant error <10-fold) prediction of binding properties of individual compound-target interactions, but they do provide guidance to focus efforts on compound classes or libraries with the best chances for success [59–64].…”

Section: Introductionmentioning

confidence: 99%

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Narayanan¹,

Gruber²,

Engh³

2017

J Cheminform

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Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0229-8) contains supplementary material, which is available to authorized users.

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“…Note that there are clear cases in docking where water molecules should be included explicitly while implicit solvation models continue to improve[59][60][61]. The choices of which pH and which salt concentration to choose should be matched to the local environment of the selected protein.…”

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confidence: 98%