Journal Article

FZJ-2019-02799

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Cosolvent-enhanced Sampling and Unbiased Identification of Cryptic Pockets Suitable for Structure-based Drug Design

Schmidt, D. ; Boehm, M. ; McClendon, C. L. ; Torella, R. ; Gohlke, H. (Corresponding author)FZJ*

2019
Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/22180  doi:10.1021/acs.jctc.8b01295

Abstract: Modulating protein activity with small molecules binding to cryptic pockets offers great opportunities to overcome hurdles in drug design. Cryptic sites are atypical binding sites in proteins that are closed in the absence of a stabilizing ligand and are thus inherently difficult to identify. Many studies have proposed methods to predict cryptic sites. However, a general approach to prospectively sample open conformations of these sites and to identify cryptic pockets in an unbiased manner suitable for structure-based drug design remains elusive. Here, we describe an all-atom, explicit cosolvent, molecular dynamics (MD) simulations-based workflow to sample the open states of cryptic sites and identify opened pockets, in a manner that does not require a priori knowledge about these sites. Furthermore, the workflow relies on a target-independent parameterization that only distinguishes between binding pockets for peptides or small-molecules. We validated our approach on a diverse test set of seven proteins with crystallographically determined cryptic sites. The known cryptic sites were found among the three highest-ranked predicted cryptic sites, and an open site conformation was sampled and selected for most of the systems. Crystallographic ligand poses were well reproduced by docking into these identified open conformations for five of the systems. When the fully open state could not be reproduced, we were still able to predict the location of the cryptic site, or identify other cryptic sites that could be retrospectively validated with knowledge of the protein target. These characteristics render our approach valuable for investigating novel protein targets without any prior information.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. John von Neumann - Institut für Computing (NIC)
3. Strukturbiochemie (ICS-6)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
2. Forschergruppe Gohlke (hkf7_20170501) (hkf7_20170501)

Appears in the scientific report 2019

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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