Literature DB >> 26414598

Understanding the challenges of protein flexibility in drug design.

Dinler A Antunes1, Didier Devaurs1, Lydia E Kavraki2.   

Abstract

INTRODUCTION: Protein-ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein-ligand complexes, and protein flexibility stands out as one of the most important and challenging issues for binding mode prediction. Various docking methods accounting for protein flexibility have been proposed, tackling problems of ever-increasing dimensionality. AREAS COVERED: This paper presents an overview of conformational sampling methods treating target flexibility during molecular docking. Special attention is given to approaches considering full protein flexibility. Contrary to what is frequently done, this review does not rely on classical biomolecular recognition models to classify existing docking methods. Instead, it applies algorithmic considerations, focusing on the level of flexibility accounted for. This review also discusses the diversity of docking applications, from virtual screening (VS) of small drug-like compounds to geometry prediction (GP) of protein-peptide complexes. EXPERT OPINION: Considering the diversity of docking methods presented here, deciding which one is the best at treating protein flexibility depends on the system under study and the research application. In VS experiments, ensemble docking can be used to implicitly account for large-scale conformational changes, and selective docking can additionally consider local binding-site rearrangements. In other cases, on-the-fly exploration of the whole protein-ligand complex might be needed for accurate GP of the binding mode. Among other things, future methods are expected to provide alternative binding modes, which will better reflect the dynamic nature of protein-ligand interactions.

Keywords:  conformational sampling; geometry prediction; molecular docking; protein flexibility; virtual screening

Mesh:

Substances:

Year:  2015        PMID: 26414598     DOI: 10.1517/17460441.2015.1094458

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  29 in total

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2.  HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy.

Authors:  Dinler A Antunes; Jayvee R Abella; Sarah Hall-Swan; Didier Devaurs; Anja Conev; Mark Moll; Gregory Lizée; Lydia E Kavraki
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Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2017-08-23       Impact factor: 3.686

5.  Using machine learning to improve ensemble docking for drug discovery.

Authors:  Tanay Chandak; John P Mayginnes; Howard Mayes; Chung F Wong
Journal:  Proteins       Date:  2020-05-25

6.  Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4.

Authors:  Andrea Basciu; Panagiotis I Koukos; Giuliano Malloci; Alexandre M J J Bonvin; Attilio V Vargiu
Journal:  J Comput Aided Mol Des       Date:  2019-11-13       Impact factor: 3.686

7.  DINC 2.0: A New Protein-Peptide Docking Webserver Using an Incremental Approach.

Authors:  Dinler A Antunes; Mark Moll; Didier Devaurs; Kyle R Jackson; Gregory Lizée; Lydia E Kavraki
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

8.  Computational discovery of novel inhibitory candidates targeting versatile transcriptional repressor MBD2.

Authors:  Zihni Onur Çalışkaner
Journal:  J Mol Model       Date:  2022-09-06       Impact factor: 2.172

9.  Protein-peptide docking using CABS-dock and contact information.

Authors:  Maciej Blaszczyk; Maciej Pawel Ciemny; Andrzej Kolinski; Mateusz Kurcinski; Sebastian Kmiecik
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

10.  Modeling of Hidden Structures Using Sparse Chemical Shift Data from NMR Relaxation Dispersion.

Authors:  R Bryn Fenwick; David Oyen; Henry van den Bedem; H Jane Dyson; Peter E Wright
Journal:  Biophys J       Date:  2020-12-08       Impact factor: 4.033

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