Understanding the challenges of protein flexibility in drug design

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

Acknowledgments

Molecular graphics have been produced with the UCSF Chimera package. Chimera is developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, supported by National Institute of General Medical Sciences (NIGMS) grant P41-GM103311. DA Antunes & D Devaurs are contributed equally to this work.

Declaration of interest

DA Antunes is supported by the CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil) from the Ministério da Ciência, Tecnologia e Inovação. LE Kavraki and D Devaurs are supported by National Science Foundation grant CCF 1423304. LE Kavraki is also supported by National Science Foundation grants ABI 0960612 and ABI 1262491. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Notes

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