Molecular ‘time-machines’ to unravel key biological events for drug design

Aravindhan Ganesan, Michelle L. Coote, Khaled Barakat*

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    10 Citations (Scopus)

    Abstract

    Molecular dynamics (MD) has become a routine tool in structural biology and structure-based drug design (SBDD). MD offers extraordinary insights into the structures and dynamics of biological systems. With the current capabilities of high-performance supercomputers, it is now possible to perform MD simulations of systems as large as millions of atoms and for several nanoseconds timescale. Nevertheless, many complicated molecular mechanisms, including ligand binding/unbinding and protein folding, usually take place on timescales of several microseconds to milliseconds, which are beyond the practical limits of standard MD simulations. Such issues with traditional MD approaches can be effectively tackled with new generation MD methods, such as enhanced sampling MD approaches and coarse-grained MD (CG-MD) scheme. The former employ a bias to steer the simulations and reveal biological events that are usually very slow, while the latter groups atoms as interaction beads, thereby reducing the system size and facilitating longer MD simulations that can witness large conformational changes in biological systems. In this review, we outline many of such advanced MD methods, and discuss how their applications are providing significant insights into important biological processes, particularly those relevant to drug design and discovery. WIREs Comput Mol Sci 2017, 7:e1306. doi: 10.1002/wcms.1306. For further resources related to this article, please visit the WIREs website.

    Original languageEnglish
    Article numbere1306
    JournalWiley Interdisciplinary Reviews: Computational Molecular Science
    Volume7
    Issue number4
    DOIs
    Publication statusPublished - 1 Jul 2017

    Fingerprint

    Dive into the research topics of 'Molecular ‘time-machines’ to unravel key biological events for drug design'. Together they form a unique fingerprint.

    Cite this