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

dc.contributor.authorGanesan, Aravindhan
dc.contributor.authorCoote, Michelle
dc.contributor.authorBarakat, Khaled
dc.date.accessioned2020-09-02T01:29:09Z
dc.date.available2020-09-02T01:29:09Z
dc.date.issued2017
dc.description.abstractMolecular dynamics (MD) has become a routine tool in structural biology andstructure-based drug design (SBDD). MD offers extraordinary insights into thestructures and dynamics of biological systems. With the current capabilities ofhigh-performance supercomputers, it is now possible to perform MD simula-tions of systems as large as millions of atoms and for several nanoseconds time-scale. Nevertheless, many complicated molecular mechanisms, including ligandbinding/unbinding and protein folding, usually take place on timescales of sev-eral microseconds to milliseconds, which are beyond the practical limits of stand-ard MD simulations. Such issues with traditional MD approaches can beeffectively tackled with new generation MD methods, such as enhanced sam-pling MD approaches and coarse-grained MD (CG-MD) scheme. The formeremploy a bias to steer the simulations and reveal biological events that are usu-ally very slow, while the latter groups atoms as interaction beads, thereby redu-cing the system size and facilitating longer MD simulations that can witnesslarge conformational changes in biological systems. In this review, we outlinemany of such advanced MD methods, and discuss how their applications areproviding significant insights into important biological processes, particularlythose relevant to drug design and discovery.en_AU
dc.description.sponsorshipThis work has been funded through the Alberta Cancer Foundation (ACF), Li Ka Shing Applied Virology Insti-tute (LKSAVI), and The Natural Sciences and Engineering Research Council of Canada (NSERC)en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1759-0876en_AU
dc.identifier.urihttp://hdl.handle.net/1885/209210
dc.language.isoen_AUen_AU
dc.provenancehttps://v2.sherpa.ac.uk/id/publication/19426..."The Accepted Version can be archived in a Non-Commercial Institutional Repository. 12 months embargo" from SHERPA/RoMEO site (as at 2/09/2020). This is the peer reviewed version of the following article: [Ganesan, Aravindhan, Michelle L. Coote, and Khaled Barakat. "Molecular ‘time‐machines’ to unravel key biological events for drug design." Wiley Interdisciplinary Reviews: Computational Molecular Science 7.4 (2017): e1306.], which has been published in final form at [https://dx.doi.org/10.1002/wcms.1306]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versionsen_AU
dc.publisherWileyen_AU
dc.rights© 2017 Wiley Periodicals, Incen_AU
dc.sourceWiley Interdisciplinary Reviews: Computational Molecular Scienceen_AU
dc.titleMolecular ‘time-machines’ to unravel key biological events for drug designen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.issue4en_AU
local.bibliographicCitation.startpagee1306en_AU
local.contributor.affiliationCoote, Michelle, Research School of Chemistry, The Australian National Universityen_AU
local.contributor.authoremailmichelle.coote@anu.edu.auen_AU
local.contributor.authoruidu4031074en_AU
local.identifier.citationvolume7en_AU
local.identifier.doi10.1002/wcms.1306en_AU
local.identifier.uidSubmittedByu1005913en_AU
local.publisher.urlhttps://www.wiley.com/en-gben_AU
local.type.statusAccepted Versionen_AU

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