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dc.contributor.authorWang, Jinan
dc.contributor.authorBhattarai, Apurba
dc.contributor.authorDo, Hung Nguyen
dc.contributor.authorMiao, Yinglong
dc.date.accessioned2023-07-11T15:44:52Z
dc.date.available2023-07-11T15:44:52Z
dc.date.issued2022-08-19
dc.identifier.citationWang, J., Bhattarai, A., Do, H. N., & Miao, Y. (2022). Challenges and frontiers of computational modelling of biomolecular recognition. QRB discovery, 3, e13. https://doi.org/10.1017/qrd.2022.11en_US
dc.identifier.urihttps://hdl.handle.net/1808/34586
dc.description.abstractBiomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterize biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.en_US
dc.publisherCambridge University Pressen_US
dc.rights© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectBiomolecular recognitionen_US
dc.subjectEnhanced samplingen_US
dc.subjectKineticsen_US
dc.subjectMachine learningen_US
dc.subjectMolecular dynamicsen_US
dc.subjectThermodynamicsen_US
dc.titleChallenges and frontiers of computational modelling of biomolecular recognitionen_US
dc.typeArticleen_US
kusw.kuauthorWang, Jinan
kusw.kuauthorBhattarai, Apurba
kusw.kuauthorDo, Hung Nguyen
kusw.kuauthorMiao, Yinglong
kusw.kudepartmentCenter for Computational Biologyen_US
kusw.kudepartmentMolecular Biosciencesen_US
dc.identifier.doi10.1017/qrd.2022.11en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-0162-212Xen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-6497-4096en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3714-1395en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.identifier.pmidPMC10299731en_US
dc.rights.accessrightsopenAccessen_US


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© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution license.
Except where otherwise noted, this item's license is described as: © The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution license.