dc.contributor.author | Wang, Jinan | |
dc.contributor.author | Bhattarai, Apurba | |
dc.contributor.author | Do, Hung Nguyen | |
dc.contributor.author | Miao, Yinglong | |
dc.date.accessioned | 2023-07-11T15:44:52Z | |
dc.date.available | 2023-07-11T15:44:52Z | |
dc.date.issued | 2022-08-19 | |
dc.identifier.citation | Wang, 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.2022.11 | en_US |
dc.identifier.uri | https://hdl.handle.net/1808/34586 | |
dc.description.abstract | Biomolecular 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.publisher | Cambridge University Press | en_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.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | Biomolecular recognition | en_US |
dc.subject | Enhanced sampling | en_US |
dc.subject | Kinetics | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Molecular dynamics | en_US |
dc.subject | Thermodynamics | en_US |
dc.title | Challenges and frontiers of computational modelling of biomolecular recognition | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Wang, Jinan | |
kusw.kuauthor | Bhattarai, Apurba | |
kusw.kuauthor | Do, Hung Nguyen | |
kusw.kuauthor | Miao, Yinglong | |
kusw.kudepartment | Center for Computational Biology | en_US |
kusw.kudepartment | Molecular Biosciences | en_US |
dc.identifier.doi | 10.1017/qrd.2022.11 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-0162-212X | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-6497-4096 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-3714-1395 | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.identifier.pmid | PMC10299731 | en_US |
dc.rights.accessrights | openAccess | en_US |