Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150578
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAnand, D. Vijayen_US
dc.contributor.authorMeng, Zhenyuen_US
dc.contributor.authorXia, Kelinen_US
dc.date.accessioned2021-06-14T07:02:51Z-
dc.date.available2021-06-14T07:02:51Z-
dc.date.issued2019-
dc.identifier.citationAnand, D. V., Meng, Z. & Xia, K. (2019). A complex multiscale virtual particle model based elastic network model (CMVP-ENM) for the normal mode analysis of biomolecular complexes. Physical Chemistry Chemical Physics, 21(8), 4359-4366. https://dx.doi.org/10.1039/c8cp07442aen_US
dc.identifier.issn1463-9076en_US
dc.identifier.urihttps://hdl.handle.net/10356/150578-
dc.description.abstractUnderstanding the molecular flexibility and dynamics is central to the analysis of biomolecular functions. In this work, complex multiscale virtual particle model based elastic network models (CMVP-ENMs) have been proposed for the normal mode analysis of biomolecular complexes or biomolecular assemblies. The term complex used in our CMVP-ENMs refers to the multi-material or multi-constituent. Different "materials" or constituents contribute differently to the general flexibility and dynamics of complex biomolecular structures. In our CMVP-ENMs, the key idea is to incorporate relative density or weight information of different components into the spring parameter of elastic network models. Two different models, including the CMVP based Gaussian network model (CMVP-GNM) and the CMVP based anisotropic network model (CMVP-ANM), have been proposed. With the consideration of complex component information, our CMVP-GNM, compared with the traditional GNM, can deliver a better accuracy in the B-factor prediction of protein-nucleic acid complexes. Moreover, our CMVP-ANM can be used to remove the "tip effect" by systematically suppressing the extremely-large vectors, in the highly flexible regions, of the normal modes generated by the ANM. In this way, our CMVP-ANM can be used to handle biomolecular structures with large hanging loops or extruding ends, which usually cause an irrationally-large-vector problem in ANM predictions. Finally, we explore the potential applications of our method by the cryo-EM data analysis. We find that by tuning the relative density ratio, we can systematically enhance or suppress the modes in different components, so that it can reveal the dynamics of the special regions that we are interested in.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationM4081842en_US
dc.relationRG126/16en_US
dc.relationRG31/18en_US
dc.relationMOE2018-T2-1-033en_US
dc.relation.ispartofPhysical Chemistry Chemical Physicsen_US
dc.rights© 2019 The Owner Societies. All rights reserved.en_US
dc.subjectEngineering::Bioengineeringen_US
dc.titleA complex multiscale virtual particle model based elastic network model (CMVP-ENM) for the normal mode analysis of biomolecular complexesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1039/c8cp07442a-
dc.identifier.pmid30724932-
dc.identifier.scopus2-s2.0-85061863729-
dc.identifier.issue8en_US
dc.identifier.volume21en_US
dc.identifier.spage4359en_US
dc.identifier.epage4366en_US
dc.subject.keywordsAnisotropic Networksen_US
dc.subject.keywordsBiomolecular Complexesen_US
dc.description.acknowledgementThis work was supported in part by the Nanyang Technological University Startup Grant M4081842 and the Singapore Ministry of Education Academic Research Fund Tier 1 RG126/16 and RG31/18, Tier 2 MOE2018-T2-1-033.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:SBS Journal Articles

Page view(s)

152
Updated on Jun 30, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.