Building Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins

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dc.authoridTekpinar, Mustafa/0000-0002-0207-0446
dc.contributor.authorYildirim, Ahmet
dc.contributor.authorTekpinar, Mustafa
dc.date.accessioned2024-12-24T19:27:50Z
dc.date.available2024-12-24T19:27:50Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractProteases are major drug targets for many viral diseases. However, mutations can render several antiprotease drugs inefficient rapidly even though these mutations may not alter protein structures significantly. Understanding relations between quickly mutating residues, protease structures, and the dynamics of the proteases is crucial for designing potent drugs. Due to this reason, we studied relations between the evolutionary information on residues in the amino acid sequences and protein dynamics for SARS-CoV-2 main protease. More precisely, we analyzed three dynamical quantities (Schlitter entropy, root-mean-square fluctuations, and dynamical flexibility index) and their relation to the amino acid conservation extracted from multiple sequence alignments of the main protease. We showed that a quantifiable similarity can be built between a sequence-based quantity called Jensen-Shannon conservation and those three dynamical quantities. We validated this similarity for a diverse set of 32 different proteins, other than the SARS-CoV-2 main protease. We believe that establishing these kinds of quantitative bridges will have larger implications for all viral proteases as well as all proteins.
dc.description.sponsorshipANR [ANR-21-CE17-0046]; Agence Nationale de la Recherche (ANR) [ANR-21-CE17-0046] Funding Source: Agence Nationale de la Recherche (ANR)
dc.description.sponsorshipM.T. was granted access to the Jean-Zay HPC resources of IDRIS under the allocation 2020-AP010711656 made by GENCI. A.Y. performed some of the MD simulations reported in this paper at TUBITAK (Scientific and Technical Research Council of Turkey) ULAKBIM (National Academic Information Center), High Performance and Grid Computing Center (TRUBA Resources). M.T. and A.Y. thank Jean-Zay and TRUBA HPC Resources for their superb computational resources and support. The computation on the dataset of 32 proteins was produced within the ANR project SolvingMEFVariants (ANR-21-CE17-0046) led by Alessandra Carbone, where multiple correlations with evolutionary conservation, physico-chemical and geometrical measures have been studied.
dc.identifier.doi10.1021/acs.jcim.2c01206
dc.identifier.endpage19
dc.identifier.issn1549-9596
dc.identifier.issn1549-960X
dc.identifier.issue1
dc.identifier.pmid36513349
dc.identifier.scopus2-s2.0-85144107512
dc.identifier.scopusqualityQ1
dc.identifier.startpage9
dc.identifier.urihttps://doi.org/10.1021/acs.jcim.2c01206
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6815
dc.identifier.volume63
dc.identifier.wosWOS:000897492600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAmer Chemical Soc
dc.relation.ispartofJournal of Chemical Information and Modeling
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.titleBuilding Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins
dc.typeArticle

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