Publication:
Proteochemometric model for predicting the inhibition of penicillin-binding proteins

dc.contributor.authorSunanta Nabuen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.authorWiwat Owasirikulen_US
dc.contributor.authorRatana Lawungen_US
dc.contributor.authorChartchalerm Isarankura-Na-Ayudhyaen_US
dc.contributor.authorMaris Lapinsen_US
dc.contributor.authorJarl E.S. Wikbergen_US
dc.contributor.authorVirapong Prachayasittikulen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherUppsala Universiteten_US
dc.date.accessioned2018-11-23T09:59:25Z
dc.date.available2018-11-23T09:59:25Z
dc.date.issued2015-01-01en_US
dc.description.abstract© 2014 Springer International Publishing Switzerland. Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing β-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic β-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability (R <sup>2</sup> = 0.91, Q <sup>2</sup> = 0.77, Q <inf>Ext</inf> <sup>2</sup> = 0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.en_US
dc.identifier.citationJournal of Computer-Aided Molecular Design. Vol.29, No.2 (2015), 127-141en_US
dc.identifier.doi10.1007/s10822-014-9809-0en_US
dc.identifier.issn15734951en_US
dc.identifier.issn0920654Xen_US
dc.identifier.other2-s2.0-84922005039en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/35779
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84922005039&origin=inwarden_US
dc.subjectChemistryen_US
dc.subjectComputer Scienceen_US
dc.titleProteochemometric model for predicting the inhibition of penicillin-binding proteinsen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84922005039&origin=inwarden_US

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