Publication:
Prediction of Mycobacterium tuberculosis pyrazinamidase function based on structural stability, physicochemical and geometrical descriptors

dc.contributor.author Supo-Escalante, Rydberg Roman es_PE
dc.contributor.author Medico, Aldhair es_PE
dc.contributor.author Gushiken, Eduardo es_PE
dc.contributor.author Olivos-Ramirez, Gustavo E. es_PE
dc.contributor.author Quispe, Yaneth es_PE
dc.contributor.author Torres, Fiorella es_PE
dc.contributor.author Zamudio, Melissa es_PE
dc.contributor.author Antiparra, Ricardo es_PE
dc.contributor.author Amzel, L. Mario es_PE
dc.contributor.author Gilman, Robert H. es_PE
dc.contributor.author Sheen, Patricia es_PE
dc.contributor.author Zimic, Mirko es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2020
dc.description.abstract Background Pyrazinamide is an important drug against the latent stage of tuberculosis and is used in both first- and second-line treatment regimens. Pyrazinamide-susceptibility test usually takes a week to have a diagnosis to guide initial therapy, implying a delay in receiving appropriate therapy. The continued increase in multi-drug resistant tuberculosis and the prevalence of pyrazinamide resistance in several countries makes the development of assays for prompt identification of resistance necessary. The main cause of pyrazinamide resistance is the impairment of pyrazinamidase function attributed to mutations in the promoter and/or pncA coding gene. However, not all pncA mutations necessarily affect the pyrazinamidase function. Objective To develop a methodology to predict pyrazinamidase function from detected mutations in the pncA gene. Methods We measured the catalytic constant (k(cat)), K-M, enzymatic efficiency, and enzymatic activity of 35 recombinant mutated pyrazinamidase and the wild type (Protein Data Bank ID = 3pl1). From all the 3D modeled structures, we extracted several predictors based on three categories: structural stability (estimated by normal mode analysis and molecular dynamics), physicochemical, and geometrical characteristics. We used a stepwise Akaike's information criterion forward multiple log-linear regression to model each kinetic parameter with each category of predictors. We also developed weighted models combining the three categories of predictive models for each kinetic parameter. We tested the robustness of the predictive ability of each model by 6-fold cross-validation against random models. Results The stability, physicochemical, and geometrical descriptors explained most of the variability (R-2) of the kinetic parameters. Our models are best suited to predict k(cat), efficiency, and activity based on the root-mean-square error of prediction of the 6-fold cross-validation. Conclusions This study shows a quick approach to predict the pyrazinamidase function only from the pncA sequence when point mutations are present. This can be an important tool to detect pyrazinamide resistance.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1371/journal.pone.0235643
dc.identifier.uri https://hdl.handle.net/20.500.12390/2858
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLOS ONE
dc.rights info:eu-repo/semantics/openAccess
dc.subject Multidisciplinary
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#3.03.09
dc.title Prediction of Mycobacterium tuberculosis pyrazinamidase function based on structural stability, physicochemical and geometrical descriptors
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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