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Prediction of Mycobacterium tuberculosis pyrazinamidase function based on structural stability, physicochemical and geometrical descriptors
Prediction of Mycobacterium tuberculosis pyrazinamidase function based on structural stability, physicochemical and geometrical descriptors
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Fecha
2020
Autores
Supo-Escalante, Rydberg Roman
Medico, Aldhair
Gushiken, Eduardo
Olivos-Ramirez, Gustavo E.
Quispe, Yaneth
Torres, Fiorella
Zamudio, Melissa
Antiparra, Ricardo
Amzel, L. Mario
Gilman, Robert H.
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Public Library of Science (PLoS)
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Abstracto
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.
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Multidisciplinary