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. 2020 Feb 5;10:1875. doi: 10.1038/s41598-020-58635-x

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Methodology workflow. The methodology can be divided into three steps. In step 1, data is collected and curated from various tuberculosis databases and articles with experimental evidence like availability of DST results or high-precision laboratory screening study. The curated mutations are shown across both the protein sequence and 3D structure, respectively. The protein sequence and structure of PncA is colored by whether resistant (red) or susceptible (blue) mutations have been observed at that location. Highlighting the difficulty of genomic analysis of pncA, both resistant and susceptible mutations have been observed across many residue positions (cyan). In step 2, effects of mutations on protein stability, dynamics, complementary information regarding the environment characteristics of the wild-type residue (e.g. relative solvent accessibility, residue depth and secondary structure), PZA binding affinity are calculated using different in-silico tools. Step 3, all the features are used as evidence to train a supervised machine learning algorithm and after evaluating the performance of the predictive model, the consensus predictions are integrated into a server and can be used to guide clinical resistance detection.