Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus
Abstract Background Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the trad...
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doaj-dbac6f2f83b84a689e2cc764956fac7b2021-04-25T11:50:40ZengBMCBMC Bioinformatics1471-21052021-04-0122111610.1186/s12859-021-04137-0Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensusQing Ning0Dali Wang1Fei Cheng2Yuheng Zhong3Qi Ding4Jing You5Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan UniversityGuangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan UniversityGuangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan UniversityGuangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan UniversityGuangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan UniversityGuangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan UniversityAbstract Background Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly. Results We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively. Conclusion The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery.https://doi.org/10.1186/s12859-021-04137-0Resistance mutationMachine learningClassifierRifampicinPrediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qing Ning Dali Wang Fei Cheng Yuheng Zhong Qi Ding Jing You |
spellingShingle |
Qing Ning Dali Wang Fei Cheng Yuheng Zhong Qi Ding Jing You Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus BMC Bioinformatics Resistance mutation Machine learning Classifier Rifampicin Prediction |
author_facet |
Qing Ning Dali Wang Fei Cheng Yuheng Zhong Qi Ding Jing You |
author_sort |
Qing Ning |
title |
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus |
title_short |
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus |
title_full |
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus |
title_fullStr |
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus |
title_full_unstemmed |
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus |
title_sort |
predicting rifampicin resistance mutations in bacterial rna polymerase subunit beta based on majority consensus |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2021-04-01 |
description |
Abstract Background Mutations in an enzyme target are one of the most common mechanisms whereby antibiotic resistance arises. Identification of the resistance mutations in bacteria is essential for understanding the structural basis of antibiotic resistance and design of new drugs. However, the traditionally used experimental approaches to identify resistance mutations were usually labor-intensive and costly. Results We present a machine learning (ML)-based classifier for predicting rifampicin (Rif) resistance mutations in bacterial RNA Polymerase subunit β (RpoB). A total of 186 mutations were gathered from the literature for developing the classifier, using 80% of the data as the training set and the rest as the test set. The features of the mutated RpoB and their binding energies with Rif were calculated through computational methods, and used as the mutation attributes for modeling. Classifiers based on five ML algorithms, i.e. decision tree, k nearest neighbors, naïve Bayes, probabilistic neural network and support vector machine, were first built, and a majority consensus (MC) approach was then used to obtain a new classifier based on the classifications of the five individual ML algorithms. The MC classifier comprehensively improved the predictive performance, with accuracy, F-measure and AUC of 0.78, 0.83 and 0.81for training set whilst 0.84, 0.87 and 0.83 for test set, respectively. Conclusion The MC classifier provides an alternative methodology for rapid identification of resistance mutations in bacteria, which may help with early detection of antibiotic resistance and new drug discovery. |
topic |
Resistance mutation Machine learning Classifier Rifampicin Prediction |
url |
https://doi.org/10.1186/s12859-021-04137-0 |
work_keys_str_mv |
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