Prediction of prokaryotic transposases from protein features with machine learning approaches

Abstract

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

Machine learning‐based approach for prediction of ion channels and their subclasses

“…TRP IDRs have an impact on channel function, regulation, and associations with proteins. In Wang et al, 12 the authors used machine learning methodologies to create a classifier for predicting the identification of prokaryotic transposases (Tnps) in bacteria and archaea. They retrieved 2751 protein features from the data set, containing 14 852 Identification of prokaryotic transposases as well as 14 852 controls and with the help of the combined mutual information, selection operator, as well as least absolute shrinkage techniques, choose 75 features as predictive signatures.…”