Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

• Published in: Briefings in bioinformatics Vol. 21; no. 1; pp. 106 - 119
• Main Authors: Wei, Leyi, Hu, Jie, Li, Fuyi, Song, Jiangning, Su, Ran, Zou, Quan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 17.01.2020; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bacteria; Cell interactions; Comparative analysis; Gene expression; Gene regulation; Gram-positive bacteria; Learning algorithms; Machine learning; Peptides; Performance prediction; Predictions; Representations; Signal processing; Strategy; sequence analysis; feature representation learning; feature descriptors; machine learning; quorum-sensing peptide
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bby107

Abstract Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell–cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs.