PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning

Phasit Charoenkwan; Pramote Chumnanpuen; Nalini Schaduangrat; Changmin Oh; Balachandran Manavalan; Watshara Shoombuatong

doi:10.1016/j.compbiomed.2023.106784

PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning

Comput Biol Med. 2023 May:158:106784. doi: 10.1016/j.compbiomed.2023.106784. Epub 2023 Mar 14.

Authors

Phasit Charoenkwan¹, Pramote Chumnanpuen², Nalini Schaduangrat³, Changmin Oh⁴, Balachandran Manavalan⁵, Watshara Shoombuatong⁶

Affiliations

¹ Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
² Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand; Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand.
³ Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
⁴ Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
⁵ Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea. Electronic address: bala2022@skku.edu.
⁶ Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand. Electronic address: watshara.sho@mahidol.ac.th.

PMID: 36989748
DOI: 10.1016/j.compbiomed.2023.106784

Abstract

Quorum sensing peptides (QSPs) are microbial signaling molecules involved in several cellular processes, such as cellular communication, virulence expression, bioluminescence, and swarming, in various bacterial species. Understanding QSPs is essential for identifying novel drug targets for controlling bacterial populations and pathogenicity. In this study, we present a novel computational approach (PSRQSP) for improving the prediction and analysis of QSPs. In PSRQSP, we develop a novel propensity score representation learning (PSR) scheme. Specifically, we utilized the PSR approach to extract and learn a comprehensive set of estimated propensities of 20 amino acids, 400 dipeptides, and 400 g-gap dipeptides from a pool of scoring card method-based models. Finally, to maximize the utility of the propensity scores, we explored a set of optimal propensity scores and combined them to construct a final meta-predictor. Our experimental results showed that combining multiview propensity scores was more beneficial for identifying QSPs than the conventional feature descriptors. Moreover, extensive benchmarking experiments based on the independent test were sufficient to demonstrate the predictive capability and effectiveness of PSRQSP by outperforming the conventional ML-based and existing methods, with an accuracy of 94.44% and AUC of 0.967. PSR-derived propensity scores were employed to determine the crucial physicochemical properties for a better understanding of the functional mechanisms of QSPs. Finally, we constructed an easy-to-use web server for the PSRQSP (http://pmlabstack.pythonanywhere.com/PSRQSP). PSRQSP is anticipated to be an efficient computational tool for accelerating the data-driven discovery of potential QSPs for drug discovery and development.

Keywords: Bioinformatics; Feature representation learning; Machine learning; Propensity score; Quorum sensing peptide; Scoring card method.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bacteria
Dipeptides / chemistry
Peptides* / chemistry
Propensity Score
Quorum Sensing*

Substances

Peptides
Dipeptides