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

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.