Charoenkwan Phasit, Chumnanpuen Pramote, Schaduangrat Nalini, Oh Changmin, Manavalan Balachandran, Shoombuatong Watshara
Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
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.
Comput Biol Med. 2023 May;158:106784. doi: 10.1016/j.compbiomed.2023.106784. Epub 2023 Mar 14.
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.
群体感应肽（QSPs）是参与多种细菌物种中细胞通讯、毒力表达、生物发光和群体运动等多种细胞过程的微生物信号分子。了解QSPs对于识别控制细菌种群和致病性的新药物靶点至关重要。在本研究中，我们提出了一种用于改进QSPs预测和分析的新型计算方法（PSRQSP）。在PSRQSP中，我们开发了一种新型倾向得分表示学习（PSR）方案。具体而言，我们利用PSR方法从基于评分卡方法的模型库中提取并学习20种氨基酸、400种二肽和400种g-gap二肽的一组综合估计倾向。最后，为了最大化倾向得分的效用，我们探索了一组最优倾向得分并将它们组合起来构建最终的元预测器。我们的实验结果表明，与传统特征描述符相比，组合多视图倾向得分对识别QSPs更有益。此外，基于独立测试的广泛基准实验足以证明PSRQSP的预测能力和有效性，其优于传统的基于机器学习的方法和现有方法，准确率为94.44%，曲线下面积为0.967。利用PSR衍生的倾向得分来确定关键的物理化学性质，以便更好地理解QSPs的功能机制。最后，我们为PSRQSP构建了一个易于使用的网络服务器（http://pmlabstack.pythonanywhere.com/PSRQSP）。PSRQSP有望成为一种高效的计算工具，用于加速数据驱动的潜在QSPs发现，以用于药物发现和开发。
