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Accurate identification of protease substrate cleavage sites is critical for understanding protease function and substrate specificity. However, experimental determination of cleavage sites remains labor\u2010intensive and time\u2010consuming, motivating the development of efficient and accurate computational prediction methods. Here, we present a detailed protocol for using ProsperousPlus, a computational platform designed for large\u2010scale prediction of protease\u2010specific substrate cleavage sites and construction of customizable models for these predictions. ProsperousPlus integrates multiple sequence\u2010derived scoring functions with an advanced machine learning framework that automates the construction of optimized predictive models. The framework's workflow includes three major functional modules: (1) prediction of cleavage sites using pre\u2010trained models covering more than 100 proteases, (2) training of custom protease\u2010specific models using user\u2010provided datasets, and (3) deployment of trained models for large\u2010scale prediction tasks. This protocol describes the required hardware and software environment, dataset preparation strategies, model training procedures, and interpretation of prediction results, including probability scores and multiple evaluation metrics such as accuracy and AUC. In addition, model interpretation using Shapley additive explanations (SHAP) enables identification of influential sequence features contributing to cleavage specificity. ProsperousPlus can be used either through a standalone software for large\u2010scale, more frequent analyses or through a user\u2010friendly web server for small\u2010scale,\n                    <jats:italic>ad hoc<\/jats:italic>\n                    use. This protocol provides a practical and reproducible workflow for researchers seeking to analyze protease substrate specificity, construct customized prediction models, and perform high\u2010throughput proteolytic analysis. \u00a9 2026 Wiley Periodicals LLC.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol<\/jats:bold>\n                    : Predicting protease\u2010specific substrate cleavage sites and constructing and deploying protease\u2010specific models using the standalone software version of ProsperousPlus\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Alternate Protocol<\/jats:bold>\n                    : Using the web server version\n                  <\/jats:p>","DOI":"10.1002\/cpz1.70487","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T13:35:12Z","timestamp":1792416912000},"source":"Crossref","is-referenced-by-count":0,"title":["ProsperousPlus: A Bioinformatics Workflow for Protease Substrate Cleavage Site Prediction and Model Construction Utilizing Machine Learning"],"prefix":"10.1002","volume":"6","author":[{"given":"Xudong","family":"Guo","sequence":"first","affiliation":[{"name":"College of Information Engineering Northwest A&amp;F University  Shaanxi China"},{"name":"South Australian immunoGENomics Cancer Institute (SAiGENCI), College of Health Adelaide University Adelaide Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heyun","family":"Sun","sequence":"additional","affiliation":[{"name":"South Australian immunoGENomics Cancer Institute (SAiGENCI), College of Health Adelaide University Adelaide Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Information Engineering Northwest A&amp;F University  Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukasz","family":"Kurgan","sequence":"additional","affiliation":[{"name":"Department of Computer Science Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University  Clayton Victoria Australia"},{"name":"Monash AI Institute Monash University  Clayton Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering Northwest A&amp;F University  Shaanxi China"},{"name":"South Australian immunoGENomics Cancer Institute (SAiGENCI), College of Health Adelaide University Adelaide Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,19]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010025\u201002975\u2010x"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf016"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010025\u201009721\u20105"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/pmic.202300471"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1142\/S021972000500117X"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.virs.2022.04.006"},{"issue":"4","key":"e_1_2_7_8_1","first-page":"1","article-title":"Xgboost: Extreme gradient boosting","volume":"1","author":"Chen T.","year":"2015","journal-title":"R Package version 0.4\u20102"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059\u2010026\u201003972\u20109"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adj2011"},{"key":"e_1_2_7_11_1","article-title":"CatBoost: Gradient boosting with categorical features support","author":"Dorogush A. 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