{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T06:48:21Z","timestamp":1785739701946,"version":"3.56.0"},"reference-count":46,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Staphylococcus aureus, including methicillin-resistant lineages, is a leading cause of device- and catheter-related infection, and rising resistance motivates the search for antimicrobial peptides (AMPs) with strong anti-staphylococcal activity and low host toxicity. Machine learning can prioritize candidate peptides. However, the literature-derived AMP datasets are prone to homology-driven optimism, and computational studies frequently overstate their translational reach. Methods: We curated 4007 deduplicated S. aureus-active AMP records and 582 binary-labeled hemolysis records. Each peptide was encoded with a transparent 538-dimensional physicochemical and compositional feature vector. Five classifiers and five regressors were evaluated for four endpoints (potency classification, log10 MIC regression, hemolysis classification, normalized hemolytic index) under both conventional random 5-fold cross-validation and homology-aware cross-validation, in which sequences were clustered by 3-mer similarity and whole clusters were confined to single folds. Class imbalance was handled by class weighting. Model behavior was interpreted with SHAP and Fisher-exact k-mer enrichment, and candidates were ranked by a multi-objective score that combines the independently trained heads. Results: Under homology-aware validation, performance was lower than under random splitting, as expected. Potency classification reached an AUROC of about 0.71 (Random Forest), compared with 0.797 under random cross-validation. Hemolysis classification remained strong at AUROC 0.90 (95% CI 0.88 to 0.93), which indicates that its high accuracy is not a homology leakage artifact. MIC regression was modest (homology-aware R2 0.17, Spearman \u03c1 0.39) and is therefore treated only as a rank-ordering signal. SHAP and k-mer analyses recovered interpretable structure\u2013activity relationships. Net positive charge and amphipathicity drove potency, whereas bulk hydrophobicity drove hemolysis. Applying the pipeline to a generated pool prioritized 20 candidates. Nearest-neighbor analysis shows that these are close optimized variants of known potent scaffolds, with a median identity of 95% to a known peptide, rather than novel sequences. Conclusions: We present an interpretable, honestly benchmarked multi-objective pipeline that optimizes known anti-S. aureus AMP scaffolds toward lower predicted hemolysis. The prioritized peptides are computational hypotheses for future synthesis and experimental testing. Their low predicted hemolysis reflects a selection criterion rather than validated safety, and cross-species selectivity was not assessed.<\/jats:p>","DOI":"10.3390\/diagnostics16152430","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T06:17:40Z","timestamp":1785737860000},"page":"2430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Staphylococcus aureus Antimicrobial Peptides"],"prefix":"10.3390","volume":"16","author":[{"given":"Jianguo","family":"Xu","sequence":"first","affiliation":[{"name":"Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Population Health Research Institute, Hamilton, ON L8L 2X2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghua","family":"Yang","sequence":"additional","affiliation":[{"name":"Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Qinghai University Affiliated Hospital, Xining 810000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengfei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yating","family":"Cui","sequence":"additional","affiliation":[{"name":"Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhui","family":"Tian","sequence":"additional","affiliation":[{"name":"Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Key Laboratory of Evidence-Based Medicine of Gansu Province, Lanzhou University, No. 199, Donggang West Road, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"391","DOI":"10.7326\/0003-4819-132-5-200003070-00009","article-title":"Prevention of intravascular catheter-related infections","volume":"132","author":"Mermel","year":"2000","journal-title":"Ann. 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