Uniform Design-Embedded Predictions of (Tetra-)Peptide Physicochemical Properties.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41555497", "kind": "science-record", "revision": 3, "content_hash": "sha256:0cf91621d034c0745bdd6924f86df6cfbe00e58c7c1e4e9f7e6e7a7c3e18e055", "created_at": "2026-11-01T20:31:31Z", "updated_at": "2026-11-01T22:15:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Uniform Design-Embedded Predictions of (Tetra-)Peptide Physicochemical Properties.", "doi": "10.1093/bioinformatics/btag036", "pmid": "41555497", "authors": ["Zhihui Zhu", "Huapeng Liu", "Xuechen Li", "Haojin Zhou", "Jiaqi Wang"], "journal": "Bioinformatics (Oxford, England)", "year": 2026, "abstract": "MOTIVATION: Short peptides hold significant promise in drug discovery and materials science due to their biocompatibility, multifunctionality, ease of synthesis, etc. However, accurately predicting their physicochemical properties, a prerequisite for application development, remains a grand challenge due to the sheet quantity of peptides.\nRESULTS: This study presents an innovative approach integrating uniform design (UD) on the sampling over the whole space with artificial intelligence (AI) on the sampled data to enhance prediction of key physicochemical properties, including aggregation propensity (AP), hydrophilicity (logP), and isoelectric point (pI), within the complete sequence space of tetrapeptides (160,000 sequences). Using UD, we generate 31 distinct peptide datasets, with a consistent amino acid occupation fraction of 5% at each position, thereby creating unbiased training data without any amino acid preferences for training AI models. This work provides comprehensive datasets on the physicochemical properties of all tetrapeptides, develops robust AI-based predictive models, and quantitatively elucidates the relationships between key physicochemical attributes and self-assembly behaviors of short peptides by Shapley Additive Explanations (SHAP) analysis. By integrating the strategic experimental design (i.e., UD), AI modeling, and peptide domain knowledge, our approach facilitates the discovery and optimization of functional peptides, offering new opportunities for peptide-based therapeutic applications.\nAVAILABILITY: The complete datasets, source code, and pre-trained models are made available at the Github repository (https://github.com/JiaqiBenWang/UD-AI-Peptide) and Zenodo (https://doi.org/10.5281/zenodo.17984124).\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "Hyperparameter optimization involved random search over 50 combinations, evaluated via five-fold cross-validation; the optimal hyperparameter configurations were selected based on validation set RMSE.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41555497", "locator": "sections[9] \"2.4 Training and testing of AI models\" paragraph 2", "quote": "Hyperparameter optimization involved random search over 50 combinations, evaluated via five-fold cross-validation; the optimal hyperparameter configurations were selected based on validation set RMSE."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supporting information including the supplementary data, code, results are available at the Github repository (https://github.com/JiaqiBenWang/UD-AI-Peptide), from Zenodo (https://doi.org/10.5281/zenodo.17984124), or from the author. 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