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Updated: Jul 10, 2026
Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
RLAnOxPeptide: an integrated framework combining transformer and reinforcement learning for efficient antioxidant
Changsheng Han1,2,3, Jianda Yue1,2,3, Yaqi Li1,2,3
1The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China.
Motivation:
Bioactive peptides exhibit immense potential in pharmaceutical and food science domains, with antioxidant peptides (AOPs) garnering significant attention for their roles in scavenging free radicals. However, traditional discovery methods are inefficient and costly. This study introduces RLAnOxPeptide, an integrated computational framework that merges machine learning and reinforcement learning for the efficient prediction and de novo design of AOPs.
Results:
The framework initially establishes a high-precision predictor, RLP-T5Pred, based on the ProtT5 model via a 'protein-to-peptide' knowledge transfer strategy. By employing label smoothing and logit penalty regularization, it achieves state-of-the-art accuracy (AUC-ROC: 0.9692) and robust calibration. The second component is the generator, RLP-T5Gen, which is trained in an iterative 'Yin-Yang' loop combining supervised learning (to maintain sequence syntax) and reinforcement learning (to drive innovation). Guided by RLP-T5Pred serving as a fixed evaluator and a multi-objective reward function, the generator efficiently designs novel AOPs with high predicted activity. We experimentally validated the framework by synthesizing 17 designed peptides. Most candidates demonstrated potent radical scavenging abilities in chemical assays (DPPH and ABTS), leading to the selection of the top five candidates for cellular validation. In a t-BHP-induced HepG2 cell model, peptides Pep4, Pep5, Pep10, and Pep11 exhibited significant protective effects against oxidative damage. Consequently, the RLAnOxPeptide framework provides a powerful, experimentally verified paradigm for accelerating the discovery of novel antioxidant peptides.
Availability:
The datasets generated and/or analysed during the current study, along with model outputs and representative peptide sequences, have been deposited in a public repository. The RLAnOxPeptide framework source code is available at GitHub: https://github.com/changshh/RLAnOxPeptide. An archival snapshot of the code used to perform the experiments described in this manuscript has been deposited in Zenodo with the DOI: 10.5281/zenodo.20078425. An interactive online demonstration is also available via Hugging Face Spaces: https://huggingface.co/spaces/chshan/RLAnOxPeptide.
