Innovative Computational Approaches for Bioactive Peptide Discovery and Functional Prediction

The field of peptide science has gained significant attention due to bioactive peptides' critical roles as antimicrobial agents, hormones, immunomodulators, and signaling molecules. Their unique properties, including selectivity, potency, and low toxicity, make them appealing candidates for therapeutic and nutraceutical applications. However, traditional methods for discovering bioactive peptides are often time-intensive and economically demanding. The inherent challenges of dealing with small sizes and high sequence variability further complicate efficient annotation and discovery processes. With recent advances in computational methods offering robust solutions for efficient peptide screening, annotation, and design, there's a compelling need to leverage these technological innovations to overcome current obstacles in the discovery and functional prediction of bioactive peptides across diverse biological systems.

This Research Topic aims to tackle the ongoing challenges in the discovery and functional prediction of bioactive peptides through computational advancements. Although some strides have been made, difficulties remain in identifying novel peptides and accurately annotating their functions due to the constraints of experimental screening and the complexities of short peptides. Many computational tools are hindered by insufficient training data, elevated false-positive rates, and limited generalizability across various peptide types and functions. This initiative seeks contributions that either develop new or apply existing computational frameworks for bioactive peptide discovery, function prediction, and mechanism elucidation. Techniques of interest include machine learning models, sequence- and structure-based strategies, generative design algorithms, and integrative omics methods. We also encourage submissions demonstrating cross-disciplinary methodologies or experimental validation. By amalgamating diverse viewpoints and innovative approaches, this Research Topic aspires to enrich the computational repertoire for peptide science and enable the discovery of peptides with potential applications in therapeutics, biotechnology, and other fields.

To gather further insights into comprehensive computational approaches for peptide discovery and function prediction, we welcome articles addressing, but not limited to, the following themes:

• Development of machine learning models for peptide prediction
• Integrative omics-based approaches for peptide identification
• Generative algorithms for peptide sequence and structure design
• Evaluation of computational tools for diverse peptide functions
• Cross-disciplinary and experimental validation of computational predictions

Submissions encompassing Reviews, Original Research, and Methodological Papers will be considered.