Computational Approaches to Bioactive Peptide Prediction and Discovery

Dear Colleagues,

Peptides derived from the hydrolysis of naturally occurring proteins are known to contain a large number of interesting bioactivities (antidiabetic, antihypertensive, antimicrobial, etc.). In addition, they can be obtained from a wide variety of sources (even by-products) and, depending on hydrolytic conditions (e.g., hydrolases, pH, temperature), the same source can provide a different set of peptides. There is a large number of peptides readily available today and computational tools can be very useful in: (i) predicting their bioactivity; (ii) help to find easy ways to obtain them from the available raw materials.

Therefore, a first goal of the current Special Issue is to describe the state of the art of the computational tools that can be used for this bioactivity prediction. This includes, but is not limited to, protein–peptide docking tools, protein–peptide complex free-energy prediction, and deep/machine-learning approaches. In all cases, only manuscripts that contain a computational or in vitro assessment of the reliability of their predictions will be considered for peer review.

The second goal of this Special Issue is to emphasize web servers and databases that help to discover easy ways: (1) to obtain a specific bioactive peptide from available sources; (2) to obtain different bioactive peptides from a specific protein source.

Dr. Gerard Pujadas
Guest Editor

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