Accelerating the Discovery and Characterization of Antimicrobial Peptides

Dear Colleagues,

The last decade has witnessed a remarkable increase in the number of alignment-free, machine learning-based approaches for the prediction of antimicrobial peptides. The accuracy of shallow and deep learning methods to discriminate AMPs from non-AMPs has reached 95% or more. Once a sequence is identified as antimicrobial, its specific activity, target pathogen and MIC must be determined. Furthermore, antimicrobial peptides are also able to penetrate cells and activate autophagy in mammalian cells, as well as to modulate the immune system. In summary, the activities embedded within these peptides include anti-viral, anti-fungal, anti-parasite, anti-bacterial, pro-autophagy and immunomodulator. Such multi-functionality imposes the need to further characterize these peptides to evaluate which activities are present, not only out of curiosity, but also as a requirement if these peptides are aimed to be used as pharmaceuticals. Thus, further developments of these predictors and experimental systems are required to match the current development of the field.

Methodological challenges to match this increasing need to further characterize antimicrobial peptides include the lack of a profound analysis of the available data, representation aspects of both shallow and deep models and high-throughput assays. A well-known drawback of machine-learning methods is the lack of negative experimentally validated sets. The development of high-throughput experiments assessing the different activities of antimicrobial peptides will be of extreme value.

This Special Issue of Antibiotics invites authors to publish original research including peptide data analysis, methodological aspects of machine learning-based approaches and high-throughput assays intended to achieve the abovementioned goal.

Dr. Carlos A. Brizuela
Dr. Gabriel Del Río Guerra
Guest Editors

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