The Human Cathelicidin LL-37, a Defensive Peptide Against Rotavirus Infection

Machine Learning Prediction of Antimicrobial Peptides

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

Design and heterologous expression of a novel dimeric LL37 variant in Pichia pastoris

BACKGROUND

The antimicrobial peptide LL37 is produced by white blood cells (mainly neutrophils) and various epithelial cells, and has the outstanding advantages of participating in immune regulation, causing chemotaxis of immune cells and promoting wound healing. However, the central domain of LL37 needs to be improved in terms of antimicrobial activity.

RESULTS

In this study, the amino acid substitution method was used to improve the antimicrobial activity of the LL37 active center, and a dimeric design with a better selection index was selected. A flexible linker was selected and combined with the 6 × His-SUMO tag and LG was successfully expressed using Pichia pastoris as a host. Recombinant LG displayed strong antimicrobial activity by destroying the cell membrane of bacteria but had low hemolytic activity. In addition, compared with monomeric peptide FR, rLG had improved ability to tolerate salt ions.

CONCLUSION

This research provides new ideas for the production of modified AMPs in microbial systems and their application in industrial production.