AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning.

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources.

De novo design of short antimicrobial lipopeptides.

The increase in bacterial resistance to antibiotics available leads to the search for new compounds with antimicrobial potential, such as peptides and lipopeptides. In this work, eight short lipopeptides with the structural pattern Cn-X1 X2 X3-NH2 were de novo designed, synthesized by Fmoc solid phase and characterized by instrumental techniques. The results of the in vitro tests indicated that two of them, LIP 4 and LIP 12 display antibacterial activity against 4 pathogenic bacteria with minimum inhibitory concentrations (MIC) between 9.

Cytolytic activity of peptides derived from the Cry11Bb insecticidal toxin of B. thuringiensis subsp. medellin.

A few Bacillus thuringiensis Cry proteins, known as parasporins, have demonstrated cell proliferation inhibition of human cancer cells in vitro after protease activation. In this work, eight peptides derived from the Cry11Bb protoxin produced by B. thuringiensis subsp.

Design of antimicrobial and cytolytic peptides by computational analysis of bacterial, algal, and invertebrate proteomes.

The increase of antibiotic resistance in bacterial species has raised the need to search for novel antimicrobial molecules. Antimicrobial peptides are molecules that commonly display an amphipathic character. In this work, we developed a computational strategy to search for new peptide sequences within the proteome of any organism that includes in-house developed software and the use of artificial intelligence tools available online.