Musical Peptide: Fragmentation of Musical Protein for AI-driven Bioactive Peptide Discovery Based on a New DeepPepQSAR Pipeline.

Abstract

Musical protein (MPro), the protein defined by music (namely biologization of music), is a reverse operation of the traditional life music that addresses the sonification of biomolecule to musical audio. We previously described a computational strategy for MPro facilitation, which maps the time sequence of music onto the spatial architecture of protein. Here, we further extend MPro to musical peptide (MPep) by fragmentizing MPro protein at either music time sequence level or protein residue sequence level, which represents a new technique to de novo generate distinct peptidic big data with intra-sequence organization, thus particularly suitable for AI-oriented tasks. On the other side, DeepPepQSAR methodology is raised as an integration of deep learning and traditional peptide quantitative structure-activity relationship (PepQSAR) in big-data bioactive peptide discovery, and we herein practice it on AI-driven high-throughput antimicrobial peptide (AMP) screening. Both the structural properties of length-varying peptide sequences and the bacterial species tested for these peptides are visually characterized by using PepImage representation, which is then fed into a dual-path convolutional neural network (CNN) to train a predictive DeepPepQSAR regression scoring function for further discovering potent AMPs from millions of MPep candidates. Consequently, 32 MPep peptides are identified to have a potential antibacterial profile, in which 4 and 12 peptides exhibit high and moderate activities, respectively, that are generally comparable with the sophisticated AMP peptide KR-12, but lower than the FDA-approved antibiotic Levofloxacin.