Computational Design of Peptides for Biomaterials Applications

Abstract

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure−function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota

“…Besides naturally produced AMPs, the design of new peptides with enhanced antimicrobial activity is an active area of research [122,123]. This includes improving the antimicrobial activity by modifying the peptide sequence and their cationic, hydrophobic, and amphipathic properties [124], where bioinformatic tools and machine learning or deep algorithms play a crucial role in improving antimicrobial peptides AMPs by aiding in their design, prediction, and analysis [125,126]. Some of these tools include HydrAMP [127], PepGAN [128], AMPAGAN v2 [129], PepCVAE [130], PandoraGan [131], among others [123].…”