Deciphering optimal molecular determinants of non-hemolytic, cell-penetrating antimicrobial peptides through bioinformatics and Random Forest.

Antimicrobial peptides (AMPs) are promising molecules for combating resistant pathogens, offering several advantages like broad-spectrum effectiveness and multi-targeted action. While most AMPs exhibit membranolytic activity similar to hemolytic peptides (HPs), some act by entering cells like cell-penetrating peptides (CPPs). The toxicity of AMPs towards the host is the major hurdle in their development and application. Given the peptides' function and toxicity largely depend on their molecular properties, identifying and fine-tuning these factors is imperative for developing effective and safe AMPs. To address these knowledge gaps, we present a study that employs a holistic strategy by investigating the molecular descriptors of AMPs, CPPs, HPs, and non-functional equivalents. The prediction of functional properties categorized datasets of 3697 experimentally validated peptides into six groups and three clusters. Predictive and statistical analyses of physicochemical and structural parameters revealed that AMPs have a mean hydrophobic moment of 1.2, a net charge of 4.5, and a lower isoelectric point of 10.9, with balanced hydrophobicity. For cluster AC-nHPs containing peptides with antimicrobial, cell-penetrating, and non-hemolytic properties, disordered conformation and aggregation propensities, followed by amphiphilicity index, isoelectric point, and net charge were identified as the most critical properties. In addition, this work also explains why most AMPs and HPs are membrane-disruptive, while CPPs are non-membranolytic. In conclusion, the study identifies optimal molecular descriptors and offers valuable insights for designing effective, non-toxic AMPs for therapeutic use.