Mechanistic principles of antimicrobial peptides uncovered by charge density-based machine learning

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Antimicrobial peptides (AMPs) are a promising alternative to conventional antibiotics.
• The diverse mechanisms of action of AMPs complicate their rational design.
• Understanding the physicochemical principles governing AMP activity is crucial for developing new therapeutics.

Purpose of the Study:

• To develop an electrostatics-stratified computational framework to uncover key physicochemical principles of AMP activity.
• To identify distinct molecular signatures across different electrostatic regimes of AMPs.
• To provide design guidelines for next-generation antimicrobial peptides.

Main Methods:

• Experimentally validated AMPs were stratified based on average charge per residue.
• Integrated sequence-, structure-, and chemistry-based descriptors were analyzed.
• Distinct molecular signatures were identified for low-, intermediate-, and high-charge AMP groups.

Main Results:

• Low-charge/length AMPs utilize amphipathic organization and structural compactness.
• Intermediate-charge/length AMPs show a balance between hydrophobicity and electrostatics.
• High-charge AMPs employ cationic attraction, lipophilicity, and tryptophan anchoring for membrane disruption.
• Hydrophobic moment is a key factor across all AMP classes.

Conclusions:

• Distinct physicochemical principles govern AMP activity across different electrostatic regimes.
• The study provides insights into the structure-activity relationships of AMPs.
• Identified design guidelines can aid in developing selective and potent next-generation AMPs.