Structure-Guided Antiviral Peptides Identification Targeting the HIV-1 Integrase

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• HIV-1 integrase (IN) is crucial for viral replication and a key drug target.
• Small peptides offer a promising avenue for antiviral therapeutics due to their selectivity and safety.
• Identifying effective peptide inhibitors from large libraries presents a significant challenge.

Purpose of the Study:

• To virtually screen a library of 280 peptides to identify novel inhibitors of HIV-1 integrase.
• To evaluate the binding affinity and interaction mechanisms of top-ranked peptides through molecular dynamics simulations.
• To elucidate structure-activity relationships for optimizing peptide-based HIV-1 drug design.

Main Methods:

• Multistep virtual screening, including minimum inhibitory concentration (MIC) assessment and molecular docking.
• Molecular dynamics (MD) simulations of peptide-integrase complexes (up to 1 microsecond).
• Binding free energy (BFE) calculations and structure-activity relationship (SAR) analysis.

Main Results:

• Identified several potent HIV-1 integrase inhibitory peptides, including HIP776, HIP777, and HIP1142.
• MD simulations revealed strong interactions with key HIV-1 integrase residues (e.g., LYS159, HIS67).
• SAR analysis indicated aromatic amino acids and peptide volume are critical for high docking scores.

Conclusions:

• Selected peptides demonstrate significant potential as therapeutic agents against HIV-1.
• The identified interactions and SAR provide a basis for rational design of next-generation peptide inhibitors.
• Further experimental validation is planned to advance these peptides towards clinical application.