Optimisation of peptides targeting reverse transcriptase HIV-1 using QSAR, machine learning, and computational...

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Virology

Background:

• Current HIV-1 reverse transcriptase (RT) inhibitors face challenges with drug resistance and adverse effects.
• Peptide-based drugs offer potential for high specificity and reduced toxicity but require extensive screening.
• Novel therapeutic strategies are crucial for combating HIV-1 infection.

Purpose of the Study:

• To identify potent tripeptide inhibitors targeting HIV-1 RT using computational methods.
• To evaluate the binding affinity, stability, and drug-like properties of candidate peptides.
• To discover new therapeutic leads for HIV treatment.

Main Methods:

• Screening of 2,197 tripeptides using a computational pipeline.
• Machine learning, Quantitative Structure-Activity Relationship (QSAR) modeling, and in silico validation.
• Molecular mechanics with generalized Born surface area (MM/GBSA), Density Functional Theory (DFT), and molecular dynamics (MD) simulations.

Main Results:

• Three promising tripeptides (FHW, HFW, HHW) were identified.
• FHW demonstrated superior predicted affinity (-63.50 kcal/mol binding energy) and stability.
• FHW exhibited favorable electronic properties and membrane translocation characteristics, outperforming Nevirapine.

Conclusions:

• The FHW peptide is a highly promising candidate for next-generation HIV therapeutics.
• FHW shows potential for improved efficacy and safety profiles.
• HFW and HHW represent viable alternative candidates for further investigation.