Analogue and structure based approaches for modelling HIV-1 integrase inhibitors

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• HIV-1 integrase is a critical target for antiretroviral therapy.
• Developing novel and potent HIV-1 integrase inhibitors remains a priority.

Purpose of the Study:

• To computationally investigate structure-activity relationships of HIV-1 integrase inhibitors.
• To design and predict the activity of novel HIV-1 integrase inhibitors.

Main Methods:

• Optimization of 220 known inhibitors using B3LYP/6-31+G(d) level of theory.
• Molecular docking against four HIV-1 integrase receptors (4LH5, 5KRS, 3ZSQ, 3ZSV).
• Molecular Dynamics (MD) simulations, analysis of root-mean-square deviation (RMSD) and root mean square fluctuation (RMSF).
• Quantitative Structure-Activity Relationship (QSAR) modeling using various molecular descriptors.
• Design and prediction of new inhibitors based on structure-activity relationships.

Main Results:

• Identified key interacting residues (GLU170, ALA169, GLN168, HIS171, ASP167) in the 4LH5 receptor.
• Hydrogen bonding and stacking interactions are crucial for inhibitor binding.
• Developed validated QSAR models for predicting pIC50 values.
• Designed novel inhibitors with predicted high potency against HIV-1 integrase.

Conclusions:

• Computational approaches, including docking and MD simulations, are effective for understanding inhibitor-receptor interactions.
• QSAR models provide a reliable basis for predicting the activity of new compounds.
• The designed inhibitors show significant potential for development as new antiretroviral agents.