Comparing the quality and predictiveness between 3D QSAR models obtained from manual and automated alignment

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Structural Biology

Background:

• Human immunodeficiency virus protease (HIV-1 PR) is a key target for antiviral therapy.
• Quantitative Structure-Activity Relationship (QSAR) models are crucial for drug discovery.
• Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) are widely used 3D QSAR methods.

Purpose of the Study:

• To compare the predictive power of CoMFA and CoMSIA models using manual versus automated inhibitor alignment.
• To evaluate the robustness of automated alignment methods in generating reliable 3D QSAR models for HIV-1 PR inhibitors.
• To identify key interactions between inhibitors and the HIV-1 PR active site.

Main Methods:

• A set of 113 flexible cyclic urea inhibitors of HIV-1 PR was analyzed.
• Inhibitor alignment was performed manually and automatically using molecular docking.
• CoMFA and CoMSIA models were generated and statistically validated (q² and r²).
• Model performance was assessed using an external test set.

Main Results:

• Both manual and automated alignment yielded statistically significant CoMFA and CoMSIA models.
• Automated alignment, guided by molecular docking, showed good agreement with X-ray crystallography data.
• The best predictive r² was 0.754, and the best q² was 0.649.
• Automated alignment provided more robust models for external prediction.
• Key interactions involved hydrogen bonds with specific amino acid residues in the HIV-1 PR active site.

Conclusions:

• Automated inhibitor alignment can generate predictive 3D QSAR models comparable to manual methods.
• Automated alignment is a viable and encouraging approach for developing QSAR models, especially when target protein structures are available.
• This study validates the utility of automated alignment in drug discovery for HIV-1 PR inhibitors.