Alanine scanning combined with interaction entropy studying the differences of binding mechanism on HIV-1 and HIV-2 proteases with inhibitor

Abstract

Quantitative characterization of binding affinity in protein-ligand and residue-ligand is critical for understanding binding mechanisms of protein-ligand and predicting hot-spot residues. In this paper, binding free energies between two HIV (HIV-1 and HIV-2) proteases and four inhibitors are calculated by molecular mechanics/generalized Born surface area (MM/GBSA) combined with the newly developed interaction entropy (IE) approach. The internal dielectric constant is set on the basis of different types of amino acids. The entropy change in protein-ligand binding is computed by IE method which is superior to the traditional normal mode (Nmode) method in the analysis of the ranking of binding free energy, statistical stability and enthalpy-entropy compensation. Importantly, IE method combined with alanine scanning is applied to calculate residue-specific binding free energy. And the calculated total binding free energy using the current method is in excellent with the experimental observed. Our research indicates that HIV-1 and HIV-2 proteases share the common hot-spot residues with ILE50/50’ and ILE84/ILE84’ which provide the major favorable contribution to the binding of protein and inhibitor in all systems. The predicted hot-spot residues are more in HIV-1 complex than HIV-2 complex and some hot-spot residues contributing to HIV-1 don’t play a significant role in HIV-2. To some extent, this explains the reason of decrease in potency inhibitors against HIV-2 compared to HIV-1 protease. The study is expected to understand quantitatively the binding mechanism of HIV-inhibitor and provide important theoretical guidance for the design of equipotent HIV-1/HIV-2 protease inhibitors.

Communicated by Ramaswamy H. Sarma

Keywords:

• Molecular dynamics simulations
• binding free energy calculation
• alanine scanning
• interaction entropy
• HIV protease

Acknowledgements

We thank the ECNU Public Platform for Innovation 001 for providing supercomputer time.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Key R&D Program of China (Grant no. 2016YFA0501700), National Natural Science Foundation of China (Grant nos. 11774207, 11574184, 21433004, 91753103), and NYU Global Seed Grant.