Effective prediction model and determination of binding residues influential for inhibitors targeting HIV-1 integrase-LEDGF/p75 interface by employing solvent accessible surface area energy as key determinant

Abstract

Development of a highly accurate prediction model for protein–ligand inhibition has been a major challenge in drug discovery. Herein, we describe a novel predictive model for the inhibition of HIV-1 integrase (IN)-LEDGF/p75 protein-protein interaction. The model was constructed using energy parameters approximated from molecular dynamics (MD) simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations. Chemometric analysis using partial least squares (PLS) regression revealed that solvent accessible surface area energy (ΔG_SASA) is the major determinant parameter contributing greatly to the prediction accuracy. PLS prediction model on the ΔG_SASA values collected from 41 complexes yielded a strong correlation between the predicted and the actual inhibitory activities (R² = 0.9666, RMSEC of pIC₅₀ values = 0.0890). Additionally, for the test set of 14 complexes, the model performed satisfactorily with very low pIC₅₀ errors (Q² = 0.5168, RMSEP = 0.3325). A strong correlation between the buried surface areas on the IN protein, when bound with IN-LEDGF/p75 inhibitors, and the respective ΔG_SASA values was also obtained. Furthermore, the current method could identify ‘hot spots’of amino acid residues highly influential to the inhibitory activity prediction. This could present fruitful implications in binding site determination and future inhibitor developments targeting protein-protein interactions.

Communicated by Ramaswamy H. Sarma

Keywords:

• HIV-1 integrase
• LEDGF/p75
• molecular dynamics (MD)
• simulation
• MM-PBSA
• solvent accessible surface area (SASA)

Acknowledgements

We would like to thank the Computer Technology Centre of NECTEC for the SYBYL software, the Drug Discovery Research Laboratory and the Department of Chemistry, Faculty of Science, Chiang Mai University, for all research facilities.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the Junior Science Talent Project (JSTP) from the National Science and Technology Development Agency (NSTDA) of Thailand and the Research Fund for DPST Graduate with First Placement (Grant: 037/2555). This research work was partially supported by Chiang Mai University. N.S. was supported by the Grant for New Researcher (Grant: TRG5880271) from the Thailand Research Fund (TRF); Chiang Mai University Young Faculty Research Grant; the National Research Council of Thailand (Grants: 2556NRCT51390 and 2558NRCT350269); the National Research University Project under Thailand’s Office of the Higher Education Commission; P.T. was supported by the JSTP-NSTDA research grant for graduate study (Grant: JSTP-06-55-35E).