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
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. 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...
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| Main Authors: | , , , |
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| Format: | Journal |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85062372620&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/63582 |
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| Institution: | Chiang Mai University |
| Summary: | © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. 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 (R 2 = 0.9666, RMSEC of pIC 50 values = 0.0890). Additionally, for the test set of 14 complexes, the model performed satisfactorily with very low pIC 50 errors (Q 2 = 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. |
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