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Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches

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Abstract

Improving performance of scoring functions for drug docking simulations is a challenging task in the modern discovery pipeline. Among various ways to enhance the efficiency of scoring function, tuning of energetic component approach is an attractive option that provides better predictions. Herein we present the first development of rapid and simple tuning models for predicting and scoring inhibitory activity of investigated ligands docked into catalytic core domain structures of HIV-1 integrase (IN) enzyme. We developed the models using all energetic terms obtained from flexible ligand-rigid receptor dockings by AutoDock4, followed by a data analysis using either partial least squares (PLS) or self-organizing maps (SOMs). The models were established using 66 and 64 ligands of mercaptobenzenesulfonamides for the PLS-based and the SOMs-based inhibitory activity predictions, respectively. The models were then evaluated for their predictability quality using closely related test compounds, as well as five different unrelated inhibitor test sets. Weighting constants for each energy term were also optimized, thus customizing the scoring function for this specific target protein. Root-mean-square error (RMSE) values between the predicted and the experimental inhibitory activities were determined to be <1 (i.e. within a magnitude of a single log scale of actual IC50 values). Hence, we propose that, as a pre-functional assay screening step, AutoDock4 docking in combination with these subsequent rapid weighted energy tuning methods via PLS and SOMs analyses is a viable approach to predict the potential inhibitory activity and to discriminate among small drug-like molecules to target a specific protein of interest.

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Acknowledgments

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). 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 (Grant: 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). We would like to thank the Computer Technology Center 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. We also thank Prof. Richard Deming (CSU Fullerton) for helpful suggestions.

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Authors and Affiliations

  1. Graduate Program in Biotechnology, The Graduate School, Chiang Mai University, Chiang Mai, Thailand

    Patcharapong Thangsunan

  2. Division of Biochemistry and Biochemical Technology, Department of Chemistry, Faculty of Science, Chiang Mai University, 239 Huay Kaew Rd, Suthep, Muang, Chiang Mai, 50200, Thailand

    Patcharapong Thangsunan, Panchika Prangkio & Nuttee Suree

  3. Department of Chemistry, Faculty of Science, Chiang Mai University, 239 Huay Kaew Rd, Suthep, Muang, Chiang Mai, 50200, Thailand

    Sila Kittiwachana, Puttinan Meepowpan & Nawee Kungwan

  4. Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand

    Supa Hannongbua

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  1. Patcharapong Thangsunan
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Thangsunan, P., Kittiwachana, S., Meepowpan, P. et al. Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches. J Comput Aided Mol Des 30, 471–488 (2016). https://doi.org/10.1007/s10822-016-9917-0

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