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A Hybrid Knowledge-Based and Empirical Scoring Function for Protein–Ligand Interaction: SMoG2016

Abstract : We present the third generation of our scoring function for the prediction of protein-ligand binding free energy. This function is now a hybrid between a knowledge-based potential and an empirical function. We constructed a diversified set of ~1000 complexes from the PDBBinding-CN database for the training of the function and we show that this number of complexes generate enough data to build the potential. The occurrence of 420 different types of atomic pair wise interactions is computed in up to five different ranges of distances to derive the knowledge-based part. All parameters were optimized and we were able to considerably improve the accuracy of the scoring function with a Pearson correlation coefficient against experimental binding free energies of up to 0.57, which ranks our new scoring function as one of the best currently available and the second-best in term of standard deviation (SD=1.68). The function is then further improved by inclusion of different terms taking into account repulsion and loss of entropy upon binging, and we show it is capable of recovering native binding pose up to 80% of times. All programs, tools and protein sets are released in Supporting Information or as open-source programs.
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Submitted on : Wednesday, October 21, 2020 - 2:22:01 PM
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Théau Debroise, Eugene Shakhnovich, Nicolas Chéron. A Hybrid Knowledge-Based and Empirical Scoring Function for Protein–Ligand Interaction: SMoG2016. Journal of Chemical Information and Modeling, American Chemical Society, 2017, 57 (3), pp.584-593. ⟨10.1021/acs.jcim.6b00610⟩. ⟨hal-02973960⟩



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