Customizing scoring functions for docking |
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Authors: | Tuan A Pham Ajay N Jain |
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Institution: | (1) University of California, San Francisco, Box 0128, San Francisco, CA 94143-0128, USA |
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Abstract: | Empirical scoring functions used in protein-ligand docking calculations are typically trained on a dataset of complexes with
known affinities with the aim of generalizing across different docking applications. We report a novel method of scoring-function
optimization that supports the use of additional information to constrain scoring function parameters, which can be used to
focus a scoring function’s training towards a particular application, such as screening enrichment. The approach combines
multiple instance learning, positive data in the form of ligands of protein binding sites of known and unknown affinity and
binding geometry, and negative (decoy) data of ligands thought not to bind particular protein binding sites or known not to bind in particular geometries. Performance of the method for the Surflex-Dock scoring function is shown in cross-validation
studies and in eight blind test cases. Tuned functions optimized with a sufficient amount of data exhibited either improved
or undiminished screening performance relative to the original function across all eight complexes. Analysis of the changes
to the scoring function suggest that modifications can be learned that are related to protein-specific features such as active-site
mobility. |
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