Background: Protein-protein docking, which aims to predict the structure of a protein-protein complex from its\r\nunbound components, remains an unresolved challenge in structural bioinformatics. An important step is the\r\nranking of docked poses using a scoring function, for which many methods have been developed. There is a need\r\nto explore the differences and commonalities of these methods with each other, as well as with functions\r\ndeveloped in the fields of molecular dynamics and homology modelling.\r\nResults: We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering\r\n118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%.\r\nHierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets\r\nof complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly\r\nscoring different complexes. This shows that functions in different clusters capture different aspects of binding and\r\nare likely to work together synergistically.\r\nConclusions: All functions designed specifically for docking perform well, indicating that functions are transferable\r\nbetween sampling methods. We also identify promising methods from the field of homology modelling. Further,\r\ndifferential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring.\r\nInvestigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a\r\nnumber of novel approaches, indicating promising augmentations of traditional scoring methods. Such\r\naugmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm
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