Abstract
RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.
Highlights
The formation of highly specific protein complexes is a fundamental process in biology, and the structures of these complexes can yield deep insight into the mechanisms of protein function
Blind structure-prediction efforts, such as the Critical Assessment of Protein Interactions (CAPRI) [1,2] have showcased a number of successful docking strategies using a range of methods from course-grained fast-Fourier transform approaches which identify surface complementarity between two partners [3,4] to all-atom stochastic methods that can accommodate intricate protein conformational changes [5,6]
Overall benchmark results We applied RosettaDock v3.2 to the Docking Benchmark 3.0, which contains a range of docking targets that vary in both complex type and difficulty
Summary
The formation of highly specific protein complexes is a fundamental process in biology, and the structures of these complexes can yield deep insight into the mechanisms of protein function. Blind structure-prediction efforts, such as the Critical Assessment of Protein Interactions (CAPRI) [1,2] have showcased a number of successful docking strategies using a range of methods from course-grained fast-Fourier transform approaches which identify surface complementarity between two partners [3,4] to all-atom stochastic methods that can accommodate intricate protein conformational changes [5,6]. RosettaDock has been used for a wide range of applications from antibody-antigen docking [11,12], to peptide docking and specificity [16,17] to multi-body [18] and symmetric docking.[19]
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