Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6–12

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Abstract
In CAPRI rounds 6–12, RosettaDock successfully predicted 2 of 5 unbound–unbound targets to medium accuracy. Improvement over the previous method was achieved with computational mutagenesis to select decoys that match the energetics of experimentally determined hot spots. In the case of Target 21, Orc1/Sir1, this resulted in a successful docking prediction where RosettaDock alone or with simple site constraints failed. Experimental information also helped limit the interacting region of TolB/Pal, producing a successful prediction of Target 26. In addition, we docked multiple loop conformations for Target 20, and we developed a novel flexible docking algorithm to simultaneously optimize backbone conformation and rigid‐body orientation to generate a wide diversity of conformations for Target 24. Continued challenges included docking of homology targets that differ substantially from their template (sequence identity <50%) and accounting for large conformational changes upon binding. Despite a larger number of unbound–unbound and homology model binding targets, Rounds 6–12 reinforced that RosettaDock is a powerful algorithm for predicting bound complex structures, especially when combined with experimental data Proteins 2007.