Chaudhury Sidhartha, Sircar Aroop, Sivasubramanian Arvind, Berrondo Monica, Gray Jeffrey J
Program in Molecular and Computational Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Proteins. 2007 Dec 1;69(4):793-800. doi: 10.1002/prot.21731.
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.
在CAPRI第6 - 12轮中，RosettaDock成功地以中等准确率预测了5个未结合 - 未结合靶点中的2个。通过计算诱变来选择与实验确定的热点能量相匹配的诱饵，实现了相对于先前方法的改进。在靶点21（Orc1/Sir1）的情况下，这导致了一次成功的对接预测，而单独使用RosettaDock或带有简单位点约束时均失败。实验信息也有助于限制TolB/Pal的相互作用区域，从而成功预测了靶点26。此外，我们为靶点20对接了多个环构象，并开发了一种新颖的灵活对接算法，以同时优化主链构象和刚体方向，为靶点24生成多种不同的构象。持续存在的挑战包括对接与其模板差异很大（序列同一性<50%）的同源靶点，以及考虑结合时的大构象变化。尽管未结合 - 未结合和同源模型结合靶点的数量增加，但第6 - 12轮强化了RosettaDock是一种预测结合复合物结构的强大算法，特别是与实验数据结合时。
