Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12
Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12
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DOI：
10.1002/prot.21731
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发表时间：
2007-12-01
影响因子：
2.9
通讯作者：
Gray, Jeffrey J.
中科院分区：
文献类型：
--
作者：
Chaudhury, Sidhartha;Sircar, Aroop;Gray, Jeffrey J.
在CAPRI第6-12轮中，RosettaDock成功预测了5个未绑定目标中的2个，准确率中等。利用计算诱变技术对先前的方法进行了改进，以选择与实验确定的热点能量相匹配的诱饵。在目标21 Orc1/Sir1的情况下，这导致了成功的对接预测，而RosettaDock单独或简单的位点约束失败了。实验信息也有助于限制TolB/Pal的相互作用区域，从而成功地预测了靶标26。此外，我们为目标20对接了多个环路构象，并开发了一种新颖的柔性对接算法，同时优化骨干构象和刚体方向，为目标24生成广泛的多样性构象。持续的挑战包括对接与模板有很大差异的同源靶标（序列同一性< 50%），以及考虑结合后的大构象变化。尽管有大量的未结合-未结合和同源模型结合靶标，但第6-12轮验证了RosettaDock是预测结合复杂结构的强大算法，特别是结合实验数据时。
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.