Kim Hyungrae, Kihara Daisuke
Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47906.
Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907.
Proteins. 2016 Sep;84 Suppl 1(Suppl 1):105-17. doi: 10.1002/prot.24920. Epub 2015 Sep 22.
An accurate scoring function that can select near-native structure models from a pool of alternative models is key for successful protein structure prediction. For the critical assessment of techniques for protein structure prediction (CASP) 11, we have built a protocol of protein structure prediction that has novel coarse-grained scoring functions for selecting decoys as the heart of its pipeline. The score named PRESCO (Protein Residue Environment SCOre) developed recently by our group evaluates the native-likeness of local structural environment of residues in a structure decoy considering positions and the depth of side-chains of spatially neighboring residues. We also introduced a helix interaction potential as an additional scoring function for selecting decoys. The best models selected by PRESCO and the helix interaction potential underwent structure refinement, which includes side-chain modeling and relaxation with a short molecular dynamics simulation. Our protocol was successful, achieving the top rank in the free modeling category with a significant margin of the accumulated Z-score to the subsequent groups when the top 1 models were considered. Proteins 2016; 84(Suppl 1):105-117. © 2015 Wiley Periodicals, Inc.
一个能够从一组备选模型中挑选出接近天然结构模型的精确评分函数,是成功进行蛋白质结构预测的关键。对于蛋白质结构预测技术的关键评估(CASP)11,我们构建了一个蛋白质结构预测方案,该方案具有新颖的粗粒度评分函数,用于选择诱饵模型作为其流程的核心。我们团队最近开发的名为PRESCO(蛋白质残基环境评分)的评分方法,在考虑空间相邻残基侧链位置和深度的情况下,评估结构诱饵中残基局部结构环境的天然相似性。我们还引入了螺旋相互作用势作为选择诱饵模型的附加评分函数。通过PRESCO和螺旋相互作用势选择出的最佳模型进行了结构优化,包括侧链建模和通过短分子动力学模拟进行松弛。我们的方案取得了成功,在自由建模类别中排名第一,当考虑前1个模型时,累积Z分数与后续组相比有显著优势。《蛋白质》2016年;84(增刊1):105 - 117。© 2015威利期刊公司。