Tosatto Silvio C E
Department of Biology, University of Padova, Italy.
J Comput Biol. 2005 Dec;12(10):1316-27. doi: 10.1089/cmb.2005.12.1316.
Scoring functions are widely used in the final step of model selection in protein structure prediction. This is of interest both for comparative modeling targets, where it is important to select the best model among a set of many good, "correct" ones, as well as for other (fold recognition or novel fold) targets, where the set may contain many incorrect models. A novel combination of four knowledge-based potentials recognizing different features of native protein structures is introduced and tested. The pairwise, solvation, hydrogen bond, and torsion angle potentials contain largely orthogonal information. Of these, the torsion angle potential is found to show the strongest correlation with model quality. Combining these features with a linear weighting function, it was possible to construct a robust energy function capable of discriminating native-like structures on several benchmarking sets. In a recent blind test (CAFASP-4 MQAP), the scoring function ranked consistently well and was able to reliably distinguish the correct template from an ensemble of high quality decoys in 52 of 70 cases (33 of 34 for comparative modeling). An executable version of the Victor/FRST function for Linux PCs is available for download from the URL http://protein.cribi.unipd.it/frst/.
评分函数在蛋白质结构预测的模型选择最后一步中被广泛使用。这对于比较建模目标很有意义，在这种情况下，从一组许多良好的“正确”模型中选择最佳模型很重要，对于其他（折叠识别或新折叠）目标也是如此，在这些目标中，模型集可能包含许多不正确的模型。本文介绍并测试了一种识别天然蛋白质结构不同特征的基于知识的四种势能的新组合。成对、溶剂化、氢键和扭转角势能包含很大程度上正交的信息。其中，扭转角势能与模型质量的相关性最强。将这些特征与线性加权函数相结合，有可能构建一个强大的能量函数，能够在几个基准数据集上区分类似天然的结构。在最近的一次盲测（CAFASP - 4 MQAP）中，该评分函数始终排名良好，并且在70个案例中的52个案例（比较建模的34个案例中的33个）中能够可靠地从一组高质量诱饵中区分出正确的模板。可从URL http://protein.cribi.unipd.it/frst/下载适用于Linux个人电脑的Victor/FRST函数的可执行版本。
