重新访问的FoldX力场,改进版本。

Javier Delgado, Raul Reche, Damiano Cianferoni, Gabriele Orlando, Rob van der Kant, Frederic Rousseau, Joost Schymkowitz, Luis Serrano
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引用次数: 0

摘要

动机:FoldX力场最初是在高分辨率结构很少的时候用1000个突变体的数据库进行验证的。在这里,我们手工整理了一个包含5556个影响蛋白质稳定性的突变的数据库,产生了2484个高度自信的突变,命名为FoldX稳定性数据集(FSD),以低于2.5 Å分辨率的非冗余x射线结构表示,不涉及重复,金属或假体基。利用该数据库,我们通过引入Pi叠加、所有带电残基的pH依赖性、改善芳香-芳香相互作用、修改Ncap贡献和α-螺旋偶极子、重新校准蛋氨酸的侧链熵、调整氢键参数、修改色氨酸等的溶剂化贡献,创建了一个新版本的FoldX力场。结果:这些变化使得对涉及上述残基/相互作用的特定突变体的预测有了显著改善,对FoldX的预测也有了统计学上的显著提高,对大多数20aa的预测也有了统计学上的显著提高。与之前发布的版本相比,从FSD (VFSD数据集)中删除所有训练集数据导致预测从R = 0.693 (RMSE = 1.277 kcal/mol)改善到R = 0.706 (RMSE = 1.252 kcal/mol)。在预测误差为±0.85 kcal/mol的情况下,FoldX达到95%的准确度,对于VFSD,预测突变时能量变化的符号,AUC = 0.78。可用性:FoldX 4.1和5.1版本可在https://foldxsuite.crg.eu/.Supplementary上免费为学者提供信息:补充数据可在Bioinformatics在线获得。
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FoldX force field revisited, an improved version.

Motivation: The FoldX force field was originally validated with a database of 1000 mutants at a time when there were few high-resolution structures. Here, we have manually curated a database of 5556 mutants affecting protein stability, resulting in 2484 highly confident mutations denominated FoldX stability dataset (FSD), represented in non-redundant X-ray structures with <2.5 Å resolution, not involving duplicates, metals, or prosthetic groups. Using this database, we have created a new version of the FoldX force field by introducing pi stacking, pH dependency for all charged residues, improving aromatic-aromatic interactions, modifying the Ncap contribution and α-helix dipole, recalibrating the side-chain entropy of methionine, adjusting the H-bond parameters, and modifying the solvation contribution of tryptophan and others.

Results: These changes have led to significant improvements for the prediction of specific mutants involving the above residues/interactions and a statistically significant increase of FoldX predictions, as well as for the majority of the 20 aa. Removing all training sets data from FSD [Validation FoldX Stability Dataset (VFSD) dataset] resulted in improved predictions from R = 0.693 (RMSE = 1.277 kcal/mol) to R = 0.706 (RMSE = 1.252 kcal/mol) when compared with the previously released version. FoldX achieves 95% accuracy considering an error of ±0.85 kcal/mol in prediction and an area under the curve = 0.78 for the VFSD, predicting the sign of the energy change upon mutation.

Availability and implementation: FoldX versions 4.1 and 5.1 are freely available for academics at https://foldxsuite.crg.eu/.

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