Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations

被引:1
|
作者
Rollo, Cesare [1 ]
Pancotti, Corrado [1 ]
Birolo, Giovanni [1 ]
Rossi, Ivan [1 ]
Sanavia, Tiziana [1 ]
Fariselli, Piero [1 ]
机构
[1] Univ Torino, Dept Med Sci, I-10126 Turin, Italy
基金
欧盟地平线“2020”;
关键词
protein stability; single-point mutation; stability change; performance evaluation; machine learning; SERVER;
D O I
10.3390/genes14122228
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.
引用
收藏
页数:10
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