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.

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

Rollo, Cesare
First
;
Pancotti, Corrado;Birolo, Giovanni;Rossi, Ivan;Sanavia, Tiziana;Fariselli, Piero
Last
2023-01-01

Abstract

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.
2023
14
12
1
10
https://www.mdpi.com/2073-4425/14/12/2228
machine learning; performance evaluation; protein stability; single-point mutation; stability change
Rollo, Cesare; Pancotti, Corrado; Birolo, Giovanni; Rossi, Ivan; Sanavia, Tiziana; Fariselli, Piero
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1992830
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