4.7 Article

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 2, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab555

Keywords

protein stability; single-point mutation; stability change; antisymmetry; machine learning

Funding

  1. Italian Ministry for Education, University and Research [PRIN 2017 201744NR8S, 20182022D15D18000410001]

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Predicting the difference in thermodynamic stability between protein variants is important for protein design and understanding genotype-phenotype relationships. This study introduces a new dataset and evaluates the prediction performance of 21 different tools. The results suggest that considering both direct and reverse variants improves the prediction accuracy.
Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and 'all' available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21-0.5 and 0-0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51-0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the Delta Delta G predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before.

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