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Mutations

Mutations

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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

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Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

Feifan Zheng¹, Yang Liu¹, Yan Yang¹

¹MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.

Protein Science : a Publication of the Protein Society

|December 12, 2023

Summary

This summary is machine-generated.

Predicting protein stabilizing mutations remains challenging for computational methods. A new dataset and evaluation of 27 tools reveal limitations in current approaches for accurately forecasting stability changes.

Keywords:

computational tools missense mutations protein stability changes stabilizing mutations

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Area of Science:

Structural Biology
Bioinformatics
Computational Biology

Background:

Understanding mutation effects on protein stability is vital for protein engineering, disease research, and evolutionary studies.
Existing computational methods for predicting mutation impacts on protein stability face challenges in direct comparison due to varied training data.
Current predictive tools often show better performance for destabilizing mutations than for stabilizing ones.

Purpose of the Study:

To create a new, non-overlapping dataset of protein mutations for evaluating computational prediction methods.
To assess the performance of 27 computational methods, including recent deep learning approaches, on predicting protein stability changes.
To identify limitations in current methods, particularly for predicting stabilizing mutations.

Main Methods:

Compiled a novel dataset of 4038 single-point mutations from ThermoMutDB, FireProtDB, and ProThermDB, excluding overlaps with the S2648 dataset.
Evaluated 27 computational prediction tools using this new dataset, ensuring no overlap with their training data.
Analyzed prediction accuracy using Pearson correlation coefficients and assessed performance trends for stabilizing versus destabilizing mutations.

Main Results:

Pearson correlation coefficients for the tested tools on unseen data ranged from 0.20 to 0.53.
No tested method accurately predicted stabilizing mutations, even those performing well in other analyses.
Destabilizing mutations showed consistent prediction trends across properties, while stabilizing mutations lacked clear patterns.

Conclusions:

Current computational methods struggle to accurately predict the impact of mutations on protein stability, especially for stabilizing mutations.
Addressing training dataset bias alone may not be sufficient to improve the prediction of stabilizing mutations.
There is a critical need for developing more precise computational methods specifically designed for predicting stabilizing mutations.