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Performance of Web tools for predicting changes in protein stability caused by mutations.
Anna Marabotti1, Eugenio Del Prete2, Bernardina Scafuri3
1Department of Chemistry and Biology "A. Zambelli", University of Salerno, Fisciano, SA, Italy. amarabotti@unisa.it.
BMC Bioinformatics
|July 6, 2021
Summary
Predicting protein stability changes from mutations remains challenging. Combining multiple prediction tools offers a more reliable approach, though current methods still have limitations.
Area of Science:
- Biochemistry
- Computational Biology
- Protein Science
Background:
- Developing web tools to predict protein thermodynamic stability changes due to mutations has been ongoing for decades.
- Accurate prediction of these stability changes remains a significant challenge in the field.
Purpose of the Study:
- To assess the reliability of five recently developed web tools for predicting mutation-induced changes in protein thermodynamic stability.
- To evaluate the progress made in the field of computational prediction of protein stability.
Main Methods:
- Evaluation of five distinct web-based prediction tools.
- Analysis of prediction accuracy, focusing on biases and reliability intervals.
Main Results:
- Despite improvements, current predictors are not ideal, showing bias towards destabilizing mutations.
- Predictions are generally unreliable for mutations causing a change in Gibbs free energy (ΔΔG) within ±0.5 kcal/mol.
- A consensus approach, combining results from multiple predictors, offers a practical method to enhance prediction reliability.
Conclusions:
- Future tool development should prioritize balanced datasets for training predictors.
- Users are advised to integrate results from multiple prediction tools for improved accuracy in assessing mutation effects on protein thermodynamic stability.
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