4.7 Review

Predicting the stability of mutant proteins by computational approaches: an overview

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 3, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa074

Keywords

mutations; machine learning; protein structure; protein sequence; thermodynamic stability

Funding

  1. University of Salerno, Fondi di Ateneo per la Ricerca di base [ORSA170308, ORSA180380]
  2. Italian Ministry of University and Research, FFABR 2017 program [2017483NH8]
  3. Italian Ministry of University and Research, PRIN 2017 program [2017483NH8]

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This review summarizes the computational methods developed over the past 30 years for predicting the change in thermodynamic stability of proteins due to mutations, as well as their current applications. It discusses the limitations of existing methods and provides guidance on selecting the most suitable tools for different needs.
A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naive users in finding the best option for their needs.

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