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

Abstract

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 naïve users in finding the best option for their needs.

Keywords: machine learning; mutations; protein sequence; protein structure; thermodynamic stability.