Prediction of the stability of proteins by computational approaches: a case report

Institute of Food Sciences, National Research Council of Italy, Avellino, Italy
Department of Chemistry and Biology "A. Zambelli", University of Salerno, Fisciano, Italy
DOI
10.7287/peerj.preprints.27319v1
Subject Areas
Biochemistry, Bioinformatics, Computational Biology
Keywords
Proteins, Mutations, Stability, Predictors
Copyright
© 2018 Scafuri et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Scafuri B, Facchiano A, Marabotti A. 2018. Prediction of the stability of proteins by computational approaches: a case report. PeerJ Preprints 6:e27319v1

Abstract

The prediction of the stability of a protein is a very important issue in computational biology. Indeed, missense mutations are frequently associated to a change in protein stability, leading usually to destabilization, unfolding and aggregation. However, the direct measurement of the effect of mutations on proteins' stability is often impaired by the large number of mutations that can affect a protein sequence. Therefore, predicting the impact of a mutation on this feature is of remarkable interest to infer the phenotypic effects associated to a genotypic variation. For this reason, many different predictors of the effects of mutations on protein stability have been developed during the past years, and they are available online as Web servers. In the present work, we applied several tools based on different approaches to predict the stability of three proteins involved in the different forms of the rare disease galactosemia, and we compare their different results, describing also the problems that we had to face, the solutions that we have adopted and the lessons learnt from this case study.

Author Comment

This is an abstract which has been accepted for the BBCC2018 Conference