Literature DB >> 32496523

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

Anna Marabotti1, Bernardina Scafuri1, Angelo Facchiano2.   

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.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

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

Year:  2021        PMID: 32496523     DOI: 10.1093/bib/bbaa074

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  5 in total

1.  Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset.

Authors:  Corrado Pancotti; Silvia Benevenuta; Giovanni Birolo; Virginia Alberini; Valeria Repetto; Tiziana Sanavia; Emidio Capriotti; Piero Fariselli
Journal:  Brief Bioinform       Date:  2022-03-10       Impact factor: 11.622

2.  Understanding the mutational frequency in SARS-CoV-2 proteome using structural features.

Authors:  Puneet Rawat; Divya Sharma; Medha Pandey; R Prabakaran; M Michael Gromiha
Journal:  Comput Biol Med       Date:  2022-06-07       Impact factor: 6.698

3.  Electrostatic interaction optimization improves catalytic rates and thermotolerance on xylanases.

Authors:  Vinícius de Godoi Contessoto; Felipe Cardoso Ramos; Ricardo Rodrigues de Melo; Vinícius Martins de Oliveira; Josiane Aniele Scarpassa; Amanda Silva de Sousa; Letıcia Maria Zanphorlin; Gabriel Gouvea Slade; Vitor Barbanti Pereira Leite; Roberto Ruller
Journal:  Biophys J       Date:  2021-04-05       Impact factor: 3.699

4.  SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability.

Authors:  Gen Li; Shailesh Kumar Panday; Emil Alexov
Journal:  Int J Mol Sci       Date:  2021-01-09       Impact factor: 5.923

5.  Investigating the Effects of Amino Acid Variations in Human Menin.

Authors:  Carmen Biancaniello; Antonia D'Argenio; Deborah Giordano; Serena Dotolo; Bernardina Scafuri; Anna Marabotti; Antonio d'Acierno; Roberto Tagliaferri; Angelo Facchiano
Journal:  Molecules       Date:  2022-03-07       Impact factor: 4.411

  5 in total

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