Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 1, Pages 601-603Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz168
Keywords
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Funding
- Italian Ministry for Education, University and Research under the programme Dipartimenti di Eccellenza [20182022 D15D18000410001, PRIN 2017 201744NR8S]
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This study discusses biases in predicting protein stability changes upon mutation, presents a more general perspective on the problem, and introduces a machine learning-based method that directly addresses the bias issue. Analysis shows that this method is nearly insensitive to the addressed problem.
A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A-> B) and its reverse (B-> A) must have the opposite value of the free energy difference (Delta Delta G(AB) = - Delta Delta G(BA)). In this letter, we complement the Fang's paper presenting a more general view of the problem. In particular, a machine learning-based method, published in 2015 (INPS), addressed the bias issue directly. We include the analysis of the missing method, showing that INPS is nearly insensitive to the addressed problem.
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