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 (ΔΔGAB = - ΔΔGBA). 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.
On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation / Savojardo C.; Martelli P.L.; Casadio R.; Fariselli P.. - In: BRIEFINGS IN BIOINFORMATICS. - ISSN 1477-4054. - ELETTRONICO. - 22:1(2021), pp. 601-603. [10.1093/bib/bbz168]
On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation
Savojardo C.;Martelli P. L.
;Casadio R.;
2021
Abstract
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 (ΔΔGAB = - ΔΔGBA). 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.File | Dimensione | Formato | |
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