On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.
On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.
Brief Bioinform. 2019 Dec 28;:
Authors: Savojardo C, Martelli PL, Casadio R, Fariselli P
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.
PMID: 31885042 [PubMed - as supplied by publisher]
Source: Briefings in Bioinformatics - Category: Bioinformatics Authors: Savojardo C, Martelli PL, Casadio R, Fariselli P Tags: Brief Bioinform Source Type: research