On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation

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.

Automated summary

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.