Machine learning: how much does it tell about protein folding rates?

Corrales, Marc; Cuscó, Pol; Usmanova, Dinara R.; Chen, Heng-Chang; Bogatyreva, Natalya S.; Filion, Guillaume; Ivankov, Dmitry N.

doi:http://dx.doi.org/10.1371/journal.pone.0143166

Machine learning: how much does it tell about protein folding rates?

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Corrales M, Cuscó P, Usmanova DR, Chen HC, Bogatyreva NS, Filion GJ, et al. Machine learning: how much does it tell about protein folding rates? PLoS ONE. 2015 Nov 25;10(11):e0143166. DOI: 10.1371/journal.pone.0143166

Abstract

The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also attracted the attention of scientists from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are exaggerated due to large fluctuations and overfitting of the estimates. When we confronted three selected published models with new data, we found a much lower predictive power than reported in the original publications. Overly optimistic predictive powers appear from violations of the basic principles of machine-learning. We highlight common misconceptions in the studies claiming excessive predictive power and propose to use learning curves as a safeguard against those mistakes. As an example, we show that the current amount of experimental data is insufficient to build a linear predictor of logarithms of folding rates based on protein amino acid composition.