SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Abstract

Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers' code.

Keywords: machine learning; sequence-based; single point mutation; thermodynamics stability; web server.

Figure 1

SAAFEC-SEQ predicted ΔΔG against experimental ΔΔG in case of 20% of mutations as a test set.

Figure 2

Importance level of each feature selected for SAAFEC-SEQ.

Figure 3

Performance comparison of SAAFEC-SEQ with other existing sequence-based methods on PTEN and TPMT datasets.