Journal Article

Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability

Miller, Craig R., Sapozhnikov, Yesol, Patel, Jagdish Suresh, Ytreberg, F. Marty

2023

Overview

Background Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability. Results We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation. Conclusions Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Binding / Bioinformatics / Biomedical and Life Sciences / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer applications / Datasets / Energy / Error prediction / Folding / Gene mutations / Life Sciences / Mathematical models / Microarrays / Missense mutation / Molecular dynamics / Molecular Dynamics Simulation / Mutation / Mutation, Missense / Predictions / Protein Binding / Protein Folding / Protein mutations / Protein Stability / Proteins / Regression analysis / Regression models / Simulation / Software / Stability / Stability prediction / Statistical analysis / Statistical model / Statistical models / Uncertainty / Upper bounds / Variables / Workflow