MGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion.

ABSTRACT:

Motivation

Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data.

Results

We have developed mGPfusion, a novel Gaussian process (GP) method for predicting protein's stability changes upon single and multiple mutations. This method complements the limited experimental data with large amounts of molecular simulation data. We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and th

SUBMITTER: Jokinen E 

PROVIDER: S-EPMC6022679 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature