title: Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.
abstract: Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada.
authors.forename: Niklas
authors.initials: N
authors.lastname: Berliner
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada.
authors.forename: Joan
authors.initials: J
authors.lastname: Teyra
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Recep
authors.initials: R
authors.lastname: Colak
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada; Universidad Nacional de Colombia, Manizales, Colombia.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Garcia Lopez
authors.affiliation: Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Philip M
authors.initials: PM
authors.lastname: Kim
mesh_terms: D001185:Artificial Intelligence; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D009154:Mutation; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding; D055550:Protein Stability; D011506:Proteins / Q000378:metabolism*; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins