ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations. Issue 11 (28th May 2021)

• Record Type: Journal Article
• Authors: Strokach, Alexey; Lu, Tian Yu; Kim, Philip M.
• Is Part Of: Journal of molecular biology. Volume 433:Issue 11(2021)
• Publication Date: 2021-05-28
• Subjects: variant effect prediction -- stability prediction -- affinity prediction -- machine learning -- graph convolutional neural network; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2021.166810
• Languages: English
• ISSNs: 0022-2836

Abstract

Highlights: ELASPIC2 (EL2) are machine learning models trained to predict the effect of mutations on protein folding and binding. EL2 models leverage heterogeneous training data and pre-trained deep neural networks. EL2 models show superior performance on a test dataset of mutations in the SARS-CoV-2 spike protein. EL2 models have been incorporated into the ELASPIC web server: http://elaspic.kimlab.org . Abstract: The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org .