Decomposing Structural Response Due to Sequence Changes in Protein Domains with Machine Learning. Issue 16 (24th July 2020)

• Record Type: Journal Article
• Authors: Bryant, Patrick; Elofsson, Arne
• Is Part Of: Journal of molecular biology. Volume 432:Issue 16(2020)
• Page Start: 4435
• Page End: 4446
• Publication Date: 2020-07-24
• Subjects: protein evolution -- protein structure -- evolutionary distance -- mutations; RSA relative surface area -- RCO relative contact order -- CD contact density -- lDDT local Distance Difference Test; 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.2020.05.021
• Languages: English
• ISSNs: 0022-2836

Abstract

Abstract: How protein domain structure changes in response to mutations is not well understood. Some mutations change the structure drastically, while most only result in small changes. To gain an understanding of this, we decompose the relationship between changes in domain sequence and structure using machine learning. We select pairs of evolutionarily related domains with a broad range of evolutionary distances. In contrast to earlier studies, we do not find a strictly linear relationship between sequence and structural changes. We train a random forest regressor that predicts the structural similarity between pairs with an average accuracy of 0.029 lDDT ( local Distance Difference Test) score, and a correlation coefficient of 0.92. Decomposing the feature importance shows that the domain length, or analogously, size is the most important feature. Our model enables assessing deviations in relative structural response, and thus prediction of evolutionary trajectories, in protein domains across evolution. Graphical abstract: Unlabelled Image Highlights: Predicting evolutionary trajectories with machine learning Changes in protein domain structure in response to sequence mutations Decomposing feature importance for domains in evolution Domain length is the most important for the prediction of evolutionary trajectories.