Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods

Gianmarc Grazioli; Rachel W Martin; Carter T Butts

doi:10.3389/fmolb.2019.00042

Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods

Front Mol Biosci. 2019 Jun 12:6:42. doi: 10.3389/fmolb.2019.00042. eCollection 2019.

Authors

Gianmarc Grazioli^{1

2}, Rachel W Martin^{2

3}, Carter T Butts^{1

4

5}

Affiliations

¹ California Institute for Telecommunications and Information Technology (Calit2), University of California, Irvine, Irvine, CA, United States.
² Department of Chemistry, University of California, Irvine, Irvine, CA, United States.
³ Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States.
⁴ Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
⁵ Department of Sociology, Statistics, and Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States.

Abstract

Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ_1-40) and its "Arctic" variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.

Keywords: amyloid beta; amyloid fibrils; clustering; intrinsically disordered proteins; machine learning; molecular dynamics; protein structure networks; support vector machines.