Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods
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-...
Published in: | Frontiers in Molecular Biosciences |
---|---|
Main Authors: | , , |
Format: | Text |
Language: | English |
Published: |
Frontiers Media S.A.
2019
|
Subjects: | |
Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581705/ https://doi.org/10.3389/fmolb.2019.00042 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:6581705 |
---|---|
record_format |
openpolar |
spelling |
ftpubmed:oai:pubmedcentral.nih.gov:6581705 2023-05-15T15:04:03+02:00 Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods Grazioli, Gianmarc Martin, Rachel W. Butts, Carter T. 2019-06-12 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581705/ https://doi.org/10.3389/fmolb.2019.00042 en eng Frontiers Media S.A. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581705/ http://dx.doi.org/10.3389/fmolb.2019.00042 Copyright © 2019 Grazioli, Martin and Butts. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. CC-BY Molecular Biosciences Text 2019 ftpubmed https://doi.org/10.3389/fmolb.2019.00042 2019-06-30T01:01:53Z 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. Text Arctic PubMed Central (PMC) Arctic Frontiers in Molecular Biosciences 6 |
institution |
Open Polar |
collection |
PubMed Central (PMC) |
op_collection_id |
ftpubmed |
language |
English |
topic |
Molecular Biosciences |
spellingShingle |
Molecular Biosciences Grazioli, Gianmarc Martin, Rachel W. Butts, Carter T. Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods |
topic_facet |
Molecular Biosciences |
description |
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. |
format |
Text |
author |
Grazioli, Gianmarc Martin, Rachel W. Butts, Carter T. |
author_facet |
Grazioli, Gianmarc Martin, Rachel W. Butts, Carter T. |
author_sort |
Grazioli, Gianmarc |
title |
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods |
title_short |
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods |
title_full |
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods |
title_fullStr |
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods |
title_full_unstemmed |
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods |
title_sort |
comparative exploratory analysis of intrinsically disordered protein dynamics using machine learning and network analytic methods |
publisher |
Frontiers Media S.A. |
publishDate |
2019 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581705/ https://doi.org/10.3389/fmolb.2019.00042 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581705/ http://dx.doi.org/10.3389/fmolb.2019.00042 |
op_rights |
Copyright © 2019 Grazioli, Martin and Butts. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3389/fmolb.2019.00042 |
container_title |
Frontiers in Molecular Biosciences |
container_volume |
6 |
_version_ |
1766335866996785152 |