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MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning
MutSα is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer’s post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cance...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6959842/ https://www.ncbi.nlm.nih.gov/pubmed/31938712 http://dx.doi.org/10.3389/fphy.2017.00010 |
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author | Melvin, Ryan L. Thompson, William G. Godwin, Ryan C. Gmeiner, William H. Salsbury, Freddie R. |
author_facet | Melvin, Ryan L. Thompson, William G. Godwin, Ryan C. Gmeiner, William H. Salsbury, Freddie R. |
author_sort | Melvin, Ryan L. |
collection | PubMed |
description | MutSα is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer’s post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cancer agents—carboplatin, cisplatin, and FdU. Through a combination of supervised and unsupervised machine learning techniques along with more traditional structural and kinetic analysis applied to all-atom molecular dynamics (MD) calculations, we predict that MutSα has a distinct response to each of the three damage types. Via a binary classification tree (a supervised machine learning technique), we identify key hydrogen bond motifs unique to each type of damage and suggest residues for experimental mutation studies. Through a combination of a recently developed clustering (unsupervised learning) algorithm, RMSF calculations, PCA, and correlated motions we predict that each type of damage causes MutSα to explore a specific region of conformation space. Detailed analysis suggests a short range effect for carboplatin—primarily altering the structures and kinetics of residues within 10 angstroms of the damaged DNA—and distinct longer-range effects for cisplatin and FdU. In our simulations, we also observe that a key phenylalanine residue—known to stack with a mismatched or unmatched bases in MMR—stacks with the base complementary to the damaged base in 88.61% of MD frames containing carboplatinated DNA. Similarly, this Phe71 stacks with the base complementary to damage in 91.73% of frames with cisplatinated DNA. This residue, however, stacks with the damaged base itself in 62.18% of trajectory frames with FdU-substituted DNA and has no stacking interaction at all in 30.72% of these frames. Each drug investigated here induces a unique perturbation in the MutSα complex, indicating the possibility of a distinct signaling event and specific repair or death pathway (or set of pathways) for a given type of damage. |
format | Online Article Text |
id | pubmed-6959842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-69598422020-01-14 MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning Melvin, Ryan L. Thompson, William G. Godwin, Ryan C. Gmeiner, William H. Salsbury, Freddie R. Front Phys Article MutSα is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer’s post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cancer agents—carboplatin, cisplatin, and FdU. Through a combination of supervised and unsupervised machine learning techniques along with more traditional structural and kinetic analysis applied to all-atom molecular dynamics (MD) calculations, we predict that MutSα has a distinct response to each of the three damage types. Via a binary classification tree (a supervised machine learning technique), we identify key hydrogen bond motifs unique to each type of damage and suggest residues for experimental mutation studies. Through a combination of a recently developed clustering (unsupervised learning) algorithm, RMSF calculations, PCA, and correlated motions we predict that each type of damage causes MutSα to explore a specific region of conformation space. Detailed analysis suggests a short range effect for carboplatin—primarily altering the structures and kinetics of residues within 10 angstroms of the damaged DNA—and distinct longer-range effects for cisplatin and FdU. In our simulations, we also observe that a key phenylalanine residue—known to stack with a mismatched or unmatched bases in MMR—stacks with the base complementary to the damaged base in 88.61% of MD frames containing carboplatinated DNA. Similarly, this Phe71 stacks with the base complementary to damage in 91.73% of frames with cisplatinated DNA. This residue, however, stacks with the damaged base itself in 62.18% of trajectory frames with FdU-substituted DNA and has no stacking interaction at all in 30.72% of these frames. Each drug investigated here induces a unique perturbation in the MutSα complex, indicating the possibility of a distinct signaling event and specific repair or death pathway (or set of pathways) for a given type of damage. 2017-03-30 2017-03 /pmc/articles/PMC6959842/ /pubmed/31938712 http://dx.doi.org/10.3389/fphy.2017.00010 Text en 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) or licensor 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. |
spellingShingle | Article Melvin, Ryan L. Thompson, William G. Godwin, Ryan C. Gmeiner, William H. Salsbury, Freddie R. MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning |
title | MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning |
title_full | MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning |
title_fullStr | MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning |
title_full_unstemmed | MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning |
title_short | MutSα’s Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning |
title_sort | mutsα’s multi-domain allosteric response to three dna damage types revealed by machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6959842/ https://www.ncbi.nlm.nih.gov/pubmed/31938712 http://dx.doi.org/10.3389/fphy.2017.00010 |
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