Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations
Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved..
Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.
Errataetall: |
ErratumIn: Biophys J. 2021 Oct 19;120(20):4636. - PMID 34480851 |
---|---|
Medienart: |
E-Artikel |
Erscheinungsjahr: |
2021 |
---|---|
Erschienen: |
2021 |
Enthalten in: |
Zur Gesamtaufnahme - volume:120 |
---|---|
Enthalten in: |
Biophysical journal - 120(2021), 2 vom: 19. Jan., Seite 189-204 |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
McCoy, Matthew D [VerfasserIn] |
---|
Links: |
---|
Themen: |
---|
Anmerkungen: |
Date Completed 14.05.2021 Date Revised 20.01.2022 published: Print-Electronic ErratumIn: Biophys J. 2021 Oct 19;120(20):4636. - PMID 34480851 Citation Status MEDLINE |
---|
doi: |
10.1016/j.bpj.2020.12.002 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM318980363 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM318980363 | ||
003 | DE-627 | ||
005 | 20231225170507.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231225s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.bpj.2020.12.002 |2 doi | |
028 | 5 | 2 | |a pubmed24n1063.xml |
035 | |a (DE-627)NLM318980363 | ||
035 | |a (NLM)33333034 | ||
035 | |a (PII)S0006-3495(20)33203-3 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a McCoy, Matthew D |e verfasserin |4 aut | |
245 | 1 | 0 | |a Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations |
264 | 1 | |c 2021 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 14.05.2021 | ||
500 | |a Date Revised 20.01.2022 | ||
500 | |a published: Print-Electronic | ||
500 | |a ErratumIn: Biophys J. 2021 Oct 19;120(20):4636. - PMID 34480851 | ||
500 | |a Citation Status MEDLINE | ||
520 | |a Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved. | ||
520 | |a Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Research Support, N.I.H., Extramural | |
700 | 1 | |a Hamre, John |c 3rd |e verfasserin |4 aut | |
700 | 1 | |a Klimov, Dmitri K |e verfasserin |4 aut | |
700 | 1 | |a Jafri, M Saleet |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Biophysical journal |d 1960 |g 120(2021), 2 vom: 19. Jan., Seite 189-204 |w (DE-627)NLM000067571 |x 1542-0086 |7 nnns |
773 | 1 | 8 | |g volume:120 |g year:2021 |g number:2 |g day:19 |g month:01 |g pages:189-204 |
856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.bpj.2020.12.002 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 120 |j 2021 |e 2 |b 19 |c 01 |h 189-204 |