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Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?
Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing metho...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6058471/ https://www.ncbi.nlm.nih.gov/pubmed/30079234 http://dx.doi.org/10.12688/f1000research.14870.1 |
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author | Delarue, Marc Koehl, Patrice |
author_facet | Delarue, Marc Koehl, Patrice |
author_sort | Delarue, Marc |
collection | PubMed |
description | Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences. |
format | Online Article Text |
id | pubmed-6058471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-60584712018-08-02 Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)? Delarue, Marc Koehl, Patrice F1000Res Review Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences. F1000 Research Limited 2018-07-24 /pmc/articles/PMC6058471/ /pubmed/30079234 http://dx.doi.org/10.12688/f1000research.14870.1 Text en Copyright: © 2018 Delarue M and Koehl P http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Delarue, Marc Koehl, Patrice Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)? |
title | Combined approaches from physics, statistics, and computer science for
ab initio protein structure prediction:
ex unitate vires (unity is strength)? |
title_full | Combined approaches from physics, statistics, and computer science for
ab initio protein structure prediction:
ex unitate vires (unity is strength)? |
title_fullStr | Combined approaches from physics, statistics, and computer science for
ab initio protein structure prediction:
ex unitate vires (unity is strength)? |
title_full_unstemmed | Combined approaches from physics, statistics, and computer science for
ab initio protein structure prediction:
ex unitate vires (unity is strength)? |
title_short | Combined approaches from physics, statistics, and computer science for
ab initio protein structure prediction:
ex unitate vires (unity is strength)? |
title_sort | combined approaches from physics, statistics, and computer science for
ab initio protein structure prediction:
ex unitate vires (unity is strength)? |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6058471/ https://www.ncbi.nlm.nih.gov/pubmed/30079234 http://dx.doi.org/10.12688/f1000research.14870.1 |
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