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Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures

Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants pro...

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Autores principales: Pacini, Lorenza, Dorantes-Gilardi, Rodrigo, Vuillon, Laurent, Lesieur, Claire
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543124/
https://www.ncbi.nlm.nih.gov/pubmed/34708077
http://dx.doi.org/10.3389/fmolb.2021.744646
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author Pacini, Lorenza
Dorantes-Gilardi, Rodrigo
Vuillon, Laurent
Lesieur, Claire
author_facet Pacini, Lorenza
Dorantes-Gilardi, Rodrigo
Vuillon, Laurent
Lesieur, Claire
author_sort Pacini, Lorenza
collection PubMed
description Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.
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spelling pubmed-85431242021-10-26 Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures Pacini, Lorenza Dorantes-Gilardi, Rodrigo Vuillon, Laurent Lesieur, Claire Front Mol Biosci Molecular Biosciences Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined. Frontiers Media S.A. 2021-10-11 /pmc/articles/PMC8543124/ /pubmed/34708077 http://dx.doi.org/10.3389/fmolb.2021.744646 Text en Copyright © 2021 Pacini, Dorantes-Gilardi, Vuillon and Lesieur. https://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.
spellingShingle Molecular Biosciences
Pacini, Lorenza
Dorantes-Gilardi, Rodrigo
Vuillon, Laurent
Lesieur, Claire
Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures
title Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures
title_full Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures
title_fullStr Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures
title_full_unstemmed Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures
title_short Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures
title_sort mapping function from dynamics: future challenges for network-based models of protein structures
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543124/
https://www.ncbi.nlm.nih.gov/pubmed/34708077
http://dx.doi.org/10.3389/fmolb.2021.744646
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