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The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the...

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Autores principales: Tang, Qian-Yuan, Ren, Weitong, Wang, Jun, Kaneko, Kunihiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550990/
https://www.ncbi.nlm.nih.gov/pubmed/36108094
http://dx.doi.org/10.1093/molbev/msac197
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author Tang, Qian-Yuan
Ren, Weitong
Wang, Jun
Kaneko, Kunihiko
author_facet Tang, Qian-Yuan
Ren, Weitong
Wang, Jun
Kaneko, Kunihiko
author_sort Tang, Qian-Yuan
collection PubMed
description The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic–hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.
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spelling pubmed-95509902022-10-11 The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database Tang, Qian-Yuan Ren, Weitong Wang, Jun Kaneko, Kunihiko Mol Biol Evol Discoveries The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic–hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives. Oxford University Press 2022-09-15 /pmc/articles/PMC9550990/ /pubmed/36108094 http://dx.doi.org/10.1093/molbev/msac197 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Discoveries
Tang, Qian-Yuan
Ren, Weitong
Wang, Jun
Kaneko, Kunihiko
The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
title The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
title_full The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
title_fullStr The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
title_full_unstemmed The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
title_short The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
title_sort statistical trends of protein evolution: a lesson from alphafold database
topic Discoveries
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550990/
https://www.ncbi.nlm.nih.gov/pubmed/36108094
http://dx.doi.org/10.1093/molbev/msac197
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