The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
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Abstract
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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Compound effect and mechanism of oxidative damage induced by nanoplastics and benzo [a] pyrene
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Self-citationProtein structure and dynamics in the era of integrative structural biology
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Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development
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Evolutionary tinkering enriches the hierarchical and nested structures in amino acid sequences
Zhang, Z., Liu, C., Zhu, Y., Lu, P., Qiu, W., Tang, Q.-Y., He, L., Zhang, K., Di, Z., & Liu, Y. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.023215 · Cited by 11 · see citing works
Self-citationUnifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold
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Universal Principles of Protein Dynamics and Evolution Revealed by Data-Driven Studies
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Atomic density distributions in proteins: structural and functional implications
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From prediction to discovery: AI as an observatory of physical organization in protein space
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Self-citationMolecular shape evolution of cyanobacterial circadian clock protein KaiC
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- Authors
- Tang, Q.-Y., Ren, W., Wang, J., & Kaneko, K.
- Affiliations
- RIKEN Center for Brain Science (JP)
- RIKEN (JP)
- Collaborative Innovation Center of Advanced Microstructures (CN)
- University of Copenhagen (DK)
- Corresponding author
- Tang, Qian-Yuan
- Kaneko, Kunihiko
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-09-15
- Volume
- 39
- Issue
- 10
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- PMC
- PMC9550990
- OpenAlex
- W4296025735
- Cited by
- 31
- References
- 110
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 5 · 2023: 6 · 2022: 4
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Biology and Evolution (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Funders
- Japan Agency for Medical Research and Development (JP21dm0207001)
- National Natural Science Foundation of China (11774157, 17H06386)
- Ministry of Education, Culture, Sports, Science and Technology of Japan ((A)20H00123)
- Japanese Society for the Promotion of Science
- Novo Nordisk Fonden
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- protein evolution, organismal complexity, AlphaFold, radius of gyration, protein dynamics, fractal dimension, assortativity, phylogenetic relatedness, normal mode analysis, origin of life
- MeSH terms
- Artificial Intelligence, Phylogeny, Proteome, Databases, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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