ArticleMolecular biology and evolution2022
The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Unraveling competitive protein interactions: A critical step toward improved production and therapeutics.Journal of advanced research · 2026Review
- Article
- The Norway-Japan bilateral symposium: Dialogue in biophysics beginning in Nara.Biophysics and physicobiology · 2026Article
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- The alfalfa U-box E3 ligase MsPUB210 interacts with MsICE1 and positively regulates cold tolerance in transgenic Arabidopsis.Frontiers in plant science · 2026Article
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- Artificial intelligence-driven framework for discovering synthetic binding protein-like scaffolds from the entire protein universe.Briefings in bioinformatics · 2025Article
- Cell reprogramming in cancer: Interplay of genetic, epigenetic mechanisms, and the tumor microenvironment in carcinogenesis and metastasis.World journal of clinical oncology · 2025Review
- Learning molecular traits of human pain disease via voltage-gated sodium channel structure renormalization.Computational and structural biotechnology journal · 2025Article
- AlphaFold2 and its applications in the fields of biology and medicine.Signal transduction and targeted therapy · 2023Review
- Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader.Communications biology · 2023Article
- Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond.Biomolecules · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.