ArticleProceedings of the National Academy of Sciences of the United States of America2018
Power law tails in phylogenetic systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom.Cell systems · 2025Article
- Natural diversifying evolution of nonribosomal peptide synthetases in a defensive symbiont reveals nonmodular functional constraints.PNAS nexus · 2024Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins.Protein science : a publication of the Protein Society · 2024Article
- Is Novelty Predictable?Cold Spring Harbor perspectives in biology · 2024Review
- An evolution-based framework for describing human gut bacteria.bioRxiv : the preprint server for biology · 2023Article
- scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching.Nature biotechnology · 2023Article
- Article
- Impact of phylogeny on structural contact inference from protein sequence data.Journal of the Royal Society, Interface · 2023Article
- General strategies for using amino acid sequence data to guide biochemical investigation of protein function.Biochemical Society transactions · 2022Review
- Protein language models trained on multiple sequence alignments learn phylogenetic relationships.Nature communications · 2022Article
- Singular value decomposition of protein sequences as a method to visualize sequence and residue space.Protein science : a publication of the Protein Society · 2022Article
- Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes.Nature communications · 2022Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Covariance predicts conserved protein residue interactions important to the emergence and continued evolution of SARS-CoV-2 as a human pathogen.bioRxiv : the preprint server for biology · 2022Article
- Extracting phylogenetic dimensions of coevolution reveals hidden functional signals.Scientific reports · 2022Article
- Article
- SiGMoiD: A super-statistical generative model for binary data.PLoS computational biology · 2021Article
- On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.PLoS computational biology · 2021Article
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2 authors.
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Abstract
Covariance analysis of protein sequence alignments uses coevolving pairs of sequence positions to predict features of protein structure and function. However, current methods ignore the phylogenetic relationships between sequences, potentially corrupting the identification of covarying positions. Here, we use random matrix theory to demonstrate the existence of a power law tail that distinguishes the spectrum of covariance caused by phylogeny from that caused by structural interactions. The power law is essentially independent of the phylogenetic tree topology, depending on just two parameters-the sequence length and the average branch length. We demonstrate that these power law tails are ubiquitous in the large protein sequence alignments used to predict contacts in 3D structure, as predicted by our theory. This suggests that to decouple phylogenetic effects from the interactions between sequence distal sites that control biological function, it is necessary to remove or down-weight the eigenvectors of the covariance matrix with largest eigenvalues. We confirm that truncating these eigenvectors improves contact prediction.
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