ArticlePLoS computational biology2019
The intrinsic dimension of protein sequence evolution.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification.Scientific reports · 2026Article
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A metric and its derived protein network for evaluation of ortholog database inconsistency.BMC bioinformatics · 2025Article
- Latent generative landscapes as maps of functional diversity in protein sequence space.Nature communications · 2023Article
- Manifold Learning for fMRI time-varying FC.bioRxiv : the preprint server for biology · 2023Article
- Manifold learning for fMRI time-varying functional connectivity.Frontiers in human neuroscience · 2023Article
- The generative capacity of probabilistic protein sequence models.Nature communications · 2021Article
- Exploring the sequence fitness landscape of a bridge between protein folds.PLoS computational biology · 2020Article
- Size and structure of the sequence space of repeat proteins.PLoS computational biology · 2019Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is well known that, in order to preserve its structure and function, a protein cannot change its sequence at random, but only by mutations occurring preferentially at specific locations. We here investigate quantitatively the amount of variability that is allowed in protein sequence evolution, by computing the intrinsic dimension (ID) of the sequences belonging to a selection of protein families. The ID is a measure of the number of independent directions that evolution can take starting from a given sequence. We find that the ID is practically constant for sequences belonging to the same family, and moreover it is very similar in different families, with values ranging between 6 and 12. These values are significantly smaller than the raw number of amino acids, confirming the importance of correlations between mutations in different sites. However, we demonstrate that correlations are not sufficient to explain the small value of the ID we observe in protein families. Indeed, we show that the ID of a set of protein sequences generated by maximum entropy models, an approach in which correlations are accounted for, is typically significantly larger than the value observed in natural protein families. We further prove that a critical factor to reproduce the natural ID is to take into consideration the phylogeny of sequences.
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.