ArticlePLoS computational biology2015
Protein sectors: statistical coupling analysis versus conservation.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- The ecological context of enzymatic variation.bioRxiv : the preprint server for biology · 2026Article
- Allostery is a widespread cause of loss-of-function variant pathogenicity.Nature communications · 2026Article
- HVIface: sequence-based deep learning for decoding human-virus protein-protein interfaces.Frontiers in bioinformatics · 2026Article
- Widespread epistasis shapes RNA polymerase II active site function and evolution.Nature communications · 2025Article
- Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.RSC chemical biology · 2025Review
- Integrative modeling of FOXO-mediated autophagy in NSCLC: linking cGAS-STING signaling to IL-6 dynamics.Frontiers in oncology · 2025Article
- Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase.The journal of physical chemistry. B · 2024Article
- Hybrid Exb/Mot stators require substitutions distant from the chimeric pore to power flagellar rotation.Journal of bacteriology · 2024Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase.bioRxiv : the preprint server for biology · 2024Article
- Evolution-guided engineering ofScience (New York, N.Y.) · 2024Article
- Article
- Allosteric regulation of kinase activity in living cells.bioRxiv : the preprint server for biology · 2023Article
- Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Knowledge-guided data mining on the standardized architecture of NRPS: Subtypes, novel motifs, and sequence entanglements.PLoS computational biology · 2023Article
- Article
- Coevolution combined with molecular dynamics simulations provides structural and mechanistic insights into the interactions between the integrator complex subunits.Computational and structural biotechnology journal · 2023Article
- Extracting phylogenetic dimensions of coevolution reveals hidden functional signals.Scientific reports · 2022Article
- Saturation Mutagenesis of the Transmembrane Region of HokC inInternational journal of molecular sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed "sectors". The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.
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.