ArticleeLife2018
Inferring joint sequence-structural determinants of protein functional specificity.
Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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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.
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Who cites it
8 citing papers in PubMed.
- Evolutionary and Functional Analysis of Caspase-8 and ASC Interactions to Drive Lytic Cell Death, PANoptosis.Molecular biology and evolution · 2025Article
- Clustering protein functional families at large scale with hierarchical approaches.Protein science : a publication of the Protein Society · 2024Article
- General strategies for using amino acid sequence data to guide biochemical investigation of protein function.Biochemical Society transactions · 2022Review
- Identifying Function Determining Residues in Neuroimmune Semaphorin 4A.International journal of molecular sciences · 2022Article
- SPARC: Structural properties associated with residue constraints.Computational and structural biotechnology journal · 2022Article
- Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins.PLoS genetics · 2021Article
- Obtaining extremely large and accurate protein multiple sequence alignments from curated hierarchical alignments.Database : the journal of biological databases and curation · 2020Article
- Statistical investigations of protein residue direct couplings.PLoS computational biology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Residues responsible for allostery, cooperativity, and other subtle but functionally important interactions remain difficult to detect. To aid such detection, we employ statistical inference based on the assumption that residues distinguishing a protein subgroup from evolutionarily divergent subgroups often constitute an interacting functional network. We identify such networks with the aid of two measures of statistical significance. One measure aids identification of divergent subgroups based on distinguishing residue patterns. For each subgroup, a second measure identifies structural interactions involving pattern residues. Such interactions are derived either from atomic coordinates or from Direct Coupling Analysis scores, used as surrogates for structural distances. Applying this approach to N-acetyltransferases, P-loop GTPases, RNA helicases, synaptojanin-superfamily phosphatases and nucleases, and thymine/uracil DNA glycosylases yielded results congruent with biochemical understanding of these proteins, and also revealed striking sequence-structural features overlooked by other methods. These and similar analyses can aid the design of drugs targeting allosteric sites.
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Registered trials
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