Evidence map›Paper›PMID 21079665›Full record

ArticlePLoS computational biology2010

Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

Cristina Marino Buslje, Elin Teppa, Tomas Di Doménico, José María Delfino, Morten Nielsen

Abstract read
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Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

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0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

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  6. Integrated Computational Approaches and Tools forAllosteric Drug Discovery.International journal of molecular sciences · 2020
    Review
  7. Article
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  11. Applications of sequence coevolution in membrane protein biochemistry.Biochimica et biophysica acta. Biomembranes · 2018
    Review
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Cristina Marino BusljeFundación Instituto Leloir, Buenos Aires, Argentina. cmb@leloir.org.ar
Elin Teppa
Tomas Di Doménico
José María Delfino
Morten Nielsen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

Indexed as

Area Under CurveCatalytic DomainComputational BiologyDatabases, ProteinEnzymesModels, MolecularModels, StatisticalProtein ConformationReproducibility of ResultsSequence HomologyEnzymes

Identifiers

PMID21079665
PMCPMC2973806

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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.