Evidence map›Paper›PMID 23815589›Full record

ArticleBMC bioinformatics2013

Identification and analysis of conserved pockets on protein surfaces.

Marco Cammisa, Antonella Correra, Giuseppina Andreotti, Maria Vittoria Cubellis

Abstract read
In one paragraph

Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Elucidating the druggability of the human proteome with eFindSite.Journal of computer-aided molecular design · 2019
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  7. The Analysis of Variants in the General Population Reveals ThatInternational journal of molecular sciences · 2018
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  9. E-Learning for Rare Diseases: An Example Using Fabry Disease.International journal of molecular sciences · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marco CammisaDepartment of Biology, University Federico II, Via Cinthia, 80126, Naples, Italy.
Antonella Correra
Giuseppina Andreotti
Maria Vittoria Cubellis

Funding

Telethon GGP12108
6 · The paper itself

Abstract

backgroundThe interaction between proteins and ligands occurs at pockets that are often lined by conserved amino acids. These pockets can represent the targets for low molecular weight drugs. In order to make the research for new medicines as productive as possible, it is necessary to exploit "in silico" techniques, high throughput and fragment-based screenings that require the identification of druggable pockets on the surface of proteins, which may or may not correspond to active sites.

resultsWe developed a tool to evaluate the conservation of each pocket detected on the protein surface by CastP. This tool was named DrosteP because it recursively searches for optimal input sequences to be used to calculate conservation. DrosteP uses a descriptor of statistical significance, Poisson p-value, as a target to optimize the choice of input sequences. To benchmark DrosteP we used monomeric or homodimer human proteins with known 3D-structure whose active site had been annotated in UniProt. DrosteP is able to detect the active site with high accuracy because in 81% of the cases it coincides with the most conserved pocket. Comparing DrosteP with analogous programs is difficult because the outputs are different. Nonetheless we could assess the efficacy of the recursive algorithm in the identification of active site pockets by calculating conservation with the same input sequences used by other programs.We analyzed the amino-acid composition of conserved pockets identified by DrosteP and we found that it differs significantly from the amino-acid composition of non conserved pockets.

conclusionsSeveral methods for predicting ligand binding sites on protein surfaces, that combine 3D-structure and evolutionary sequence conservation, have been proposed. Any method relying on conservation mainly depends on the choice of the input sequences. DrosteP chooses how deeply distant homologs must be collected to evaluate conservation and thus optimizes the identification of active site pockets. Moreover it recognizes conserved pockets other than those coinciding with the sites annotated in UniProt that might represent useful druggable sites. The distinctive amino-acid composition of conserved pockets provides useful hints on the fundamental principles underlying protein-ligand interaction. AVAILABILITY: http://www.icb.cnr.it/project/drosteppy/

Indexed as

AlgorithmsCatalytic DomainAmino Acid SequenceBinding SitesComputer SimulationConserved SequenceHumansLigandsModels, MolecularProtein ConformationProteinsLigandsProteins

Identifiers

PMID23815589
PMCPMC3633052

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Registered trials

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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.