Evidence map›Paper›PMID 25977292›Full record

ArticleNucleic acids research2015

BCSearch: fast structural fragment mining over large collections of protein structures.

Frédéric Guyon, François Martz, Marek Vavrusa, Jérôme Bécot, Julien Rey, Pierre Tufféry

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
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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

6 authors.

Frédéric GuyonMolécules Thérapeutiques in Silico, INSERM UMR-S 973, Université Paris Diderot, Sorbone Paris Cité, 75205 Paris Cedex 13, France.
François MartzMolécules Thérapeutiques in Silico, INSERM UMR-S 973, Université Paris Diderot, Sorbone Paris Cité, 75205 Paris Cedex 13, France.
Marek VavrusaMolécules Thérapeutiques in Silico, INSERM UMR-S 973, Université Paris Diderot, Sorbone Paris Cité, 75205 Paris Cedex 13, France.
Jérôme BécotMolécules Thérapeutiques in Silico, INSERM UMR-S 973, Université Paris Diderot, Sorbone Paris Cité, 75205 Paris Cedex 13, France.
Julien ReyMolécules Thérapeutiques in Silico, INSERM UMR-S 973, Université Paris Diderot, Sorbone Paris Cité, 75205 Paris Cedex 13, France.
Pierre TufféryMolécules Thérapeutiques in Silico, INSERM UMR-S 973, Université Paris Diderot, Sorbone Paris Cité, 75205 Paris Cedex 13, France pierre.tuffery@univ-paris-diderot.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resources to mine the large amount of protein structures available today are necessary to better understand how amino acid variations are compatible with conformation preservation, to assist protein design, engineering and, further, the development of biologic therapeutic compounds. BCSearch is a versatile service to efficiently mine large collections of protein structures. It relies on a new approach based on a Binet-Cauchy kernel that is more discriminative than the widely used root mean square deviation criterion. It has statistics independent of size even for short fragments, and is fast. The systematic mining of large collections of structures such as the complete SCOPe protein structural classification or comprehensive subsets of the Protein Data Bank can be performed in few minutes. Based on this new score, we propose four innovative applications: BCFragSearch and BCMirrorSearch, respectively, search for fragments similar and anti-similar to a query and return information on the diversity of the sequences of the hits. BCLoopSearch identifies candidate fragments of fixed size matching the flanks of a gaped structure. BCSpecificitySearch analyzes a complete protein structure and returns information about sites having few similar fragments. BCSearch is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/BCSearch.

Indexed as

Protein ConformationSoftwareDatabases, ProteinData MiningInternetModels, Molecular

Identifiers

PMID25977292
PMCPMC4489267

What OpenQuestion holds

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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.