Evidence map›Paper›PMID 22270458›Full record

ArticleJournal of structural and functional genomics2012

Structure- and sequence-based function prediction for non-homologous proteins.

Lee Sael, Meghana Chitale, Daisuke Kihara

Abstract read
In one paragraph

Article in Journal of structural and functional genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

3 authors.

Lee SaelDepartment of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
Meghana Chitale
Daisuke Kihara

Funding

Surface Shape Based Screening of Large Protein DatabasesR01GM075004 · NIGMS · PURDUE UNIVERSITY WEST LAFAYETTE · PI KIHARA, DAISUKE · 2005 to 2009
$1.5M
Identification of protein-metabolite interactome.R01GM097528 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2011 to 2014
$1.1M
NIGMS NIH HHS R01 GM075004NIGMS NIH HHS R01GM075004NIGMS NIH HHS R01 GM097528NIGMS NIH HHS R01GM097528
6 · The paper itself

Abstract

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.

Indexed as

AlgorithmsDatabases, ProteinSoftwareBinding SitesComputational BiologyInternetMolecular Sequence AnnotationProtein ConformationProteinsReproducibility of ResultsSequence Analysis, ProteinSequence Homology, Amino AcidStructure-Activity RelationshipProteins

Identifiers

PMID22270458
PMCPMC3375349

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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.