Evidence map›Paper›PMID 17658943›Full record

ArticlePLoS computational biology2007

Natively unstructured loops differ from other loops.

Avner Schlessinger, Jinfeng Liu, Burkhard Rost

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

47 citing papers in PubMed, 100 citations in OpenAlex.

  1. Article
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  6. Predicting Protein Conformational Disorder and Disordered Binding Sites.Methods in molecular biology (Clifton, N.J.) · 2022
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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 at 1 institution in 1 country.

Avner SchlessingerDepartment of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, USA. as2067@columbia.edu
Jinfeng Liu
Burkhard Rost
Columbia University · US

Funding

Sub-project 9U54GM074958 · NIGMS · RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK · PI KENNEDY, MICHAEL ALLEN · 2005 to 2009
$56.6M
Predictions of structure/function by PredictProteinR01LM007329 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI PE'ER, ITSHACK G · 2003 to 2010
$2.6M
NIGMS NIH HHS U54-GM072980NIGMS NIH HHS U54 GM074958NLM NIH HHS 2R01-LM07329-01NLM NIH HHS R01 LM007329
6 · The paper itself

Abstract

Natively unstructured or disordered protein regions may increase the functional complexity of an organism; they are particularly abundant in eukaryotes and often evade structure determination. Many computational methods predict unstructured regions by training on outliers in otherwise well-ordered structures. Here, we introduce an approach that uses a neural network in a very different and novel way. We hypothesize that very long contiguous segments with nonregular secondary structure (NORS regions) differ significantly from regular, well-structured loops, and that a method detecting such features could predict natively unstructured regions. Training our new method, NORSnet, on predicted information rather than on experimental data yielded three major advantages: it removed the overlap between testing and training, it systematically covered entire proteomes, and it explicitly focused on one particular aspect of unstructured regions with a simple structural interpretation, namely that they are loops. Our hypothesis was correct: well-structured and unstructured loops differ so substantially that NORSnet succeeded in their distinction. Benchmarks on previously used and new experimental data of unstructured regions revealed that NORSnet performed very well. Although it was not the best single prediction method, NORSnet was sufficiently accurate to flag unstructured regions in proteins that were previously not annotated. In one application, NORSnet revealed previously undetected unstructured regions in putative targets for structural genomics and may thereby contribute to increasing structural coverage of large eukaryotic families. NORSnet found unstructured regions more often in domain boundaries than expected at random. In another application, we estimated that 50%-70% of all worm proteins observed to have more than seven protein-protein interaction partners have unstructured regions. The comparative analysis between NORSnet and DISOPRED2 suggested that long unstructured loops are a major part of unstructured regions in molecular networks.

Indexed as

Artificial IntelligenceProtein Structure, SecondaryAmino Acid SequenceAnimalsBinding SitesCaenorhabditis elegansComputational BiologyComputer SimulationDatabases, ProteinModels, ChemicalModels, MolecularNeural Networks, ComputerPredictive Value of TestsProtein FoldingProtein Interaction MappingProteomeProteome

Identifiers

PMID17658943
PMCPMC1924875
OpenAlexW2094005653

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.