Evidence map›Paper›PMID 16618368›Full record

ArticleBMC bioinformatics2006

Length-dependent prediction of protein intrinsic disorder.

Kang Peng, Predrag Radivojac, Slobodan Vucetic, A Keith Dunker, Zoran Obradovic

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 476 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
476citing papers in PubMed, 1 pooled it
12.5field-weighted citation impact, top 1% 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

476 citing papers in PubMed, 1 synthesis or guideline pooled it, 946 citations in OpenAlex.

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416 more citing papers are in PubMed but not listed here.

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

5 authors at 3 institutions in 1 country.

Kang PengCenter for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA. kangpeng@ist.temple.edu
Predrag Radivojac
Slobodan Vucetic
A Keith Dunker
Zoran Obradovic
Temple University · USIndiana University Bloomington · USIndiana University School of Medicine

Funding

Bioinformatics linkage of protein disorder and functionR01LM007688 · NLM · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI DUNKER, ALAN KEITH · 2003 to 2006
$1.3M
NLM NIH HHS R01 LM007688NLM NIH HHS R01 LM007688-01
6 · The paper itself

Abstract

backgroundDue to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (< or =30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions.

resultsWe proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (< or = 30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder.

conclusionThe VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php.

Indexed as

AlgorithmsModels, ChemicalModels, MolecularSoftwareAmino Acid SequenceComputer SimulationMolecular Sequence DataProteinsProtein Structure, SecondarySequence AlignmentSequence Analysis, ProteinProteins

Identifiers

PMID16618368
PMCPMC1479845
OpenAlexW1792685479

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.