Evidence map›Paper›PMID 23867606›Full record

ArticleInternational journal of molecular sciences2013

Structure prediction of partial-length protein sequences.

Adrian Laurenzi, Ling-Hong Hung, Ram Samudrala

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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.

Adrian LaurenziDepartment of Computer Science & Engineering, University of Washington, Seattle, WA 98195-2350, USA. alaurenz@uw.edu
Ling-Hong Hung
Ram Samudrala

Funding

NOVEL PARADIGMS FOR DRUG DISCOVERY: COMPUTATIONAL MULTITARGET SCREENINGDP1LM011509 · NLM · UNIVERSITY OF WASHINGTON · PI SAMUDRALA, RAM · 2012 to 2014
$2.4M
NOVEL PARADIGMS FOR DRUG DISCOVERY: COMPUTATIONAL MULTITARGET SCREENINGDP1OD006779 · OD · UNIVERSITY OF WASHINGTON · PI SAMUDRALA, RAM · 2010 to 2011
$1.7M
NIH HHS DP1 OD006779NIH HHS DP1OD006779NLM NIH HHS DP1 LM011509
6 · The paper itself

Abstract

Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial structures of proteins encoded by sequences that contain approximately 50% or more of the full-length protein sequence. We hypothesize that structure prediction may be useful for predicting functions of proteins whose corresponding genes are mapped expressed sequence tags (ESTs) that encode partial-length amino acid sequences. Additionally, we identify a confidence score representing the quality of a predicted structure as a useful means of predicting the likelihood that an arbitrary polypeptide sequence represents a portion of a foldable protein sequence ("foldability"). This work has ramifications for the prediction of protein structure with limited or noisy sequence information, as well as genome annotation.

Indexed as

Databases, ProteinExpressed Sequence TagsProtein FoldingProteinsProtein Structure, TertiarySoftwareProteins

Identifiers

PMID23867606
PMCPMC3742278

What OpenQuestion holds

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LicenceCC BY
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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.