Evidence map›Paper›PMID 19326457›Full record

ArticleProteins2009

Building and assessing atomic models of proteins from structural templates: learning and benchmarks.

Brinda Kizhakke Vallat, Jaroslaw Pillardy, Peter Májek, Jaroslaw Meller, Thomas Blom, Baoqiang Cao, Ron Elber

Abstract read
In one paragraph

Article in Proteins, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Diversity, expression, and structural modeling of sugar transporters inFrontiers in cellular and infection microbiology · 2025
    Article
  2. Tandem Mass Tagging (TMT) Reveals Tissue-Specific Proteome of L4 Larvae ofInternational journal of molecular sciences · 2022
    Article
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  13. Crystal structure of the central axis DF complex of the prokaryotic V-ATPase.Proceedings of the National Academy of Sciences of the United States of America · 2011
    Article
  14. Fast geometric consensus approach for protein model quality assessment.Journal of computational biology : a journal of computational molecular cell biology · 2011
    Article
  15. Article
  16. 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

7 authors.

Brinda Kizhakke VallatDepartment of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
Jaroslaw Pillardy
Peter Májek
Jaroslaw Meller
Thomas Blom
Baoqiang Cao
Ron Elber

Funding

LONG TIME DYNAMICS OF BIOMOLECULESR01GM059796 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI ELBER, RON · 2000 to 2021
$5.7M
Optimization of Stuctures and Networks of ProteinsR01GM067823 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI ELBER, RON · 2004 to 2010
$1.9M
NIGMS NIH HHS GM067823NIGMS NIH HHS R01 GM059796NIGMS NIH HHS R01 GM067823
6 · The paper itself

Abstract

One approach to predict a protein fold from a sequence (a target) is based on structures of related proteins that are used as templates. We present an algorithm that examines a set of candidates for templates, builds from each of the templates an atomically detailed model, and ranks the models. The algorithm performs a hierarchical selection of the best model using a diverse set of signals. After a quick and suboptimal screening of template candidates from the protein data bank, the current method fine-tunes the selection to a few models. More detailed signals test the compatibility of the sequence and the proposed structures, and are merged to give a global fitness measure using linear programming. This algorithm is a component of the prediction server LOOPP (http://www.loopp.org). Large-scale training and tests sets were designed and are presented. Recent results of the LOOPP server in CASP8 are discussed.

Indexed as

AlgorithmsStructural Homology, ProteinComputer SimulationModels, MolecularProtein FoldingProteinsProteins

Identifiers

PMID19326457
PMCPMC2719020

What OpenQuestion holds

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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.