Evidence map›Paper›PMID 20225257›Full record

ArticleProteins2010

Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3D structure prediction method ASTRO-FOLD.

R Rajgaria, Y Wei, C A Floudas

Abstract read
In one paragraph

Article in Proteins, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction.AIChE journal. American Institute of Chemical Engineers · 2012
    Article
  10. Article
  11. 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.

R RajgariaDepartment of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA.
Y Wei
C A Floudas

Funding

STRUCTURE PREDICTION OF PEPTIDES VIA GLOBAL OPTIMIZATIONR01GM052032 · NIGMS · PRINCETON UNIVERSITY · PI FLOUDAS, CHRISTODOULOS ACHILLEUS · 1996 to 2011
$1.9M
Computer Aided Design of Complement TherapeuticsR24GM069736 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI LAMBRIS, JOHN D · 2004 to 2007
$1.8M
NIGMS NIH HHS R01 GM052032NIGMS NIH HHS R01 GM52032NIGMS NIH HHS R24 GM069736
6 · The paper itself

Abstract

An integer linear optimization model is presented to predict residue contacts in beta, alpha + beta, and alpha/beta proteins. The total energy of a protein is expressed as sum of a C(alpha)-C(alpha) distance dependent contact energy contribution and a hydrophobic contribution. The model selects contact that assign lowest energy to the protein structure as satisfying a set of constraints that are included to enforce certain physically observed topological information. A new method based on hydrophobicity is proposed to find the beta-sheet alignments. These beta-sheet alignments are used as constraints for contacts between residues of beta-sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of beta, alpha + beta, alpha/beta proteins and it was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was approximately 61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 A and 15.88 A, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO-FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins.

Indexed as

AlgorithmsProtein Structure, TertiaryAmino AcidsComputational BiologyDatabases, ProteinHydrophobic and Hydrophilic InteractionsModels, MolecularProteinsProtein Structure, SecondarySequence AlignmentAmino AcidsProteins

Identifiers

PMID20225257
PMCPMC2858251

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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.