Evidence map›Paper›PMID 18775963›Full record

ArticleBiophysical journal2008

Alpha-helical topology prediction and generation of distance restraints in membrane proteins.

Scott R McAllister, Christodoulos A Floudas

Abstract read
In one paragraph

Article in Biophysical journal, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
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

2 authors.

Scott R McAllisterDepartment of Chemical Engineering, Princeton University, Princeton, New Jersey, USA.
Christodoulos 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

The field of protein structure prediction has seen significant advances in recent years. Researchers have followed a multitude of approaches, including methods based on comparative modeling, fold recognition and threading, and first-principles techniques. It is noteworthy that the structure prediction of membrane proteins is comparatively less studied by researchers in the field. A membrane protein is characterized by a protein structure that extends into or through the lipid-lipid bilayer of a cell. The structure is influenced by the combination of the hydrophobic bilayer region, the direct interaction with the bilayer, and the aqueous external environment. Due to the difficulty in obtaining reliable experimental structures, accurate computational prediction of membrane proteins is of paramount importance. An optimization model has been developed to predict the interhelical interactions in alpha-helical membrane proteins. A database of alpha-helical membrane proteins of known structure and limited sequence identity can be constructed to develop interaction probabilities. By then maximizing the occurrence of highly probable pairwise or three-residue interactions, realistic contacts can be predicted by imposing a number of geometrical constraints. The development of these low distance contacts can provide additional distance restraints for first principles-based approaches to the tertiary structure prediction problem. The proposed approach is shown to successfully predict interhelical contacts in several membrane protein systems, including bovine rhodopsin and the recently released human beta2 adrenergic receptor protein structure.

Indexed as

Models, MolecularMembrane ProteinsProbabilityProtein Structure, SecondaryProtein Structure, TertiaryMembrane Proteins

Identifiers

PMID18775963
PMCPMC2586566

What OpenQuestion holds

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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.