Evidence map›Paper›PMID 24445405›Full record

ArticleMolecular & cellular proteomics : MCP2014

Modeling protein assemblies in the proteome.

Guray Kuzu, Ozlem Keskin, Ruth Nussinov, Attila Gursoy

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2014. 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. Review
  2. Article
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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

4 authors.

Guray KuzuCenter for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey;
Ozlem Keskin
Ruth Nussinov
Attila Gursoy

Funding

Biomolecular Recognition and Binding MechanismsZIABC010441 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI NUSSINOV, RUTH · 2009 to 2025
$9.4M
Resource for Biocomputing Visualization and InformaticsP41GM103311 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2012 to 2017
$8.2M
Method Development: Efficient Computer Vision Based AlgorithmsZIABC010442 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI NUSSINOV, RUTH · 2009 to 2025
$2.4M
CCR NIH HHS HHSN261200800001CIntramural NIH HHSNCI NIH HHS HHSN261200800001ENIGMS NIH HHS P41 GM103311
6 · The paper itself

Abstract

Most (if not all) proteins function when associated in multimolecular assemblies. Attaining the structures of protein assemblies at the atomic scale is an important aim of structural biology. Experimentally, structures are increasingly available, and computations can help bridge the resolution gap between high- and low-resolution scales. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; and still others exploit the symmetry of the complexes and thus are not applicable to nonsymmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy for the construction of protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (Protein Interactions by Structural Matching). Previously, we have shown its power in predicting pairwise interactions. Here we take a step toward multimolecular assemblies, reflecting the more prevalent cellular scenarios. With this method we are able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components. The method considers conformational changes and is applicable to large-scale studies. We also exploit electron microscopy density maps to select a solution from among the predictions. Here we present the method, illustrate its results, and highlight its current limitations.

Indexed as

Models, MolecularAlgorithmsComputational BiologyDatabases, ProteinMolecular Docking SimulationProtein BindingProtein ConformationProteomeStatic ElectricityThermodynamicsProteome

Identifiers

PMID24445405
PMCPMC3945916

What OpenQuestion holds

Textmetadata
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.