Evidence map›Paper›PMID 27756103›Full record

ArticleProteins2017

Structural quality of unrefined models in protein docking.

Ivan Anishchenko, Petras J Kundrotas, Ilya A Vakser

Abstract read
In one paragraph

Article in Proteins, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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

3 authors.

Ivan AnishchenkoCenter for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.
Petras J KundrotasCenter for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.
Ilya A VakserCenter for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.

Funding

Integrated Resource for Protein Recognition StudiesR01GM074255 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI VAKSER, ILYA · 2005 to 2024
$6.2M
NIGMS NIH HHS R01 GM074255
6 · The paper itself

Abstract

Structural characterization of protein-protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template-based docking techniques that utilize structural similarity between target protein-protein interaction and cocrystallized protein-protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template-free docking. However, the template-based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template-based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template-based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template-based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template-based docking are comparable to those in the free docking, due to the overall higher quality of the template-based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template-based docking predictions as well. Proteins 2016; 85:39-45. © 2016 Wiley Periodicals, Inc.

Indexed as

Molecular Docking SimulationBinding SitesComputational BiologyCrystallography, X-RayProtein BindingProtein Interaction Domains and MotifsProtein Interaction MappingProteinsProtein Structure, SecondarySoftwareStructural Homology, ProteinProteinsinteractomemodel refinementprotein modelingprotein recognitionsteric clashstructure prediction

Identifiers

PMID27756103
PMCPMC5167671

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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.