Evidence map›Paper›PMID 20607702›Full record

ArticleProteins2010

Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions?

Nir London, Barak Raveh, Dana Movshovitz-Attias, Ora Schueler-Furman

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 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing 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

65 citing papers in PubMed.

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5 more citing papers are in PubMed but not listed here.

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.

Nir LondonDepartment of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University, POB 12272, Jerusalem, 91120 Israel.
Barak Raveh
Dana Movshovitz-Attias
Ora Schueler-Furman

Funding

Optimizing the Clinical Efficacy of Opioids by TLR4 BlockadeR21DA026950 · NIDA · UNIVERSITY OF COLORADO · PI YIN, HANG HUBERT · 2009 to 2010
$273k
NIDA NIH HHS R21 DA026950NIDA NIH HHS R21DA026950
6 · The paper itself

Abstract

In this study, we assess on a large scale the possibility of deriving self-inhibitory peptides from protein domains with globular architectures. Such inhibitory peptides would inhibit interactions of their origin domain by mimicking its mode of binding to cognate partners, and could serve as promising leads for rational design of inhibitory drugs. For our large-scale analysis, we analyzed short linear segments that were cut out of protein interfaces in silico in complex structures of protein-protein docking Benchmark 3.0 and CAPRI targets from rounds 1-19. Our results suggest that more than 50% of these globular interactions are dominated by one short linear segment at the domain interface, which provides more than half of the original interaction energy. Importantly, in many cases the derived peptides show strong energetic preference for their original binding mode independently of the context of their original domain, as we demonstrate by extensive computational peptide docking experiments. As an in depth case study, we computationally design a candidate peptide to inhibit the EphB4-EphrinB2 interaction based on a short peptide derived from the G-H loop in EphrinB2. Altogether, we provide an elaborate framework for the in silico selection of candidate inhibitory molecules for protein-protein interactions. Such candidate molecules can be readily subjected to wet-laboratory experiments and provide highly promising starting points for subsequent drug design.

Indexed as

Models, ChemicalProtein Interaction Domains and MotifsAnimalsBinding SitesComputer SimulationHumansMiceModels, MolecularPeptidesProtein BindingProtein ConformationProtein Interaction MappingProteinsProtein StabilitySoftwareThermodynamicsPeptidesProteins

Identifiers

PMID20607702
PMCPMC2952690

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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.