Evidence map›Paper›PMID 34063976›Full record

ArticleBiomolecules2021

Pepscan Approach for the Identification of Protein-Protein Interfaces: Lessons from Experiment.

Angelita Rebollo, Eric Savier, Pierre Tuffery

Open access · goldAbstract read
In one paragraph

Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 41% of its field
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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  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

3 authors at 3 institutions in 1 country.

Angelita RebolloUTCBS, Inserm 1265, Faculté de Pharmacie, Université de Paris, 75013 Paris, France.
Eric SavierDepartment of Hepatobiliary and Liver Transplantation Surgery, Pitié-Salpêtrière Hospital, AP-HP, Sorbonne Université, 75013 Paris, France.
Pierre TufferyBFA, CNRS, UMR 8251, Inserm U1133, Université de Paris, F-75013 Paris, France.ORCID 0000-0003-1033-9895
Centre National de la Recherche Scientifique · FRInserm · FRSorbonne Université · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PEPscan is an old approach that has recently gained renewed interest for the identification of interfering peptides (IPs), i.e., peptides able to interfere with protein-protein interactions (PPIs). Its principle is to slice a protein sequence as a series of short overlapping peptides that are synthesized on a peptide array and tested for their ability to bind a partner, with positive spots corresponding to candidate IPs. PEPscan has been applied with a rather large success in various contexts, but the structural determinants underlying this success remain obscure. Here, we analyze the results of 14 PEPscan experiments, and confront the in vitro results with the available structural information. PEPscan identifies candidate IPs in limited numbers that in all cases correspond to solvent-accessible regions of the structures, their location at the protein-protein interface remaining to be further demonstrated. A strong point of PEPscan seems to be its ability to identify specific IPs. IPs identified from the same protein differ depending on the target PPI, and correspond to patches not frequently involved in the interactions seen in the 3D structures available. Overall, PEPscan seems to provide a cheap and rapid manner to identify candidate IPs, that also comes with room for improvement.

Indexed as

Models, MolecularProtein Interaction MappingPeptidesProteinsPeptidesProteinsbinding specificityinterfering peptidePEP-scanprotein–protein interaction

Identifiers

PMID34063976
PMCPMC8224071
OpenAlexW3162286721

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.