Evidence map›Paper›PMID 37827517›Full record

ArticleJournal of chemical information and modeling2023

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization.

Yasaman Karami, Samuel Murail, Julien Giribaldi, Benjamin Lefranc, Florian Defontaine, Olivier Lesouhaitier, Jérôme Leprince, Sjoerd de Vries, Pierre Tufféry

Open access · greenAbstract read
In one paragraph

Article in Journal of chemical information and modeling, 2023. 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
1.2field-weighted citation impact, top 20% 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. Review
  2. Review
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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

9 authors at 3 institutions in 1 country.

Yasaman KaramiUniversité Paris Cité, CNRS UMR 8251, INSERM ERL U1133, 75013 Paris, France.ORCID 0000-0001-8413-2665
Samuel MurailUniversité Paris Cité, CNRS UMR 8251, INSERM ERL U1133, 75013 Paris, France.ORCID 0000-0002-3842-5333
Julien GiribaldiInstitut des Biomolécules Max Mousseron, UMR 5247, Université de Montpellier-CNRS, 34293 Montpellier, France.
Benjamin LefrancUniversité de Rouen Normandie, INSERM U1239 NorDiC, Neuroendocrine, Endocrine and Germinal Differentiation and Communication, INSERM US51 HeRacLeS, F-76000 Rouen, France.
Florian DefontaineUniversité de Rouen Normandie, UR CBSA, Research Unit Bacterial Communication and Anti-infectious Strategies, 27000 Evreux, France.
Olivier LesouhaitierUniversité de Rouen Normandie, UR CBSA, Research Unit Bacterial Communication and Anti-infectious Strategies, 27000 Evreux, France.
Jérôme LeprinceUniversité de Rouen Normandie, INSERM U1239 NorDiC, Neuroendocrine, Endocrine and Germinal Differentiation and Communication, INSERM US51 HeRacLeS, F-76000 Rouen, France.ORCID 0000-0002-7814-9927
Sjoerd de VriesUniversité Paris Cité, CNRS UMR 8251, INSERM ERL U1133, 75013 Paris, France.
Pierre TufféryUniversité Paris Cité, CNRS UMR 8251, INSERM ERL U1133, 75013 Paris, France.ORCID 0000-0003-1033-9895
Centre National de la Recherche Scientifique · FRCommunication Bactérienne et Stratégie Anti-infectieusesInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peptides have recently regained interest as therapeutic candidates, but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization, i.e., setting a covalent peptide bond linking the last amino acid with the first one, is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity, and preventing enzymatic degradation. Starting from an active peptide, it usually requires the design of a linker of a few amino acids to make it possible to cyclize the peptide, possibly preserving the conformation of the initial peptide and not affecting its activity. However, very little is known about the sequence-structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large-scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to linkers, allowing head-to-tail peptide cyclization. First we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation. Second, and more importantly, we show that it seems possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational design of head-to-tail cyclization linkers. Finally, we illustrate this for two peptides using a limited set of amino-acids likely not to interfere with peptide function. For a linear peptide derived from Nrf2, the peptide cyclized starting from the experimental structure showed a 26-fold increase in the binding affinity. For urotensin II, a peptide already cyclized by a disulfide bond that exerts a broad array of biological activities, we were able, starting from models of the structure, to design a head-to-tail cyclized peptide, the first synthesized bicyclic 14-residue long urotensin II analogue, showing a retention of

Indexed as

PeptidesPeptides, CyclicAmino AcidsCyclizationProtein ConformationAmino AcidsPeptidesPeptides, Cyclic

Identifiers

PMID37827517
PMCPMC10599322
OpenAlexW4387568030

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.