Evidence mapPaperPMID 38822212Full record

ReviewAmino acids2024

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives.

Ayca Aslan, Selcen Ari Yuka

Abstract readReview
In one paragraph

Review in Amino acids, 2024. 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
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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

2 authors.

Ayca AslanDepartment of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey.ORCID http://orcid.org/0000-0001-8105-7970
Selcen Ari YukaDepartment of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey. selcenay@yildiz.edu.tr.ORCID http://orcid.org/0000-0002-0028-2453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Indexed as

Coronary Artery DiseasePeptidesComputer SimulationHumansMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationPeptidesAtherosclerosisCoronary artery diseaseIn silico modelingMolecular dockingMolecular dynamics simulationsPeptide therapeutics

Identifiers

PMID38822212
PMCPMC11143054

What Socratic holds

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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 Socratic graph.