Evidence map›Paper›PMID 38999985›Full record

ArticleInternational journal of molecular sciences2024

TriplEP-CPP: Algorithm for Predicting the Properties of Peptide Sequences.

Maria Serebrennikova, Ekaterina Grafskaia, Dmitriy Maltsev, Kseniya Ivanova, Pavel Bashkirov, Fedor Kornilov, Pavel Volynsky, Roman Efremov, Eduard Bocharov, Vassili Lazarev

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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. Article
  2. Article
  3. Review
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

10 authors.

Maria SerebrennikovaLaboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.ORCID 0009-0002-1663-9311
Ekaterina GrafskaiaLaboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.ORCID 0000-0001-8957-6142
Dmitriy MaltsevFederal Center of Brain Research and Neurotechnologies, Federal Medical Biological Agency, Moscow 117997, Russia.ORCID 0000-0001-9478-9222
Kseniya IvanovaLaboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.
Pavel BashkirovMoscow Center for Advanced Studies 20, Kulakova Str., Moscow 123592, Russia.
Fedor KornilovMoscow Center for Advanced Studies 20, Kulakova Str., Moscow 123592, Russia.
Pavel VolynskyShemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997, Russia.ORCID 0000-0002-0471-4188
Roman EfremovMoscow Center for Advanced Studies 20, Kulakova Str., Moscow 123592, Russia.ORCID 0000-0002-5474-4721
Eduard BocharovMoscow Center for Advanced Studies 20, Kulakova Str., Moscow 123592, Russia.ORCID 0000-0002-3635-1609
Vassili LazarevLaboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.

Funding

Russian Science Foundation 23-15-00084
6 · The paper itself

Abstract

Advancements in medicine and pharmacology have led to the development of systems that deliver biologically active molecules inside cells, increasing drug concentrations at target sites. This improves effectiveness and duration of action and reduces side effects on healthy tissues. Cell-penetrating peptides (CPPs) show promise in this area. While traditional medicinal chemistry methods have been used to develop CPPs, machine learning techniques can speed up and reduce costs in the search for new peptides. A predictive algorithm based on machine learning models was created to identify novel CPP sequences using molecular descriptors using a combination of algorithms like k-nearest neighbors, gradient boosting, and random forest. Some potential CPPs were found and tested for cytotoxicity and penetrating ability. A new low-toxicity CPP was discovered from the

Indexed as

AlgorithmsCell-Penetrating PeptidesMachine LearningAmino Acid SequenceAnimalsHumansProteomeWasp VenomsCell-Penetrating PeptidesProteomeWasp Venomscell penetrating peptides (CPP)functional activity predictionintracellular deliverymachine learningprotein-lipid interactionstructural-dynamic properties

Identifiers

PMID38999985
PMCPMC11241344

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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.