Evidence map›Paper›PMID 33173846›Full record

ArticleAdvanced theory and simulations2020

Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2.

Parth Chaturvedi, Yanxiao Han, Petr Král, Lela Vuković

Open access · bronzeAbstract read
In one paragraph

Article in Advanced theory and simulations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.4field-weighted citation impact, top 24% 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

14 citing papers in PubMed, 19 citations in OpenAlex.

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  11. Retrained Generic Antibodies Can Recognize SARS-CoV-2.The journal of physical chemistry letters · 2021
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Parth ChaturvediDepartment of Chemistry and Biochemistry University of Texas at El Paso El Paso TX 79968 USA.
Yanxiao HanDepartment of Chemistry University of Illinois at Chicago Chicago IL 60607 USA.
Petr KrálDepartment of Chemistry University of Illinois at Chicago Chicago IL 60607 USA.
Lela VukovićDepartment of Chemistry and Biochemistry University of Texas at El Paso El Paso TX 79968 USA.
The University of Texas at El Paso · USUniversity of Chicago · USUniversity of Illinois Chicago · US

Funding

Strengthening Stakeholder Engagement in Human Research Protections.UL1TR002003 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KARNIK, NIRANJAN SUBHASH, MERMELSTEIN, ROBIN J. · 2016 to 2024
$33.7M
NCATS NIH HHS UL1 TR002003
6 · The paper itself

Abstract

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the past decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of adaptive (smart) therapeutics. Here, a computational strategy is developed to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2). Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), the templates are gradually modified by random mutations, while retaining those mutations that maximize their RBD-binding free energies. In this adaptive evolution, atomistic molecular dynamics simulations of the template-RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions. The computational search will provide libraries of optimized therapeutics capable of reducing the SARS-CoV-2 infection on a global scale.

Indexed as

adaptive evolutionmolecular dynamicspeptide librariestherapeutic peptides

Identifiers

PMID33173846
PMCPMC7646009
OpenAlexW3042989289

What OpenQuestion holds

Textmetadata
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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.