Evidence map›Paper›PMID 32916095›Full record

ArticleCell systems2020

Estimating the Binding of Sars-CoV-2 Peptides to HLA Class I in Human Subpopulations Using Artificial Neural Networks.

Caterina A M La Porta, Stefano Zapperi

Open access · bronzeAbstract read
In one paragraph

Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

24 citing papers in PubMed, 38 citations in OpenAlex.

  1. Article
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  10. Study of HLA-A, -B, -C, -DRB1 and -DQB1 polymorphisms in COVID-19 patients.Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi · 2022
    Article
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  14. Human genetic basis of coronavirus disease 2019.Signal transduction and targeted therapy · 2021
    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

2 authors at 1 institution in 2 countries.

Caterina A M La PortaCenter for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, via Celoria 26, Milano 20133, Italy; CNR - Consiglio Nazionale delle Ricerche, Istituto di Biofisica, via Celoria 26, Milano 20133, Italy. Electronic address: caterina.laporta@unimi.it.
Stefano ZapperiCenter for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano 20133, Italy; CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia, via R. Cozzi 53, Milano 20125, Italy.
University of Milan · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemiological studies show that SARS-CoV-2 infection leads to severe symptoms only in a fraction of patients, but the determinants of individual susceptibility to the virus are still unknown. The major histocompatibility complex (MHC) class I exposes viral peptides in all nucleated cells and is involved in the susceptibility to many human diseases. Here, we use artificial neural networks to analyze the binding of SARS-CoV-2 peptides with polymorphic human MHC class I molecules. In this way, we identify two sets of haplotypes present in specific human populations: the first displays weak binding with SARS-CoV-2 peptides, while the second shows strong binding and T cell propensity. Our work offers a useful support to identify the individual susceptibility to COVID-19 and illustrates a mechanism underlying variations in the immune response to SARS-CoV-2. A record of this paper's transparent peer review process is included in the Supplemental Information.

Indexed as

Neural Networks, ComputerPolymorphism, GeneticBetacoronavirusHaplotypesHistocompatibility Antigens Class IHumansPeptidesProtein BindingSARS-CoV-2Viral ProteinsHistocompatibility Antigens Class IPeptidesViral Proteinsartificial neural networkshaplotypesHLApeptidesSARS-CoV-2T cell propensity

Identifiers

PMID32916095
PMCPMC7488596
OpenAlexW3085247423

What OpenQuestion holds

Textmetadata
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.