Evidence map›Paper›PMID 41342182›Full record

ArticleJMIR bioinformatics and biotechnology2025

In Silico Analysis and Validation of A Disintegrin and Metalloprotease (ADAM) 17 Gene Missense Variants: Structural Bioinformatics Study.

Abdelilah Mechnine, Asmae Saih, Lahcen Wakrim, Ahmed Aarab

Abstract read
In one paragraph

Article in JMIR bioinformatics and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Abdelilah MechnineBiotechnology and Biomolecule Engineering Research Team, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Ancienne Route de l'Aéroport, Km 10, Ziaten. BP:416, Tangier, 90030, Morocco.ORCID http://orcid.org/0000-0001-7797-8988
Asmae SaihLaboratory of Biology and Health, URAC 34, Faculty of Sciences Ben M'Sik, University of Hassan II Casablanca, Casablanca, Morocco.ORCID http://orcid.org/0000-0002-5487-5684
Lahcen WakrimVirology Unit, Immunovirology Laboratory, Institut Pasteur du Maroc, Casablanca, Morocco.ORCID http://orcid.org/0000-0003-0491-7556
Ahmed AarabBiotechnology and Biomolecule Engineering Research Team, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Ancienne Route de l'Aéroport, Km 10, Ziaten. BP:416, Tangier, 90030, Morocco.ORCID http://orcid.org/0000-0001-6221-2381

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The protein A disintegrin and metalloprotease (ADAM) domain containing 17, also called tumor necrosis factor alpha-converting enzyme, is mainly responsible for cleaving a specific sequence Pro-Leu-Ala-Gln-Ala-/-Val-Arg-Ser-Ser-Ser in the membrane-bound precursor of tumor necrosis factor alpha. This cleavage process has significant implications for inflammatory and immune responses, and recent research indicates that genetic variants of ADAM17 may influence susceptibility to and severity of SARS-CoV-2 infection. Objective: The aim of the study is to identify the most deleterious missense variants of ADAM17 that impact protein stability, structure, and function and to assess specific variants potentially involved in SARS-CoV-2 infection. Methods: A bioinformatics approach was used on 12,042 single-nucleotide polymorphisms using tools including SIFT (Sorting Intolerant From Tolerant), PolyPhen2.0, PROVEAN (Protein Variation Effect Analyzer), PANTHER (Protein Analysis Through Evolutionary Relationships), SNP&GO (Single Nucleotide Polymorphisms and Gene Ontology), PhD-SNP (Predictor of Human Deleterious Single Nucleotide Polymorphisms), Mutation Assessor, SNAP2 (Screening for Non-Acceptable Polymorphisms 2), MUpro, I-Mutant, iStable, InterPro, Sparks-x, PROCHECK (Programs to Check the Stereochemical Quality of Protein Structures), PyMol, Project HOPE (Have (y)Our Protein Explained), ConSurf, and SWISS-MODEL. Missense variants of ADAM17 were collected from the Ensembl database for analysis. Results: In total, 7 nonsynonymous single-nucleotide polymorphisms (P556L, G550D, V483A, G479E, G349E, T339P, and D232E) were identified as high-risk pathogenic by all prediction tools, and these variants were found to potentially have deleterious effects on the stability, structure, and function of the ADAM17 protein, potentially destroying the entire cleavage process. Additionally, 4 missense variants (Q658H, D657G, D654N, and F652L) in positions related to SARS-CoV-2 infection exhibited high conservation scores and were predicted to be deleterious, suggesting that they play an important role in SARS-CoV-2 infection. Conclusions: Specific missense variants of ADAM17 are predicted to be highly pathogenic, potentially affecting protein stability and function and contributing to SARS-CoV-2 pathogenesis. These findings provide a basis for understanding their clinical relevance, aiding in early diagnosis, risk assessment, and therapeutic development.

Indexed as

bioinformaticsCOVID-19in silicomolecular modelingSARS-CoV-2

Identifiers

PMID41342182
PMCPMC12377791

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