Analysis and Interpretation of the Impact of Missense Variants in Cancer.
free-energy change
protein function
protein stability
protein structure
putative cancer driving variant
single amino acid variant
Journal
International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791
Informations de publication
Date de publication:
21 May 2021
21 May 2021
Historique:
received:
30
03
2021
revised:
03
05
2021
accepted:
17
05
2021
entrez:
2
6
2021
pubmed:
3
6
2021
medline:
23
6
2021
Statut:
epublish
Résumé
Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.
Identifiants
pubmed: 34063805
pii: ijms22115416
doi: 10.3390/ijms22115416
pmc: PMC8196604
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministero Istruzione, Università e Ricerca, PRIN project, "Integrative tools for defining the molecular basis of the diseases: Computational and Experimental methods for Protein Variant Interpretation"
ID : 201744NR8S
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