Literature DB >> 28444810

Large differences in proportions of harmful and benign amino acid substitutions between proteins and diseases.

Gerard C P Schaafsma1, Mauno Vihinen.   

Abstract

Genes and proteins are known to have differences in their sensitivity to alterations. Despite numerous sequencing studies, proportions of harmful and harmless substitutions are not known for proteins and groups of proteins. To address this question, we predicted the outcome for all possible single amino acid substitutions (AASs) in nine representative protein groups by using the PON-P2 method. The effects on 996 proteins were studied and vast differences were noticed. Proteins in the cancer group harbor the largest proportion of harmful variants (42.1%), whereas the non-disease group of proteins not known to have a disease association and not involved in the housekeeping functions had the lowest number of harmful variants (4.2%). Differences in the proportions of the harmful and benign variants are wide within each group, but they still show clear differences between the groups. Frequently appearing protein domains show a wide spectrum of variant frequencies, whereas no major protein structural class-specific differences were noticed. AAS types in the original and variant residues showed distinctive patterns, which are shared by all the protein groups. The observations are relevant for understanding genetic bases of diseases, variation interpretation, and for the development of methods for that purpose.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  disease groups; pathogenicity; proteins; sensitivity; variation

Mesh:

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Year:  2017        PMID: 28444810     DOI: 10.1002/humu.23236

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  8 in total

Review 1.  Biophysical and Mechanistic Models for Disease-Causing Protein Variants.

Authors:  Amelie Stein; Douglas M Fowler; Rasmus Hartmann-Petersen; Kresten Lindorff-Larsen
Journal:  Trends Biochem Sci       Date:  2019-01-31       Impact factor: 13.807

2.  Large Scale Identification of Variant Proteins in Glioma Stem Cells.

Authors:  Ekaterina Mostovenko; Ákos Végvári; Melinda Rezeli; Cheryl F Lichti; David Fenyö; Qianghu Wang; Frederick F Lang; Erik P Sulman; K Barbara Sahlin; György Marko-Varga; Carol L Nilsson
Journal:  ACS Chem Neurosci       Date:  2017-12-21       Impact factor: 4.418

3.  Finding driver mutations in cancer: Elucidating the role of background mutational processes.

Authors:  Anna-Leigh Brown; Minghui Li; Alexander Goncearenco; Anna R Panchenko
Journal:  PLoS Comput Biol       Date:  2019-04-29       Impact factor: 4.475

4.  New insights into the pathogenicity of non-synonymous variants through multi-level analysis.

Authors:  Hong Sun; Guangjun Yu
Journal:  Sci Rep       Date:  2019-02-07       Impact factor: 4.379

5.  An analysis of mutational signatures of synonymous mutations across 15 cancer types.

Authors:  Yannan Bin; Xiaojuan Wang; Le Zhao; Pengbo Wen; Junfeng Xia
Journal:  BMC Med Genet       Date:  2019-12-09       Impact factor: 2.103

6.  ProTstab - predictor for cellular protein stability.

Authors:  Yang Yang; Xuesong Ding; Guanchen Zhu; Abhishek Niroula; Qiang Lv; Mauno Vihinen
Journal:  BMC Genomics       Date:  2019-11-04       Impact factor: 3.969

7.  Problems in variation interpretation guidelines and in their implementation in computational tools.

Authors:  Mauno Vihinen
Journal:  Mol Genet Genomic Med       Date:  2020-03-11       Impact factor: 2.183

Review 8.  Individual Genetic Heterogeneity.

Authors:  Mauno Vihinen
Journal:  Genes (Basel)       Date:  2022-09-10       Impact factor: 4.141

  8 in total

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