Scinovex
article Open Access

Computational Prediction of deleterious mutation in ACTN3 gene in cattle

International Journal of Advanced Biochemistry Research · 2024 · Vol. 8(1S) · pp. 832–834
Ayushi SinghGaurav PatelAshok ChaudharyPriyanshi Yadav

Abstract

Actinin Alpha 3 gene (ACTN3) has profound role in muscle development and feed efficiency. Therefore, it forms fundamentally important gene in whole mammalian class including beef cattle. Non-synonymous mutations in ACTN3 gene in human has previously been known to be usually non-tolerant in nature and causes decrease in muscle mass. To extend the knowledge for bovine ACTN3 gene led to the formulation of in-silico prediction of mutations in the present study. In this study, about 708 mutations were found to be non-synonymous initially and were filtered to eight SNPs using SIFT score. Out of which, one nsSNP (rs723349530, Y392D) was found to have significant deleterious effect as per different scores and algorithms. Protein interaction network revealed connection with proteins having functions on muscle twitching and strength. This is one of its first attempt to characterise non-synonymous mutation of ACTN3 gene in bovine genome reflecting its importance in muscle growth and hereby, feed efficiency in beef cattle.

Genetics and Physical PerformanceGeneticsMutationGeneBiologyComputational biology
Citations
0
FWCI
0.00
field-weighted impact
References
15
Percentile
2%
vs. same field & year
References
A method and server for predicting damaging missense mutations
Nature Methods · 2010 · 13,461 citations
Prediction of protein stability changes for single‐site mutations using support vector machines
Proteins Structure Function and Bioinformatics · 2005 · 1,175 citations
SIFT: predicting amino acid changes that affect protein function
Nucleic Acids Research · 2003 · 6,783 citations
Proteins: Structure, Function, and Bioinformatics
Proteins Structure Function and Bioinformatics · 2006 · 966 citations
Citation Network

How this paper connects to the literature. Drag to explore, click any node to open that paper.