作者
Panagiotis Katsonis, Amanda Koire, Stephen Joseph Wilson, Teng‐Kuei Hsu, Rhonald C Lua, Angela Dawn Wilkins, Olivier Lichtarge
发表日期
2014/12
来源
Protein Science
卷号
23
期号
12
页码范围
1650-1666
简介
Genome‐wide association studies (GWAS) and whole‐exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease‐causing mutations are exonic non‐synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these …
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P Katsonis, A Koire, SJ Wilson, TK Hsu, RC Lua… - Protein Science, 2014