DEEPON CHAKRABORTY, UNIVERSITY OF CALCUTTA
Interpretation of a large number of genetic variants identified from sequencing is one of the breakthroughs of clinical genetics. It is also termed as genetic screening. The main challenge is the identification of the effect of amino acid substituting missense variation on protein structure and function. Its several applications are coming out each day especially for the diagnosis of rare monogenic diseases and cancer.
It has several applications, among which identification of a rapidly growing number of genetic variations is noteworthy. These genetic variations are mostly missense variations which cause an amino acid substitution upon a single nucleotide change in the protein-coding region of the genome. These missense variations can be either benign or pathogenic and the most important thing is that both types coexist in almost every disease-associated gene. By knowing the consequence of amino acid substitution on the protein structure and function we can Discover how a missense variant is implicated in a disease. The ability to predict pathogenicity is improving day by day but the output scores of the predictors do not advance our knowledge about the molecular pathology of associated disorders. So, various in-silico methods are employed in pathogenicity prediction and it employs a variety of machine learning algorithms which are based on various pathogenic and population variant data which uses many features such as evolutionary pieces of information, gene-level properties and specific amino acid exchanges in protein sequences.
Biological insights into the effect of pathogenic missense variations can be gained by analysing the relationship between point mutations and protein structures. Several studies in this field focus light on the fact that the damaging consequences of missense variations are linked to the properties and localisation of the altered amino acid residues in the protein structure. Several resources have been developed to predict the impact of amino acid substitution on protein structure. Some of them are missense 3D, SuSPect, VarSite etc. They can predict a change in structure, free energy etc. due to mutation.
American College of Medical Genetics and Genomics (ACMG) propose some guidelines where they list the presence of an amino acid substitution in mutational hotspot as moderate evidence of pathogenicity. A mutational hotspot is a site that displays frequent occurrence of pathogenic mutations and depleted in benign variants. that hotspot can be located at a region of the protein known to be critical for its function or functional domain and also in less well-characterized regions too.
Recently some scientist of MIT, Harvard, Cambridge and some renowned worldwide institution had tried to bridge the gap between genetic variation data and Molecular phenotype. They are mainly focusing on the analysis of features of single amino acid in the context of the native 3D protein structure. We can conclude that the 3D sites those are more frequently mutated in pathogenic variants than benign variants are more likely to be important in protein fitness and it contributes more in the determination of pathogenicity. In near future, it is easier and in hand to determine variant pathogenicity based on this approach.
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