Single nucleotide polymorphism analysis comparing different TTR (transthyretin) protein mutations using support vector machine and neural network

The purpose of this research is to conduct a single nucleotide polymorphism (SNP) analysis of the TTR protein using support vector machines and neural networks. The goal is to determine which mutations are more frequently found in neurodegenerative illnesses. Methodologies and Instruments for Research: Obtaining the protein sequence from Uniport was the first step in carrying out the snp analysis of the TTR protein. After that, we pasted the sequence into a number of different bioinformatics programmes. The results show that we are able to characterise the native state of the TTR protein by examining the free energy values of the structural assemblies and interfaces of both the revealed and hidden regions of the protein. When it comes to PQ set 1, the surface area that is visible to the naked eye is 18250, whereas the area that is hidden or buried is 9140. All things considered, the results of our statistical research lead us to the realisation that the three TTR protein mutations that occur the most frequently are V50G, D38E, and L32P. All things considered, our findings are related to the structural interaction of the TTR protein.