Sequence similarity-based candidate gene prioritization for Type 1 diabetes mellitus using moment of inertia tensor.

Abstract

In this paper, the study focuses on Type 1 Diabetes Mellitus (T1D), a chronic condition that affects the insulin-producing cells of the pancreas, requiring individuals to depend on external insulin for survival. We introduce a novel method for analyzing protein sequences by treating them as rigid bodies with mass and moment of inertia to assess sequence similarity. This method transforms the protein sequences into vectors using the moment of inertia tensor, with similarity calculated using Euclidean distance. Using this technique, we identified 24 genes linked to T1D, showing significant similarities to known T1D-related genes and highlighting their potential importance in the disease. Further, we conduct functional enrichment analysis for better understanding, which is very helpful for investigating their roles in various biological processes and molecular functions. The Gene Ontology (GO)analysis is crucial for prioritizing the identified genes and providing insights into their contributions to T1D pathophysiology. To combine the concepts from physics with computational biology, our research not only increases the understanding of T1D disease but also introduces an innovative approach for gene discovery and functional analysis in autoimmune diseases.