Abstract
Hypotrichosis is an uncommon type of alopecia (hair loss) characterized by coarse scalp hair caused by the reduced or fully terminated activity of the Lipase-H (LIPH) enzyme. LIPH gene mutations contribute to the development of irregular or non-functional proteins. Because several cellular processes, including cell maturation and proliferation, are inhibited when this enzyme is inactive, the hair follicles become structurally unreliable, undeveloped, and immature. This results in brittle hair, as well as altered hair shaft development and structure. Because of these nsSNPs, the protein's structure and/or function may be altered. Given the difficulty in discovering functional SNPs in genes associated with disease, it is possible to assess potential functional SNPs before conducting broader population investigations. As a result, in our in silico analysis, we separated potentially hazardous nsSNPs of the LIPH gene from benign representatives using a variety of sequencing and architecture-based bioinformatics approaches. Using seven prediction algorithms, 9 out of a total of 215 nsSNPs were shown to be the most likely to cause harm. In order to distinguish between potentially harmful and benign nsSNPs of the LIPH gene, in our in silico investigation, we employed a range of sequence- and architecture-based bioinformatics techniques. Three nsSNPs (W108R, C246S, and H248N) were chosen as potentially harmful. The present findings will likely be helpful in future large population-based studies, as well as in drug discovery, particularly in the creation of personalized medicine, since this study provides an initial thorough investigation of the functional nsSNPs of LIPH.