In Silico Characterization and Analysis of Clinically Significant Variants of Lipase-H (LIPH Gene) Protein Associated with Hypotrichosis

Author:

Khan Hamza Ali1,Asif Muhammad Umair2,Ijaz Muhammad Khurram3,Alharbi Metab4,Ali Yasir5ORCID,Ahmad Faisal5,Azhar Ramsha5,Ahmad Sajjad6ORCID,Irfan Muhammad7,Javed Maryana5,Naseer Noorulain5,Aziz Abdul1

Affiliation:

1. Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak 27200, Pakistan

2. Basic Health Unit, 196 RB, Faisalabad 38000, Pakistan

3. Rural Health Center, Faislaabad 38000, Pakistan

4. Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia

5. National Center for Bioinformatics, Quaid-i-Azam University, Islamabad 45320, Pakistan

6. Department of Health and Biological Sciences, Abasyn University, Peshawar 25000, Pakistan

7. Department of Oral Biology, College of Dentistry, University of Florida, Gainesville, FL 32611, USA

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.

Funder

Researchers Supporting Project

Publisher

MDPI AG

Subject

Drug Discovery,Pharmaceutical Science,Molecular Medicine

Reference42 articles.

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