Exploring Missense Variants in the Human PNPLA3 Protein: An In Silico Analysis

Background: Missense variants in humans are genetic variations that lead to amino acid substitutions in protein-coding regions, which can modify protein structure, function, and phenotype. The Patatin-like phospholipase domain-containing protein 3 (PNPLA3) gene has received considerable attention because of its link to several metabolic disorders, such as non-alcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD). Methods: The PNPLA3 gene was extracted from the National Center for Biotechnology Information (NCBI) databases, and non-synonymous single-nucleotide polymorphisms (nsSNPs) were analyzed using computational software (SIFT, Polyphen-2, SNPs&GO, PhD-SNP, I-Mutant 3.0, MUpro, and Project Hope). Results: A total of 108 nsSNPs were selected from the coding region for Homo sapiens. The SIFT server was used to distinguish between tolerant and intolerant nsSNPs. A total of 21 deleterious nsSNPs were identified, with a tolerance index ranging from 0.000 to 0.03. Polyphen-2 software predicted 19 damaging polymorphisms, with a score range of 0.970 to 1.000. Using Mupro and I-Mutant 3.0, 18 nsSNPs were identified as decreasing the stability of the mutated protein, while one nsSNP was found to increase the stability of the mutated protein. According to the SNPs&GO software, six nsSNPs were predicted to be disease-related, whereas the PhD-SNP software predicted 11 nsSNPs as disease-related. Six nsSNPs were selected for submission to the Project Hope software based on their prediction by SNPs&GO as the most damaging, with a score range of 0.998 to 1.000. Conclusion: In this study, six deleterious mutations affecting the protein of the PNPLA3 gene were detected, each with a high score, as indicated by a PSIC SD range of 0.998– 1.000, implying pathological polymorphisms that alter the protein’s structure, stability, and function. These mutations were regarded as significant nsSNPs for the PNPLA3 gene in relation to NAFLD. Computational tools have inherent limitations, including biases from training data and challenges in modeling complex biological systems, making experimental validation crucial for their implications and practical applications.