Prediction of potential deleterious nonsynonymous single nucleotide polymorphisms of HIF1A gene: A computational approach. (October 2020) - Jamali, Zeinab; Taheri-Anganeh, Mortaza; Entezam, Mona
Graphical abstract: Highlights: HIF1A gene sequence changes can influence its expression and probably be associated with different kinds of diseases such as many types of cancers. There are many uncharacterized nonsynonymous SNPs in HIF1A gene that may have a potentially damaging effect on the protein function. Computational analysis and in silico tools are considered as a beneficial alternative to screen deleterious SNPs from enormous neutral ones. Abstract: Hypoxia-inducible factor-1α (HIF-1α) is the oxygen sensitive subunit of HIF1 transcription factor. Its variations is associated with several diseases including different type of cancer, cardiovascular diseases, and liver and kidney failure. Despite all the investigations carried out on the single nucleotide polymorphisms (SNPs) of HIF1A gene and diseases, there are many uncharacterized nonsynonymous SNPs of this gene, which might have damaging effect on the protein function. Therefore, it is worthwhile to analyze these potential damaging nsSNPs, using different bioinformatics tools before launching large population studies. The objective of the present study was to predict the possible deleterious nsSNPs of HIF1A gene and their effects on the function and structure of HIF-1alpha protein, using different bioinformatics tools. Various prediction servers were used including SIFT, PROVEAN, PolyPhen-2, PANTHER, phD-SNP, SNP-GO, I-Mutant 2.0, Fathmm, SNPeffect 4.0, Mutation taster, CADD and RAMPAGE in a stepwise approach. After analyzing all 454 missense variants of the HIF1A gene using the abovementioned tools, we reported 11 variants with a significant impact on the function or structure of HIF-1α protein. Furthermore, among these variants only S274 P was predicted as stability enhancing variant with effect on protein function by increasing its stability. Although there are many advantages for computational analysis, the results has to be confirmed by experimental investigations.