Novel Mutations within PRSS1 Gene that Could Potentially Cause Hereditary Pancreatitis: Using Bioinformatics Approach

Abstract

Hereditary pancreatitis (HP) is a rare heterogeneous disease with partial penetrance identified by frequent episodes of severe abdominal pain, often showing in young aged children. It is complicating by chronic pancreatitis, and high rate of pancreatic cancer (up to 40-50%). The aim of this work was to classify the most deleterious mutation in PRSS1 gene and to predict their influence on the functional and structural level by a variety of bioinformatics analysis tools. The raw data of PRSS1 gene were recovered from SNP database, and further used to examine a deleterious effect using SIFT, PolyPhen-2, PROVEAN, SNAP2, SNPs&GO, PHD-SNP, PANTHER and P-Mut. The functional analysis predicted that two SNPs “rs1366278558 and rs767036052” have a deleterious effect at functional level. Additionally, we submitted them to I-mutant 3.0, and MUPro respectively to investigate their effect on structural level; the two tools revealed that; two mutations have a dramatic decrease of the protein stability, thus suggesting that the M1R and L4P mutations of PRSS1 gene could destabilize the amino acid interactions causing functional abnormalities of PRSS1 protein. The 3D structure of PRSS1 was predicted by RaptorX and modeled using UCSF Chimera to compare the differences between the native and the mutant amino acids. From the comparative analysis at the functional and structural level, these two SNPs “M1R and L4P” have a deleterious effect and thus could be used as diagnostic markers to predict HP. These findings can be used as a platform to develop large-scale studies in the future.

Identification of High risk nsSNPs in Human TP53 Gene Associated with Li–Fraumeni Syndrome: An In Silico Analysis Approach

“…Structure-based approaches are restricted to a known 3D structure; on the other hand, sequence-based approaches can be employed in proteins with unknown 3D structures. A combination of multiple predictors revealed better predictions in many recent reports for identifying deleterious nsSNPs in many genes [46][47][48][49][50][51][52]. Typically, at least five of in silico tools should be considered increasing the consensus on the effect of SNPs [53]; nevertheless, 14 different computational algorithms were engaged to categorize the most pathogenic nsSNPs from PT53 gene.…”