Identification of Novel Key Biomarkers in Simpson-Golabi-Behmel Syndrome (SGBS): Evidence from Bioinformatics Analysis

Abstract

The Simpson-Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is an uncommon genetic X-linked disorder highlighted by macrosomia, renal defects, cardiac weaknesses and skeletal abnormalities. The purpose of the work was to classify the functional nsSNPs of GPC3 to serve as genetic biomarkers for overgrowth syndrome. The raw data of GPC3 gene were retrieved from dbSNP database and used to examine the most damaging effect using eight functional analysis tools, while we used I-mutant and MUPro to examine the effect of SNPs on GPC3 protein structure; The 3D structure of GPC3 protein is not found in the PDB, so RaptorX was used to create a 3D structural prototype to visualize the amino acids alterations by UCSF Chimera; For biophysical validation we used project HOPE; Lastly we run conservational analysis by BioEdit and Consurf web server respectively. Our results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are that are more likely to be responsible for disturbance in the function and structure of GPC3. This work provides new insight into the molecular basis of overgrowth Syndrome by evidence from bioinformatics analysis. Three novel missense mutations (rs757475450, rs1295603457 and rs1460413167) are more likely to be responsible for disturbance in the function and structure of GPC3; therefore, they may be assisting as genetic biomarkers for overgrowth syndrome. As well as these SNPs can be used for the larger population-based studies of overgrowth syndrome.

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.…”