Prediction of functionally significant single nucleotide polymorphisms inPTENtumor suppressor gene: Anin silicoapproach

Abstract

The phosphatase and tensin homolog (PTEN) gene plays a crucial role in signal transduction by negatively regulating the PI3K signaling pathway. It is the most frequent mutated gene in many human-related cancers. Considering its critical role, a functional analysis of missense mutations of PTEN gene was undertaken in this study. Thirty five nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of the PTEN gene were selected for our in silico investigation, and five nsSNPs (G129E, C124R, D252G, H61D, and R130G) were found to be deleterious based on combinatorial predictions of different computational tools. Moreover, molecular dynamics (MD) simulation was performed to investigate the conformational variation between native and all the five mutant PTEN proteins having predicted deleterious nsSNPs. The results of MD simulation of all mutant models illustrated variation in structural attributes such as root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and total energy; which depicts the structural stability of PTEN protein. Furthermore, mutant PTEN protein structures also showed a significant variation in the solvent accessible surface area and hydrogen bond frequencies from the native PTEN structure. In conclusion, results of this study have established the deleterious effect of the all the five predicted nsSNPs on the PTEN protein structure. Thus, results of the current study can pave a new platform to sort out nsSNPs that can be undertaken for the confirmation of their phenotype and their correlation with diseased status in case of control studies.

Integrative Molecular Analysis of Patients With Advanced and Metastatic Cancer

“…A deleterious effect has been predicted for this variant. 33 In a previously published analysis of 13 uterine carcinosarcomas analyzed by targeted sequencing, eight cases demonstrated 100% identical mutations in both the carcinoma and sarcoma part. 34 In contrast, we observed an early divergence with only the one PTEN mutation of 54 non-synonymous shared mutations.…”

Prediction of a highly deleterious mutation E17K in AKT-1 gene: An in silico approach

“…These limitations in predicting the genotype-phenotype correlation with disease-association makes it costly and highly challenging [29] . To overcome above restrictions, recently bioinformatics tools have emerged as a valuable option for mutation analysis [18] , [20] , [25] , [26] , [27] , [28] , [45] , [47] . Aberrant activation of AKT-1 has been implicated in various human cancers and is also associated with drug resistance [2] , [55] , [63] .…”

“…For this purpose, various bioinformatics tools, designed on the basis of recent findings in protein structure research and evolutionary biology, may prove useful in predicting the functional importance of nsSNPs [6] , [13] , [24] , [37] (Conde et al, 2006). Over past few years, several in silico studies have attempted to screen missense/ nsSNPs within the protein coding region of a gene and have shown these bioinformatics tools to be efficient and effective platform to prioritize SNPs for their association in disease pathology [18] , [20] , [25] , [26] , [27] , [28] , [31] .Over past few years, several in silico studies have attempted to screen missense/non-synonymous single nucleotide polymorphisms (nsSNPs) within the protein coding region of a gene and have shown these bioinformatics tools to be efficient and effective platform to prioritize SNPs for their association in disease pathology [18] , [20] , [25] , [26] , [27] , [28] , [45] . These nsSNPs within the coding region alter the encoded amino acid and further resulting in altered physiochemical properties of native protein [43] , [44] , [61] , [67] .…”

Computational Fine-Tuning of Functional Single Nucleotide Polymorphisms Associated with ACP5 Gene to Characterize Missense Mutations

“…All the nsSNPs of the ACP5 gene were uncovered from various computational tools like SIFT Blink, Polyphen-1, PolyPhen-2, MAPP, SNAP, Predict SNP, and PhD-SNP to find its deleterious effect [39,40]. The selected tools follow different algorithms like sequence-homology and evolution-based methods with diverse criteria.…”