Functional alteration in SMAD proteins leads to dis-regulation of its mechanism results in possibilities of high risk diseases like fibrosis, cancer, juvenile polyposis etc. Studying single nucleotide polymorphism (SNP) in SMAD genes helps understand the malfunction of these proteins. In this study, we focused on deleterious effects of nsSNPs in both structural and functional level using publically available bioinformatics tools. We have mainly focused on identifying deleterious nsSNPs in both structural and functional level in SMAD genes by using SIFT, PolyPhen, SNPs&amp;GO, I-Mutant 3.0, MUpro and PANTHER. Structure analysis was carried out with the major mutation that occurred in the native protein coded by SMAD genes and its amino acid positions (R358W, K306S, R310G, S433R and R361C). SRide was used to check the stability of the native and mutant modelled proteins. In addition, we used MAPPER to identify SNPs present in transcription factor binding sites. These findings demonstrate that the in silico approaches can be used efficiently to identify potential candidate SNPs in large scale analysis. © 2012 International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg.

George Priya Doss C

Department of Integrative Biology

School of Bio Sciences and Technology

Vellore Campus

Nagasundaram N

Tanwar H.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Interdisciplinary Sciences: Computational Life Sciences

Mutations in the fibrinogen gamma chain (FGG) gene have been associated with various disorders, such as dysfibrinogenemia, thrombophilia, and hypofibrinogenemia. A literature survey showed that a residue exchange in fibrinogen Milano I from γ Asp to Val at position 330 impairs fibrin polymerization. The D356V (D330V) mutation located in the C-terminus was predicted to be highly deleterious and to affect the function of the protein. The pathogenicity of the altered gene and changes in protein functions were predicted using in silico methods, such as SIFT, PolyPhen 2, I-Mutant 3.0, Align GV-GD, PhD-SNP, and SNPs&amp;GO. The secondary structure of the mutant protein was unwound by the end of the 50-ns simulation period, and a structural change in the helix-turn transition of the alpha-helical (352-356) region residues was observed. Moreover, a change in the length of the helical region was visualized in the mutant trajectory file, indicating the local transient unfolding of the protein. The obtained computational results suggest that the substitution of the neutral amino acid valine for the acidic amino acid aspartic acid at position 356 results in an unwound conformation within 50 ns, which might contribute to defective polymerization. Our analysis also provides insights into the effect of the conformational change in the D356V (D330V) mutant on protein structure and function.

Journal of Biomolecular Structure and Dynamics

Molecular dynamics-based analyses of the structural instability and secondary structure of the fibrinogen gamma chain protein with the D356V mutation

Background: A major area of effort in current genomics is to distinguish mutations that are functionally neutral from those that contribute to disease. Single Nucleotide Polymorphisms (SNPs) are amino acid substitutions that currently account for approximately half of the known gene lesions responsible for human inherited diseases. As a result, the prediction of non-synonymous SNPs (nsSNPs) that affect protein functions and relate to disease is an important task. Principal Findings: In this study, we performed a comprehensive analysis of deleterious SNPs at both functional and structural level in the respective genes associated with red blood cell metabolism disorders using bioinformatics tools. We analyzed the variants in Glucose-6-phosphate dehydrogenase (G6PD) and isoforms of Pyruvate Kinase (PKLR &amp; PKM2) genes responsible for major red blood cell disorders. Deleterious nsSNPs were categorized based on empirical rule and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for evaluation of protein structure stability. Significance: We argue here that bioinformatics tools can play an important role in addressing the complexity of the underlying genetic basis of Red Blood Cell disorders. Based on our investigation, we report here the potential candidate SNPs, for future studies in human Red Blood Cell disorders. Current study also demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies. Our approach will present the application of computational tools in understanding functional variation from the perspective of structure, expression, evolution and phenotype. © 2011 C, B.

Fulltext

PLoS ONE

Path to Facilitate the Prediction of Functional Amino Acid Substitutions in Red Blood Cell Disorders – A Computational Approach

Single nucleotide polymorphisms (SNPs) are being intensively studied to understand the biological basis of complex traits and diseases. Deleterious mutations of the human beta-globin gene (HBB) are responsible for beta-thalassaemia and other haemoglobinopathies, which are the most common genetic diseases of blood. Single amino acid substitutions in the globin chain are the commonest forms of haemoglobinopathy. Although many haemoglobinopathies present similar structural abnormal points, their functions sometimes are different. Here, using computational methods, we analysed the genetic variations that can alter the expression and function of the HBB gene. We applied an evolutionary perspective to screen the SNPs using a sequence homology-based SIFT tool, which suggested that 210 (90%) non-synonymous (ns)SNPs were found to be deleterious. The structure-based approach PolyPhen server suggested that 134 (57%) nsSNPS may disrupt protein function and structure. The PupaSuite tool predicted the phenotypic effect of SNPs on the structure and function of the affected protein. Structure analysis was carried out with the major mutation that occurred in the native protein coded by the HBB gene in HbC, HbD, HbE and HbS. The amino acid residues in the native and mutant modelled protein were further analysed for solvent accessibility, and secondary structure to check the stability of the proteins. The functional analysis presented here may be a good model for further research.

New Biotechnology

Impact of single nucleotide polymorphisms in HBB gene causing haemoglobinopathies: in silico analysis

Background. A central focus of cancer genetics is the study of mutations that are causally implicated in tumorigenesis. The identification of such causal mutations not only provides insight into cancer biology but also presents anticancer therapeutic targets and diagnostic markers. Missense mutations are nucleotide substitutions that change an amino acid in a protein, the deleterious effects of these mutations are commonly attributed to their impact on primary amino acid sequence and protein structure. Methods. The method to identify functional SNPs from a pool, containing both functional and neutral SNPs is challenging by experimental protocols. To explore possible relationships between genetic mutation and phenotypic variation, we employed different bioinformatics algorithms like Sorting Intolerant from Tolerant (SIFT), Polymorphism Phenotyping (PolyPhen), and PupaSuite to predict the impact of these amino acid substitutions on protein activity of mismatch repair (MMR) genes causing hereditary nonpolyposis colorectal cancer (HNPCC). Results. SIFT classified 22 of 125 variants (18%) as 'Intolerant." PolyPhen classified 40 of 125 amino acid substitutions (32%) as "Probably or possibly damaging". The PupaSuite predicted the phenotypic effect of SNPs on the structure and function of the affected protein. Based on the PolyPhen scores and availability of three-dimensional structures, structure analysis was carried out with the major mutations that occurred in the native protein coded by MSH2 and MSH6 genes. The amino acid residues in the native and mutant model protein were further analyzed for solvent accessibility and secondary structure to check the stability of the proteins. Conclusion. Based on this approach, we have shown that four nsSNPs, which were predicted to have functional consequences (MSH2-Y43C, MSH6-Y538S, MSH6-S580L, and MSH6-K854M), were already found to be associated with cancer risk. Our study demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies.

Journal of Biomedical Science

Investigation on the role of nsSNPs in HNPCC genes – a bioinformatics approach

Single Nucleotide Polymorphisms (SNPs) are being intensively studied to understand the biological basis of complex traits and diseases. The Genetics of human phenotype variation could be understood by knowing the functions of SNPs. In this study using computational methods, we analyzed the genetic variations that can alter the expression and function of the CFTR gene responsible candidate for causing cystic fibrosis. We applied an evolutionary perspective to screen the SNPs using a sequence homology-based SIFT tool, which suggested that 17 nsSNPs (44%) were found to be deleterious. The structure-based approach PolyPhen server suggested that 26 nsSNPS (66%) may disrupt protein function and structure. The PupaSuite tool predicted the phenotypic effect of SNPs on the structure and function of the affected protein. Structure analysis was carried out with the major mutation that occurred in the native protein coded by CFTR gene, and which is at amino acid position F508C for nsSNP with id (rs1800093). The amino acid residues in the native and mutant modeled protein were further analyzed for solvent accessibility, secondary structure and stabilizing residues to check the stability of the proteins. The SNPs were further subjected to iHAP analysis to identify htSNPs, and we report potential candidates for future studies on CFTR mutations. © 2008 Springer Science+Business Media B.V.

Genomic Medicine

A novel computational and structural analysis of nsSNPs in CFTR gene

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans. Understanding the functions of SNPs can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. The method to identify functional SNPs from a pool, containing both functional and neutral SNPs is challenging by experimental protocols. To explore possible relationships between genetic mutation and phenotypic variation, different computational algorithm tools like Sorting Intolerant from Tolerant, Polymorphism Phenotyping, UTRscan, FASTSNP, and PupaSuite were used for prioritization of high-risk SNPs in coding region (exonic nonsynonymous SNPs) and noncoding regions (intronic and exonic 5′ and 3′-untranslated region (UTR) SNPs). In this work, we have analyzed the SNPs that can alter the expression and function of transcriptional factor TP53 as a pipeline and for providing a guide to experimental work. We identified the possible mutations and proposed modeled structure for the mutant proteins and compared them with the native protein. These nsSNPs play a critical role in cancer association studies aiming to explain the disparity in cancer treatment responses as well as to improve the effectiveness of the cancer treatments. Our results endorse the study with in vivo experimental protocols. © Springer-Verlag 2008.

Journal	Data powered by TypesetInterdisciplinary Sciences: Computational Life Sciences
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1913-2751
Open Access	No