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

Doss CG, Sethumadhavan R - J. Biomed. Sci. (2009)

Bottom Line: 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.The PupaSuite predicted the phenotypic effect of SNPs on the structure and function of the affected protein.Our study demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Division, School of Biotechnology, Chemical and Biomedical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India. georgecp77@yahoo.co.in

ABSTRACT

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.

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Proposed methodology for the functional nonsynonymous coding SNPs analysis.
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Figure 1: Proposed methodology for the functional nonsynonymous coding SNPs analysis.

Mentions: Over the past few years, quite a lot of studies have attempted to predict the functional consequences of an nsSNPs whether it is disease-related or neutral, based on sequence information and structural attributes [15] using computational algorithms such as SIFT and PolyPhen algorithms to screen for deleterious nsSNPs [16,17]. The structure of a protein can change in various ways due to the biochemical differences of the amino acid variant (acidic, basic, or hydrophobic) and by the location of the variant in the protein sequence (by affecting tertiary or quaternary structure or the active site where substrate binds) which can have a deleterious effect on the structure and/or function of the proteins [18]. Therefore, it is important to determine whether an nsSNP that affects the amino acid sequence of a gene product can alter protein function and contribute to disease will be a challenge in the coming years [19]. Several groups have tried to evaluate the deleterious nsSNPs based on 3-dimensional (3D) structure information of proteins by in-silico analysis. They indicated that the residue solvent accessibility, which could identify the buried residues, was confidently proposed as predictors of deleterious substitutions [20,21]. Deleterious nsSNPs analyses for the HNPCC genes have not been estimated computationally until now, although they have been the focus for experimental researchers. Therefore, in this work, the computational algorithms namely SIFT, PolyPhen, PupaSuite, ASA View and DSSP were used to identify the deleterious nsSNPs that are likely to affect the function and structure of the protein. Based on PolyPhen, we identified the possible mutation, proposed a model structure for the mutant proteins and compared this with the native protein in the 3-D modeled structure of the MSH2 and MSH6 gene. We further analyzed native and mutant modeled proteins for solvent accessibility and secondary structure analysis. Secondary structures and solvent accessibilities of amino acid residues give a useful insight into the structure and function of a protein [22-25]. We have described our approach using computational tools to provide related information of SNPs and a guide to experimental biologists (Figure 1). Our computational study also demonstrates the presence of other deleterious mutations in other HNPCC genes in which there is no availability of three- dimensional structure that may affect the expression and function of proteins with possible roles in colon cancer.


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

Doss CG, Sethumadhavan R - J. Biomed. Sci. (2009)

Proposed methodology for the functional nonsynonymous coding SNPs analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2682794&req=5

Figure 1: Proposed methodology for the functional nonsynonymous coding SNPs analysis.
Mentions: Over the past few years, quite a lot of studies have attempted to predict the functional consequences of an nsSNPs whether it is disease-related or neutral, based on sequence information and structural attributes [15] using computational algorithms such as SIFT and PolyPhen algorithms to screen for deleterious nsSNPs [16,17]. The structure of a protein can change in various ways due to the biochemical differences of the amino acid variant (acidic, basic, or hydrophobic) and by the location of the variant in the protein sequence (by affecting tertiary or quaternary structure or the active site where substrate binds) which can have a deleterious effect on the structure and/or function of the proteins [18]. Therefore, it is important to determine whether an nsSNP that affects the amino acid sequence of a gene product can alter protein function and contribute to disease will be a challenge in the coming years [19]. Several groups have tried to evaluate the deleterious nsSNPs based on 3-dimensional (3D) structure information of proteins by in-silico analysis. They indicated that the residue solvent accessibility, which could identify the buried residues, was confidently proposed as predictors of deleterious substitutions [20,21]. Deleterious nsSNPs analyses for the HNPCC genes have not been estimated computationally until now, although they have been the focus for experimental researchers. Therefore, in this work, the computational algorithms namely SIFT, PolyPhen, PupaSuite, ASA View and DSSP were used to identify the deleterious nsSNPs that are likely to affect the function and structure of the protein. Based on PolyPhen, we identified the possible mutation, proposed a model structure for the mutant proteins and compared this with the native protein in the 3-D modeled structure of the MSH2 and MSH6 gene. We further analyzed native and mutant modeled proteins for solvent accessibility and secondary structure analysis. Secondary structures and solvent accessibilities of amino acid residues give a useful insight into the structure and function of a protein [22-25]. We have described our approach using computational tools to provide related information of SNPs and a guide to experimental biologists (Figure 1). Our computational study also demonstrates the presence of other deleterious mutations in other HNPCC genes in which there is no availability of three- dimensional structure that may affect the expression and function of proteins with possible roles in colon cancer.

Bottom Line: 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.The PupaSuite predicted the phenotypic effect of SNPs on the structure and function of the affected protein.Our study demonstrates the presence of other deleterious mutations and also endorses with in vivo experimental studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Division, School of Biotechnology, Chemical and Biomedical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India. georgecp77@yahoo.co.in

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus