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

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.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 & 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.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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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).</p> <p>Results</p> <p>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 <it>MSH2 and MSH6 </it>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.</p> <p>Conclusion</p> <p>Based on this approach, we have shown that four nsSNPs, which were predicted to have functional consequences (<it>MSH2</it>-Y43C, <it>MSH6</it>-Y538S, <it>MSH6</it>-S580L, <it>and MSH6</it>-K854M), were already found to be associated with cancer risk. Our study demonstrates the presence of other deleterious mutations and also endorses with <it>in vivo </it>experimental studies.</p

Analyzing Effects of Naturally Occurring Missense Mutations

Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well

Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines

<p>Abstract</p> <p>Background</p> <p>Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs) that lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases. One of the key problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. As such, the development of computational tools that can identify such nsSNPs would enhance our understanding of genetic diseases and help predict the disease.</p> <p>Results</p> <p>We propose a method, named Parepro (Predicting the amino acid replacement probability), to identify nsSNPs having either deleterious or neutral effects on the resulting protein function. Two independent datasets, HumVar and NewHumVar, taken from the PhD-SNP server, were applied to train the model and test the robustness of Parepro. Using a 20-fold cross validation test on the HumVar dataset, Parepro achieved a Matthews correlation coefficient (MCC) of 50% and an overall accuracy (Q2) of 76%, both of which were higher than those predicted by the methods, such as PolyPhen, SIFT, and HydridMeth. Further analysis on an additional dataset (NewHumVar) using Parepro yielded similar results.</p> <p>Conclusion</p> <p>The performance of Parepro indicates that it is a powerful tool for predicting the effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.</p

Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene

Here we report an in-silico approach for identification, characterization and validation of deleterious non-synonymous SNPs (nsSNPs) in the interleukin-8 gene using three steps. In first step, sequence homology-based genetic analysis of a set of 50 coding SNPs associated with 41 rsIDs using SIFT (Sorting Intolerant from Tolerant) and PROVEAN (Protein Variation Effect Analyzer) identified 23 nsSNPs to be putatively damaging/deleterious in at least one of the two tools used. Subsequently, structure-homology based PolyPhen-2 (Polymorphism Phenotyping) analysis predicted 9 of 23 nsSNPs (K4T, E31A, E31K, S41Y, I55N, P59L, P59S, L70P and V88D) to be damaging. According to the conditional hypothesis for the study, only nsSNPs that score damaging/deleterious prediction in both sequence and structural homology-based approach will be considered as 'high-confidence' nsSNPs. In step 2, based on conservation of amino acid residues, stability analysis, structural superimposition, RSMD and docking analysis, the possible structural-functional relationship was ascertained for high-confidence nsSNPs. Finally, in a separate analysis (step 3), the IL-8 deregulation has also appeared to be an important prognostic marker for detection of patients with gastric and lung cancer. This study, for the first time, provided in-depth insights on the effects of amino acid substitutions on IL-8 protein structure, function and disease association

Computational Refinement of Functional Single Nucleotide Polymorphisms Associated with ATM Gene

gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown. gene. gene function can aid in better understanding of genetic differences in disease susceptibility

Using structural bioinformatics to investigate the impact of non synonymous SNPs and disease mutations: scope and limitations

BACKGROUND: Linking structural effects of mutations to functional outcomes is a major issue in structural bioinformatics, and many tools and studies have shown that specific structural properties such as stability and residue burial can be used to distinguish neutral variations and disease associated mutations. RESULTS: We have investigated 39 structural properties on a set of SNPs and disease mutations from the Uniprot Knowledge Base that could be mapped on high quality crystal structures and show that none of these properties can be used as a sole classification criterion to separate the two data sets. Furthermore, we have reviewed the annotation process from mutation to result and identified the liabilities in each step. CONCLUSION: Although excellent annotation results of various research groups underline the great potential of using structural bioinformatics to investigate the mechanisms underlying disease, the interpretation of such annotations cannot always be extrapolated to proteome wide variation studies. Difficulties for large-scale studies can be found both on the technical level, i.e. the scarcity of data and the incompleteness of the structural tool suites, and on the conceptual level, i.e. the correct interpretation of the results in a cellular context.status: publishe