Prediction of the deleterious nsSNPs in ABCB transporters

The non-synonymous SNPs (nsSNPs) in coding regions, neutral or deleterious, could lead to the alteration of the function or structure of proteins. We have developed the computational models to analyze the deleterious nsSNPs in the transporters and predict ones in ABCB (ATP-binding cassette B) transporters of interest. The RPLS (ridge partial least square) and LDA (linear discriminant analysis) methods were applied to the problem, by training on a selection of datasets from a specified source, i.e., human transporters. The best combination of datasets and prediction attributes was ascertained. The prediction accuracy of the theoretical RPLS model for the training and testing sets is 84.8% and 80.4%, respectively (LDA: 84.3% and 80.4%), which indicates the models are reasonable and may be helpful for pharmacogenetics studies.     

The Importance of Emerging Model Systems in Plant Biology

   apd: 9 March 2001     

Prediction and Experimental Characterization of nsSNPs Altering Human PDZ-Binding Motifs

Single nucleotide polymorphisms (SNPs) are a major contributor to genetic and phenotypic variation within populations. Non-synonymous SNPs (nsSNPs) modify the sequence of proteins and can affect their folding or binding properties. Experimental analysis of all nsSNPs is currently unfeasible and therefore computational predictions of the molecular effect of nsSNPs are helpful to guide experimental investigations. While some nsSNPs can be accurately characterized, for instance if they fall into strongly conserved or well annotated regions, the molecular consequences of many others are more challenging to predict. In particular, nsSNPs affecting less structured, and often less conserved regions, are difficult to characterize. Binding sites that mediate protein-protein or other protein interactions are an important class of functional sites on proteins and can be used to help interpret nsSNPs. Binding sites targeted by the PDZ modular peptide recognition domain have recently been characterized. Here we use this data to show that it is possible to computationally identify nsSNPs in PDZ binding motifs that modify or prevent binding to the proteins containing the motifs. We confirm these predictions by experimentally validating a selected subset with ELISA. Our work also highlights the importance of better characterizing linear motifs in proteins as many of these can be affected by genetic variations.     

Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins

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Predicting deleterious nsSNPs: an analysis of sequence and structural attributes

Background  

There has been an explosion in the number of single nucleotide polymorphisms (SNPs) within public databases. In this study we focused on non-synonymous protein coding single nucleotide polymorphisms (nsSNPs), some associated with disease and others which are thought to be neutral. We describe the distribution of both types of nsSNPs using structural and sequence based features and assess the relative value of these attributes as predictors of function using machine learning methods. We also address the common problem of balance within machine learning methods and show the effect of imbalance on nsSNP function prediction. We show that nsSNP function prediction can be significantly improved by 100% undersampling of the majority class. The learnt rules were then applied to make predictions of function on all nsSNPs within Ensembl.     

In-Silico Computing of the Most Deleterious nsSNPs in HBA1 Gene

Background

α-Thalassemia (α-thal) is a genetic disorder caused by the substitution of single amino acid or large deletions in the HBA1 and/or HBA2 genes.

Method

Using modern bioinformatics tools as a systematic in-silico approach to predict the deleterious SNPs in the HBA1 gene and its significant pathogenic impact on the functions and structure of HBA1 protein was predicted.

Results and Discussion

A total of 389 SNPs in HBA1 were retrieved from dbSNP database, which includes: 201 non-coding synonymous (nsSNPs), 43 human active SNPs, 16 intronic SNPs, 11 mRNA 3′ UTR SNPs, 9 coding synonymous SNPs, 9 5′ UTR SNPs and other types. Structural homology-based method (PolyPhen) and sequence homology-based tool (SIFT), SNPs&Go, PROVEAN and PANTHER revealed that 2.4% of the nsSNPs are pathogenic.

Conclusions

A total of 5 nsSNPs (G60V, K17M, K17T, L92F and W15R) were predicted to be responsible for the structural and functional modifications of HBA1 protein. It is evident from the deep comprehensive in-silico analysis that, two nsSNPs such as G60Vand W15R in HBA1 are highly deleterious. These “2 pathogenic nsSNPs” can be considered for wet-lab confirmatory analysis.     

Candidate nsSNPs that can affect the functions and interactions of cell cycle proteins

Nonsynonymous single nucleotide polymorphisms (nsSNPs) alter the encoded amino acid sequence, and are thus likely to affect the function of the proteins, and represent potential disease-modifiers. There is an enormous number of nsSNPs in the human population, and the major challenge lies in distinguishing the functionally significant and potentially disease-related ones from the rest. In this study, we analyzed the genetic variations that can alter the functions and the interactions of a group of cell cycle proteins (n = 60) and the proteins interacting with them (n = 26) using computational tools. As a result, we extracted 249 nsSNPs from 77 cell cycle proteins and their interaction partners from public SNP databases. Only 31 (12.4%) of the nsSNPs were validated. The majority (64.5%) of the validated SNPs were rare (minor allele frequencies < 5%). Evolutionary conservation analysis using the SIFT tool suggested that 16.1% of the validated nsSNPs may disrupt the protein function. In addition, 58% of the validated nsSNPs were located in functional protein domains/motifs, which together with the evolutionary conservation analysis enabled us to infer possible biological consequences of the nsSNPs in our set. Our study strongly suggests the presence of naturally occurring genetic variations in the cell cycle proteins that may affect their interactions and functions with possible roles in complex human diseases, such as cancer.     

Importance of Hydrophobic Cavities in Allosteric Regulation of Formylglycinamide Synthetase: Insight from Xenon Trapping and Statistical Coupling Analysis

Formylglycinamide ribonucleotide amidotransferase (FGAR-AT) is a 140 kDa bi-functional enzyme involved in a coupled reaction, where the glutaminase active site produces ammonia that is subsequently utilized to convert FGAR to its corresponding amidine in an ATP assisted fashion. The structure of FGAR-AT has been previously determined in an inactive state and the mechanism of activation remains largely unknown. In the current study, hydrophobic cavities were used as markers to identify regions involved in domain movements that facilitate catalytic coupling and subsequent activation of the enzyme. Three internal hydrophobic cavities were located by xenon trapping experiments on FGAR-AT crystals and further, these cavities were perturbed via site-directed mutagenesis. Biophysical characterization of the mutants demonstrated that two of these three voids are crucial for stability and function of the protein, although being ∼20 Å from the active centers. Interestingly, correlation analysis corroborated the experimental findings, and revealed that amino acids lining the functionally important cavities form correlated sets (co-evolving residues) that connect these regions to the amidotransferase active center. It was further proposed that the first cavity is transient and allows for breathing motion to occur and thereby serves as an allosteric hotspot. In contrast, the third cavity which lacks correlated residues was found to be highly plastic and accommodated steric congestion by local adjustment of the structure without affecting either stability or activity.     

A novel computational and structural analysis of nsSNPs in CFTR gene

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.     

胆红素对人胎盘谷胱甘肽S-转移酶的别构效应

胆红素是人胎盘谷胱甘肽S—转移酶(GST—π)的别构效应剂,在胆红素存在下,底物谷胱甘肽(GSH)呈同促正协同效应:胆红素浓度愈高,Hill氏系数(n_H)也愈大,胆红素本身对酶的结合也呈同促正协同效应。胆红素还能加速GST-π在缺乏疏基保护剂时的自然失活,加速GST-π的氨基被2,4,6—三硝基苯磺酸(TNBS)、胍基被丁二酮以及羧基被N-乙基-N’-(3-二甲胺基丙基)羧二亚胺(EDC)的修饰,但却抑制N-乙基顺丁二酰亚胺(NEMI)对疏基的修饰,胆红素这种对失活作用的影响可能和胆红素引起GST-π空间构象的变化有关,对其他可能性也作了讨论。     

Effect of Diethylpyrocarbonate on the Allosteric Properties of Phosphoenolpyruvate Carboxylase from Crassula argentea

Phosphoenolpyruvate carboxylase from the Crassulacean acid metabolism plant Crassula argentea was substantially desensitized to the effects of regulatory ligands by treatment with diethylpyrocarbonate, a reagent which selectively modifies histidyl residues. Desensitization of the enzyme to the inhibitor malate and the activator glucose 6-phosphate was accompanied by the appearance of a peak in the ultraviolet difference spectrum at 240 nanometers, indicating the formation of ethoxyformylhistidyl derivatives. Hydroxylamine reversed part of the spectral change under native conditions, and almost all of the change under denaturing conditions, but failed to restore sensitivity to effectors. The pH profiles of desensitization to malate and glucose 6-phosphate indicated the involvement of groups on the enzyme with pK, values of 6.8 and 6.4, respectively. Under denaturing conditions, a total of 15 histidine residues per subunit were modified by diethylpyrocarbonate, whereas for the native enzyme nine histidines were modified per subunit. Effector desensitization occurs after the modification of two to three histidyl residues per subunit. The presence of malate reduced the apparent rate constant for desensitization by 60%, suggesting that the modification occurred at the malate binding site. Diethylpyrocarbonate treatment also eliminated the kinetic lag caused by malate. Glucose 6-phosphate did not protect the enzyme against diethylpyrocarbonate-induced desensitization.     

Effect of Explosion on the Crystalline Polymorphism of Chitin and Chitosan

For identification of how explosion increases the reactivity of chitin and chitosan, changes in the crystalline polymorphism of these polysaccharides were studied by X-ray diffraction measurements. The α-chitin form of chitin did not change after being exploded, but an X-ray diagram of chitosan showed a hydrated crystal of low crystallinity before the explosion, and increased crystallinity of the hydrated form plus a small amount of an anhydrous crystal after the explosion. The improvement of accessibility to both polysaccharides caused by the explosion seemed not to arise from changes in their crystalline polymorphism or crystallinity     

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

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.     

An ANN model for the identification of deleterious nsSNPs in tumor suppressor genes

Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occurs approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs), lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases and cancer. One of the main problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. An attempt was made to develop a new approach to predict such nsSNPs. This would enhance our understanding of genetic diseases and helps to predict the disease. We detect nsSNPs and all possible and reliable alleles by ANN, a soft computing model using potential SNP information. Reliable nsSNPs are identified, based on the reconstructed alleles and on sequence redundancy. The model gives good results with mean specificity (95.85&), sensitivity (97.40&) and accuracy (96.25&). Our results indicate that ANNs can serve as a useful method to analyze quantitative effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data. AVAILABILITY: The database is available for free at http://www.snp.mirworks.in.     

On Spectral Identification of DNA-Base Pairs Polymorphism

The results of MNDO-PM3 theoretical study of H-bonds liability of watson-crick base pairs are discussed. Some microwave and IR spectral criteria are suggested for identification of hidden polymorphism of DNA base pairing.     

Allosteric Inhibition of the Neuropeptidase Neurolysin

Neuropeptidases specialize in the hydrolysis of the small bioactive peptides that play a variety of signaling roles in the nervous and endocrine systems. One neuropeptidase, neurolysin, helps control the levels of the dopaminergic circuit modulator neurotensin and is a member of a fold group that includes the antihypertensive target angiotensin converting enzyme. We report the discovery of a potent inhibitor that, unexpectedly, binds away from the enzyme catalytic site. The location of the bound inhibitor suggests it disrupts activity by preventing a hinge-like motion associated with substrate binding and catalysis. In support of this model, the inhibition kinetics are mixed, with both noncompetitive and competitive components, and fluorescence polarization shows directly that the inhibitor reverses a substrate-associated conformational change. This new type of inhibition may have widespread utility in targeting neuropeptidases.