Structural location of disease-associated single-nucleotide polymorphisms

Non-synonymous single-nucleotide polymorphism (nsSNP) of genes introduces amino acid changes to proteins, and plays an important role in providing genetic functional diversity. To understand the structural characteristics of disease-associated SNPs, we have mapped a set of nsSNPs derived from the online mendelian inheritance in man (OMIM) database to the structural surfaces of encoded proteins. These nsSNPs are disease-associated or have distinctive phenotypes. As a control dataset, we mapped a set of nsSNPs derived from SNP database dbSNP to the structural surfaces of those encoded proteins. Using the alpha shape method from computational geometry, we examine the geometric locations of the structural sites of these nsSNPs. We classify each nsSNP site into one of three categories of geometric locations: those in a pocket or a void (type P); those on a convex region or a shallow depressed region (type S); and those that are buried completely in the interior (type I). We find that the majority (88%) of disease-associated nsSNPs are located in voids or pockets, and they are infrequently observed in the interior of proteins (3.2% in the data set). We find that nsSNPs mapped from dbSNP are less likely to be located in pockets or voids (68%). We further introduce a novel application of hidden Markov models (HMM) for analyzing sequence homology of SNPs on various geometric sites. For SNPs on surface pocket or void, we find that there is no strong tendency for them to occur on conserved residues. For SNPs buried in the interior, we find that disease-associated mutations are more likely to be conserved. The approach of classifying nsSNPs with alpha shape and HMM developed in this study can be integrated with additional methods to improve the accuracy of predictions of whether a given nsSNP is likely to be disease-associated.     

PicSNP: a browsable catalog of nonsynonymous single nucleotide polymorphisms in the human genome

Recent progress in identification and mapping of single nucleotide polymorphisms (SNPs) in the human genome generates an unprecedented opportunity to explore cause-effect relationships between genetic variations and susceptibility to common diseases. For this purpose, one promising strategy would be to select a set of SNPs that potentially alter the function of proteins involved in the pathogenesis of the diseases and compare their frequencies in the affected individuals and the healthy population. In this respect, SNPs that change amino acid sequences (nonsynonymous SNPs; nsSNPs) are of particular interest, since they are more likely to affect protein functions. In this study, we have constructed a catalog of nsSNPs (PicSNP), whose unique features are (i) nsSNPs are classified according to the functions of the affected genes and are searchable under the guidance of hierarchical lists of protein functions and (ii) nsSNPs that lead to amino acid changes in the known functional sites and domains of proteins are highlighted. Out of 1,190,295 SNPs extracted from public database, we identified 3793 nsSNPs and classified them in 1247 categories of protein functions. 495 sites and domains annotated in the Swiss-Prot database were found to include nsSNPs, including 2 nsSNPs in disulfide-binding sites and 38 nsSNPs in transmembrane regions. PicSNP is available via the World Wide Web (http://picsnp.org) and would support research questing for SNPs involved in common diseases.     

Applications of computational algorithm tools to identify functional SNPs in cytokine genes

Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, how to identify functional SNPs from a pool containing both functional and neutral SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of a group of cytokine proteins using computational tools. As a result, we extracted 4552 SNPs from 45 cytokine proteins from SNPper database. Of particular interest, 828 SNPs were in the 5'UTR region, 961 SNPs were in the 3' UTR region, and 85 SNPs were non-synonymous SNPs (nsSNPs), which cause amino acid change. Evolutionary conservation analysis using the SIFT tool suggested that 8 nsSNPs may disrupt the protein function. Protein structure analysis using the PolyPhen tool suggested that 5 nsSNPs might alter protein structure. Binding motif analysis using the UTResource tool suggested that 27 SNPs in 5' or 3'UTR might change protein expression levels. Our study demonstrates the presence of naturally occurring genetic variations in the cytokine proteins that may affect their expressions and functions with possible roles in complex human disease, such as immune diseases.     

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources

MOTIVATION: The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. RESULTS: We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. AVAILABILITY: http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org SUPPLEMENTARY INFORMATION: http://salilab.org/LS-SNP/supp-info.pdf.     

Computational refinement of functional single nucleotide polymorphisms associated with ATM gene

Background

Understanding and predicting molecular basis of disease is one of the major challenges in modern biology and medicine. SNPs associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the ATM 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.

Principal findings

We analyzed the deleterious nsSNPs associated with ATM gene based on different computational methods. An integrative scoring system and sequence conservation of amino acid residues was adapted for a priori nsSNP analysis of variants associated with cancer. We further extended our approach on SNPs that could potentially influence protein Post Translational Modifications in ATM gene.

Significance

In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in both coding and non coding region that can alter the expression and function of ATM gene. In silico characterization of nsSNPs affecting ATM gene function can aid in better understanding of genetic differences in disease susceptibility.     

SNPeffect v2.0: a new step in investigating the molecular phenotypic effects of human non-synonymous SNPs

Single nucleotide polymorphisms (SNPs) constitute the most fundamental type of genetic variation in human populations. About 75 000 of these reported variations cause an amino acid change in the translated protein. An important goal in genomic research is to understand how this variability affects protein function, and whether or not particular SNPs are associated to disease susceptibility. Accordingly, the SNPeffect database uses sequence- and structure-based bioinformatics tools to predict the effect of non-synonymous SNPs on the molecular phenotype of proteins. SNPeffect analyses the effect of SNPs on three categories of functional properties: (1) structural and thermodynamic properties affecting protein dynamics and stability (2) the integrity of functional and binding sites and (3) changes in posttranslational processing and cellular localization of proteins. The search interface of the database can be used to search specifically for polymorphisms that are predicted to cause a change in one of these properties. Now based on the Ensembl human databases, the SNPeffect database has been remodeled to better fit an automatically updatable structure. The current edition holds the molecular phenotype of 74 567 nsSNPs in 23 426 proteins. AVAILABILITY: SNPeffect can be accessed through http://snpeffect.vib.be.     

Non-synonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association

Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80(th) base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3' untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.     

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.     

Functional dynamics of PDZ binding domains: a normal-mode analysis

Postsynaptic density-95/disks large/zonula occludens-1 (PDZ) domains are relatively small (80-120 residues) protein binding modules central in the organization of receptor clusters and in the association of cellular proteins. Their main function is to bind C-terminals of selected proteins that are recognized through specific amino acids in their carboxyl end. Binding is associated with a deformation of the PDZ native structure and is responsible for dynamical changes in regions not in direct contact with the target. We investigate how this deformation is related to the harmonic dynamics of the PDZ structure and show that one low-frequency collective normal mode, characterized by the concerted movements of different secondary structures, is involved in the binding process. Our results suggest that even minimal structural changes are responsible for communication between distant regions of the protein, in agreement with recent NMR experiments. Thus, PDZ domains are a very clear example of how collective normal modes are able to characterize the relation between function and dynamics of proteins, and to provide indications on the precursors of binding/unbinding events.     

Energetics of peptide recognition by the second PDZ domain of human protein tyrosine phosphatase 1E

Formation of protein-protein assemblies is essential in maintaining cell structure and function. Conservation of structural motifs and binding sites is the result of evolutionary pressure for solutions compatible with both molecular economy and regulation. PDZ domains are a typical example: A conserved fold governs specificity toward recognition of C-terminal protein sequences by small sequential and/or structural deviations within a canonical binding mode. The energetic principles underlying the strength and specificity of PDZ-protein interactions are practically unknown. We use the second PDZ domain (PDZ2) of the human protein tyrosine phosphatase (hPTP1E) as a model to study the energetics of peptide binding to a class I PDZ domain. Calorimetric experiments reveal the enthalpy, entropy, and heat capacity changes accompanying PDZ2 binding to the C-terminal pentadecapeptide derived from the guanine nucleotide exchange factor RA-GEF2. Association is driven by favorable enthalpy and entropy changes below 18 degrees C. Above that temperature the entropy change opposes complex formation. Structure-based predictions poorly reproduce the observed thermodynamic profile of the PDZ-peptide complex. On the basis of MD simulations and experimental findings by others we suggest that changes in the dynamics of the PDZ domain upon peptide binding make a large contribution to the observed thermodynamic parameters. Possible impacts of subtle, ligand-induced structural "stiffening" of PDZ domains are discussed. In our hands, the C-terminal segment of the tumor suppressor APC binds much less tightly to PDZ2 than what has been proposed earlier from surface plasmon resonance experiments.     

Phage display can select over-hydrophobic sequences that may impair prediction of natural domain-peptide interactions

MOTIVATION: The phage display peptide selection approach is widely used for defining binding specificities of globular domains. PDZ domains recognize partner proteins via C-terminal motifs and are often used as a model for interaction predictions. Here, we investigated to which extent phage display data that were recently published for 54 human PDZ domains can be applied to the prediction of human PDZ-peptide interactions. RESULTS: Promising predictions were obtained for one-third of the 54 PDZ domains. For the other two-thirds, we detected in the phage display peptides an important bias for hydrophobic amino acids that seemed to impair correct predictions. Therefore, phage display-selected peptides may be over-hydrophobic and of high affinity, while natural interaction motifs are rather hydrophilic and mostly combine low affinity with high specificity. We suggest that potential amino acid composition bias should systematically be investigated when applying phage display data to the prediction of specific natural domain-linear motif interactions.     

Computational Analysis of Functional Single Nucleotide Polymorphisms Associated with the CYP11B2 Gene

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans and play a major role in the genetics of human phenotype variation and the genetic basis of human complex diseases. Recently, there is considerable interest in understanding the possible role of the CYP11B2 gene with corticosterone methyl oxidase deficiency, primary aldosteronism, and cardio-cerebro-vascular diseases. Hence, the elucidation of the function and molecular dynamic behavior of CYP11B2 mutations is crucial in current genomics. In this study, we investigated the pathogenic effect of 51 nsSNPs and 26 UTR SNPs in the CYP11B2 gene through computational platforms. Using a combination of SIFT, PolyPhen, I-Mutant Suite, and ConSurf server, four nsSNPs (F487V, V129M, T498A, and V403E) were identified to potentially affect the structure, function, and activity of the CYP11B2 protein. Furthermore, molecular dynamics simulation and structure analyses also confirmed the impact of these nsSNPs on the stability and secondary properties of the CYP11B2 protein. Additionally, utilizing the UTRscan, MirSNP, PolymiRTS and miRNASNP, three SNPs in the 3′UTR region were predicted to exhibit a pattern change in the upstream open reading frames (uORF), and eight microRNA binding sites were found to be highly affected due to 3′UTR SNPs. This cataloguing of deleterious SNPs is essential for narrowing down the number of CYP11B2 mutations to be screened in genetic association studies and for a better understanding of the functional and structural aspects of the CYP11B2 protein.     

Three-Dimensional Models of Immune Cell Surface Proteins and Identification of Binding Sites

The extracellular regions of many cell surface proteins of the immune system contain distinct domains that may be linked in many different ways and are often only loosely tethered to the transmembrane segment. In efforts to identify regions critical for binding, molecular models of these domains are used to select residues for mutagenesis and to map binding sites. Many immune cell surface proteins belong to protein superfamilies and display only limited sequence identity compared to proteins of known three-dimensional (3D) structure, often 30% or less. Therefore, detailed 3D structures are difficult to predict, and structure-based sequence analysis and model assessment are particularly important components of the model building process. In some cases, experimentally determined structures have made it possible to assess the accuracy of predictions, which illustrates the opportunities and shortcomings of the approach. Herein the model-based identification of binding sites in cell surface proteins is described and representative examples are discussed.     

Where in the genome are significant single nucleotide polymorphisms from genome-wide association studies located?

Recent technological progress has permitted the efficient performance of genome-wide association studies (GWAS) to map genetic variants associated with common diseases. Here, we analyzed 2,893 single nucleotide polymorphisms (SNPs) that have been identified in 593 published GWAS as associated with a disease phenotype with respect to their genomic location. In absolute numbers, most significant SNPs are located in intergenic regions and introns. When compared to their representation on the chips, there is essentially overrepresentation of nonsynonymous coding SNPs (nsSNPs), synonymous coding SNPs, and SNPs in untranscribed regions upstream of genes among the disease associated SNPs. A Gene Ontology term analysis showed that genes putatively causing a phenotype often code for membrane associated proteins or signal transduction genes.     

Using RNA secondary structures to guide sequence motif finding towards single-stranded regions

RNA binding proteins recognize RNA targets in a sequence specific manner. Apart from the sequence, the secondary structure context of the binding site also affects the binding affinity. Binding sites are often located in single-stranded RNA regions and it was shown that the sequestration of a binding motif in a double-strand abolishes protein binding. Thus, it is desirable to include knowledge about RNA secondary structures when searching for the binding motif of a protein. We present the approach MEMERIS for searching sequence motifs in a set of RNA sequences and simultaneously integrating information about secondary structures. To abstract from specific structural elements, we precompute position-specific values measuring the single-strandedness of all substrings of an RNA sequence. These values are used as prior knowledge about the motif starts to guide the motif search. Extensive tests with artificial and biological data demonstrate that MEMERIS is able to identify motifs in single-stranded regions even if a stronger motif located in double-strand parts exists. The discovered motif occurrences in biological datasets mostly coincide with known protein-binding sites. This algorithm can be used for finding the binding motif of single-stranded RNA-binding proteins in SELEX or other biological sequence data.     

In silico analysis of structural and functional consequences in p16INK4A by deleterious nsSNPs associated CDKN2A gene in malignant melanoma

In this study, we identified the most deleterious nsSNP in CDKN2A gene through structural and functional properties of its protein (p16INK4A) and investigated its binding affinity with cdk6. Out of 118 SNPs, 14 are nsSNPs in the coding region and 17 SNPs were found in the untranslated region (UTR). FastSNP suggested that 7 SNPs in the 5' UTR might change the protein expression levels. Sixty-four percent of nsSNPs are found to be damaged in PolyPhen server among the 14 nsSNPs investigated. With this effort, we modeled the mutant p16INK4A proteins based on these deleterious nsSNPs, out of which three nsSNPs associated p16INK4A had RMSD values of greater than 3.00 A with native protein. From a comparison of total energy of these three mutant proteins, we identified that the major mutation is from Aspartic acid to Tyrosine at the residue position of 84 of p16INK4A. Further, we compared the binding efficiency of both native and mutant p16INK4A with cdk6. We found that mutant p16INK4A has less binding affinity with cdk6 compared to native type. This is due to ten hydrogen bonds and eight salt bridges which exist between the native type and cdk6, whereas the mutant type makes only nine hydrogen bonds and five salt bridges with cdk6. Based on our investigation, we propose that the SNP with the ID rs11552822 could be the most deleterious nsSNP in CDKN2A gene, causing malignant melanoma, as it was well correlated with experimental studies carried out elsewhere.     

Binding of PTEN to specific PDZ domains contributes to PTEN protein stability and phosphorylation by microtubule-associated serine/threonine kinases

The tumor suppressor phosphatase PTEN is a key regulator of cell growth and apoptosis that interacts with PDZ domains from regulatory proteins, including MAGI-1/2/3, hDlg, and MAST205. Here we identified novel PTEN-binding PDZ domains within the MAST205-related proteins, syntrophin-associated serine/threonine kinase and MAST3, characterized the regions of PTEN involved in its interaction with distinctive PDZ domains, and analyzed the functional consequences on PTEN of PDZ domain binding. Using a panel of PTEN mutations, as well as PTEN chimeras containing distinct domains of the related protein TPTE, we found that the PTP and C2 domains of PTEN do not affect PDZ domain binding and that the C-terminal tail of PTEN (residues 350-403) provides selectivity to recognize specific PDZ domains from MAGI-2, hDlg, and MAST205. Binding of PTEN to the PDZ-2 domain from MAGI-2 increased PTEN protein stability. Furthermore, binding of PTEN to the PDZ domains from microtubule-associated serine/threonine kinases facilitated PTEN phosphorylation at its C terminus by these kinases. Our results suggest an important role for the C-terminal region of PTEN in the selective association with scaffolding and/or regulatory molecules and provide evidence that PDZ domain binding stabilizes PTEN and targets this tumor suppressor for phosphorylation by microtubule-associated serine/threonine kinases.