Research Article Functional and Structural Consequences of ...downloads.hindawi.com/journals/bmri/2015/271458.pdfF : e Flow Chart depicting the overall process of identi cation and

Research ArticleFunctional and Structural Consequences of DamagingSingle Nucleotide Polymorphisms in Human Prostate CancerPredisposition Gene RNASEL

Amit Datta Md Habibul Hasan MazumderAfrin Sultana Chowdhury and Md Anayet Hasan

Department of Genetic Engineering and Biotechnology Faculty of Biological Sciences University of ChittagongChittagong 4331 Bangladesh

Correspondence should be addressed to Md Anayet Hasan anayet johnyyahoocom

Received 26 April 2015 Revised 8 June 2015 Accepted 10 June 2015

Academic Editor Rituraj Purohit

Copyright copy 2015 Amit Datta et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

A commonly diagnosed cancer prostate cancer (PrCa) is being regulated by the gene RNASEL previously known as PRCA1 codesfor ribonuclease L which is an integral part of interferon regulated system that mediates antiviral and antiproliferative role of theinterferons Both somatic and germline mutations have been implicated to cause prostate cancer With an array of available SingleNucleotide Polymorphism data on dbSNP this study is designed to sort out functional SNPs in RNASEL by implementing differentauthentic computational tools such as SIFT PolyPhen SNPsampGO FathmmConSurf UTRScan PDBsumTm-Align I-Mutant andProject HOPE for functional and structural assessment solvent accessibility molecular dynamics and energy minimization studyAmong 794 RNASEL SNP entries 124 SNPs were found nonsynonymous from which SIFT predicted 13 nsSNPs as nontolerablewhereas PolyPhen-2 predicted 28 SNPs found on the 31015840 and 51015840 UTR were also assessed By analyzing six tools having differentperspectives an aggregate result was produced where nine nsSNPs were found to be most likely to exert deleterious effect 3Dmodels of mutated proteins were generated to determine the functional and structural effect of the mutations on ribonuclease LThe initial findings were reinforced by the results from I-Mutant and Project HOPE as these tools predicted significant structuraland functional instability of themutated proteins Expasy-ProSit tool defined themutations to be situated in the functional domainsof the protein Considering previous analysis this study revealed a conclusive result deducing the available SNP data on the databaseby identifying themost damaging three nsSNP rs151296858 (G59S) rs145415894 (A276V) and rs35896902 (R592H) As such studiesinvolving polymorphisms of RNASEL were none to be found the results of the current study would certainly be helpful in futureprospects concerning prostate cancer in males

1 Introduction

Single Nucleotide Polymorphism also known as SNP ac-counts for the most common form of genetic mutation inhuman It has been reported that sim93 of all human genesrepresent at least one SNP [1] Therefore they are liablefor generating the majority of biological variations amongindividuals An understanding of the relationship betweenthese genetic variations and their phenotypic effects couldtherefore be a step toward exploring the causes of variousdisorders or diseases SNPs can fall within the coding regions(coding SNPs) or noncoding regions of genes (noncoding

SNPs) or in the intergenic region between two genes [2 3]While the two others are quite natural in the human genomeand phenotypically neutral [1 4] nonsynonymous codingSNPs (nsSNPs) are thought to have the principal impacton phenotype by changing the protein sequence As theycause amino acid alteration in the corresponding proteinproduct it may exert deleterious effects on the structurefunction solubility or stability of proteins [5 6] Beside thesethe nsSNPs perturb gene regulation by modifying DNA andtranscriptional binding factors [6ndash9] and the maintenanceof the formational integrity of cells and tissues [10] Thusit is likely that nsSNPs play a major role in the functional

Hindawi Publishing CorporationBioMed Research InternationalVolume 2015 Article ID 271458 15 pageshttpdxdoiorg1011552015271458

2 BioMed Research International

diversity coded proteins in human populations and oftenassociated with human diseases Indeed earlier studies haverevealed thatmore than 50 of themutations associated withinherited genetic disorders are resulted by nsSNPs [11ndash13]Recently many researchers have focused on nsSNPs in cancercausing genes The recent studies have identified multiplensSNPs that influence susceptibility to infection as well asthe development of inflammatory disorders and autoimmunediseases [4ndash9] Nonetheless because innate immune genesare often highly polymorphic many nsSNPs in these genesremain uncharacterized

Prostate cancer (PRCA) is one of the most commonlydiagnosed cancers worldwide mostly in developed countries[14] In the United States it is the second leading cause ofcancer death in males [15] Currently no permitted curativetherapies are available for prostate cancer that has beenmetastasized In the United States it is the second leadingcause of cancer death in males [15] Therefore researcheshave focused to detect newer suitable solution for controllingprostate cancer and generating new potential targets fortherapy A fraction of PRCA patients belong to the hereditaryprostate cancer (HPC) families Linkage analyses in HPCfamilies have predicted that PRCA susceptibility genes areharbored in multiple genetic loci including HPC1 at 1q24-q25 ELAC2HPC2 at 17p11 PCAP at 1q422-q43 HPCX atXq27-q28 CAPB at 1p36 andHPC20 at 20q13 [16] As studyshows that genetic background has remarkable contributionto cause prostate cancer [15] both the identification andtreatment of cancer would potentially aid from the detectionof new genes that are particularly expressed in prostatecancer Among the large number of loci reported the HPC1locus at 1q24-q25 harbors the gene RNASEL (encodingribonuclease L or RNase L) a recently proposed candidate forthe hereditary prostate cancer (HPC) gene [17]

RNase L is a ubiquitously expressed 21015840 51015840-oligoadenylate(2-5A) dependent endoribonuclease that involves the antivi-ral action and proapoptosis activity in interferon [18 19]In virus infected and INF treated cells RNase L probablyshows its antiviral effects through a combination of activitieswhich include direct cleavage of the single-stranded viralRNAs suppression of protein synthesis via the disruption ofrRNA beginning of apoptosis and induction of associatedantiviral genes RNaseL induced apoptosis is the ultimateresult of a JNK-dependent stress-response pathway whichleads to the release of cytochrome c from mitochondria andcaspase-dependent cell death (apoptosis) Therefore RNaseL activation could lead to the elimination of virus infectedcells under different circumstances It might play a centralrole also in the regulation of mRNA turnover In control ofviral and cellular growth the role of the 2-5A system suggeststhat defects in the 2-5A-dependent RNASEL gene couldresult in decreased immunity to virus infections and cancer[20] Again RNase L is involved in the regulation of cellproliferation through the interferon-regulated 2-5A pathwayand therefore had been suggested as a tumor suppressor geneIn that connection a study found loss of heterozygosity andloss of RNase L protein in the microdissected tumors with agermline mutation [21] This indicates that RNASEL activitywas reduced in lymphoblasts from heterozygous individuals

compared with family members who were homozygous withrespect to the wild type allele Thus germline mutations inRNASEL might have diagnostic value and the 2-5A pathwaymay present opportunities for developing therapies for theprostate cancer patients In that regard recent studies onhereditary prostate cancer (HPC1) positioning at the locus1q24-q25 found two rare heterozygous inactivating germlinemutations in RNASEL linked to HPC1

From the last few years computational approaches havebeen widely used to detect the impact of deleterious nsSNPsin candidate genes by analyzing data such as conservation ofsequences across species [22] physicochemical properties ofthe polypeptides [23 24] and structural attributes [25]

By following computational algorithms the functionalSNPs out of a vast range of disease susceptible SNPs of ATMgene [26] BRCA1 gene andBRAF gene [27] have successfullydivided based on their structural and functional propertiesIn recent years computational approaches have been adoptedby many researchers in cancer studies either as a part oflarge population data analysis or only for predicting mostdeleterious SNPs from the large datasets Similar work hasbeen done extensively for breast cancer associated genesBRCA1 [28] and BRCA2 [29] where more than thousandSNPs have been analyzed altogether Insulin-like growthfactor 1 receptor (IGF1R) is another gene associated withboth breast cancer and prostate cancer and polymorphismsin the IGF1R receptor are found [30] which cause instabilityof the receptor protein Moreover SNPs which can increasedisease predisposition in colorectal cancer (HPNCC geneandMCAK gene) [31 32] haemoglobinopathies (beta-globingene) [33] and breast cancer (BARD1 gene) [34] have beenpredicted in computational studies Several oncogenes suchas cyclin-dependent kinase 7 (CDK7) gene [35] ErbB3 gene[36] polo-like kinase 1 (PLK1) [37] centromere-associatedprotein-E (CENP-E) [38] and centrosomal protein of 63 kDa(CEP63) [39] involved in cell cycle regulation and cell divi-sion are reported to contain damaging SNPs in correspondingin silico studies

Basing on the convincing data which indicates the wideinvolvement of RNASEL gene in varied range of humandiseases its functional genomics depend on mutation anal-ysis which is conceived to provide key advances in diseasediagnosis and therapy But the in silico analysis of noncodingSNPs (intronic exonic and 51015840 and 31015840 UTR SNPs) and codingSNPs (nsSNPs and sSNPs in exonic regions) in our candidateHPC1 gene remains unpredicted to date

Hence in the current investigation the in silico analysisof RNASEL gene has been carried out in order to charac-terize the deleterious mutations Our investigational studyinvolved (i) retrieval of SNPs in RNASEL gene from availabledatabases (ii) allocating the deleterious nsSNPs to theirphenotypic effects based on sequence and structure-basedhomology search and identifying the regulatory nsSNPsthat can alter the splicing and gene expression patterns(iii) predicting the specific effects of the substitutions ofamino acids on secondary structures by means of solventaccessibility and stability (iv) and prediction of change inthe domain structures due to the mutations The above insilico approaches offer a total of 9 high-risk nsSNPs sorted out

BioMed Research International 3

RNASEL dataset

Protein sequence from UniProt and wild type protein models from PDB

Nonsynonymous missense SNP variants (rsIDs)

UTR SNP rsIDs and RNASEL transcript sequence

Identification and characterization of functional SNPs with SIFT PolyPhen-2 SNPsampGO PANTHER PhD-SNP

Pmut and nsSNPAnalyzer

Most damaging SNPs selected as predicted by SNP analyzing tools

Mutated protein sequences generated bydiscovery studio

UTR signal analysis by UTRScan and identification of UTR SNPs in the UTR region

Homology models of mutated proteins generated by Phyre-2

RMSD calculation od wild type and mutated proteins done by Tm-Align

Energy minimization done by YASARA force field

SNPs in functional domains identified by Prosit-ExPasy tool

Structural stability of mutated proteins analyzed with I-Mutant 20

Functional consequence and change in domain structure of mutated proteins analyzed by Project HOPE

Cancer related SNPs identified by Fathmm

Figure 1 The Flow Chart depicting the overall process of identification and characterization of damaging SNPs in RNASEL along with thestructural and functional consequence analysis upon mutation

from the NCBI SNP database This study is the first extensivein silico analysis of the RNASEL gene and will establish astrong foundation for structure-function and population-based studies in future

2 Materials and Methods

The Flow Chart depicts the overall process of identificationand characterization of damaging SNPs in RNASEL alongwith the structural and functional consequence analysis uponmutation (Figure 1)

21 Retrieval of SNP Datasets The data on human RNASELgene was derived from web-based data sources such asOnline Mendelian Inheritance in Man (OMIM) [40] the

SNPs information (protein accession number and SNP ID)of the RNASEL gene was retrieved from the NCBI dbSNP(Database of Single Nucleotide Polymorphism) [41] and theprotein sequence and protein structure were retrieved fromUniprot (Universal Protein Resource) [42] and RCSB proteindatabank [43] subsequently

22 Analysis of Functional Consequences of nsSNPs SortingIntolerant from Tolerant (SIFT) [44] is a tool that can detectthe deleterious coding nonsynonymous SNPs This programpresumes that major amino acids will be conserved in theprotein family and changes at specific positions tend to bepredicted as deleterious [28 44] During mutagenesis studiesin human SIFT can differentiate functionally neutral anddeleterious polymorphisms [45]The algorithms for the SIFTprogram use SWISSPROT nr and TrEMBL databases to find


homologous sequences The threshold for the intoleranceindex is ge005 In this study the identification numbers(rsIDs) of each SNP of human RNASEL gene obtainedfrom NCBI were submitted as a query sequence to SIFTfor homology searching The SIFT value le005 indicatesthe deleterious effect of nonsynonymous variants on proteinfunction

23 Prediction of Functional Consequences of Coding nsSNPsby Structural Homology-Based Method To understand thefunctional significance of a protein it is crucial to analyzethe damaged coding nonsynonymous SNPs at the structurallevel [46 47] Polymorphism Phenotyping-2 or PolyPhen-2[11] which is a probabilistic classifier that computes functionalimpact of an allele change by Naive Bayes a set of super-vised learning algorithms was used to determine structuralconsequences Query was submitted in the form of proteinsequence along with mutational position and two amino acidvariants PolyPhen categorizes the SNPs as benign possiblydamaging or probably damaging on the basis of site-specificsequence conservation by estimating the position-specificindependent count (PSIC) score for every variant and alsocalculates the score difference between variants [11] Thehigher the PSIC score difference is the higher the functionalimpact a specific amino acid substitution is likely to have

24 Characterization of Functional nsSNPs For characteriza-tion of functional nsSNPs SNPampGO was used It accumu-lates five functional tools SNPampGO [48] PANTHER [49]PHD-SNP [50] nsSNP Analyzer [51] and P-MUT [52]Single Nucleotide Polymorphism Database (SNPs) and GeneOntology (GO) and Predictor of Human Deleterious SingleNucleotide Polymorphisms (PhD-SNP) use support vectormachine (SVM) based analyzing method where the ProteinANalysis THrough Evolutionary Relationships (PANTHER)estimates the function of coding nsSNPs by calculating thesubPSEC (substitution position-specific evolutionary conser-vation) score [48 53] nsSNPAnalyzer uses information con-tained in the multiple sequence alignment and informationcontained in the 3D structure to make predictions LastlyPmut is based on the use of different kinds of sequenceinformation to label mutations and neural networks toprocess this information FASTA sequence was inputted andresult was based on the differences among disease relatedand neutral variations of protein sequence Probability scorehigher than 05 reveals the disease related effect of mutationon the protein function [18]

25 Prediction of Cancer Promoting Mutations The cancer-associated variants were predicted by using the FunctionalAnalysis through Hidden Markov Models (Fathmm) whichcombine sequence conservationwithin hiddenMarkovmod-els (HMMs) [54] Fathmm server is a high-throughputweb server that can predict phenotypic molecular andfunctional consequences of protein variants both on codingand noncoding variants It uses two algorithms unweightedsequenceconservation based and weighted combined bysequence conservation with pathogenicity weights For

Fathmm server the default prediction threshold is minus075where prediction with score less than this indicates thatthe mutation is potentially associated with cancer Cancerpromoting mutations plays critical role in cell regulation andmutations falling in the conserved region can downweight thenature of the domain

26 Identification of Functional SNPs in Conserved RegionsEvolutionary conservation of amino acid substitution waspredicted by ConSurf web server [55] by using a Bayesianalgorithm (conservation scores 1ndash4 variable 5-6 interme-diate and 7ndash9 conserved) [56 57] Protein structure ofRNase L was submitted and the conserved regions werepredicted by means of colouring scheme and conservationscore Functional and structural residues were also predictedHighly conserved amino acids located at high-risk nsSNPsites were nominated for further analysis

27 Scanning ofUTR SNPs The51015840 and 31015840 untranslated regions(UTRs) have important roles in the posttranscriptional regu-lation of gene expression translational efficiency and stability[58] To predict the functional SNPs we used UTRScan [59]a pattern matcher tool that searches protein or nucleotidesequences to findUTRmotifs collected in the UTRsite UTR-site derives data fromUTRdb a curated database that updatesUTRdatasets throughprimary datamining and experimentalvalidation [60 61] After performing with probable differentnucleotide at an SNP position if different sequences for eachUTR SNP are found to have varying functional patternsthis UTR SNP is expected to have functional impact Toperform this primary FASTA format data was submitted andthe results showed predicted UTRs at the specific region (51015840and 31015840)

28 Modeling of the Mutated Protein To find proteins relatedto RNASEL gene The EMBL-EBI Web-based tool PDBsum[62 63] was used that performs a FASTA search againstsequences submitted in the protein data bank (PDB) to obtainthe closest matchesThe RNASEL FASTA sequence was givenin the query space and from the results closest matches wereselected

Virtual Mutation (VM) refers to the substitution of asingle or multiple amino acids in the atomic 3D model of themolecule [64 65] Accelrys Discovery Studio 40 was used togenerate mutated sequence for the corresponding amino acidsubstitutions [66 67] Regenerated mutant sequences wereused further for mutant modeling Determining propertiesand three-dimensional structure of a macromolecule such asenzymes antibodies DNA or RNA is a vital element to awide range of research activities The modeling of mutatedproteins were performed through Phyre2 (Protein Homol-ogyAnalogy Recognition Engine) [68] the most popularonline protein fold identification server Phyre2 selects thebest suited template and creates a protein model throughsequential steps such as profile construction similarityanalysis and structural properties Intensive mode of proteinmodeling was selected to get an accurate model The inputdata of the proteins were in FASTA format


29 Energy Minimization and RMSD Calculation of theProtein Models Tm-Align is a sequence-order independentprotein structure comparison algorithm Tm-Align performsoptimized residue to residue alignment based on structuralsimilarity using dynamic programming iterationsThis serverwas used for RMSD calculation of the protein structures [69]

YASARA-minimization server was used to perform theenergy minimization of the mutated protein models YetAnother Scientific Artificial Reality Application (YASARA)is a molecular graphics modeling and simulation programwith diverged application where YASARA-minimizationserver uses YASARA force field for energy minimization thatcan optimize the damage of the mutant proteins and thusprecisely calculates the energy To perform this strategy thepdb file of themutant proteins were inserted as input data andthe result was further analyzed for forthcoming steps [70]

210 Prediction of Change in Stability upon Mutation I-Mutant 20 server was used to predict the change in stabilitydue to mutations I-Mutant is a support vector machine(SVM) based tool server This tool can automatically predictthe change in structural stability analyzing the structureor the sequence of the protein I-Mutant 20 can be usedas a classifier for predicting the sign of protein stabilityupon mutation and a regression estimator which predicts thechange in Gibbs free energy The resulting DDG value is thedifference between the Gibbs free energy of mutated proteinand wild type protein in kcalmol [71]

211 Prediction of Structural Effects upon Mutation ProjectHOPE was utilized to see the structural effect of the aminoacid substitutions Project Have yOur Protein Explained(HOPE) was used for molecular dynamics simulation toobserve the effect of the mutations on the structure ofRNASEL This web server after given input of the proteinsequence performs a BLAST against the PDB and builds ahomology model of the protein if possible through YASARAand collects tertiary structure information from What IFweb services and then access the UniProt database forsequence features like active site domains and motifs andso forth Finally to predict the features of the protein ituses Distributed Annotation System (DAS) servers whichcan exchange annotations on genomic and protein sequences[72]

3 Results

31 SNP Dataset The polymorphism data is available fromseveral databases NCBI dbSNP database the Ensemblgenome browser and the UniProt database for such TheNCBI dbSNP database is the most extensive SNP database ofthe aforementioned databases but it contains both validatedand nonvalidated polymorphisms Despite this drawbackthe SNP data for RNASEL gene was collected from dbSNPbecause it houses the largest polymorphism database [73]

The dbSNP contains total of 794 SNPs for the geneRNASEL Of the 794 SNPs 122 were missense SNPs and 151were in the UTRs Among 151 UTR SNPs 142 SNPs were in

the 31015840 UTR and 9 SNPs were found in the 51015840 UTRThere were2 nonsensestop gained SNPs but only the missense and 31015840and 51015840 UTR SNPs were selected for further analysis

32 Nonsynonymous SNP Analysis A sequence homologybased tool SIFT can determine the conservation of a partic-ular position of any amino acids in a given protein sequenceSIFT aligns paralogous and orthologous protein sequencesto determine the influence of an amino acid substitutionconsidering its functional significance and physical proper-ties SIFT has been confirmed to be sufficiently accurate todetect disease related SNPs by predicting the known diseaserelated SNPs from the database compromising only a 20false positive result Furthermore a large number of SNP dataavailable in the database lack structural information relatedto the SNP but SIFT algorithm performs analysis using thesequence data so it can predict a significantly large number ofSNPs from the database As a result SIFT provides advantagesover other deleterious SNP prediction algorithms [44 74]

SIFT takes rsID of SNPs as input and a txt file wasuploaded containing the rsIDs to the SIFT server SIFTcalculates the tolerance index (TI) of a particular amino acidsubstitution SIFT score is categorized as tolerant (0201ndash100)or intolerant (0051ndash010) and borderline (0101ndash020) So aSingle Nucleotide Polymorphisms functional consequence isinversely proportional to the tolerance index (TI) Among 122submitted nsSNP rsIDs fromdbSNP SIFT analyzed 11 nsSNPsto bear a deleterious effect with TI score le005 results areshown in Table 1 The corresponding 4 nsSNPs rs35896902rs145415894 rs145787003 and rs182539049 had a toleranceindex of 001The following 5 rsIDs rs114166108 rs142939718rs145581875 rs146781980 and rs190359946 had the toleranceindex 002 and rs143374873 rs150721457 had a score of 003and 005 respectively All of the 11 amino acid substitutionswere mutually exclusive

33 Prediction of Functional Modification of Coding nsSNPsnsSNPs with the potential to cause structural modificationsdue to the amino acid substitution were determined throughPolyPhen program It can predict the structural fate of anyamino acid substitution with approximately 82 accuracyby applying some empirical rules on the sequence of theprotein compromising only an 8 chance of false positiveresult The PolyPhen server functions by accessing UniPro-tKB nonredundant protein sequence and PDBDSSP proteinstructure database PolyPhen uses the TMHMM algorithmfor sequence based characterization of the sequence Coils2program and SignalP program for transmembrane coiledcoil and signal peptide regions prediction of the proteinsequence For identifying the homologous proteins PolyPhenperforms a BLAST query of the given protein sequenceand calculates position-specific independent count (PSIC)for every input variant and estimates the difference betweenthe variant scores the score difference of more than 0339is detrimental PolyPhen scores were allocated probablydamaging (200 or more) possibly damaging (140ndash190)potentially damaging (120ndash150) benign (000ndash090) andborderline (100ndash120) To determine the structural effects


Table 1 List of SNPs predicted by SIFT as damaging

SNP Position Amino acid change Prediction Score Medianrs145581875 50 N50S Damaging 002 281rs114166108 62 P62S Damaging 002 281rs182539049 184 L184S Damaging 001 281rs146781980 195 V195I Damaging 002 281rs145415894 276 A276V Damaging 001 281rs142939718 361 L361P Damaging 002 278rs145787003 406 S406F Damaging 001 281rs190359946 483 H483Q Damaging 002 291rs35896902 592 R592H Damaging 001 31rs143374873 727 C727Y Damaging (low confidence) 003 363rs150721457 673 I673T Damaging 005 312

PolyPhen also does a BLAST query of the input proteinsequence against PDB and PQS structure databases andmaps the query substitutions to known 3D structures Thesolvent accessible surface areas of themapped amino acids areattained fromDSSP database to generate a complete report ofthe deleterious effect of the nsSNPs on protein structure [11]

A total of 122 nsSNP rsIDs were submitted to thePolyPhen server and in the resulting output 27 amino acidsubstitutions have been reported to be probably damag-ing with PSIC score range from 0539 to 1 as shown inTable 2 Eight nsSNPs (rs114166108 rs182539049 rs146781980rs145415894 rs142939718 rs145787003 rs35896902 andrs150721457) were identified by SIFT as deleterious alsomarked to be damaging by PolyPhen-2 program as well

To further validate the results of the tools usedbeforehand we analyzed the nsSNPs with the followingin silico SNP prediction algorithms nsSNP analyzer PhD-SNP PANTHER P-MUT and SNPsampGO Primarily weselected the nsSNPs which are marked as deleterious byboth SIFT and Poly-Phen-2 server The SIFT and PolyPhenserver is distinctly precise so we combined the predictionof the five abovementioned tools and compared them withthe result of SIFT and PolyPhen server In the combinedresult 6 (rs114166108 rs182539049 rs145415894 rs142939718rs35896902 and rs150721457) of the previously selected 8nsSNPs (predicted deleterious by SIFT and PolyPhen) werepredicted as disease related by at least 3 out of the 5 tools TwonsSNPs rs142939718 and rs35896902 showed positive resultsin all the 7 tools Though predicted by SIFT as toleratedwe also selected rs151296858 rs138685180 and rs147890567for further analysis as found deleterious by at least 6 SNPanalyzers So in the final result the 9 nsSNPs that cameout through the process are rs151296858 rs114166108rs182539049 rs138685180 rs145415894 rs142939718rs35896902 rs147890567 and rs150721457 The resultsare shown in Table 3

34 Conservation Profile of High-Risk Nonsynonymous SNPsFunctional sites of proteins such as the enzymatic sites andprotein-protein interaction sites are important for biologicalprocesses Amino acids located in this biologically active sitestend to be highly conserved more than other residues Any

substitution of these functionally involved residues generallyleads towards the complete loss of biological functions andrender severe deleterious effects compared to other polymor-phisms of nonconserved site [13 75]

ConSurf web server was used to calculate the degree ofevolutionary conservation at each amino acid position ofthe RNase L protein ConSurf identifies putative structuraland functional residues and determine their evolutionaryconservation by applying empirical Bayesian method [55]

Although a complete analysis was done we focused onthe conservation profile of the selected 9 high-risk nsSNPlocations The analysis showed that residues G59 P62 L184L224 A276 L361 R592 T595 and I 673 are highly conservedhaving the conservation score between 7 and 9 Accordingto ConSurf the residues A276 and L361 are conserved andburied denoting them as critical structural residues and G59P62 and T592 are conserved exposed residues emphasizingtheir critical functional importance The highly conservedresidues are identified as functional or structural based ontheir location relative to the protein surface or the proteincore To identify the functional and structural sites ConSurfcombines evolutionary data and solvent accessibility predic-tions [76] Table 4 shows the result from ConSurf

In the analysis 5 residues (G59 P62 A276 L361 andT592) that were detected as essential functional and struc-tural residues correspond to the nsSNPs which were pre-dicted to be deleterious by at least 6 out of 7 SNP analyzingalgorithms So correspondingmutations G59S P62S A276VL361P and T592H to the nsSNPs rs151296858 rs114166108rs145415894 rs142939718 and rs35896902 can severely dis-rupt structural and functional properties of RNase L

35 Identifying Cancer Associated Variants From Fathmmfor Q05823 cancer association was predicted for the aminoacid substitution P62S G59S and A276V with the scoresminus158 minus182 and minus153 respectively

36 Functional SNPs in UTR Found by the UTRscan ServerGene expression is affected by the SNPs in the 31015840 UTR regionpurposely due to defective ribosomal RNA translation or byaffecting RNA half-life [77]The UTRscan server was used toanalyze 142 31015840UTR SNPs and 9 51015840 UTR SNPs of PRCA gene


Table 2 List of SNPs that were predicted by PolyPhen-2 server as significantly damaging

rsIDs Amino acid position Mutation PSIC score Effectrs192719507 44 L44F 1 Probably damagingrs151296858 59 G59S 1 Probably damagingrs114166108 62 P62S 0997 Probably damagingrs113469614 65 N65K 0989 Probably damagingrs141809307 94 T94T 1 Probably damagingrs56250729 97 I97L 0997 Probably damagingrs138740835 98 L98F 0539 Possibly damagingrs115634589 101 I101T 0999 Probably damagingrs182539049 184 L184S 0992 Probably damagingrs146781980 195 V195I 098 Probably damagingrs138685180 224 L224P 0999 Probably damagingrs143565946 258 L258H 0999 Probably damagingrs145415894 276 A276V 1 Probably damagingrs140715742 285 L285M 0995 Probably damagingrs35553278 289 A289T 0591 Possibly damagingrs151056294 318 K318T 0651 Probably damagingrs148464936 355 R355C 1 Probably damagingrs142939718 361 L361P 1 Probably damagingrs145787003 406 S406F 0976 Probably damagingrs486907 462 R462Q 0998 Probably damagingrs190359946 483 H483Q 1 Probably damagingrs148411578 528 V528I 0982 Probably damagingrs193195484 532 V532I 0999 Probably damagingrs35896902 592 R592H 1 Probably damagingrs147890567 595 T595M 1 Probably damagingrs142133260 601 N601S 1 Probably damagingrs150721457 673 I673T 1 Probably damagingPSIC = position-specific independent count score[Damaging rsID predicted by both SIFT and PolyPhen-2 server are shown in bold letters]

Table 3 List of SNPs predicted to be disease related by 7 SNP analyzer algorithms (T = tolerated as predicted by SIFT)

rsIDs Mutation PolyPhen-2 score SIFT score nsSNPs PhD-SNP PANTHER SNPsampGO P-Mutrs151296858 G59S 1 T Disease Disease Disease Disease mdashrs114166108 P62S 0997 002 Disease Disease Disease Neutral mdashrs182539049 L184S 0992 001 Disease Disease Disease Neutral mdashrs138685180 L224P 0999 T Disease Disease Disease Disease Pathologicalrs145415894 A276V 1 001 Disease Disease mdash Neutral Pathologicalrs142939718 L361P 1 002 Disease Disease Disease Disease Pathologicalrs35896902 R592H 1 001 Disease Disease Disease Disease Pathologicalrs147890567 T595M 1 T Disease Disease Disease Neutral Pathologicalrs150721457 I673T 1 005 Disease mdash mdash Disease mdash

collected from dbSNP database The UTRscan server looksfor patterns in the UTR database for regulatory regionmotifsand according to the given SNP information predicts if anymatched regulatory region is damaged [78] UTRscan found5 UTRsite motif matches in the RNASEL transcript Total 20matches were found for 5 motifs The results are shown inTable 5

37 Comparative Modeling of High-Risk NonsynonymousSNPs As the SAAPdb was unavailable for maintenance wewere unable to map the mutations in the protein structureTo find out the structure of the closest related proteins wesubmitted the structure to the NCBI protein BLAST tool andperformed BLAST against the Protein Database (PDB) TheBLAST result showed two PDB entries 4OAUand 4OAVwith


Table 4 Conservation profile of amino acids in RNASEL that coincide in location with high-risk nsSNPs

rsIDs Residue pos Residue CS Color Buried or exposed Functionrs151296858 59 G minus0834 8 e frs114166108 62 P minus099 9 e frs182539049 184 L minus0296 6 brs138685180 224 L minus0146 6 brs145415894 276 A minus1202 9 b srs142939718 361 L minus116 9 b srs35896902 592 R minus0915 8 e frs147890567 595 T minus0345 6 brs150721457 673 I minus0552 7 bCS conservation score (1ndash4 = variable 5 = average and 6ndash9 = conserved) (f) predicted functional site (s) predicted structural site

Table 5 Result showing UTR regions in RNASEL transcript fromUTRScan server

Signal name UTRregion

Matchtotal

Position intranscript

BRD BOX 31015840 1 2679ndash2685GY BOX 31015840 1 39ndash45uORF(upstream open reading frames) 51015840 13 mdash

MBE(Musashi Binding Element) 31015840 4

901ndash9053132ndash31363231ndash34554220ndash4258

PAS(Polyadenylation Signal) 31015840 1 4220ndash4258

100 identity We selected the structure with PDB id 4OAVand manually scanned the structure and with YASARA viewmutation tool carried out the mutations (G59S P62S L184SL224P A276V L361P R592H T595M and I673T) separatelyon the RNase L protein Energy minimization results fromYASARA-minimization server showed decreased free energyfor all the mutant models than the wild type models Theresults shown in Table 6 RMSD calculation was done by Tm-Align tool where the results showed a RMSD score of 224 forthemutation R59H and lowest 42 for G59SMutationA276Vin the mutant model scored 194 in the RMSD calculationThese results indicate significant change in the structure ofthe protein that can hamper its natural function

38 Prediction of Protein Structural Stability We used theneural network based routine tool I-Mutant 20 for analyzingthe potential alteration in protein stability due to mutationsThis tool took input of the mutated protein models derivedfromPHYRE-2 server in pdb format I-Mutant 20 generatesresults based on ProTherm database which contains themost extensive amount of thermodynamic experimental dataon free energy alterations of proteins stability owing tomutations 80 or 70 accurate prediction can be achievedby using protein structure or sequence respectively by thistool In addition to that this tool also provides the score of

Table 6 RMSD (A) values and total free energy after energyminimization of the wild type and mutated protein models RMSDcalculated by Tm-Align and energy minimization was performed byYASARA force field

Mutated models Energy afterminimization (kjmol) RMSD (A)

Wild Typeminus4364378

R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148

free energy change prediction due tomutations incorporatingthe energy based FOLD-X tool This increases the precisionto 93 on one-third of the database if FOLD-X analysis isincorporated along with I-Mutant [73]

Models with following mutations G59S P62S L184SL224P A276V L361P R592H T595M and I673T were sub-mitted to the server for DDG stability prediction and RSAcalculation All the mutations decreased protein stabilityexcept A76V which is shown to be increasing structuralstability but with a reliability index score 3 Mutation I673Taccounted for the lowest DDG value (minus246 kcalmol) fol-lowed by L184S (minus224 kcalmol) All other mutationsrsquo DDGvalues ranged from minus054 kcalmol to minus213 kcalmol thissuggests decreased protein stability due to DDG values beingless than 0 The results are shown in Table 7

39 Effect of Mutations on Domain Structures of RNSEL andTheir Functional Consequences The Prosit-ExPasy tool wasused to search for domain structures in RNase L and mapthe mutations in the domains for determining the changesthey might cause in the domain structures The tool searchesUNIProtKB database for motifs and in the produced resultshowed 9 Ankyrin Repeat domains in RNase L consisting of


Table 7 I-mutant predictions for selected nsSNPs

Mutation Sign of DDG DDG value predictionkcalmol RI RSA

G59S Decreaseminus136 5 38

P62S Decreaseminus215 8 07

L184S Decreaseminus224 5 00

L224P Decreaseminus213 9 158

A276V Increase 066 3 09L361P Decrease

minus159 8 00R592H Decrease

minus099 9 66T595M Decrease

minus054 7 22I673T Decrease

minus246 9 92DDG free energy change value in Kcalmol (gt0 increase lt0 decrease gt05large increase and lt25 large decrease) sign of DDG the direction of thechange (increase or decrease) the reliability index (RI) from 0 to 9 is shownin parentheses where 0 is the lowest RI and 9 is the highest RSA relativesurface accessibility

residues 24 to 329 RNase L also contains a protein kinasedomain and the functional KEN domain from residues 365to 586 and 589 to 723 respectively

Mutations G59S and P62S are located in the ANK 2repeat L184 is located in ANK 5 repeat L224P in ANK 6 andA276V in ANK 8 domain whereas L361P is buried in a B-sheet and the remaining threemutations R592H T595M andI673T are located in the KEN domain None of the selectednsSNPs in this study were in the protein kinase domain ofRNase L

The effects of the amino acid substitutions on the domainstructure of the protein were received in detail from ProjectHOPE server The mutation G59S results in a serine residuein place of Glycine at 59th position located in the ANK2repeat region This region is necessary for protein bindingand alteration of the buriedGlycine residuewith larger Serineresidue might abolish the core structure of the domain andaffect protein binding A graphical view of the mutation isshown in Figure 2

Similar destabilizing condition is created by the mutationP62S Replacing a buriedProlinewith a smaller Serine residuecreates a hollow space in the core of the domain that mightdisturb the function of the domain as shown in Figure 3 InANK6 repeat regionmutation L184S also introduces a smallerresidue than the wild type and the result alters the repeatregion and hampers the function The mutations are shownin Figure 4 The next mutation (L224P) disrupts an alphahelix in the protein located in ANK6 repeat As the mutatedresidue is located on the surface of a binding domain andis smaller in size than the wild type it is likely to disturbthe external interaction of the domain (Figure 5) MutationA276V replaces Alanine residue on an ankyrin repeat domain(ANK8) to Valine Valine is larger than Alanine which isburied and the size difference will probably hamper the corestructure of the protein (Figure 6)

Protein loses flexibility when a flexible residue (Leucine)is replaced with a rigid one like Proline Mutation L361P as aresult can disrupt the core structure of the protein (Figure 7)

Figure 2 Close-up of the mutation G59S The protein is coloredgrey the side chains of both the wild type (Glycine) and the mutant(Serine) residue are shown and colored green and red respectively

Figure 3 Close-up of the mutation P62S The protein is coloredgrey the side chains of both the wild type (Proline) and the mutant(Serine) residue are shown and colored green and red respectively

The KEN domain of the protein RNase L is accounted for itsendonuclease activity and a functional domain of the proteinThe mutation at position 592 (R592H) replaces the wild typeresidue with a smaller residue Residue 592 is exposed sothe mutation is likely to cause loss of functional property forreplacing an exposed residueMoreover the wild type residueat position 592 formed a salt bridge with Aspartic Acid at 556and Glutamic Acid at 457 and 558 The mutation will disturbthe ionic interaction of the wild type residue and affect thecatalytic activity of the domain The mutation is shown inFigure 8 The other two mutations T595M and I673T in theKENdomain also abolish the function of the domain Replac-ing the 595th residue Threonine with Methionine mightcause loss of hydrogen bonding and incorrect folding asMethionine is bigger and more hydrophobic thanThreonineSimilarly mutation I673T can also disrupt the function of theKEN domain by introducing a less hydrophobic and smallerresidue (Isoleucine toThreonine) resulting in an empty spaceand loss of hydrophobic interaction in protein core Theresults are shown in Figures 9 and 10

4 Discussion

SNP stands for Single Nucleotide Polymorphism Thesesingle nucleic acid variations are themajor cause of variations


Figure 4 Close-up of the mutation L184S The protein is coloredgrey the side chains of both the wild type (Leucine) and the mutant(Serine) residue are shown and colored green and red respectively

Figure 5 Close-up of the mutation L224P The protein is coloredgrey the side chains of both the wild type (Leucine) and the mutant(Proline) residue are shown and colored green and red respectively

among people besides SNPs are also accounted for majorityof inherited diseases as more than half of known geneticdisorders involve amino acid substitutions To date nearlyfour million SNPs could be found on NCBI SNP database butmany SNPs do not pose any significant change or any changeat all on protein structure and function due to degeneracyof amino acids and natural selection which will ultimatelyremove mutations in functionally important regions As aresult genetic studies to distinguish between functionallyneutral and disease associated polymorphisms have becomea major affair Since SNPs are dispersed throughout thegenome they are excellent geneticmarkers and an inseparableutility in disease research Although most disease associatedSNPs are found in the exons or coding regions also knownas nonsynonymous SNPs there is also evidence of SNPsoccurring in the intronic regions of gene and disrupting theregulatory region which in turn affects splicing process andgene expression

With the increasing number of reported and recordedSNPs in several databases expensive population based sur-veys have become challenging as the sheer number of SNPsdata makes it difficult to choose a target SNP for study whichare most likely to contribute in disease development In silicoapproach in this situation is a handy way to distinguish the

Figure 6 Close-up of the mutation A276V The protein is coloredgrey the side chains of both the wild type (Alanine) and the mutant(Valine) residue are shown and colored green and red respectively


deleterious SNPs using specialized algorithms that can dif-ferentiate between neutral and deleterious SNPs by analyzingthe databases and incorporating functional and structuralevidence about the ultimate effect of a polymorphism Byusing phylogenetic analysis and structural analysis ofmutatedproteins using hypothetical models can provide a fair amountof information with recognizable precision

RNASEL resides in one of the prostate cancer (PRCA)susceptibility loci HPC1 discovered in 1996 In linkageanalysis it is showed that a number of loci contain PRCA sus-ceptibility genes and that implies the heterogeneous nature ofthe disease incorporating several genetic and environmentalfactors [79 80]

Recent studies have found several accounts indicatingRNASEL linkage to prostate cancer but there remains asignificant amount of polymorphism data on RNASEL thatawaits extensive population based and clinical studies Inthis current study the SNP databases were analyzed to findout SNPs that might potentially be deleterious for RNASELemploying computational methods

Search for nsSNPs against RNASEL resulted in 122 hitsThe rsIDs were submitted to SIFT and PolyPhen-2 serversSIFT found 11 nsSNPs as nontolerable and PolyPhen found28 nsSNPs to be probably damaging (Table 1) 9 SNPs werepredicted deleterious in common Furthermore we analyzed


Figure 8 Close-up of the mutation R592H The protein is coloredgrey the side chains of both the wild type (Arginine) and themutant (Histidine) residue are shown and colored green and redrespectively

Figure 9 Close-up of the mutation T595M The protein is coloredgrey the side chains of both the wild type (Threonine) and themutant (Methionine) residue are shown and colored green and redrespectively

the data with several other SNP analyzing algorithms andin a combined result 9 nsSNPs were predicted deleteriousor disease related by at least 6 or more out of the used 7algorithms The rsIDs and their corresponding mutationsare listed in Table 2 among them rs151296858 (G59S) andrs35896902 (R592H) are previously known to be associatedwith the risk of prostate cancer [21 81]

UTRScan results confirmed the experimental finding thatthere are SNPs in the UTR region of RNASEL associated withcancer predisposition One 51015840 UTR RNASEL SNP rs3738579reportedly increases the risk of several cancers includingprostate cancer Three 31015840 UTR SNPs rs12135247 rs1048260and rs11072 have also been reported in RNASEL UTRScanprovided a preview of the UTR region of RNASEL From theresults two SNPs (rs566253706 rs566253706) were found inthe Mushashi Binding Elements (MBE) of 31015840 UTR althoughno experimental evidence is available [82]

The protein data bank contained two structures (4OAV4OAU) with 100 identity with RNase L We performedenergy minimization and RMSD calculation of the mutantand wild type models The free energy of all the mutatedmodels decreased significantly from the wild type models

Figure 10 Close-up of the mutation I673T The protein is coloredgrey the side chains of both the wild type (Isoleucine) and themutant (Threonine) residue are shown and colored green and redrespectively

The RMSD also indicated significant change in mutant mod-els Highest RMSD 259 was score by rs35896902 (R592H)and lowest calculated for rs142939718 (L361P) was 111 OtherRMSD values ranged from 114 to 194 According to RMSDvalue themutation R592H causes themost deviation from thewild type structure of RNase L followed by A276V (194)

The lowest total free energy after energy minimiza-tion was minus4310219 kjmol for mutation A276V followed byminus4222875 kjmol for T595M Significant decrease in freeenergy is recorded for all the mutation Considering theRMSD value and decrease being free energymutation A276Vseems to cause notable deviation from the wild type model ofRNase L The other mutation R592H which is also found tobe associatedwith PRCA in experimental data was also foundto be more deleterious than other mutations in our studyThis finding is also supported by the phylogenetic analysisdata from ConSurf The 276th residue Alanine is a buriedstructural residue with conservation score 9 denoting it is acrucial change in protein stability due to themutation A276Vaccording to Project HOPE

Residue Alanine 592 is an exposed functional residue inthe KEN domain of the protein and probably the mutationR592H will result in loss of catalytic activity of the domain aspredicted by Project HOPE

Although all the mutations are likely to disrupt proteinfunction and structure mutations G59S P62S and L361Palong with previously mentioned L224P and R592H aremost likely do cause severe dysfunction as all the residuesare strictly conserved and either functionally or structurallyinvolved

The RNase L synthesis is induced by interferon upon viralinfection After binding with 2-5A (51015840-phosphorylated 2101584051015840-linked oligoadenylates) activation occurs through subsequenthomodimerization Now the ankyrin repeat domains are alsocalled 2-5A sensors and they constitute the 2-5A bindingdomain As a result the mutations in the ankyrin repeatregions (G59S P62S L184S L224P A276V and L361P) canhamper RNase L activation by hampering 2-5A bindingThe formation and stability of the homodimer depend onankyrin repeat domains and kinase domain of the proteinAlthough no mutations in the kinase domain remain in the


final list of damaging SNPs in this study all the mutations inANK region potentially causes instability in protein structureas discussed above and shown by Project HOPE analysisThe other mutations (R592H T595M and I673T) in theribonuclease domain of the protein can possibly inhibit thecatalytic function of the protein Mutation R592H replaces aconserved and exposed residue in the functional domainThemolecular analysis of this amino acid substitution showedthis mutation can substantially destabilize the functionalcapability of RNase L because this substitution eliminates sev-eral molecular interactions of the wild type Arginine residueAccording to the molecular analysis the mutations mighteither retard structural integrity andproduce or interferewiththe functional features which ultimately lead to inhibition ofRNase L activity

So far studies involving RNASEL nsSNPs have beendone in several populations but only with a few numbers ofSNPs under consideration A study on German populationfound mutations E265X and R400P to be involved in PRCAsusceptibility [83]

E265X is also reported in several studies in FinnishGerman and Swedish populations [81 83 84] The substitu-tion R462Q is a common variant reported to both increase[73] and decrease the susceptibility to PRCA [85] D541Ea missense variant was found associated with increasedPRCA risk in Japan [85] and several other studies found noassociation [81 86 87] But none of these mutations exceptG59S and R592H [21 81] met the selection criteria of thiscurrent in silico analysis because several SNP analyzing algo-rithms failed to predict their deleterious effects and nonsensevariants (E265X) were not taken into consideration in thisstudy Nevertheless the deleterious nsSNPs that are foundin this study especially rs151296858 (G59S) rs145415894(A276V) 11 and rs35896902 (R592H) two out of these threemutations have been reported in population based studiesand proved to be potentially destabilizing both functionallyand structurally in our analysis In the process of this study itwas seen that despite some correct assumption the web-basedtools need to be more precise in detecting deleterious SNPsand population based studies are necessary to identify andtest the predicted SNPs in different populations

5 Conclusion

In this study available data from the NCBI dbSNP databasefor the prostate cancer susceptibility gene RNASEL hasbeen analyzed through several SNP analyzing tools andthe predicted deleterious SNPs were evaluated for theirpotential deleterious effect on the protein function andstability Nine SNPs were predicted deleterious thoseare rs151296858 rs114166108 rs182539049 rs138685180rs145415894 rs142939718 rs35896902 rs147890567 andrs150721457 among them three nsSNPs rs151296858 (G59S)rs145415894 (A276V) and rs35896902 (R592H) have themost probability to increase PRCA susceptibility Thepredicted SNPs are located in the ANK repeat domainsof the protein which facilitate binding of other moleculesand the catalytically active KEN domain which bears

the ribonuclease activity of the protein So it is verylikely that there are unreported nsSNPs increasing diseasepredisposition by altering protein function or structure Thefindings of this study will hopefully help to distinguish thedamaging SNPs which increase the risk of prostate cancerin patients of different populationsrsquo in future genome-widestudies Therefore extensive population based studies andclinical studies are required to characterize the vast SNP dataand for the verification of the findings of the current study

Conflict of Interests

The authors declare that they have no competing interests

Authorsrsquo Contribution

Amit Datta has made substantial contributions to con-ception and design acquisition of data and analysis andinterpretation of data Md Habibul Hasan Mazumder andAfrin Sultana Chowdhury carried out the molecular geneticstudies participated in the sequence alignment and draftedthe paper Md Anayet Hasan worked for computationalanalysis conceived of the study and participated in its designand coordination and helped to draft the paper All authorsread and approved the final paper

Acknowledgment

The authors cordially thank Adnan Mannan (Assistant Pro-fessor) of the Department of Genetic Engineering and Bio-technology University of Chittagong for his suggestions andinspiration during their research proceedings

References

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[2] P Carninci T Kasukawa S Katayama et al ldquoThe transcrip-tional landscape of the mammalian genomerdquo Science vol 309no 5740 pp 1559ndash1563 2005

[3] J Liu J Gough and B Rost ldquoDistinguishing protein-codingfrom non-coding RNAs through support vector machinesrdquoPLoS Genetics vol 2 no 4 article e29 2006

[4] P C Ng and S Henikoff ldquoPredicting the effects of amino acidsubstitutions on protein functionrdquo Annual Review of Genomicsand Human Genetics vol 7 pp 61ndash80 2006

[5] T P Dryja T L Mcgee L B Hahn et al ldquoMutations within therhodopsin gene in patients with autosomal dominant retinitispigmentosardquoTheNew England Journal of Medicine vol 323 no19 pp 1302ndash1307 1990

[6] E P Smith J Boyd G R Frank et al ldquoEstrogen resistancecaused by a mutation in the estrogen-receptor gene in a manrdquoThe New England Journal of Medicine vol 331 no 16 pp 1056ndash1061 1994

[7] I Barroso M Gurnell V E F Crowley et al ldquoDominantnegative mutations in human PPAR120574 associated with severeinsulin resistance diabetes mellitus and hypertensionrdquo Naturevol 402 no 6764 pp 880ndash883 1999


[8] D Chasman and R M Adams ldquoPredicting the functionalconsequences of non-synonymous single nucleotide polymor-phisms Structure-based assessment of amino acid variationrdquoJournal of Molecular Biology vol 307 no 2 pp 683ndash706 2001

[9] E S Lander ldquoThe new genomics global views of biologyrdquoScience vol 274 no 5287 pp 536ndash539 1996

[10] R Thomas R McConnell J Whittacker P Kirkpatrick JBradley and R Sandford ldquoIdentification of mutations in therepeated part of the autosomal dominant polycystic kidneydisease type 1 gene PKD1 by long-range PCRrdquo The AmericanJournal of Human Genetics vol 65 no 1 pp 39ndash49 1999

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[12] P Radivojac V Vacic C Haynes et al ldquoIdentification analysisand prediction of protein ubiquitination sitesrdquo Proteins Struc-ture Function and Bioinformatics vol 78 no 2 pp 365ndash3802010

[13] S W Doniger H S Kim D Swain et al ldquoA catalog of neutraland deleterious polymorphism in yeastrdquo PLoS Genetics vol 4no 8 Article ID e1000183 2008

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[16] E A Ostrander and J L Stanford ldquoGenetics of prostate cancertoo many loci too few genesrdquo American Journal of HumanGenetics vol 67 no 6 pp 1367ndash1375 2000

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[19] A Zhou J Paranjape T L Brown et al ldquoInterferon action andapoptosis are defective in mice devoid of 2101584051015840-oligoadenylate-dependent RNase Lrdquo The EMBO Journal vol 16 no 21 pp6355ndash6363 1997

[20] I M Kerr and R E Brown ldquopppA21015840p51015840A21015840p51015840A an inhibitorof protein synthesis synthesized with an enzyme fraction frominterferon-treated cellsrdquo Proceedings of the National Academy ofSciences of the United States of America vol 75 no 1 pp 256ndash260 1978

[21] J CarptenNNupponen S Isaacs et al ldquoGermlinemutations inthe ribonuclease L gene in families showing linkagewithHPC1rdquoNature Genetics vol 30 no 2 pp 181ndash184 2002

[22] R Grantham ldquoAmino acid difference formula to help explainprotein evolutionrdquo Science vol 184 no 4154 pp 813ndash873 1974

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[25] D Chasman and R M Adams ldquoPredicting the functionalconsequences of non-synonymous single nucleotide polymor-phisms structure-based assessment of amino acid variationrdquoJournal of Molecular Biology vol 307 no 2 pp 683ndash706 2001

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[34] A A Alshatwi T N Hasan N A Syed G Shafi and B LGrace ldquoIdentification of functional snps in BARD1 gene and InSilico analysis of damaging SNPs based on data procured fromdbSNP databaserdquo PLoS ONE vol 7 no 10 Article ID e439392012

[35] C G P Doss N Nagasundaram C Chakraborty L Chenand H Zhu ldquoExtrapolating the effect of deleterious nsSNPs inthe binding adaptability of flavopiridol with CDK7 protein amolecular dynamics approachrdquo Human Genomics vol 7 no 1article 10 2013

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[39] A Kumar and R Purohit ldquoComputational investigation ofpathogenic nsSNPs in CEP63 proteinrdquo Gene vol 503 no 1 pp75ndash82 2012

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[41] S T SherryM-HWardMKholodov et al ldquoDbSNP theNCBIdatabase of genetic variationrdquo Nucleic Acids Research vol 29no 1 pp 308ndash311 2001


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[44] P C Ng and S Henikoff ldquoSIFT predicting amino acid changesthat affect protein functionrdquo Nucleic Acids Research vol 31 no13 pp 3812ndash3814 2003

[45] P C Ng and S Henikoff ldquoPredicting deleterious amino acidsubstitutionsrdquoGenomeResearch vol 11 no 5 pp 863ndash874 2001

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[47] A A Alshatwi T NHasan N A Syed andG Shaf ldquoPredictingthe possibility of two newly isolated phenetheren ring con-taining compounds from Aristolochia manshuriensis as CDK2inhibitorsrdquo Bioinformation vol 7 no 7 pp 334ndash338 2011

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determinants within their homologuesrdquo Bioinformatics vol 18no 1 pp S71ndashS77 2002

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[77] S J H Van Deventer ldquoCytokine and cytokine receptor poly-morphisms in infectious diseaserdquo Intensive Care Medicine vol26 no 1 pp S98ndashS102 2000

[78] G Pesole and S Liuni ldquoInternet resources for the functionalanalysis of 51015840 and 31015840 untranslated regions of eukaryoticmRNAsrdquoTrends in Genetics vol 15 no 9 article 378 1999

[79] D F Easton D J Schaid A S Whittemore and W J IsaacsldquoWhere are the prostate cancer genes A summary of eightgenomewide searchesrdquoProstate vol 57 no 4 pp 261ndash269 2003

[80] J R Smith D Freije J D Carpten et al ldquoMajor susceptibilitylocus for prostate cancer on chromosome 1 suggested by agenome-wide searchrdquo Science vol 274 no 5291 pp 1371ndash13741996

[81] A Rokman T Ikonen E H Seppala et al ldquoGermline alter-ations of the RNASEL gene a candidate HPC1 gene at 1q25 inpatients and families with prostate cancerrdquo American Journal ofHuman Genetics vol 70 no 5 pp 1299ndash1304 2002

[82] B E Madsen E M Ramos M Boulard et al ldquoGermlinemutation in RNASEL predicts increased risk of head and neckuterine cervix and breast cancerrdquoPLoSONE vol 3 no 6 ArticleID e2492 2008

[83] CMaier J Haeusler K Herkommer et al ldquoMutation screeningand association study of RNASEL as a prostate cancer suscep-tibility generdquo British Journal of Cancer vol 92 no 6 pp 1159ndash1164 2005

[84] F Wiklund B-A Jonsson A J Brookes et al ldquoGenetic analysisof the RNASEL gene in hereditary familial and sporadicprostate cancerrdquo Clinical Cancer Research vol 10 no 21 pp7150ndash7156 2004

[85] H Nakazato K Suzuki H Matsui N Ohtake S Nakata andH Yamanaka ldquoRole of genetic polymorphisms of the RNASELgene on familial Prostate cancer risk in a Japanese populationrdquoBritish Journal of Cancer vol 89 no 4 pp 691ndash696 2003

[86] G Casey P J Neville S J Plummer et al ldquoRNASELArg462Glnvariant is implicated in up to 13 of prostate cancer casesrdquoNature Genetics vol 32 no 4 pp 581ndash583 2002

[87] L Wang S K McDonnell D A Elkins et al ldquoAnalysis of theRNASEL gene in familial and sporadic prostate cancerrdquo TheAmerican Journal of Human Genetics vol 71 no 1 pp 116ndash1232002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

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International Journal of

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Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

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diversity coded proteins in human populations and oftenassociated with human diseases Indeed earlier studies haverevealed thatmore than 50 of themutations associated withinherited genetic disorders are resulted by nsSNPs [11ndash13]Recently many researchers have focused on nsSNPs in cancercausing genes The recent studies have identified multiplensSNPs that influence susceptibility to infection as well asthe development of inflammatory disorders and autoimmunediseases [4ndash9] Nonetheless because innate immune genesare often highly polymorphic many nsSNPs in these genesremain uncharacterized

Prostate cancer (PRCA) is one of the most commonlydiagnosed cancers worldwide mostly in developed countries[14] In the United States it is the second leading cause ofcancer death in males [15] Currently no permitted curativetherapies are available for prostate cancer that has beenmetastasized In the United States it is the second leadingcause of cancer death in males [15] Therefore researcheshave focused to detect newer suitable solution for controllingprostate cancer and generating new potential targets fortherapy A fraction of PRCA patients belong to the hereditaryprostate cancer (HPC) families Linkage analyses in HPCfamilies have predicted that PRCA susceptibility genes areharbored in multiple genetic loci including HPC1 at 1q24-q25 ELAC2HPC2 at 17p11 PCAP at 1q422-q43 HPCX atXq27-q28 CAPB at 1p36 andHPC20 at 20q13 [16] As studyshows that genetic background has remarkable contributionto cause prostate cancer [15] both the identification andtreatment of cancer would potentially aid from the detectionof new genes that are particularly expressed in prostatecancer Among the large number of loci reported the HPC1locus at 1q24-q25 harbors the gene RNASEL (encodingribonuclease L or RNase L) a recently proposed candidate forthe hereditary prostate cancer (HPC) gene [17]

RNase L is a ubiquitously expressed 21015840 51015840-oligoadenylate(2-5A) dependent endoribonuclease that involves the antivi-ral action and proapoptosis activity in interferon [18 19]In virus infected and INF treated cells RNase L probablyshows its antiviral effects through a combination of activitieswhich include direct cleavage of the single-stranded viralRNAs suppression of protein synthesis via the disruption ofrRNA beginning of apoptosis and induction of associatedantiviral genes RNaseL induced apoptosis is the ultimateresult of a JNK-dependent stress-response pathway whichleads to the release of cytochrome c from mitochondria andcaspase-dependent cell death (apoptosis) Therefore RNaseL activation could lead to the elimination of virus infectedcells under different circumstances It might play a centralrole also in the regulation of mRNA turnover In control ofviral and cellular growth the role of the 2-5A system suggeststhat defects in the 2-5A-dependent RNASEL gene couldresult in decreased immunity to virus infections and cancer[20] Again RNase L is involved in the regulation of cellproliferation through the interferon-regulated 2-5A pathwayand therefore had been suggested as a tumor suppressor geneIn that connection a study found loss of heterozygosity andloss of RNase L protein in the microdissected tumors with agermline mutation [21] This indicates that RNASEL activitywas reduced in lymphoblasts from heterozygous individuals

compared with family members who were homozygous withrespect to the wild type allele Thus germline mutations inRNASEL might have diagnostic value and the 2-5A pathwaymay present opportunities for developing therapies for theprostate cancer patients In that regard recent studies onhereditary prostate cancer (HPC1) positioning at the locus1q24-q25 found two rare heterozygous inactivating germlinemutations in RNASEL linked to HPC1

From the last few years computational approaches havebeen widely used to detect the impact of deleterious nsSNPsin candidate genes by analyzing data such as conservation ofsequences across species [22] physicochemical properties ofthe polypeptides [23 24] and structural attributes [25]

By following computational algorithms the functionalSNPs out of a vast range of disease susceptible SNPs of ATMgene [26] BRCA1 gene andBRAF gene [27] have successfullydivided based on their structural and functional propertiesIn recent years computational approaches have been adoptedby many researchers in cancer studies either as a part oflarge population data analysis or only for predicting mostdeleterious SNPs from the large datasets Similar work hasbeen done extensively for breast cancer associated genesBRCA1 [28] and BRCA2 [29] where more than thousandSNPs have been analyzed altogether Insulin-like growthfactor 1 receptor (IGF1R) is another gene associated withboth breast cancer and prostate cancer and polymorphismsin the IGF1R receptor are found [30] which cause instabilityof the receptor protein Moreover SNPs which can increasedisease predisposition in colorectal cancer (HPNCC geneandMCAK gene) [31 32] haemoglobinopathies (beta-globingene) [33] and breast cancer (BARD1 gene) [34] have beenpredicted in computational studies Several oncogenes suchas cyclin-dependent kinase 7 (CDK7) gene [35] ErbB3 gene[36] polo-like kinase 1 (PLK1) [37] centromere-associatedprotein-E (CENP-E) [38] and centrosomal protein of 63 kDa(CEP63) [39] involved in cell cycle regulation and cell divi-sion are reported to contain damaging SNPs in correspondingin silico studies

Basing on the convincing data which indicates the wideinvolvement of RNASEL gene in varied range of humandiseases its functional genomics depend on mutation anal-ysis which is conceived to provide key advances in diseasediagnosis and therapy But the in silico analysis of noncodingSNPs (intronic exonic and 51015840 and 31015840 UTR SNPs) and codingSNPs (nsSNPs and sSNPs in exonic regions) in our candidateHPC1 gene remains unpredicted to date

Hence in the current investigation the in silico analysisof RNASEL gene has been carried out in order to charac-terize the deleterious mutations Our investigational studyinvolved (i) retrieval of SNPs in RNASEL gene from availabledatabases (ii) allocating the deleterious nsSNPs to theirphenotypic effects based on sequence and structure-basedhomology search and identifying the regulatory nsSNPsthat can alter the splicing and gene expression patterns(iii) predicting the specific effects of the substitutions ofamino acids on secondary structures by means of solventaccessibility and stability (iv) and prediction of change inthe domain structures due to the mutations The above insilico approaches offer a total of 9 high-risk nsSNPs sorted out


RNASEL dataset






































3 Results
































Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


RNASEL dataset






































3 Results
































Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology
















3 Results
































Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






3 Results
































Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


























Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






Matchtotal











R592Hminus3935433 259

P361minus3964560 111

G59Sminus3996150 119

P62Sminus4052230 114

A276Vminus4310219 194

L184Sminus4030940 174

L224Pminus4026040 157

T595Mminus4222875 145

I673Tminus4039672 148
























4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





















4 Discussion





























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




























5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

















5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





5 Conclusion







Acknowledgment


References



































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




























































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






















Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Documents

Research Article Functional and Structural Consequences of ...downloads.hindawi.com/journals/bmri/2015/271458.pdfF : e Flow Chart depicting the overall process of identi cation and