International Journal of Computational Bioinformatics and In Silico Modeling Vol. 6, No. 5 (2017): 965-981 Research Article Open Access
ISSN: 2320-0634
In silico analysis of Single Nucleotide Polymorphisms (SNPs) in Human JAK2 Gene Mohamed M. Babeker1*, Maye M. Merghani2, Iman M Shammat3, Anwaar A.Y Kordofani4, Mashaer T. Edirs5, Huda A. Elhassan6, A.O Ibrahim7, Mosab M Gasemelseed8, Noor A Abdulha9, L. M.A.Hassan10 and Mohamed A. Hassan11 Department of Hematology, Omdurman Ahlia University, Sudan. Department of Hematology, Nahda College, Sudan. 3 Faculty of Science, Taibah University, KSA and Faculty of Medical Laboratory Sciences, Omdurman Islamic University. 4 Department of Pathology, Faculty of Medicine, University of Khartoum, Sudan. 5 Department of Bioinformatics, Africa city of Technology, Sudan. 6 Department of Hematology, Nahda College, Sudan. 7 Faculty of medicine, University of Khartoum, Sudan. 8 Alneelain university faculty of Medical laboratory Sciences. 9 Department of Histopathology and cytology, Omdurman Ahlia University, Sudan. 10 Wildlife Research Center- Animal Resource Research Corporation Federal Ministry of livestock, Fisheries and Rangelands, Khartoum - Sudan 11 Department of Bioinformatics, Africa city of Technology, Sudan. 1 2
*Corresponding author: Mohamed M. Babeker; email:
[email protected] Received: 05 July 2017
Accepted: 23 July 2017
Online: 19 September 2017
ABSTRACT JAK2 gene is a member of a family of four Janus kinases. Each JAK has an active tyrosine kinase domain, which binds to type 1 cytokine receptors. Under normal physiological circumstances when a ligand binds with a receptor a conformational change occurs. In this study, a bioinformatics’ analysis of JAK2 gene initiated by Polyphen-2 and SIFT server is used to review 60 pathological polymorphisms. Among these 60, ten pathological polymorphisms were found to be very damaging, with higher Polyphen-2 score (=1) and SIFT tolerance index of 0.000-0.005. Protein structural analysis was done by modeling amino acid substitutions using Project Hope, Chimera and I-Mutant to check their stability and the effect of the native and mutant residues in the protein structure for all these pathological polymorphisms. 18 SNPs in the 3’URT containing 39 alleles can be disrupted by a conserved miRNA site and therefore might change the protein expression levels. We hope our results will provide useful information that is needed to help researchers to do further studies.
Keywords: In silico Analysis, JAK2 gene, SNPs, SIFT, PolyPhen-2, I-Mutant 3.0 and Project Hope. 1. INTRODUCTION
JAK is a family of tyrosine kinases, JAK1, JAK2, JAK3 and tyrosine kinase 2 (TYK2) [1]. JAKs named after a Roman god with two faces because they contain two symmetrical kinase-like domains; the C-terminal JAK homology 1 (JH1) and JH2 or pseudo kinase domain [2,3]. The main function of JAK kinases through interaction with cytokine receptors that lack intrinsic kinase activity and binding Ligand (e.g. erythropoietin, http://bioinfo.aizeonpublishers.net/content/2017/5/bioinfo965-981.pdf
thrombopoietin) to the appropriate cytokine receptor results in juxtaposition of JAKs followed by phosphorylation and activation, cytokine receptor phosphorylation and this creates a docking and stimulation for signal transducers and activators of transcription (STATs) [4]. The JAK2 gene is located on chromosome 9p24 [5]. A mutation in JAK 2 gene was first identified in the patients with MPNs (P.V, E.T and M.F) [6,7,8,9]. The mutation is a somatic mutation from 965
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G to T which results in a substitution of valine for phenylalanine at codon 617 within the JH2 domain of JAK2 (JAK2V617F). Valine 617 plays an important role in JAK2 kinase autoinhibition [10]. High prevalence of JAK2V617F mutations were reported in over 95% of patients with PV and 50% of patients with ET or MF [11]. Moreover there are many publications covering the most important forms of the JAK2 SNPs like, relationship between polymorphism ofJAK2,using four SNPs (rs6503695, rs744166, rs2293152, and rs12948909) and Bechet’s disease (BCS) in high risk Chinese population [12], and association between JAK2 (rs4495487) polymorphism and risk of Budd-Chiari Syndrome in Chinese population [13]. In the present study we aimed to determine the influence of various polymorphisms in JAK2 gene on its protein structure that may have an important role in disease susceptibility. The harmful SNPs for the JAK2 gene have not been predictable to date in silico.
2. MATERIALS AND METHODS
All data about Single Nucleotide Polymorphisms (SNPs) in human JAK2 gene was obtained for computational analysis software from National Center for Biotechnology Information (NCBI)SNPs database (dbSNPs) (http://www.ncbi.nlm.nih.gov/snp/) for SNPs in coding region (Exon) and SNPs at untranslated region at 3' ends (3'UTR). JAK2 gene contain 9778 SNPs, 486 of them found on homo sapiens, of which 437 were in the coding region, 77 were deleterious and 29 in the 3'UTR. All deleterious SNPs were investigated by different soft wares such as SIFT, Polyphen2, I mutant suite and Project hope. The FASTA format of the protein obtained from Uniprot at Expasy database (http://www.uniprot.org). The SNPs at the 3’UTR region was analyzed by Polymirt database. 2.1 The BioGRID: The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species[14]. 2.2 Predicting the effects of the amino acid substitution on protein function using SIFT: The type of genetic mutation that causes a single amino acid substitution (AAS) in a protein sequence is called a non-synonymous single nucleotide polymorphism (nsSNP). An nsSNP could potentially affect the function of the protein, subsequently altering the carrier's phenotype [15]. SIFT ( Sorting Intolerant From Tolerant)is an online computational tool to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids (http://blocks.fhcrc. org/sift/SIFT.html). SIFT has been applied to human variant databases and was able to distinguish mutations (deleterious) involved in disease from neutral polymorphisms[16].The main underlying principle of this program is that it generates alignments with a large number of homologous sequences, and http://bioinfo.aizeonpublishers.net/content/2017/5/bioinfo965-981.pdf
assigns scores to each residue ranging from zero to one. Scores close to zero indicate evolutionary conservation of the genes intolerance to substitution, while scores close to one indicate tolerance to substitution only [17]. 2.3 Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2: All protein sequences of nsSNPs submitted to SIFT were also submitted to PolyPhen. Unlike SIFT, it does not solely depend on sequence homology alone to make SNP functional predictions as its modeling of the amino acid substitutions is also based on structural information(18). PolyPhen-2 (polymorphism phenotyping) is an online bioinformatics program to predict the possible impact of amino acid substitutions on the stability and function of human proteins using structural and comparative evolutionary considerations [19] by analysis of multiple sequence alignment and protein 3D structure (http://genetics.bwh.harvard.edu/pph2/), in addition it calculates position-specific independent count scores (PSIC) for each of two variants, and then calculates the PSIC scores difference between two variantsfor quantitative assessment of the severity of the effect on protein function. Prediction outcomes could be classified as benign, possibly damaging or probably damaging according to the value of PSIC [20] and corresponding to posterior probability intervals (0.030) , (0.4-0.95), and (0.96-1) respectively. 2.4 Predicting the protein stability changes upon single point mutations using I-mutant 3.0: Again, all protein sequences of nsSNPs submitted to SIFT and PolyPhenwere also submitted to I-Mutant 3.0. The protein stability change due to single point mutation was predicted using I-Mutant 3.0. I-Mutant tested to predict the value of the free energy stability change upon single point mutation, starting from the protein structure or sequence [17]. The method was trained and tested on a data set derived from ProTherm, which is presently the most comprehensive available database of thermodynamic experimental data of free energy changes of protein stability upon mutation under different conditions [21].The output result of the predicted free energy change (DDG) classifies the prediction into one of three classes: largely unstable DDG< -0.5kcal/mol), largely stable (DDG> 0. 5kcal /mol ),or neutral (-0.5≤DDG≤0.5 kcal/mol ) [22]. 2.5 Analyzing the structural and functional effects of point mutations using A) Project Hope : Project hope is a new online web-server (http://www.cmbi.ru.nl/hope) to search protein 3D structures by collecting structural information from a series of sources, including calculations on the 3D coordinates of the protein, sequence annotations from the UniProt database, and predictions by DAS services [23]. Protein sequences were submitted to project hope server in order to analyze the structural and 966
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conformational variations that have resulted from single amino acid substitution corresponding to single nucleotide substitution [20]. B) Chimera 1.8: Modeling the SNPs on the 3D structure of the proteins is a very helpful action in order to predict the impact of SNPs on structural level. Therefore we used Raptor X web server to get the pdb(protein data bank) for protein with unknown 3D structure. (http://raptorx.uchicago.edu/StructurePrediction/pre dict/ ). Investigating 3D (three-dimensional) structure of proteins is helpful in predicting the effect of SNPs on the structural level and in displaying the degrees of alteration. UCSF Chimera (http://www.cgl.ucsf.edu/chimera/) is highly extensible software for interactive visualization and analysis of molecular structures; Chimera (version 1.8) software was used to scan the 3D structure of specific protein and then modify the original or native amino acid with the candidate to display the impact that can be produced [20].
2.6 Analyzing the functional impact of genetic polymorphisms in miRNA seed regions and miRNA target sites: PolymiRTS database was designed specifically for the analysis of non-coding SNPs namely 3'UTR.We used this computational server (http://compbio.uthsc.edu/miRSNP) in order to determine 3'UTR SNPs in JAK2 gene that may alter miRNA binding on target sites resulting in diverse functional consequences. The polymorphic miRNA target sites are assigned into four classes: ‘D’ (the derived allele disrupts a conserve miRNA site), ‘N’ (the derived allele disrupts a non conserved miRNA site), ‘C’ (the derived allele creates anew miRNA site) and ‘O’ (other cases when the ancestral allele cannot be determined unambiguously). The class ‘C’ may cause abnormal gene repression and class‘D’ may cause loss of normal repression control. So these two classes of PolymiRTS are most likely to have functional impacts [22].
3. RESULTS AND DISCUSSION
Figure 1: Functional interaction between JAK2 and its related genes.
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Table 1: Functional interaction between JAK2 and its related genes. Interactor AGTR1 ALK ASB2 ASB2 ASS1 CALM1 CCR5 CDKN1B CDKN1B CDKN1B CSF2RB CSF2RB CTLA4 CXCR4 DNAJA3 DNAJA3 DTX3L EGFR EGFR EGFR ELP2 EMD EPOR EPOR EPOR EPOR ERBB2 ERBB3 EZH2 FGFR1 FYN FYN GHR GHR GRB2 GRB2 GRB2 GRB10 GTF2I HES1 HES5 HIST2H3C HIST3H3 HSP90AA1 HSP90AB1 HSPA8 HTR2A IFNGR1 Ifngr1 IFNGR2 IKBKG IL2RB IL2RG IL4R IL5RA IL5RA IL6ST IL12RB2 IL12RB2 INSR IRS1 IRS1 JAK2 JAK3 KIT KPNB1 LYN MAP3K5 MAP3K5 NAP1L1 PIK3R1 PLCG1 PPIA PPP1CC
Role BAIT BAIT BAIT HIT BAIT HIT BAIT HIT BAIT HIT BAIT HIT BAIT BAIT BAIT HIT BAIT HIT BAIT HIT BAIT HIT HIT BAIT BAIT HIT BAIT HIT HIT BAIT BAIT BAIT BAIT BAIT HIT BAIT BAIT BAIT HIT BAIT BAIT BAIT HIT HIT HIT HIT HIT BAIT HIT BAIT BAIT BAIT BAIT BAIT BAIT BAIT BAIT HIT BAIT BAIT HIT HIT HIT BAIT BAIT HIT BAIT HIT HIT HIT BAIT HIT BAIT BAIT
Organism H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens M. musculus H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens
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Experimental Code Evidence Affinity Capture-Western Affinity Capture-MS Affinity Capture-Western Affinity Capture-Western Two-hybrid Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Biochemical Activity Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Protein-peptide Affinity Capture-Western Affinity Capture-MS Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Affinity Capture-Western Protein-peptide Biochemical Activity Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Co-fractionation Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Biochemical Activity Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Biochemical Activity Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Co-localization Reconstituted Complex Reconstituted Complex Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Biochemical Activity Biochemical Activity Affinity Capture-Western Affinity Capture-Western Affinity Capture-MS Reconstituted Complex Affinity Capture-Western Biochemical Activity Affinity Capture-MS Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Two-hybrid
968
Mohamed M. Babeker et al. / Int J Comput Bioinfo In Silico Model. 2017, 6(5): 965-981 PPP2R1B PRLR PRLR PRMT5 PRMT5 PRMT5 PRMT5 PRMT5 PTAFR PTK2B PTK2 PTPN1 PTPN1 PTPN6 PTPN11 PTPN11 PTPN11 PTPN12 RAF1 RBMX RCN1 SH2B1 SH2B1 SH2B1 SH2B1 SH2B2 SHC1 SHC1 SIRPA SKP2 SKP2 SLC2A1 SOCS1 SOCS1 SOCS1 SOCS1 SOCS1 SOCS1 SOCS3 SOCS3 SOCS3 SOCS3 SOCS3 SOCS3 SRC STAM2 STAM STAM STAT1 STAT1 STAT1 STAT1 STAT3 STAT3 STAT3 STAT5A STAT5A STAT5A STAT5A STAT5B STAT5B STAT5B TEC TEC TNFRSF1A TNFRSF1A TNFRSF1A TRAF6 TSHR TUB UBP1 VAV1 VAV1 VHL VHL
HIT BAIT HIT HIT BAIT HIT BAIT HIT BAIT HIT BAIT BAIT BAIT HIT HIT HIT BAIT HIT BAIT HIT HIT BAIT BAIT HIT BAIT HIT BAIT BAIT HIT HIT BAIT HIT BAIT HIT BAIT HIT HIT HIT HIT BAIT BAIT HIT HIT BAIT BAIT BAIT BAIT HIT HIT BAIT HIT BAIT HIT BAIT BAIT BAIT HIT HIT HIT BAIT HIT HIT HIT BAIT BAIT BAIT BAIT HIT HIT HIT HIT HIT HIT BAIT HIT
H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens H. sapiens
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Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Biochemical Activity Reconstituted Complex Two-hybrid Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Biochemical Activity Affinity Capture-Western Affinity Capture-Western Biochemical Activity Reconstituted Complex Affinity Capture-Western Reconstituted Complex Two-hybrid Affinity Capture-MS Affinity Capture-Western Reconstituted Complex Two-hybrid Two-hybrid Biochemical Activity Reconstituted Complex Reconstituted Complex Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Affinity Capture-MS Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Two-hybrid Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Reconstituted Complex Two-hybrid Two-hybrid Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Biochemical Activity Affinity Capture-Western Affinity Capture-Western Affinity Capture-Western Co-localization Affinity Capture-Western Affinity Capture-Western Co-localization Affinity Capture-Western Biochemical Activity Reconstituted Complex Two-hybrid Affinity Capture-Western Biochemical Activity Two-hybrid Affinity Capture-Western Biochemical Activity Affinity Capture-Western Affinity Capture-Western Reconstituted Complex Two-hybrid Affinity Capture-Western Biochemical Activity Affinity Capture-MS Affinity Capture-Western Biochemical Activity Affinity Capture-Western Affinity Capture-Western
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Table 2: Prediction result of SIFT and Polyphen programs Gene Name JAK2
SNP ID
Nucleotide Change T/A
AMINO ACID CHANGE D435E
Polyphen result
PSIC SD
SIFT result
rs17490221
Chromosome Location Chr9: 5072602
probably damaging
1
DELETERIOUS
Tolerance Index 0.033
rs17490221 rs55873896 rs55953208 rs55953208 rs77375493 rs77375493 rs77375493 rs77375493 rs139087749 rs139127951 rs139127951 rs140392449 rs143124074 rs143124074 rs145273013 rs145273013 rs145561071 rs145561071 rs147483622 rs149705816 rs149705816 rs150675431 rs150675431 rs151160183 rs151160183 rs190968273 rs190968273 rs199942090 rs199942090 rs200018153 rs200018153 rs200282557 rs200282557 rs200778413 rs200778413 rs201846579 rs201846579 rs201992086 rs368219482 rs368359929 rs368359929 rs368688124 rs368688124 rs369815812 rs369815812 rs371734553 rs371734553 rs371826393 rs372254348 rs372254348 rs373353534 rs373353534 rs375678155 rs375678155 rs376070326 rs376125987 rs376125987 rs377212884 rs377212884
Chr9:2072602 Chr9:5090810 Chr9:50644956 Chr9:5064956 Chr9:5073770 Chr9:5073770 Chr9:5073770 Chr9:5073770 Chr9:5022172 Chr9:5126352 Chr9:5126352 Chr9:5066682 Chr9:5064973 Chr9:5064973 Chr9:5080293 Chr9:5080293 Chr9:5073739 Chr9:5073739 Chr9:5044416 Chr9:5072609 Chr9:5072609 Chr9:5050742 Chr9:5050742 Chr9: 5078384 Chr9:5078384 Chr9:5066712 Chr9:5066712 Chr9:5123006 Chr9:5123006 Chr9:506500 Chr9:506500 Chr9:5089798 Chr9:5089798 Chr9:5054706 Chr9:5054706 Chr9:5066767 Chr9:5066767 Chr9:5080316 Chr9:5080316 Chr9:5069203 Chr9:5069203 Chr9:5081820 Chr9:5081820 Chr9:5080663 Chr9:5080663 Chr9:5072566 Chr9:5072566 Chr9:5044449 Chr9:5080268 Chr9:5080268 Chr9:5081805 Chr9:5081805 Chr9:5064922 Chr9:5064922 Chr9:5044450 Chr9:5054657 Chr9:5054657 Chr9:5126396 Chr9:5126396
T/A C/A C/A C/A G/A G/A G/T G/T A/G G/A G/A G/A C/G C/G G/C G/C C/A C/A C/T C/A C/A G/C G/C A/C A/C G/A G/A A/G A/G G/A G/A T/C T/C A/G A/G A/G A/G C/G G/A C/G C/G T/C T/C T/C T/C A/T A/T C/T T/C T/C C/T C/T G/A G/A G/A A/T A/T C/T C/T
D584E N986K A228E A377E V468I V617I V468F V617F Y62C G1066D G917D D407N L234V L383V L732F L583F H606Q H457Q R122C H587N H438N Q175H Q26H N691H N542H G417S G268S Y1021C Y872C V392M V243M I899T I750T K104R K253R Y286C Y435C S648C G591D P503R P354R F844L F695L L805P L656P Q423H Q572H R133W I724T I575T R839W R690W V366M V217M R133Q I237F I88F L932F L1081F
probably damaging possibly damaging possibly damaging probably damaging possibly damaging possibly damaging possibly damaging probably damaging probably damaging probably damaging probably damaging possibly damaging probably damaging probably damaging probably damaging probably damaging possibly damaging possibly damaging possibly damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging probably damaging possibly damaging possibly damaging probably damaging probably damaging probably damaging probably damaging possibly damaging possibly damaging probably damaging possibly damaging possibly damaging probably damaging probably damaging possibly damaging probably damaging probably damaging possibly damaging probably damaging probably damaging probably damaging
1 0.911 0.928 0.969 0.899 0.934 0.899 0.966 1 1 1 0.837 0.995 0.967 1 0.999 0.723 0.489 0.712 0.999 0.975 1 0.993 0.997 0.995 1 0.998 1 1 0.979 0.986 1 1 0.987 0.999 1 1.000 0.999 1 0.893 0.589 0.996 0.98 1 1 0.797 0.862 1 0.863 0.95 0.993 0.995 0.734 0.993 1 0.947 0.96 0.999 1
DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS DELETERIOUS
0.037 0.039 0.009 0.012 0.021 0.022 0.006 0.007 0.019 0.002 0.002 0.028 0.036 0.037 0.018 0.025 0.003 0.003 0.004 0.006 0.006 0.002 0.002 0.016 0.017 0.024 0.025 0.001 0.001 0.007 0.007 0.006 0.006 0.017 0.018 0.007 0.011 0.036 0000 0.01 0.015 0.005 0.005 0.001 0.001 0.022 0.031 0000 0.008 0.008 0.004 0.004 0.032 0.032 0.003 0.008 0.008 0.043 0.044
Table 3: Prediction result of I-Mutant software. Gene name
SNP ID
Amino position
JAK2
rs17490221 rs17490221 rs55873896 rs55953208 rs55953208
435 584 986 228 377
acid
WT
MT
PH
Temp (Cº)
D D N A A
E E K E E
7 7 7 7 7
25 25 25 25 25
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SVM2 Prediction Effect Increase Increase Decrease Decrease Decrease
DDG Value prediction Kcal/mol -0.24 -0.24 -0.58 -0.39 -0.39
RI 3 3 8 4 4
970
Mohamed M. Babeker et al. / Int J Comput Bioinfo In Silico Model. 2017, 6(5): 965-981 rs77375493 rs77375493 rs77375493 rs77375493 rs139087749 rs139127951 rs139127951 rs140392449 rs143124074 rs143124074 rs145273013 rs145273013 rs145561071 rs145561071 rs147483622 rs149705816 rs149705816 rs150675431 rs150675431 rs151160183 rs151160183 rs190968273 rs190968273 rs199942090 rs199942090 rs200018153 rs200018153 rs200282557 rs200282557 rs200778413 rs200778413 rs201846579 rs201846579 rs201992086 rs368219482 rs368359929 rs368359929 rs368688124 rs368688124 rs369815812 rs369815812 rs371734553 rs371734553 rs371826393 rs372254348 rs372254348 rs373353534 rs373353534 rs375678155 rs375678155 rs376070326 rs376125987 rs376125987 rs377212884 rs377212884
468 617 468 617 62 1066 917 407 234 383 732 583 606 457 122 587 438 175 26 691 542 417 268 1021 872 392 243 899 750 104 253 286 435 648 591 503 354 844 695 805 656 423 572 133 724 575 839 690 366 217 133 237 88 932 1081
V V V V Y G G D L L L L H H R H H Q Q N N G G Y Y V V I I K K Y Y S G P P F F L L Q Q R I I R R V V R I I L L
I I F F C D D N V V F F Q Q C N N H H H H S S C C M M T T R R C C C D R R L L P P H H W T T W W M M Q F F F F
7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Increase Increase Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease Decrease
-0.73 -0.73 -1.62 -1.62 -1.49 -0.81 -0.79 -1.11 -1.69 -1.69 -1.14 -1.14 -0.31 -0.31 -1.24 -0.62 -0.62 -0.64 -0.64 -0.64 -0.64 -1.52 -1.52 -0.75 -0.75 -0.59 -0.59 -1.91 -1.91 0.02 0.02 -1.3 -1.3 -0.82 -1.16 -0.79 -0.79 -0.98 -0.98 -2.06 -2.06 -0.85 -0.85 -0.46 -1.89 -1.89 -0.18 -0.18 -1.4 -1.4 -1.04 -1.08 -1.08 -1.14 -1.14
9 9 9 9 8 5 5 7 7 7 8 8 3 3 5 3 3 7 7 8 9 9 9 0 0 6 6 7 7 1 1 3 3 4 8 5 5 6 6 6 6 8 8 2 8 8 5 5 6 6 8 8 8 6 6
Table 4: Amino acid prosperities according to result obtained from Project Hope software. SNP ID
rs139127951
Amino Acid Change
Wild Type Properties Size
Charge
Hydropho bicity
Conservation
Size
Charge
Hydropho bicity
Conservation
G1066D
very conserved
>
- charge
- charge
