Design Of Chronic Myeloid Leukemia (Cml)Vaccine Through Computational Techniques: Free Science Essays

Chronic myeloid leukemia (CML) is a disease Of stem cell in which there is accumulation of large number of white blood cells takes place on bone marrow. some antigens such as AURORA KINASE A ,BCR/ABL ,CML-28 ,CML-66 ,ELA 2 , MPP11 ,NM 23-H2 A ,NM 23-H2 B ,PPP2R5C ,PRAME ,TELOMERASE and WT1 plays a key role as a molecular and oncoprotein for immunogenic target. Despite the success of rate of tyrosine kinase inhibitors (TKIs), as targeted therapy.There is no great success rate of CML cure, because due to the leukemic stem cells resistance against treatment. There has been development of many immunotherapies for CML in different stages and that are relapse after allogeneic stem cell transplantation. In the this report, specific immunotherapeutic approach for CML, vaccination are discussed along with leukemia-associated antigens (LAAs) such as AURORA KINASE A ,BCR/ABL ,CML-28 ,CML-66 ,ELA 2 , MPP11 ,NM 23-H2 A ,NM 23-H2 B ,PPP2R5C ,PRAME ,TELOMERASE and WT1, which are used for inducing specific response for T-cell and are sufficient for immunological targeting of CML as a targeted structure .
In the past decade, remarkable progress has been made in the field of immunology of CML, which has increased the result oriented hopes that this disease are curable by giving the supplementation to the current targeted chemotherapy with the help of immunotherapeutic approaches. There are many clinical trials have been done on the p210 BCR-ABL fusion protein, and other essential attractive targets such as Wilms’ tumor 1 (WT1) antigen and the epitope PR1 of proteinase 3, agranule protein overexpressed in CML. There is appearance of safe side chances for peptide vaccination which have result in clinical effects undoubtedly.

In this project we select the antigens that are responsible for the immunotherapeutic responses for CML such as AURORA KINASE A ,BCR/ABL ,CML-28 ,CML-66 ,ELA 2 , MPP11 ,NM 23-H2 A ,NM 23-H2 B ,PPP2R5C ,PRAME ,TELOMERASE and WT1 . We tried to predict the B-cell epitope and T-cell epitope of the selected antigen by using the computational tool. We also tried to predict of their 3D structure of the antigens which plays a crucial role in immunotherapeutic responses for CML.

INTRODUCTION

Chronic myelogenous leukemia (CML) is a disease of stem cell in which there is a accumulation of large number of white blood cells takes place on bone marrow . Chronic myelogenous leukemia (also called CML ) is a disease that progresses slowly on bone marrow and blood.This disease is usually occurs during or after middle age. It has a rare occurance in children.
In normal condition, the bone marrow produces immature blood stem cells that become mature blood cells during a period of time. A blood stem cell should become either a lymphoid stem cell or a myeloid stem cell. A lymphoid stem cell furthur becomes and matured into a white blood cell and a myeloid stem cell furthur becomes and matured into one of three types of mature blood cells and that are as follows:
‘ Red blood cells that carries oxygen and other essential substances to all tissues of the body of an organism.
‘ Platelets that are used for the formation of blood clots which stops bleeding.
‘ White blood cells also called as Granulocytes that fight against disease and infection.

FIG 1.1-Differentiation of blood cells

In blood cell development, There are several steps that are performed by a blood stem cell for the formation and production of red blood cell, platelet, or white blood cell.
In chronic myeloid leukemia(CML) there is a formation of large amount of blood stem cells that are converted into a type of white blood cell which is called as granulocytes. As the formation of these granulocytes are abnormal which never become a healthy white blood cells. These granulocytes are also called as leukemia cells. These leukemia cells can accumulated in the bone marrow and blood so that there is a less availability for healthy w, red blood cells, white blood cells and platelets. When there is an infection of this disease, there is a sign of infection, anemia, or easy bleeding may be witnessed.
For the treatment of the chronic myeloid leukemia(CML) along with chemotherapetic drugs reverse vaccinology applications also play crucial role. Keeping in mind the reverse vaccinology present study have following objectives:
‘ Prediction of antigens sequences
‘ Screening of predicted antigen as vaccine candidates.
‘ Prediction of B cell epitopes
‘ Prediction of supertypes and helper T cell epitopes
‘ Correlation with experimentally observed immunogenecity data

LITERATURE REVIEW

Chronic myelogenous leukemia (CML) is a clonal myeloproliferative hematopoietic stem cell disorder that is characterized by a translocation, which results in the expression of BCR-ABL fusion oncoproteins that are unique to the leukemic cells, necessary for oncogenesis, and potentially immunogenic . The BCR-ABL tyrosine kinase inhibitor imatinib is highly effective for first-line CML treatment and is increasingly used in patients with residual disease or relapse after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Despite the success of imatinib and other tyrosine kinase inhibitors (TKIs), CML remains largely incurable, and this is likely due to the treatment resistance of leukemic stem cells, which are responsible for rapid disease relapse after the discontinuation of therapy. How to treat patients with CML who are resistant to BCR-ABL tyrosine kinase inhibitors is an important and urgent issue for clinical hematology. Based on experimental research exploring the imatinib resistance mechanism in CML cells, second-generation TKIs were developed. Dasatinib and nilotinib, two newer drugs with higher potency than imatinib against BCR-ABL and activity against most imatinib-resistant BCR-ABL mutations, have demonstrated superior efficacy compared with imatinib for first-line chronic-phase CML treatment in randomized phase III trials . However, because successful treatment of a portion of patients with CML using allo-HSCT suggests the importance of immune mechanisms in eliminating leukemic cells including leukemia stem cells, TKI administration or HSCT may be combined with vaccination to cure patients with CML . The history of CML immunotherapetic strategies begins as early as 1975 when patients with CML
received repeated intradermal BCG-cultured cell mixture injections or were vaccinated with BCG alone in a clinical immunotherapy trial, and data from cases in which intermittent busulfan therapy was used provided evidence suggesting that immunotherapy prolonged the unmaintained remission of one-third of patients . Today, in the molecular biology and immunology era, increasing effective and specific immunotherapies involving vaccination or adoptive cellular immunotherapy are used.

Immune status in CML
In patients with leukemia, T cell function becomes suppressed with disease progression. Such immune dysfunction, which has been demonstrated in many patients with leukemia, may be due to a disorder in the thymic output function, the abnormal expression of the T cell receptor (TCR) repertoire and, in part, abnormal TCR signal transduction, possibly through altered CD3 gene
expression .In de novo CML, decreased levels of recent thymic emigrants in CD4+ and CD8 + T cells may underlie the persistent immunodeficiency found in patients. Restricted TCR V?? repertoire expression indicates T cell immunodeficiency in patients, although clonally expanded T cells suggest a specific immune response to leukemia-associated antigens. A deficiency in the level of CD3 gene expression may be a characteristic of lower T cell activation . The absence of the TCR?? chain not only influences the level of TCR expression on the cell membrane and the number of single positive (CD4+ or CD8+) circulating T cells, it also impairs the proliferative response and mature T cell activation level. T cells from patients with CML are
functionally impaired, and this is indicated by decreased TCR?? chain expression.
Moreover, imatinib impairs CD8+ T cells specifically directed against leukemia-associated antigen function in vitro; therefore, clinical imatinib administration may result in reduction of the efficacy of the graft versus-leukemia effect or other T-cell-based immunotherapies.
In contrast, it has been demonstrated that patients with CML possess T cells capable of recognizing autologous tumor cells, and clonally expanded T cells were identified in some TCR subfamilies in the peripheral blood of patients with CML, which display specific anti-leukemia
cytotoxicity such as WT1 or BCR-ABL-specific cytotoxic T cells (CTLs), indicating that specific anti-leukemic T cells could be generated in vivo This finding suggested that the host could have a specific immune response to leukemia-associated antigens despite T cell immunodeficiency. Several clinical observations, which were supported by experimental data, indicate the presence of CML-specific T cells. Leukemia-specific T cells are regularly detected in patients with CML and may be involved in the immunological control of the disease. However, recent findings demonstrated that leukemia-specific CTLs maintain only limited cytotoxic activity, do not produce interferon-?? or tumor necrosis factor-??, and do not expand after restimulation. Because CML-specific CTLs were characterized by the high expression of programmed death 1 (PD-1) and CML cells expressed PD-ligand 1 (PD-L1) , this phenomenon was found not only in a CML mouse model but in patients with CML as well .

BCR-ABL-derived peptide vaccines

Nearly all patients with CML express the BCR-ABL fusion product on their leukemia cells. The chimeric p210 BCR-ABL fusion protein, comprising products of either the b2a2 or b3a2 exon junction, represents a potentially immunogenic tumor-specific antigen. Despite the intracellular
location of this oncogenic fusion protein, it has been shown that peptides derived from its junctional region can be recognized by human T cells obtained from patients with CML or normal donors and can elicit a BCR-ABL peptide-specific T cell immune response. The first evidence of a cytolytic human immune response against CML BCR-ABL oncogene-derived peptides was described by Bocchia et al. who demonstrated that peptides derived from amino acid sequences crossing the b3a2 fusion breakpoint in p210 elicit class I restricted cytotoxic T cells and class II-mediated T cell proliferation, respectively, in vitro . Thus, such sequences may comprise definitive tumor-specific antigens in a peptide-based vaccine and provide a rationale for developing peptide-based vaccines for CML. The different BCR-ABL breakpoint peptide vaccines were evaluated in numerous clinic trials. Pinilla-Ibarz J et al. were the first to develop a BCR-ABL-derived peptide vaccination strategy . The vaccine was well tolerated and elicited specific immune responses in patients with CML. In a phase II trial from the same group, 14 patients with chronic phase CML were vaccinated 5 times. Immunological and clinical effects were detected; however, CTLs were not identified . Thus, in phase I and II trials, a tumor-specific BCR-ABL derived peptide vaccine could be safely administered to patients with chronic phase CML and elicit BCR-ABL peptidespecific CD4 immunity. However, CD8 responses were limited. Therefore, one strategy to circumvent this poor immunogenicity is to design synthetic immunogenic analog peptides that cross-react with native peptides (i.e., a heteroclitic response). A number of synthetic peptides derived from CML junctional sequences (i.e., p210/b3a2 or p210/b2a2) in which single and double amino acid substitutions were introduced at key HLA-A0201 binding positions were screened for eliciting HLA restricted, peptide-specific CTL responses using CD3+ T cells from several A0201 donors and patients with CML [26]. A significant BCR-ABL vaccination effect was described in a 63-year-old woman with CML relapse after interferon (IFN)-?? treatment who achieved a complete cytogenetic response for 6 years. The patientwas treated with a therapeutic vaccine comprising an immunogenic 25-mer b2a2 breakpoint-derived peptide (CMLb2a2-25) with binding properties for several HLADR molecules. After nine vaccine boosts, the patient developed an adequate b2a2-25 peptide-specific CD4+ T cell response, and the BCR-ABL1 transcript began declining in the peripheral blood. At the last evaluation i.e., 39 months since the vaccinations commenced, the patient is in complete molecular response with an undetectable level of BCR-ABL1 transcript in the peripheral blood and bone marrow. She continues to receive a vaccine boost every 3 months as her only treatment . Recently, BCR-ABL peptides were used as combination immunotherapy in imatinib-treated patients with CML. BCR-ABL peptide vaccination may improve the control of CML, particularly in patients responding well to imatinib. A trial with 19 imatinib-treated patients with CML in the first chronic phase were vaccinated with BCR-ABL peptides spanning the e14a2 fusion junction, and 14 of the 19 patients developed T cell responses to BCR-ABL peptides. The development of an anti-BCR-ABL T cell response correlated with a subsequent decrease in BCR-ABL transcripts. Of the 14 patients in MCR at baseline, 13 developed at least a 1 log decrease in BCR-ABL transcripts . Similar results were reported in a phase II trial in which 10 patients who had received imatinib for a median of 62 months were enrolled . These data suggested that a vaccination-related transient disruption in immune tolerance may contribute to a reduction in BCR-ABL transcripts, and this BCR-ABL peptide vaccine may transiently improve the molecular response in a subset of patients with CML. Although most patients with CML achieve clinically relevant hematologic and cytogenetic responses to imatinib, CML cells with a BCR-ABL mutation (T315I) confer drug resistance to imatinib, dasatinib and nilotinib treatment; therefore, the development of a vaccine expressing the T315I-mutated BCR-ABL antigen to stimulate an anti-BCR-ABL (T315I) immune response appears to be more important. A recombinant yeast-based vaccine expressing the T315I-mutated BCR-ABL antigen was demonstrated to significantly reduce or eliminate BCRABL (T315I) leukemia cells from the peripheral blood of immunized animals and extended leukemia-free survival in a murine BCR-ABL + leukemia model . Thus, this may be a potential vaccine for patients with CML.

WT1 vaccines

WT1 is an oncogenic protein expressed by the Wilms’ tumor gene that is overexpressed in the majority of acute myelogenous leukemias (AMLs) and CML. WT1 expression in progenitor cells is minimal or absent, and the limited WT1 tissue expression in adults suggests that WT1 may be a leukemia therapy target. In mice, WT1 vaccines elicit specific immune responses without evidence of tissue damage [31]. Moreover, humoral immune responses against the WT1 protein could be elicited in patients with WT1-expressing hematopoietic malignancies . Therefore, therapeutic vaccines directed against WT1 have the increased expectation that they will be able to elicit and/or boost an immune response to WT1. For example, an imatinib-treated patient with CML who was intradermally administered a WT1 peptide vaccine elicited WT1-specific immune responses and had a resultant reduction in persistent residual disease with the co-administration of imatinib. BCR-ABL mRNA levels were maintained below the detection limit for 8 months beginning at vaccination week 77. The decrease in BCR-ABL mRNA levels was associated with an increase in the frequency of WT1-specific CTLs . These findings indicated that WT1 peptide vaccines may become a safe and cure-oriented treatment for patients with CML who have residual disease despite imatinib treatment.

Potential LAA vaccines

The BCR-ABL fusion peptide is the predominant antigen in CML, and WT1 is also thought to be important for the identification of leukemia-associated antigens (LAAs) in CML to elicit a specific immune response in patients. However, a more effective and specific immunotherapy
with an optimal expression pattern is required for patients with CML, and the identification
of additional LAAs is a pivotal step [34,35]. Recently, a number of LAAs that are able to induce specific immune responses were identified in CML including telomerase, PR1, hyaluronan acid-mediated motility (RHAMM), CML-66, CML-28, CML-Ag165, NM23-H2, PPP2R5C, PR3, ELA2, PRAME and a novel epitope derived from the M-phase phosphoprotein 11 protein
(MPP11) [34,36-42]. Most of these LAAs have been recognized by human CD8+ T cells. Dendritic cells were DCs pulsed with peptides and then used to generate CTLs. Aurora-A kinase (Aur-A) is a member of the serine/threonine kinase family that regulates the cell division process and has been recently implicated in tumorigenesis. An antigenic 9-amino-acid epitope which was derived from Aur-A, is capable of generating leukemia-reactive CTLs .Thus, cellular immunotherapy targeting Aur-A is a promising strategy for leukemia therapy .

MATERIAL AND METHOD
Reterieval of proteome sequence database-

UniProt-

UniProt is an universal protein data base which contains high-quality comprehensive database of protein sequence and functional information, of protein. It is a free accessible database . There are many entries that are derived from genome sequencing projects. It also contains a vast amount of information of biological function of proteins that are derived from the research literature.

FIG-1.2-Home page of uniprot

3-D structure prediction tool:
SWISS-MODEL-
It is a fully automate server ,which is used for homology-modeling of a protein three dimensional structure. This server is accessible via the ExPASy web server. With the help of this server protein modeling is accessible molecular biologists and biochemists globally.

FIG-1.3-Home page of Swiss model

B cell epitope and T cell epitope prediction tool-

NETCTL 1.2 SERVER
We used a web server NetCTL 1.2 for the prediction of CTL epitopes in protein sequences, integerating prediction of MHC class -I binding affinity. TAP transport efficiency and C-terminal proteasomal clevage analysis . NetCTL 1.2 is demonstrated to have higher prediction performance than other web server such as EpiJen,MAPPP etc. For classifying the peptides into HLA class-I supertypes (5 HLA-A [A1,A2,A3,A24,A26] and 7 HLA-B [B7,B8,B27,B39,B44,B58,B62]) binders and non-binders,building affinity (IC50) less than or equal to 500 nM equivalent to the transformed binding affinity value grater than or equal to 0.426 was used, which result on average an optimal predictive performance. Peptides with a combined prediction score greater than or equal to default threshold value (0.75) were marked as potential HLA class I supertype CTL epitopes.

FIG-1.4-Home page of NETCTL 1.2 SERVER

DISCOTOPE 2.0 SERVER

DiscoTope server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers.

FIG-1.5-Home page of DISCOTOPE 2.0 SERVER

RESULTS AND DISCUSSION
The predicted 3D structure of selected antigens with their Ramachandran Plot :

AURORA KINASE A

FIG-1.6- PREDICTED 3 DIMENSIONAL STRUCTURE OF AURORA KINASE A

FIG-1.7- RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF AURORA KINASE A
BCR-ABL

FIG-1.8- PREDICTED 3 DIMENSIONAL STRUCTURE OF BCR-ABL

FIG-1.9-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF BCR-ABL

CML-28

FIG-1.10- PREDICTED 3 DIMENSIONAL STRUCTURE OF CML-28

FIG-1.11- RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF CML-28

CML-66

FIG-1.12- PREDICTED 3- DIMENSIONAL STRUCTURE OF CML-66

FIG-1.13-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF CML-66

ELA 2

FIG-1.14- PREDICTED 3 DIMENSIONAL STRUCTURE OF ELA 2

FIG-1.15-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF ELA 2

MPP11

FIG-1.16- PREDICTED 3 DIMENSIONAL STRUCTURE OF MPP11

FIG-1.17-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF MPP11
NM 23 H-2 A

FIG-1.18- PREDICTED 3 DIMENSIONAL STRUCTURE OF NM 23 H-2 A

FIG-1.19-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF NM 23 H-2 A

NM 23 H-2 B

FIG-1.20- PREDICTED 3 DIMENSIONAL STRUCTURE OF NM 23 H-2 B

FIG-1.21-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF NM 23 H-2 B

PPP2R5C

FIG-1.22- PREDICTED 3 DIMENSIONAL STRUCTURE OF PPP2R5C

FIG-1.23-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF PPP2R5C
PRAME

FIG-1.24- PREDICTED 3 DIMENSIONAL STRUCTURE OF PRAME

FIG-1.25-RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF PRAME

TELOMERASE

FIG-1.26- PREDICTED 3 DIMENSIONAL STRUCTURE OF TELOMERASE

FIG-1.27- RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF TELOMERASE

WT1

FIG- 1.28-PREDICTED 3 DIMENSIONAL STRUCTURE OF WT1

FIG-1.29- RAMACHANDRAN PLOT OF 3- DIMENSIONAL STRUCTURE OF WT1

The NETCTL result of the selected antigen are as follows:

TABLE NO 1.0 ‘Computational characterization of antigen AURORA KINASE predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the AURORA KINASE binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 40 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO SUPER TYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1. A1 287 TTLCGTLDY 0.5693 0.9495 3.0320 2.7113
2. A1 330 EANTYQETY 0.3422 0.9052 2.5740 1.7174
3. A2 271 KIADFGWSV 0.8305 0.9545 0.6150 1.4120
4. A2 207 YLILEYAPL 0.7652 0.8756 1.0700 1.3256
5. A3 158 ILALKVLFK 0.8281 0.7958 0.4190 1.6989
6. A3 163 VLFKAQLEK 0.7598 0.9608 0.7590 1.6121
7. A3 187 HLRHPNILR 0.6583 0.5594 1.5160 1.3986
8. A3 278 SVHAPSSRR 0.5840 0.7514 1.6500 1.2942
9. A24 333 TYQETYKRI 0.6767 0.8528 0.8870 1.6132
10. A24 127 QWALEDFEI 0.5741 0.5058 0.9430 1.3454
11. A26 336 ETYKRISRV 0.5225 0.9690 0.2270 1.5589
12. B7 281 APSSRRTTL 0.8849 0.9775 0.7670 1.8921
13. B7 84 VPHPVSRPL 0.7839 0.9779 0.7960 1.6988
14. B7 11 GPVKATAPV 0.7731 0.9668 -0.0370 1.6346
15. B7 31 FPCQNPLPV 0.7088 0.8014 -0.1250 1.4814
16. B8 122 ESKKRQWAL 0.5406 0.9233 0.6220 2.0159
17. B8 207 YLILEYAPL 0.4109 0.8756 1.0700 1.5879
18. B8 338 YKRISRVEF 0.3449 0.3880 2.6230 1.3673
19. B8 281 APSSRRTTL 0.3449 0.9775 0.7670 1.3630
20. B8 23 KRVLVTQQF 0.5579 0.9738 2.7810 1.7683
21. B27 219 YRELQKLSK 0.5841 0.9180 0.5540 1.7182
22. B27 285 RRTTLCGTL 0.4880 0.9715 1.4230 1.5141
23. B27 55 QRVPLQAQK 0.4881 0.9656 0.8160 1.4831
24. B27 125 KRQWALEDF 0.4041 0.9478 2.9170 1.3622
25. B27 204 TRVYLILEY 0.3961 0.9344 3.3420 1.3602
26. B27 250 KRVIHRDIK 0.4378 0.3300 0.9100 1.2587
27. B27 200 FHDATRVYL 0.7873 0.6086 0.6390 2.6438
28. B39 186 SHLRHPNIL 0.6580 0.7754 0.9840 2.2721
29. B39 254 HRDIKPENL 0.5155 0.9259 0.9030 1.8343
30. B39 153 KQSKFILAL 0.3992 0.9094 0.9660 1.4626
31. B39 236 YITELANAL 0.3850 0.9511 1.0370 1.4272
32. B39 170 EKAGVEHQL 0.3363 0.9388 0.8650 1.2607
33. B44 180 REVEIQSHL 0.7007 0.9631 1.2730 1.9445
34. B44 151 REKQSKFIL 0.7116 0.7066 1.2150 1.9302
35. B44 375 REVLEHPWI 0.6210 0.4135 0.7690 1.6395
36. B58 340 RISRVEFTF 0.7025 0.6011 3.0040 1.8235
37. B58 232 RTATYITEL 0.4665 0.9555 1.1450 1.2519
38. B62 199 YFHDATRVY 0.5476 0.9522 3.0720 1.3835
39. B62 29 QQFPCQNPL 0.5594 0.8200 1.1650 1.2918
40. B62 153 KQSKFILAL 0.5414 0.9094 0.9660 1.2595

TABLE NO 1.1 ‘Computational characterization of antigen BCR/ABL predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . . In this prediction method ,the server integrates the prediction of peptides of the BCR/ABL binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 6 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPER TYPE RESIDUE NO. PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1. A3 14 HSIPLTINK 0.6687 0.9702 0.5300 1.4305
2. A24 44 RWNSKENLL 0.4930 0.9651 1.5050 1.2697
3. B27 43 ARWNSKENL 0.4570 0.9629 1.6310 1.4407
4. B58 88 LGYNHNGEW 0.5225 0.8917 0.8560 1.3541
5. B58 37 QGLSEAARW 0.5273 0.6508 0.5040 1.3111
6. B62 26 LQRPVASDF 0.5848 0.9479 2.8170 1.4440

TABLE NO 1.2 ‘Computational characterization of antigen CML-28 predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the CML-28 binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 25 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPER TYPE RESIDUE NO. PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1. A1 51 DTSVLAGVY 0.6159 0.6943 2.5530 2.8470
2. A1 193 LMSSTKGLY 0.5084 0.9204 3.0940 2.4514
3. A1 216 ASQHVFRFY 0.3635 0.8728 3.2310 1.8360
4. A1 123 VSDAGSLLA 0.3043 0.4135 -0.5620 1.3260
5. A2 47 FLQGDTSVL 0.7330 0.9706 0.9010 1.2833
6. A3 65 KVSKEIFNK 0.6473 0.6328 0.7530 1.3508
7. A3 57 GVYGPAEVK 0.6185 0.7844 0.7650 1.3199
8. B7 20 SPRGPGCSL 0.8445 0.9759 0.8060 1.8159
9. B7 111 HPRTSITVV 0.7580 0.9063 0.0160 1.5990
10. B7 40 RPDGSASFL 0.6859 0.9629 0.7720 1.5063
11. B8 173 QEKEARAVL 0.3189 0.9623 0.9310 1.2799
12. B8 47 FLQGDTSVL 0.3146 0.9706 0.9010 1.2649
13. B8 220 VFRFYRESL 0.3098 0.9116 1.2100 1.2551
14. B27 190 RKLLMSSTK 0.5226 0.9042 0.6090 1.5553
15. B27 224 YRESLQRRY 0.4529 0.9699 2.8620 1.4925
16. B27 39 SRPDGSASF 0.3890 0.9775 2.8270 1.3220
17. B39 177 ARAVLTFAL 0.5877 0.9396 1.1550 2.0803
18. B39 159 LDSDGTLVL 0.4019 0.9752 0.8010 1.4731
19. B44 68 KEIFNKATL 0.6784 0.9283 1.0490 1.8728
20. B44 62 AEVKVSKEI 0.5159 0.6629 0.4760 1.4017
21. B44 175 KEARAVLTF 0.4322 0.9558 2.3560 1.3322
22. B44 102 CEAVVLGTL 0.4617 0.8203 0.8050 1.3075
23. B44 173 QEKEARAVL 0.4473 0.9623 0.9310 1.2994
24. B58 215 AASQHVFRF 0.5567 0.8868 2.6780 1.5215
25. B62 213 AQAASQHVF 0.6350 0.7398 2.9250 1.5179

TABLE NO 1.3 ‘Computational characterization of antigen CML-66 predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the CML-66 binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 52 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NO. PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1. A1 129 LSDGTGRLY 0.6792 0.7529 2.8030 3.1369
2. A1 15 LLDPRFEGY 0.4919 0.9785 2.3667 2.6270

3. A1 499 FACAPNYSY 0.2707 0.9000 2.7860 1.4237
4. A1 148 ASEKWEIMF 0.2390 0.8718 2.6290 1.2770
5. A2 306 FLPDHINIV 0.8633 0.9609 0.1750 1.4398
6. A3 464 LLWQPHSSK 0.6406 0.9784 0.8140 1.3932
7. A3 202 SLEWVTISK 0.6493 0.9730 0.4400 1.3900
8. A3 485 ALGYVQASK 0.6171 0.7625 0.6060 1.3061
9. A3 513 CLRRVFIYR 0.5830 0.9192 1.3450 1.3023
10. A3 570 TTKNLFLIK 0.6072 0.6415 0.4680 1.2624
11. A3 89 VMLDTALGK 0.6018 0.6204 0.6760 1.2594
12. A24 70 WYQDSVYYI 0.7772 0.7677 0.7940 1.8099
13. A24 76 YYIDTLGRI 0.7308 0.2981 0.8950 1.6456
14. A26 51 YTLEHMHAF 0.7704 0.1540 2.4620 2.2135
15. A26 551 ETNDPILGF 0.7146 0.9558 2.2630 2.1742
16. A26 43 EVKLRDDQY 0.4674 0.8474 3.0110 1.5320
17. A26 475 DMWEHIATF 0.3971 0.9679 2.3730 1.3294
18. A26 271 EKIKEPLYY 0.3819 0.8165 2.8930 1.2921
19. B7 502 APNYSYAAL 0.8151 0.9654 0.6930 1.7519
20. B7 522 QPAPMSTVL 0.7103 0.9736 0.8120 1.5569
21. B8 518 FIYRQPAPM 0.4313 0.4217 0.5720 1.5648
22. B8 164 FIIIHSISL 0.3354 0.9601 0.9120 1.3350
23. B8 51 YTLEHMHAF 0.3428 0.1540 2.4620 1.3168
24. B27 113 NRLCASIHF 0.4775 0.9558 2.6570 1.5455
25. B27 536 GRQVGQVAK 0.4922 0.9106 0.2930 1.4596
26. B27 515 RRVFIYRQP 0.4802 0.0364 0.6810 1.3161
27. B27 564 ERLFVLTTK 0.4285 0.9695 0.6260 1.3157
28. B27 82 GRIMNLTVM 0.4207 0.8823 0.4490 1.2731
29. B39 478 EHIATFNAL 0.6291 0.9052 0.7710 2.1883
30. B39 177 EHSIATLLL 0.5943 0.9680 0.5870 2.0771
31. B39 121 FSSSTWVTL 0.4287 0.9425 0.8730 1.5575
32. B39 175 AEEHSIATL 0.3418 0.9518 0.9600 1.2850
33. B44 1 MEVAANCSL 0.7352 0.8915 0.9420 2.0028
34. B44 152 WEIMFNEEL 0.7696 0.1935 0.9620 1.9843
35. B44 175 AEEHSIATL 0.6644 0.9518 0.9600 1.8372
36. B44 176 EEHSIATLL 0.6032 0.9377 0.7990 1.6755
37. B44 352 NEGLTWPEL 0.5137 0.9073 0.7940 1.4489
38. B44 299 KEDIQIQFL 0.5291 0.4721 0.7600 1.4200
39. B44 158 EELGDPFII 0.5321 0.1926 0.3540 1.3652
40. B44 53 LEHMHAFGM 0.5362 0.1624 0.2130 1.3637
41. B44 218 YEIIKRDIL 0.4786 0.5028 0.9690 1.3100
42. B44 27 LEPLPCYQL 0.4357 0.9716 0.8620 1.2687
43. B44 144 RGNSASEKW 0.7484 0.6987 0.6010 1.8215
44. B58 197 SGFYVSLEW 0.7226 0.7012 0.7530 1.7712
45. B58 469 HSSKQDDMW 0.7029 0.1281 0.6850 1.6376
46. B58 246 IVSYKSLTF 0.5025 0.8945 2.7280 1.4031
47. B58 272 KIKEPLYYW 0.4908 0.9771 0.9510 1.3002
48. B58 318 HQFLEGKLY 0.5966 0.8245 2.8820 1.4522
49. B62 433 VVNLGSNQY 0.5344 0.9741 3.0080 1.3574
50. B62 246 IVSYKSLTF 0.5182 0.8945 2.7280 1.2993
51. B62 51 YTLEHMHAF 0.5706 0.1540 2.4620 1.2791
52. B62 499 FACAPNYSY 0.5048 0.9000 2.7860 1.2764

TABLE NO 1.4 ‘Computational characterization of antigen ELA-2 predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . . In this prediction method ,the server integrates the prediction of peptides of the ELA-2 binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 43 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 341 QVAERALYY 0.4928 0.7023 2.9880 2.3469
2) A1 346 ALYYWNNEY 0.2896 0.9739 3.4070 1.5460
3) A2 246 FLLKVLLPL 0.8915 0.9544 0.9060 1.5174
4) A2 72 ALSEMVEYI 0.8264 0.2519 0.8480 1.3121
5) A3 97 HMFAVNMFR 0.8043 0.9364 1.6740 1.7379
6) A3 395 KLFMEMNQK 0.7636 0.9066 0.7510 1.6107
7) A3 261 SVYHPQLAY 0.6174 0.9739 3.3510 1.4756
8) A3 341 QVAERALYY 0.5942 0.7023 2.9880 1.3731
9) A3 184 TTLHRIYGK 0.6035 0.8016 0.5410 1.2830
10) A24 91 IYPEVVHMF 0.8644 0.9682 2.6620 2.1190
11) A24 157 KYIDQKFVL 0.6668 0.9724 1.4960 1.6405
12) A24 347 LYYWNNEYI 0.6369 0.4577 1.1440 1.4820
13) A24 96 VHMFAVNMF 0.5374 0.9482 2.7160 1.4223
14) A24 137 VYEFFLRFL 0.5288 0.9370 1.1850 1.3258
15) A24 348 YYWNNEYIM 0.5249 0.9564 0.7690 1.2997
16) B7 341 QVAERALYY 0.5007 0.7023 2.9880 1.5984
17) B8 319 EFVKIMEPL 0.4195 0.6009 1.0500 1.2685
18) B8 163 FVLQLLELF 0.3804 0.6623 2.6970 1.2550
19) B8 435 NLAKANPQY 0.3628 0.9262 2.8310 1.2540
20) B27 252 LPLHKVKSL 0.6792 0.9671 0.7200 1.4913
21) B39 65 WKEVKRAAL 0.4684 0.1761 0.9340 1.6728
22) B39 246 FLLKVLLPL 0.3983 0.9544 0.9060 1.5487
23) B39 491 PLARRKSEL 0.3574 0.8919 0.6430 1.3863
24) B39 142 LRFLESPDF 0.3956 0.8132 2.7520 1.3111
25) B44 386 IHGLIYNAL 0.6019 0.8560 0.8320 2.0969
26) B44 216 EHHNGIAEL 0.5194 0.9676 0.7980 1.8480
27) B44 217 HHNGIAELL 0.5165 0.8042 0.6100 1.8047
28) B44 178 ERDFLKTTL 0.3886 0.9441 0.9130 1.4312
29) B44 300 KEVMFLNEL 0.6741 0.8816 0.8590 1.8456
30) B58 38 QEKLFIQKL 0.5284 0.9528 0.6910 1.4870
31) B58 213 YETEHHNGI 0.5433 0.7957 0.3520 1.4834
32) B58 240 KEEHKIFLL 0.5244 0.9099 0.8300 1.4775
33) B58 93 PEVVHMFAV 0.5127 0.3979 -0.2750 1.3166
34) B58 438 KANPQYTVY 0.7084 0.9731 3.1440 1.8997
35) B62 110 SSNPTGAEF 0.5949 0.9738 2.3890 1.6063
36) B62 342 VAERALYYW 0.5522 0.5975 0.8060 1.3744
37) B62 136 LVYEFFLRF 0.4630 0.9776 2.8150 1.3309
38) B62 285 VVMALLKYW 0.5231 0.3156 1.0050 1.2766
39) B62 261 SVYHPQLAY 0.5957 0.9739 3.3510 1.4963
40) B62 346 ALYYWNNEY 0.5888 0.9739 3.4070 1.4852
41) B62 341 QVAERALYY 0.5671 0.7023 2.9880 1.3806
42) B62 340 FQVAERALY 0.5964 0.2104 2.9600 1.3636
43) B62 110 SSNPTGAEF 0.5067 0.9738 2.3890 1.2714

TABLE NO 1.5 ‘Computational characterization of antigen MPP11 predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the MPP11 binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 32 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 548 YTDFTPWTT 0.5314 0.4511 -1.0680 2.2705
2) A1 366 FSDNEAERV 0.4602 0.2592 0.0610 1.9960
3) A1 540 PSERFEGPY 0.3266 0.5030 2.3510 1.5796
4) A2 375 KMMEEVEKL 0.8378 0.9651 1.2950 1.4584
5) A3 613 VLNASRAKK 0.7768 0.8694 0.4030 1.6126
6) A3 114 MVLKHHPDK 0.6657 0.9571 0.8220 1.4375
7) A3 434 RQASKNTEK 0.5773 0.9454 0.8370 1.2702
8) A24 213 IFYSFWYNF 0.6840 0.8808 2.7730 1.7273
9) A24 99 RYKATQRQI 0.5465 0.9117 1.0880 1.3549
10) A24 156 AFNSVDPTF 0.4906 0.9020 2.8840 1.3241
11) A26 478 EVIANYMNI 0.5886 0.5967 0.4820 1.6933
12) B8 106 QIKAAHKAM 0.5091 0.6787 0.5760 1.8691
13) B8 255 RAQRKKEEM 0.4304 0.1082 0.5790 1.5151
14) B8 38 FVKRRNRNA 0.4278 0.1493 -0.3400 1.4663
15) B8 558 EQKLLEQAL 0.3601 0.7775 0.6570 1.3792
16) B27 475 SRWEVIANY 0.6330 0.9786 3.4970 2.0044
17) B27 244 RRWIEKQNR 0.6119 0.6666 1.9400 1.8237
18) B27 32 GRWFEAFVK 0.5612 0.9720 0.7110 1.6732
19) B27 594 KRYKELVEM 0.5357 0.9420 0.8410 1.6073
20) B27 332 RQQALLAKK 0.5319 0.6733 0.7990 1.5548
21) B27 104 QRQIKAAHK 0.4619 0.9483 0.6620 1.4031
22) B27 313 RQAELEAAR 0.4715 0.1532 1.7720 1.3650
23) B27 434 RQASKNTEK 0.4360 0.9454 0.8370 1.3427
24) B27 331 VRQQALLAK 0.4413 0.8799 0.5340 1.3318
25) B39 403 TKEVGKAAL 0.4771 0.7938 0.8320 1.6880
26) B44 71 EEFPMLKTL 0.6750 0.9317 0.7750 1.8512
27) B44 387 LELASLQCL 0.6006 0.8802 0.9730 1.6690
28) B44 60 SEESEDEEL 0.5777 0.9065 0.7760 1.6064
29) B44 51 QELEDKKEL 0.5587 0.9568 0.7450 1.5652
30) B44 455 SEDDLQLLI 0.4707 0.8506 0.2200 1.3051
31) B62 42 RNRNASASF 0.5083 0.9297 2.8070 1.2888
32) B62 219 YNFDSWREF 0.5240 0.8169 2.4180 1.2838

TABLE NO 1.6 ‘Computational characterization of antigen NM-23 H2 A predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the NM-23 H2 A binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 7 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 143 TSCAQNWIY 0.7369 0.8471 2.9010 3.4011
2) B7 100 KPGTIRGDF 0.5682 0.7703 2.2280 1.3230
3) B8 47 LLKEHYVDL 0.3326 0.9618 0.8370 1.3220
4) B27 26 KRFEQKGFR 0.4755 0.1990 1.9190 1.3898
5) B39 68 MHSGPVVAM 0.4145 0.7188 0.3030 1.4500
6) B44 126 AEKEIGLWF 0.5601 0.5480 2.5950 1.6001
7) B44 28 FEQKGFRLV 0.4903 0.4558 0.1840 1.2926

TABLE NO 1.7 ‘Computational characterization of antigen NM-23 H2 B predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the NM-23 H2 B binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 9 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 143 KSCAHDWVY 0.5349 0.9305 3.0290 2.5620
2) A3 58 RPFFPGLVK 0.6052 0.9753 0.3770 1.3042
3) A3 116 IIHGSDSVK 0.5749 0.9124 0.7870 1.2583
4) B7 100 KPGTIRGDF 0.5682 0.7703 2.2280 1.3230
5) B8 47 HLKQHYIDL 0.3247 0.9564 0.7140 1.2881
6) B27 26 KRFEQKGFR 0.4755 0.1720 1.9190 1.3858
7) B44 126 AEKEISLWF 0.5662 0.8156 2.5950 1.6553
8) B44 28 FEQKGFRLV 0.4903 0.4130 0.1840 1.2862
9) B58 143 KSCAHDWVY 0.6648 0.9305 3.0290 1.7892

TABLE NO 1.8 ‘Computational characterization of antigen PPP2R5C predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the PPP2R5C binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 43 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 341 QVAERALYY 0.4928 0.7023 2.9880 2.3469
2) A1 346 ALYYWNNEY 0.2896 0.9739 3.4070 1.5460
3) A2 246 FLLKVLLPL 0.8915 0.9544 0.9060 1.5174
4) A2 72 ALSEMVEYI 0.8264 0.2519 0.8480 1.3121
5) A3 97 HMFAVNMFR 0.8043 0.9364 1.6740 1.7379
6) A3 395 KLFMEMNQK 0.7636 0.9066 0.7510 1.6107
7) A3 261 SVYHPQLAY 0.6174 0.9739 3.3510 1.4756
8) A3 341 QVAERALYY 0.5942 0.7023 2.9880 1.3731
9) A3 184 TTLHRIYGK 0.6035 0.8016 0.5410 1.2830
10) A24 91 IYPEVVHMF 0.8644 0.9682 2.6620 2.1190
11) A24 157 KYIDQKFVL 0.6668 0.9724 1.4960 1.6405
12) A24 347 LYYWNNEYI 0.6369 0.4577 1.1440 1.4820
13) A24 96 VHMFAVNMF 0.5374 0.9482 2.7160 1.4223
14) A24 137 VYEFFLRFL 0.5288 0.9370 1.1850 1.3258
15) A24 348 YYWNNEYIM 0.5249 0.9564 0.7690 1.2997
16) A26 341 QVAERALYY 0.5007 0.7023 2.9880 1.5984
17) A26 319 EFVKIMEPL 0.4195 0.6009 1.0500 1.2685
18) A26 163 FVLQLLELF 0.3804 0.6623 2.6970 1.2550
19) A26 435 NLAKANPQY 0.3628 0.9262 2.8310 1.2540
20) B7 252 LPLHKVKSL 0.6792 0.9671 0.7200 1.4913
21) B8 65 WKEVKRAAL 0.4684 0.1761 0.9340 1.6728
22) B8 246 FLLKVLLPL 0.3983 0.9544 0.9060 1.5487
23) B8 491 PLARRKSEL 0.3574 0.8919 0.6430 1.3863
24) B27 142 LRFLESPDF 0.3956 0.8132 2.7520 1.3111
25) B39 386 IHGLIYNAL 0.6019 0.8560 0.8320 2.0969
26) B39 216 EHHNGIAEL 0.5194 0.9676 0.7980 1.8480
27) B39 217 HHNGIAELL 0.5165 0.8042 0.6100 1.8047
28) B39 178 ERDFLKTTL 0.3886 0.9441 0.9130 1.4312
29) B44 300 KEVMFLNEL 0.6741 0.8816 0.8590 1.8456
30) B44 38 QEKLFIQKL 0.5284 0.9528 0.6910 1.4870
31) B44 213 YETEHHNGI 0.5433 0.7957 0.3520 1.4834
32) B44 240 KEEHKIFLL 0.5244 0.9099 0.8300 1.4775
33) B44 93 PEVVHMFAV 0.5127 0.3979 -0.2750 1.3166
34) B58 438 KANPQYTVY 0.7084 0.9731 3.1440 1.8997
35) B58 110 SSNPTGAEF 0.5949 0.9738 2.3890 1.6063
36) B58 342 VAERALYYW 0.5522 0.5975 0.8060 1.3744
37) B58 136 LVYEFFLRF 0.4630 0.9776 2.8150 1.3309
38) B58 285 VVMALLKYW 0.5231 0.3156 1.0050 1.2766
39) B62 261 SVYHPQLAY 0.5957 0.9739 3.3510 1.4963
40) B62 346 ALYYWNNEY 0.5888 0.9739 3.4070 1.4852
41) B62 341 QVAERALYY 0.5671 0.7023 2.9880 1.3806
42) B62 340 FQVAERALY 0.5964 0.2104 2.9600 1.3636
43) B62 110 SSNPTGAEF 0.5067 0.9738 2.3890 1.2714

TABLE NO 1.9 ‘Computational characterization of antigen PRAME predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the PRAME binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 56 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 136 WSGNRASLY 0.6359 0.9593 2.8220 2.9847
2) A1 433 LSNLTHVLY 0.6231 0.9559 2.8450 2.9312
3) A1 267 LSHIHASSY 0.4674 0.9576 3.0470 2.2804
4) A1 294 SLQCLQALY 0.3225 0.8398 3.0130 1.6458
5) A1 390 ITDDQLLAL 0.3393 0.9604 0.7500 1.6222
6) A2 394 QLLALLPSL 0.7841 0.9729 0.8660 1.3581
7) A2 248 TLAKFSPYL 0.7742 0.7407 0.8250 1.3064
8) A2 425 SLLQHLIGL 0.7717 0.6449 0.9960 1.2969
9) A2 435 NLTHVLYPV 0.7505 0.9237 0.3890 1.2767
10) A3 5 RLWGSIQSR 0.6580 0.9388 2.0520 1.4818
11) A3 28 ELAGQSLLK 0.6396 0.9288 0.3440 1.3602
12) A3 190 ELFSYLIEK 0.6298 0.8698 0.2530 1.3284
13) A24 301 LYVDSLFFL 0.6770 0.9650 1.4550 1.6592
14) A24 447 SYEDIHGTL 0.5181 0.9785 0.9610 1.2981
15) A26 284 YIAQFTSQF 0.4776 0.9373 2.6110 1.5530
16) B7 48 LPRELFPPL 0.7522 0.9715 0.7510 1.6343
17) B7 113 RPRRWKLQV 0.7596 0.7215 0.3500 1.5912
18) B7 345 SPSVSQLSV 0.7146 0.9276 0.0100 1.5183
19) B7 53 FPPLFMAAF 0.6460 0.9697 2.1680 1.5002
20) B7 476 RPSMVWLSA 0.6907 0.9067 -0.6300 1.4369
21) B8 462 YLHARLREL 0.5697 0.9650 1.1380 2.1471
22) B8 53 FPPLFMAAF 0.5392 0.9697 2.1680 2.0951
23) B8 198 KVKRKKNVL 0.5411 0.9551 1.0310 2.0426
24) B8 111 EVRPRRWKL 0.4605 0.9379 0.8890 1.7577
25) B8 308 FLRGRLDQL 0.4050 0.5221 0.8230 1.5024
26) B8 114 PRRWKLQVL 0.3641 0.9727 0.8620 1.4324
27) B8 113 RPRRWKLQV 0.3786 0.7215 0.3500 1.4187
28) B8 466 RLRELLCEL 0.3435 0.9277 1.4210 1.3831
29) B8 210 CKKLKIFAM 0.3716 0.3423 0.3040 1.3357
30) B8 212 KLKIFAMPM 0.3522 0.6158 0.5300 1.3216
31) B8 313 LDQLLRHVM 0.3608 0.2571 0.1010 1.2756
32) B8 259 MINLRRLLL 0.3265 0.7221 0.9870 1.2727
33) B8 224 KMILKMVQL 0.3060 0.9537 1.2610 1.2511
34) B27 200 KRKKNVLRL 0.5091 0.9752 1.3130 1.5653
35) B27 263 RRLLLSHIH 0.5077 0.8014 -0.0190 1.4689
36) B27 115 RRWKLQVLD 0.5361 0.1992 -1.3380 1.3880
37) B27 24 RRLVELAGQ 0.4936 0.1445 0.2180 1.3446
38) B39 437 THVLYPVPL 0.5430 0.9688 1.0670 1.9369
39) B39 463 LHARLRELL 0.4963 0.6009 0.9310 1.7257
40) B39 93 HLETFKAVL 0.3781 0.9474 0.7870 1.3921
41) B39 447 SYEDIHGTL 0.3439 0.9785 0.9610 1.2957
42) B39 343 SQSPSVSQL 0.3359 0.9760 1.1270 1.2783
43) B39 390 ITDDQLLAL 0.3399 0.9604 0.7500 1.2696
44) B39 494 TFYDPEPIL 0.3271 0.9569 1.4200 1.2617
45) B39 175 VEVLVDLFL 0.6378 0.8695 0.8140 1.7518
46) B44 27 VELAGQSLL 0.5858 0.8705 0.9470 1.6296
47) B44 238 LEVTCTWKL 0.5915 0.8040 0.7620 1.6246
48) B44 280 KEEQYIAQF 0.4949 0.8564 2.1950 1.4646
49) B44 50 RELFPPLFM 0.5091 0.8702 0.2860 1.4065
50) B44 37 DEALAIAAL 0.4844 0.9596 0.6210 1.3755
51) B58 70 KAMVQAWPF 0.7758 0.4849 2.8070 1.9614
52) B58 418 ISISALQSL 0.4798 0.8216 1.1410 1.2617
53) B62 284 YIAQFTSQF 0.5252 0.9373 2.6110 1.3137
54) B62 267 LSHIHASSY 0.5123 0.9576 3.0470 1.3131
55) B62 454 TLHLERLAY 0.5186 0.8977 2.9230 1.3103
56) B62 89 GQHLHLETF 0.5067 0.8786 2.5620 1.2658

TABLE NO 1.10 ‘Computational characterization of antigen TELOMERASE predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the TELOMERASE binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 212 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTY-PE RESIDUE NUMBER PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A1 760 WSLNTFGKY 0.4105 0.9669 2.9260 2.0342
2) A1 823 STDLNPNDV 0.4257 0.9586 0.2180 1.9620
3) A1 59 KTMEKPHGY 0.3290 0.9623 3.1230 1.6972
4) A1 683 LTDANADRL 0.3527 0.9159 0.7090 1.6702
5) A1 793 MINVAKQLY 0.3303 0.7269 2.9760 1.6602
6) A1 200 GAETQMPSY 0.3133 0.9707 2.7550 1.6138
7) A1 2246 VSETSGLML 0.2983 0.9608 1.0670 1.4641
8) A1 969 FVGILGSRY 0.2804 0.7747 2.8530 1.4496
9) A1 1752 VLQTKAHQY 0.2559 0.9749 3.1060 1.3881
10) A1 85 LSDLKTMEK 0.2873 0.9261 0.3430 1.3759
11) A1 2327 WSSAHTFFV 0.3035 0.4231 0.3330 1.3689
12) A1 1004 VTEMEVMQF 0.2705 0.2665 2.4850 1.3127
13) A1 2482 SSDGILWNL 0.2598 0.9768 1.0260 1.3008
14) A1 621 MAMRIPVLY 0.2290 0.9582 3.2210 1.2770
15) A1 2125 SSDGSVGLW 0.2588 0.9202 0.7950 1.2767
16) A2 1726 FLSDDTLFL 0.9366 0.9448 0.9230 1.5841
17) A2 1177 FLASLVSAL 0.8261 0.9673 1.0500 1.4290
18) A2 788 SMDDGMINV 0.8457 0.8880 0.2640 1.4071
19) A2 23 AMLPDLQPL 0.7935 0.9747 1.2860 1.3933
20) A2 1974 ALAWLSPKV 0.7632 0.9580 0.5490 1.3089
21) A2 204 QMPSYSLSL 0.7228 0.9758 1.0840 1.2780
22) A2 1892 FLHAGCQLL 0.7213 0.9748 0.9570 1.2693
23) A2 1286 KLPRCVHLV 0.7315 0.8951 0.5270 1.2511
24) A3 111 CLATLSSLK 0.8063 0.8720 0.5920 1.6778
25) A3 900 RLFISSTFR 0.7688 0.8816 1.8690 1.6725
26) A3 1585 ALYASSVPK 0.7801 0.9766 0.7920 1.6544
27) A3 1516 ILIAAQLWK 0.7696 0.7665 0.5530 1.5910
28) A3 1219 CTYLRGQLK 0.7585 0.6640 0.6370 1.5591
29) A3 2463 TLISITQAK 0.6888 0.8980 0.6640 1.4642
30) A3 520 KLPFMAMLR 0.6750 0.7333 1.5660 1.4587
31) A3 376 QLAKYNPRK 0.6849 0.9695 0.4620 1.4575
32) A3 592 TLMRRILTR 0.6484 0.8990 1.5570 1.4329
33) A3 1435 LSVWRTLPK 0.6642 0.9311 0.6340 1.4214
34) A3 638 RVHKARQWK 0.7145 0.1205 0.9250 1.4090
35) A3 2544 HLKTRQRRK 0.6919 0.5213 0.2460 1.3927
36) A3 1579 RLLEAHALY 0.5672 0.9769 3.2600 1.3770
37) A3 809 CLFVGILLR 0.6065 0.8903 1.6760 1.3589
38) A24 1990 GSLQGWALK 0.6390 0.9473 0.2050 1.3549
39) A24 168 LSTCPIALK 0.6305 0.9543 0.3340 1.3465
40) A24 1793 LAFQHTYPK 0.5930 0.8991 0.5840 1.2801
41) A24 2434 GVLSFLRQK 0.5901 0.9202 0.2970 1.2635
42) A24 464 RYPSNLQLF 0.8783 0.9749 2.9760 2.1652
43) A24 2326 IWSSAHTFF 0.7554 0.8655 2.8480 1.8807
44) A24 1819 SWAGSISFF 0.6896 0.6926 2.8210 1.7133
45) A24 1235 TYRSLVWEL 0.6730 0.9699 1.3010 1.6436
46) A24 1027 IYFRDSSFL 0.6614 0.9687 1.4020 1.6238
47) A24 1700 VYLLDLRTW 0.5958 0.9019 1.2390 1.4660
48) A24 1193 VASLVFFHF 0.5353 0.9750 2.7460 1.4234
49) A24 1725 LFLSDDTLF 0.5543 0.6721 2.7170 1.4170
50) A24 976 RYGYIPPSY 0.5187 0.9573 3.3240 1.4144
51) A24 2409 SYTENPMIL 0.5423 0.9710 1.2900 1.3649
52) A24 1798 TYPKSLNCV 0.5380 0.9678 0.5200 1.3168
53) A26 978 GYIPPSYNL 0.5267 0.9357 1.0570 1.3147
54) A26 2114 AWTKDNLLI 0.5158 0.9492 0.8820 1.2847
55) A26 324 RYFCAIVQL 0.4792 0.9728 1.7070 1.2516
56) A26 1329 LVREELALY 0.5337 0.9731 3.2480 1.7406
57) A26 286 EFILKASLY 0.4924 0.9100 2.8120 1.5985
58) A26 1787 DTVRGQLAF 0.4931 0.9144 2.2190 1.5713
59) A26 275 EICRELALL 0.4836 0.4560 0.7830 1.4054
60) B7 120 STVSASPLF 0.3835 0.9452 2.7450 1.3081
61) B7 1946 YRADGIRIY 0.3574 0.9778 3.0980 1.2606
62) B7 1426 LTVDQLHGV 0.4191 0.7643 0.3450 1.2566
63) B7 1917 RPRGHLGSL 0.8619 0.9770 0.9130 1.8549
64) B7 928 RAAPHRISL 0.8188 0.8259 1.4220 1.7746
65) B7 452 KPAQHVQAL 0.8035 0.9300 0.7510 1.7271
66) B7 1287 LPRCVHLVL 0.7645 0.9783 0.8040 1.6619
67) B7 1326 RARLVREEL 0.7567 0.8164 1.4540 1.6549
68) B7 357 LPACLRTAM 0.8103 0.2106 -0.1240 1.5887
69) B7 1145 RPRLLQDTV 0.6789 0.9327 0.4570 1.4725
70) B7 1635 SPLCHQASL 0.6709 0.9680 0.5820 1.4687
71) B7 1840 APGASIRTL 0.6644 0.9644 0.6010 1.4565
72) B7 2582 RPSMQLLGL 0.6580 0.9707 0.7230 1.4513
73) B7 777 VPVDRVILL 0.6515 0.9777 0.9220 1.4496
74) B7 1232 LPSTYRSLV 0.6667 0.8788 0.1640 1.4263
75) B7 2471 KPESESSFL 0.6335 0.9753 0.7670 1.4069
76) B7 1936 SPDGDRVAV 0.6475 0.9411 -0.0650 1.3870
77) B7 1388 LPATVPLLL 0.6198 0.9431 0.6810 1.3712
78) B7 1674 AVSSSPTAV 0.6147 0.9594 0.6410 1.3618
79) B7 2276 IPRSSAAVT 0.7132 0.1511 -0.9450 1.3513
80) B7 1461 YPMGPFACL 0.6073 0.9765 0.5500 1.3457
81) B7 2379 APDGHFLIL 0.6046 0.9744 0.5620 1.3407
82) B7 2354 APGNLSLHL 0.5901 0.9727 0.6850 1.3187
83) B7 585 LPFPSNITL 0.5717 0.9768 0.9200 1.2955
84) B7 1361 RPLYLRLVT 0.6858 0.0602 -0.7780 1.2932
85) B7 143 RVNNSNCLL 0.5639 0.9413 1.1910 1.2886
86) B8 671 LPLLPGRTV 0.5946 0.8973 0.0860 1.2861
87) B8 1880 FPAHHGFVA 0.6498 0.3347 -0.7640 1.2656
88) B8 494 RPETWEREL 0.5592 0.9690 0.8040 1.2645
89) B8 577 EAQLRNQAL 0.5982 0.7397 0.8190 2.1949
90) B8 1177 FLASLVSAL 0.5094 0.9673 1.0500 1.9373
91) B8 321 HLRRYFCAI 0.5314 0.5462 0.3670 1.9151
92) B8 651 MLNRYRQAL 0.5041 0.9186 0.7600 1.8974
93) B8 240 VLQEKKMAL 0.4975 0.9153 0.9810 1.8852
94) B8 1287 LPRCVHLVL 0.4864 0.9783 0.8040 1.8481
95) B8 1326 RARLVREEL 0.4830 0.8164 1.4540 1.8447
96) B8 814 ILLRRVQYL 0.4736 0.9741 0.9850 1.8128
97) B8 518 NGKLPFMAM 0.4567 0.7639 -0.0940 1.6695
98) B8 1609 FLRQQASIL 0.4176 0.9343 0.9380 1.6130
99) B8 1799 YPKSLNCVA 0.4345 0.8151 -0.8520 1.5636
100) B8 629 YEQLKREKL 0.4207 0.4400 0.8150 1.5436
101) B8 556 VIHSRQFPF 0.3607 0.7827 2.8090 1.4898
102) B8 357 LPACLRTAM 0.4203 0.2106 -0.1240 1.4606
103) B8 1018 RLQPSAQAL 0.3711 0.8688 1.1370 1.4545
104) B8 1052 EAARRISEL 0.3736 0.9114 0.6530 1.4451
105) B8 452 KPAQHVQAL 0.3701 0.9300 0.7510 1.4409
106) B27 274 FEICRELAL 0.3605 0.3435 0.8020 1.3227
107) B27 1917 RPRGHLGSL 0.3232 0.9770 0.9130 1.2960
108) B27 415 FLREEQRKF 0.2887 0.9415 2.4780 1.2510
109) B27 1646 RRWHLQHTL 0.6652 0.9735 1.6310 1.9960
110) B27 608 RRFLCHLSR 0.6552 0.8833 2.1790 1.9831
111) B27 1055 RRISELKSY 0.5828 0.9594 3.5980 1.8731
112) B27 484 SRAGKRMKL 0.5955 0.9712 1.2850 1.7929
113) B27 595 RRILTRNEK 0.5948 0.9349 1.0200 1.7724
114) B27 463 YRYPSNLQL 0.5653 0.9782 1.2980 1.7143
115) B27 633 KREKLRVHK 0.5723 0.8872 0.6820 1.6884
116) B27 899 IRLFISSTF 0.5193 0.9665 2.8360 1.6671
117) B27 2039 RQLLTRPHK 0.5419 0.8939 0.6570 1.6074
118) B27 1860 GRLDSMVEL 0.5232 0.9737 1.0470 1.5891
119) B27 388 KRHPRRPPR 0.4847 0.9254 1.9820 1.5265
120) B27 615 SRQQLRMAM 0.5263 0.4634 0.5800 1.4975
121) B27 1946 YRADGIRIY 0.4339 0.9778 3.0980 1.4549
122) B27 2139 QRLGQFLGH 0.5227 0.5214 -0.3280 1.4514
123) B27 190 GRWFDSEEK 0.4606 0.9757 0.7120 1.4063
124) B27 1356 KRESGRPLY 0.4070 0.9113 3.1410 1.3757
125) B27 392 RRPPRSPGM 0.4446 0.9556 0.5950 1.3549
126) B27 2550 RRKIHSGSV 0.4416 0.9265 0.7140 1.3485
127) B27 323 RRYFCAIVQ 0.4882 0.0911 0.5800 1.3405
128) B27 643 RQWKYDGEM 0.4162 0.8124 0.9500 1.2758
129) B39 616 RQQLRMAMR 0.4420 0.0459 1.8260 1.2732
130) B39 619 LRMAMRIPV 0.4532 0.1517 0.5410 1.2544
131) B39 142 YRVNNSNCL 0.3923 0.9753 1.2670 1.2525
132) B39 463 YRYPSNLQL 0.7729 0.9782 1.2980 2.6862
133) B39 2553 IHSGSVTAL 0.7168 0.9645 1.0820 2.4935
134) B39 142 YRVNNSNCL 0.6694 0.9753 1.2670 2.3526
135) B39 2420 HKEYGIFVL 0.6755 0.9616 0.7790 2.3456
136) B39 1883 HHGFVAAAL 0.6492 0.8530 0.8620 2.2493
137) B39 2328 SSAHTFFVL 0.5483 0.9642 1.0090 1.9506
138) B39 1308 EQSQGAHVL 0.5422 0.9743 0.8700 1.9256
139) B39 1534 FRSCPPEAL 0.5449 0.7700 1.0720 1.9136
140) B39 542 HHELILQRL 0.5359 0.9378 0.8590 1.8993
141) B39 484 SRAGKRMKL 0.4875 0.9712 1.2850 1.7707
142) B39 1255 LHPGQTQVL 0.4863 0.9472 1.0050 1.7492
143) B39 233 HPEPTDHVL 0.4930 0.9751 0.4690 1.7481
144) B39 1578 SRLLEAHAL 0.4150 0.9638 1.2790 1.5372
145) B39 2409 SYTENPMIL 0.4136 0.9710 1.2900 1.5342
146) B39 301 VRNVANNIL 0.4069 0.9478 1.2700 1.5083
147) B39 1404 EKEHGPDVL 0.4424 0.2923 0.7040 1.4953
148) B39 754 FDENDGWSL 0.4168 0.6467 0.2410 1.4435
149) B39 2090 VRTPKTPVL 0.3608 0.9732 1.0480 1.3534
150) B39 1860 GRLDSMVEL 0.3471 0.9737 1.0470 1.3097
151) B39 2256 ASEDGSVRL 0.3448 0.9723 1.0050 1.3001
152) B44 1726 FLSDDTLFL 0.3431 0.9448 0.9230 1.2862
153) B44 1461 YPMGPFACL 0.3433 0.9765 0.5500 1.2729
154) B44 2028 ASEDFTVQL 0.3329 0.9754 1.0490 1.2645
155) B44 845 AEHGASHLL 0.7888 0.9750 1.0020 2.1512
156) B44 1092 LEEFGQLVL 0.7307 0.8839 0.6490 1.9758
157) B44 2580 WERPSMQLL 0.7008 0.8924 0.8890 1.9151
158) B44 2445 GEFEERLNF 0.5948 0.9114 2.3860 1.7300
159) B44 1357 RESGRPLYL 0.5893 0.9592 1.1600 1.6624
160) B44 274 FEICRELAL 0.6314 0.3435 0.8020 1.6564
161) B44 213 GEEEEVEDL 0.5826 0.9217 0.5440 1.6093
162) B44 961 GEVENAQLF 0.5596 0.4596 2.3460 1.5731
163) B44 1005 TEMEVMQFL 0.5517 0.9505 0.9100 1.5553
164) B44 77 LENQCLATL 0.5588 0.7314 0.7830 1.5337
165) B44 1510 LEDTAHILI 0.5540 0.8528 0.1970 1.5108
166) B44 285 PEFILKASL 0.5291 0.9639 0.2840 1.4699
167) B44 1572 LELGLVSRL 0.5030 0.9392 0.8180 1.4284
168) B44 880 LEEDTPSPL 0.5053 0.9507 0.5340 1.4215
169) B44 1321 LEASARARL 0.5108 0.6384 0.6950 1.3963
170) B44 181 TETAQEATL 0.4692 0.9478 0.9160 1.3506
171) B58 107 LENRCLATL 0.4777 0.8403 0.7830 1.3489
172) B58 2447 FEERLNFDI 0.5079 0.4330 -0.0070 1.3233
173) B58 659 LETAVNLSV 0.4623 0.9680 0.3010 1.3058
174) B58 2280 SAAVTAVAW 0.7849 0.8709 1.0610 1.9527
175) B58 1193 VASLVFFHF 0.6743 0.9750 2.7460 1.8033
176) B58 1676 SSSPTAVAF 0.6632 0.9606 2.6710 1.7722
177) B58 1863 DSMVELWAW 0.7116 0.5795 0.7220 1.7268
178) B58 1878 AAFPAHHGF 0.6401 0.9027 2.8910 1.7225
179) B58 2618 QGNVYFLNW 0.6869 0.9075 0.5840 1.7134
180) B58 558 HSRQFPFRF 0.6207 0.9189 2.5420 1.6638
181) B58 1034 FLSSVPDAW 0.6409 0.9288 0.8590 1.6266
182) B58 2480 CASSDGILW 0.6425 0.8734 0.9400 1.6260
183) B58 1861 RLDSMVELW 0.6337 0.6652 0.9420 1.5750
184) B58 120 STVSASPLF 0.5670 0.9452 2.7450 1.5570
185) B58 59 KTMEKPHGY 0.5518 0.9623 3.1230 1.5441
186) B58 149 CLLSEPPSW 0.5973 0.9544 1.0220 1.5403
187) B58 621 MAMRIPVLY 0.5211 0.9582 3.2210 1.4791
188) B58 554 KSVIHSRQF 0.5748 0.1852 2.9140 1.4688
189) B58 794 INVAKQLYW 0.5942 0.5690 0.8690 1.4680
190) B58 2595 GSVSCLEPW 0.6227 0.2034 0.6570 1.4668
191) B58 2125 SSDGSVGLW 0.5505 0.9202 0.7950 1.4185
192) B58 1515 HILIAAQLW 0.5298 0.9403 1.0940 1.3898
193) B58 2370 LGVLTSLDW 0.5364 0.8201 0.5610 1.3600
194) B58 1969 DVAVSALAW 0.5453 0.5536 0.7720 1.3507
195) B58 694 KSNPQGPPL 0.5191 0.8825 0.8630 1.3454
196) B58 888 LAPVSQQGW 0.5490 0.4287 0.7810 1.3407
197) B58 986 LPDHPHFHW 0.5072 0.9778 0.5490 1.3173
198) B58 1640 QASLLSRRW 0.5180 0.5890 0.9230 1.3019
199) B62 2337 SADEKISEW 0.4864 0.9755 0.8620 1.2856
200) B62 1818 GSWAGSISF 0.4400 0.9691 2.5780 1.2658
201) B62 935 SLHGIDLRW 0.4721 0.9763 0.9960 1.2601
202) B62 1791 GQLAFQHTY 0.5913 0.9674 2.9710 1.4675
203) B62 1818 GSWAGSISF 0.5470 0.9691 2.5780 1.3602
204) B62 1579 RLLEAHALY 0.5277 0.9769 3.2600 1.3570
205) B62 457 VQALLGYRY 0.5498 0.7296 3.0560 1.3538
206) B62 746 KLQAQVQEF 0.5420 0.9271 2.6380 1.3470
207) B62 1612 QQASILSQY 0.5275 0.8747 3.1620 1.3366
208) B62 579 QLRNQALPF 0.5702 0.4302 2.7700 1.3350
209) B62 1676 SSSPTAVAF 0.5259 0.9606 2.6710 1.3216
210) B62 1329 LVREELALY 0.4986 0.9731 3.2480 1.2982
211) B62 59 KTMEKPHGY 0.4879 0.9623 3.1230 1.2691
212) B62 620 RMAMRIPVL 0.5333 0.8788 1.3850 1.2597

TABLE NO 1.11 ‘Computational characterization of antigen WT1 predicting the peptide sequence ,binding affinity ,cleavage affinity ,tap score ,comb score with the help of antigen supertype of netctl . In this prediction method ,the server integrates the prediction of peptides of the WT1 binding with the MHC class-I molecules. In this method, server allows the prediction of CTL epitopes that are restricted to 12 MHC class-I supertypes (i.e. A1, A2, A3, A24, A26, B7, B8 , B27, B39 , B44 ,B58 , B62 ). In this method, the binding of a MHC class-I molecules and proteosomal clevage is performed with the help of artificial neural network. Prediction of a TAP transport efficiency is done by using weight matrix as in this case weight on tap are 0.05 and threshold for epitope prediction is 1.25. By calculating more than 800 known MHC class-I ligands , we predicted 37 number of epitopes which should be candidate for binding with the MHC class-I molecules.

SNO. SUPERTYPE RESIDUE NO. PEPTIDE SEQUENCE BINDING AFFINITY CLEVAGE AFFINITY TAP SCORE COMB SCORE
1) A2 10 ALLPAVPSL 0.8515 0.9769 1.2770 1.4797
2) A2 126 RMFPNAPYL 0.8021 0.9775 1.5860 1.4216
3) A3 240 QMNLGATLK 0.7003 0.9204 0.6460 1.4883
4) A3 436 NMHQRNMTK 0.6879 0.7486 0.6300 1.4385
5) A3 312 RSASETSEK 0.6698 0.7906 0.8130 1.4198
6) A3 169 AQFPNHSFK 0.6309 0.9659 0.9500 1.3797
7) A3 287 RIHTHGVFR 0.6701 0.0838 1.8350 1.3654
8) A3 343 HSRKHTGEK 0.6468 0.4143 0.4990 1.3044
9) A24 417 RWPSCQKKF 0.6097 0.7541 3.0030 1.5614
10) A26 152 VTFDGTPSY 0.6233 0.9788 3.0870 1.9738
11) A26 316 ETSEKRPFM 0.5791 0.4741 0.1290 1.6315
12) A26 4 DVRDLNALL 0.4471 0.9315 0.8800 1.3837
13) A26 92 FTVHFSGQF 0.4158 0.7989 2.6500 1.3681
14) A26 221 YSSDNLYQM 0.4368 0.9425 0.1760 1.3225
15) A26 163 TPSHHAAQF 0.6479 0.7193 2.1820 1.4669
16) B7 327 YPGCNKRYF 0.5588 0.8849 2.2010 1.3209
17) B7 334 YFKLSHLQM 0.4940 0.9640 0.5820 1.8608
18) B8 372 RHQRRHTGV 0.3819 0.7917 0.6350 1.4547
19) B8 375 RRHTGVKPF 0.5873 0.9511 2.9660 1.8522
20) B27 362 RRFSRSDQL 0.5971 0.9733 1.3500 1.8007
21) B27 332 KRYFKLSHL 0.5317 0.9772 1.5960 1.6397
22) B27 125 ARMFPNAPY 0.4573 0.9718 3.2910 1.5259
23) B27 286 YRIHTHGVF 0.4582 0.8429 2.7410 1.4815
24) B27 439 QRNMTKLQL 0.4436 0.9684 1.0570 1.3772
25) B27 437 MHQRNMTKL 0.4015 0.9689 1.1180 1.4867
26) B39 286 YRIHTHGVF 0.3168 0.8429 2.7410 1.2778
27) B39 81 AEPHEEQCL 0.5772 0.6277 0.9180 1.5704
28) B44 349 GEKPYQCDF 0.4710 0.9573 2.2280 1.4221
29) B44 252 AGSSSSVKW 0.5587 0.9547 0.9120 1.4479
30) B58 126 RMFPNAPYL 0.4815 0.9775 1.5860 1.3110
31) B58 88 CLSAFTVHF 0.4420 0.9689 2.4600 1.2644
32) B58 32 AQWAPVLDF 0.6065 0.9527 3.0800 1.5009
33) B62 152 VTFDGTPSY 0.5984 0.9788 3.0870 1.4891
34) B62 263 GQSNHSTGY 0.5492 0.9766 2.6500 1.3694
35) B62 40 FAPPGASAY 0.5000 0.9757 2.7280 1.2754
36) B62 146 NQGYSTVTF 0.4987 0.9042 2.6160 1.2564
37) B62 126 RMFPNAPYL 0.5179 0.9775 1.5860 1.2540

FIG-1.30- SUPERTYPE PREDICTION’S DISTRIBUTION OF THE SELECTED ANTIGENS .

The DISCOTOPE result of selected antigens are as follows:

TABLE NO 1.12 ‘Computational characterization of antigen AURORA KINASE A predicting the amino acid ,residue number with their discotope score for its epitope prediction. This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of AURORA KINASE A are 9 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT
NUMBER PROPENSITY SCORE DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 302 GLU 16 2.170 0.080 <=B
2. A 303 GLY 1 1.381 1.107 <=B
3. A 304 ARG 16 3.688 1.424 <=B
4. A 305 MET 1 1.638 1.334 <=B
5. A 333 THR 3 0.410 0.018 <=B
6. A 335 GLN 0 0.468 0.415 <=B
7. A 339 LYS 6 0.391 -0.344 <=B
8. A 342 SER 11 0.516 -0.808 <=B
9. A 367 ASN 2 0.642 0.338 <=B

TABLE NO 1.13 ‘Computational characterization of antigen BCR-ABL predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of BCR-ABL are 6 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY SCORE DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 43 ALA 0 -0.704 -0.623 <=B
2. A 49 GLU 2 -0.339 -0.530 <=B
3. A 52 LEU 1 -0.974 -0.977 <=B
4. A 54 GLY 2 -0.335 -0.527 <=B
5. A 55 PRO 2 -0.507 -0.679 <=B
6. A 56 SER 0 -0.700 -0.620 <=B

TABLE NO 1.14 ‘Computational characterization of antigen CML-28 predicting the amino acid ,residue number with their discotope score for its epitope prediction. This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of CML-28 are 1 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY SCORE DISCOTOPE SCORE IDENTIFIED
B CELL
EPITOPE
1. D 234 LYS 0 -0.848 -0.750 <=B

TABLE NO 1.15 ‘Computational characterization of antigen CML-66 predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of CML-66 are 2 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY SCORE DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 295 GLU 5 -0.360 -0.893 <=B
2. A 352 ASN 7 0.169 -0.656 <=B

TABLE NO 1.16 ‘Computational characterization of antigen ELA 2 predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of ELA 2 are 39 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY SCORE DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. B 34 PRO 4 1.187 0.591 <=B
2. B 35 PRO 0 1.858 1.644 <=B
3. B 36 ALA 0 0.051 0.045 <=B
4. B 112 ASN 22 1.849 -0.894 <=B
5. B 113 PRO 6 3.603 2.499 <=B
6. B 114 THR 9 3.764 2.296 <=B
7. B 115 GLY 5 4.112 3.064 <=B
8. B 116 ALA 2 4.169 3.459 <=B
9. B 117 GLU 0 4.775 4.226 <=B
10. B 118 PHE 12 4.958 3.007 <=B
11. B 119 ASP 10 3.410 1.868 <=B
12. B 121 GLU 13 1.641 -0.042 <=B
13. B 122 GLU 8 4.037 2.652 <=B
14. B 123 ASP 24 3.160 0.037 <=B
15. B 124 GLU 7 2.773 1.649 <=B
16. B 125 PRO 5 1.085 0.385 <=B
17. B 413 LYS 5 0.600 -0.044 <=B
18. B 414 ALA 5 0.563 -0.077 <=B
19. B 416 LYS 6 1.989 1.070 <=B
20. B 417 LEU 6 1.481 0.621 <=B
21. B 418 LYS 6 2.493 1.516 <=B
22. B 419 GLU 5 2.994 2.075 <=B
23. B 420 LYS 6 1.746 0.855 <=B
24. B 421 LEU 6 1.556 0.687 <=B
25. B 422 LYS 6 2.182 1.241 <=B
26. B 423 MET 5 2.957 2.042 <=B
27. B 424 LYS 6 2.481 1.506 <=B
28. B 425 GLU 6 1.974 1.057 <=B
29. B 426 ARG 5 1.749 0.973 <=B
30. B 427 GLU 7 1.723 0.720 <=B
31. B 428 GLU 5 1.071 0.373 <=B
32. B 429 ALA 6 1.003 0.198 <=B
33. B 430 TRP 6 0.873 0.083 <=B
34. B 431 VAL 6 0.365 -0.367 <=B
35. B 432 LYS 6 0.673 -0.095 <=B
36. B 433 ILE 5 0.127 -0.463 <=B
37. B 434 GLU 3 -0.222 -0.542 <=B
38. B 435 ASN 5 0.017 -0.560 <=B
39. B 436 LEU 2 -0.831 -0.965 <=B

TABLE NO 1.17 ‘Computational characterization of antigen MPP11 predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of MPP11 are 11 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY NUMBER DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 553 PRO 1 -0.962 -0.966 <=B
2. A 570 PRO 2 -0.267 -0.466 <=B
3. A 571 VAL 6 -0.049 -0.734 <=B
4. A 572 ASN 4 0.278 -0.214 <=B
5. A 574 PRO 0 1.066 0.943 <=B
6. A 575 GLU 2 0.672 0.365 <=B
7. A 578 GLU 4 0.001 -0.459 <=B
8. A 588 THR 4 -0.022 -0.480 <=B
9. A 590 LYS 0 0.576 0.510 <=B
10. A 594 LYS 7 0.094 -0.722 <=B
11. A 620 LYS 0 -1.051 -0.931 <=B

TABLE NO 1.18 ‘Computational characterization of antigen NM-23 H2 A predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of NM-23 H2 A are 12 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY NUMBER DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 59 PRO 0 -0.363 -0.322 <=B
2. A 98 ASP 7 0.073 -0.741 <=B
3. D 59 PRO 0 -0.195 -0.173 <=B
4. D 98 ASP 7 0.653 -0.227 <=B
5. B 59 PRO 0 -0.129 -0.114 <=B
6. B 98 ASP 7 0.240 -0.593 <=B
7. E 59 PRO 0 -0.364 -0.322 <=B
8. E 98 ASP 7 0.073 -0.740 <=B
9. C 59 PRO 0 -0.196 -0.173 <=B
10. C 98 ASP 7 0.651 -0.228 <=B
11. F 59 PRO 0 -0.129 -0.114 <=B
12. F 98 ASP 7 0.241 -0.592 <=B

TABLE NO 1.19 ‘Computational characterization of antigen NM-23 H2 B predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of NM-23 H2 B are 42 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY NUMBER DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 46 GLU 0 -0.869 -0.769 <=B
2. A 56 LYS 10 2.007 0.626 <=B
3. A 57 ASP 1 3.042 2.577 <=B
4. A 58 ARG 13 3.107 1.254 <=B
5. A 59 PRO 2 2.157 1.679 <=B
6. A 60 PHE 11 0.964 -0.412 <=B
7. A 62 PRO 1 1.227 0.971 <=B
8. A 98 ASP 7 0.085 -0.730 <=B
9. B 56 LYS 10 2.280 0.868 <=B
10. B 57 ASP 1 3.142 2.666 <=B
11. B 58 ARG 14 2.892 0.949 <=B
12. B 59 PRO 2 2.243 1.755 <=B
13. B 62 PRO 2 1.299 0.919 <=B
14. B 98 ASP 7 0.794 -0.102 <=B
15. C 56 LYS 9 1.623 0.401 <=B
16. C 57 ASP 1 2.225 1.854 <=B
17. C 58 ARG 12 2.897 1.184 <=B
18. C 59 PRO 0 2.947 2.608 <=B
19. C 60 PHE 9 2.096 0.820 <=B
20. C 62 PRO 1 1.968 1.626 <=B
21. C 98 ASP 8 0.453 -0.519 <=B
22. D 46 GLU 0 -0.918 -0.812 <=B
23. D 56 LYS 7 1.874 0.853 <=B
24. D 57 ASP 0 2.788 2.468 <=B
25. D 58 ARG 15 2.502 0.489 <=B
26. D 59 PRO 2 2.223 1.737 <=B
27. D 60 PHE 11 1.039 -0.345 <=B
28. D 62 PRO 2 1.518 1.114 <=B
29. D 98 ASP 7 -0.043 -0.843 <=B
30. E 56 LYS 10 1.014 -0.253 <=B
31. E 57 ASP 1 2.246 1.873 <=B
32. E 58 ARG 13 3.095 1.244 <=B
33. E 59 PRO 0 3.346 2.962 <=B
34. E 60 PHE 9 2.071 0.798 <=B
35. E 62 PRO 2 1.960 1.504 <=B
36. E 98 ASP 8 0.406 -0.561 <=B
37. F 56 LYS 10 2.180 0.779 <=B
38. F 57 ASP 1 2.855 2.412 <=B
39. F 58 ARG 13 2.540 0.753 <=B
40. F 59 PRO 1 2.013 1.667 <=B
41. F 62 PRO 2 0.979 0.637 <=B
42. F 98 ASP 7 0.506 -0.358 <=B

TABLE NO 1.20 ‘Computational characterization of antigen PPP2R5C predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of PPP2R5C are 39 that are as :

TABLE NO 1.21 ‘Computational characterization of antigen WT1 predicting the amino acid ,residue number with their discotope score for its epitope prediction. . This server predicted the discontinuous B cell epitopes from three dimensional structures of proteins.This method is performed by calculating a novel epitope propensity amino acid score and surface accessibility ( in terms of contact numbers) . In this method ,the final predicted scores are calculated by integrating the propensity scores of residues in spatial proximity and the contact numbers. The predicted discontinous B-cell epitopes of WT1 are 114 that are as :

SNO. CHAIN ID RESIDUE NUMBER AMINO ACID CONTACT NUMBER PROPENSITY SCORE DISCOTOPE SCORE IDENTIFIED B CELL EPITOPE
1. A 318 SER 0 1.086 0.961 <=B
2. A 319 GLU 0 1.066 0.944 <=B
3. A 320 LYS 2 0.718 0.406 <=B
4. A 321 ARG 6 0.657 -0.109 <=B
5. A 322 PRO 8 0.810 -0.204 <=B
6. A 324 MET 4 1.058 0.477 <=B
7. A 326 ALA 3 1.281 0.789 <=B
8. A 327 TYR 20 1.603 -0.881 <=B
9. A 328 PRO 6 1.538 0.671 <=B
10. A 329 GLY 5 2.000 1.195 <=B
11. A 331 ASN 13 1.268 -0.373 <=B
12. A 332 LYS 5 1.215 0.500 <=B
13. A 333 ARG 3 1.099 0.628 <=B
14. A 335 PHE 0 0.573 0.507 <=B
15. A 336 LYS 4 0.797 0.246 <=B
16. A 337 LEU 2 0.710 0.398 <=B
17. A 338 SER 0 1.119 0.990 <=B
18. A 339 HIS 8 1.612 0.507 <=B
19. A 340 LEU 14 1.543 -0.245 <=B
20. A 341 GLN 3 2.268 1.662 <=B
21. A 342 MET 5 2.699 1.814 <=B
22. A 343 HIS 18 2.729 0.345 <=B
23. A 344 SER 12 3.065 1.333 <=B
24. A 345 ARG 5 4.396 3.315 <=B
25. A 346 LYS 17 3.940 1.532 <=B
26. A 347 HIS 15 4.129 1.929 <=B
27. A 348 THR 7 5.538 4.096 <=B
28. A 349 GLY 4 6.519 5.309 <=B
29. A 350 GLU 12 4.782 2.852 <=B
30. A 351 LYS 15 5.105 2.793 <=B
31. A 352 PRO 5 5.153 3.986 <=B
32. A 353 TYR 19 4.878 2.132 <=B
33. A 354 GLN 4 4.735 3.730 <=B
34. A 355 CYS 13 4.910 2.851 <=B
35. A 356 ASP 14 3.875 1.820 <=B
36. A 357 PHE 19 3.448 0.866 <=B
37. A 358 LYS 2 2.473 1.959 <=B
38. A 359 ASP 3 1.933 1.365 <=B
39. A 360 CYS 16 2.511 0.382 <=B
40. A 361 GLU 0 2.918 2.582 <=B
41. A 362 ARG 18 4.961 2.320 <=B
42. A 363 ARG 6 5.184 3.898 <=B
43. A 364 PHE 19 5.542 2.720 <=B
44. A 365 SER 8 6.243 4.605 <=B
45. A 366 ARG 10 6.724 4.801 <=B
46. A 367 SER 4 6.119 4.955 <=B
47. A 368 ASP 7 7.882 6.171 <=B
48. A 369 GLN 15 8.862 6.118 <=B
49. A 370 LEU 19 7.571 4.516 <=B
50. A 371 LYS 9 7.672 5.754 <=B
51. A 372 ARG 14 9.184 6.518 <=B
52. A 373 HIS 19 7.575 4.519 <=B
53. A 374 GLN 14 6.579 4.213 <=B
54. A 375 ARG 17 6.042 3.392 <=B
55. A 376 ARG 15 5.469 3.115 <=B
56. A 377 HIS 15 4.931 2.639 <=B
57. A 378 THR 16 4.942 2.534 <=B
58. A 379 GLY 0 4.044 3.579 <=B
59. A 380 VAL 13 4.128 2.159 <=B
60. A 381 LYS 14 4.792 2.631 <=B
61. A 382 PRO 12 4.209 2.345 <=B
62. A 383 PHE 14 3.998 1.929 <=B
63. A 384 GLN 4 3.990 3.071 <=B
64. A 385 CYS 16 3.031 0.843 <=B
65. A 386 LYS 8 1.015 -0.021 <=B
66. A 387 THR 10 0.385 -0.809 <=B
67. A 389 GLN 0 1.863 1.648 <=B
68. A 390 ARG 14 3.261 1.276 <=B
69. A 391 LYS 7 3.975 2.713 <=B
70. A 392 PHE 15 4.454 2.217 <=B
71. A 393 SER 13 5.314 3.208 <=B
72. A 394 ARG 13 5.346 3.236 <=B
73. A 395 SER 3 5.492 4.516 <=B
74. A 396 ASP 6 6.130 4.735 <=B
75. A 397 HIS 16 5.042 2.623 <=B
76. A 398 LEU 17 4.803 2.295 <=B
77. A 399 LYS 6 4.261 3.081 <=B
78. A 400 THR 14 4.939 2.761 <=B
79. A 401 HIS 17 4.034 1.615 <=B
80. A 402 THR 13 2.467 0.688 <=B
81. A 403 ARG 16 2.268 0.167 <=B
82. A 404 THR 13 1.530 -0.141 <=B
83. A 405 HIS 11 1.944 0.455 <=B
84. A 406 THR 15 1.988 0.035 <=B
85. A 407 GLY 16 1.167 -0.807 <=B
86. A 408 LYS 4 0.661 0.125 <=B
87. A 409 THR 4 0.821 0.267 <=B
88. A 410 SER 5 0.330 -0.283 <=B
89. A 411 GLU 9 1.884 0.633 <=B
90. A 412 LYS 17 1.790 -0.370 <=B
91. A 413 PRO 8 2.079 0.920 <=B
92. A 414 PHE 14 2.431 0.541 <=B
93. A 415 SER 8 1.765 0.642 <=B
94. A 416 CYS 16 0.972 -0.979 <=B
95. A 417 ARG 4 0.299 -0.195 <=B
96. A 419 PRO 0 0.268 0.238 <=B
97. A 420 SER 2 -0.011 -0.240 <=B
98. A 422 GLN 0 0.819 0.725 <=B
99. A 423 LYS 13 1.998 0.273 <=B
100. A 424 LYS 13 2.560 0.770 <=B
101. A 425 PHE 17 1.424 -0.695 <=B
102. A 426 ALA 16 1.813 -0.236 <=B
103. A 427 ARG 14 2.862 0.923 <=B
104. A 428 SER 3 2.081 1.497 <=B
105. A 429 ASP 3 2.655 2.005 <=B
106. A 430 GLU 16 1.957 -0.108 <=B
107. A 431 LEU 17 2.493 0.251 <=B
108. A 432 VAL 3 2.257 1.653 <=B
109. A 433 ARG 7 2.710 1.593 <=B
110. A 434 HIS 20 3.135 0.475 <=B
111. A 435 HIS 12 1.755 0.173 <=B
112. A 436 ASN 7 1.699 0.698 <=B
113. A 437 MET 10 1.606 0.271 <=B
114. A 438 HIS 19 1.435 -0.915 <=B

DISCUSSION AND CONCLUSION
Chronic myeloid leukemia (CML) is a disease in which the bone marrow makes too many white blood cells. Inspite the success of targeted therapy using tyrosine kinase inhibitors (TKIs), CML remains largely incurable, because due to the treatment resistance of leukemic stem cells.
So the vaccination for CML may crucial role for curing this disease. As there are several antigens such as CML-66, CML-28, NM23-H2, PPP2R5C, PR3, ELA2, PRAME and a novel epitope derived from the M-phase phosphoprotein 11 protein (MPP11) ,Aurora kinase A, BCR/ABL, etcwhich make an immunological response for CML to the body. Through the prediction of the epitopes we are able to find the candidate vaccine for the CML.

As we are able to predicts the epitopes of the antigen we are able to dock with MHC molecule .So that it can make immunological response which make molecule the good candidate for vaccine.We work on many crucial antigens like WT1,BCR/ABL,AURORA KINASE A which are able to create and boost up the immunological response towards CML and make active immunity towards it. It also give ease to the research on making candidate vaccine in future aspects. Through this project we are able to learn all the immunoinformatics approaches for making of the vaccine in silico.

In this project, we are able to predict the three dimensional structure of the selected antigens such as AURORA KINASE A ,BCR/ABL ,CML-28 ,CML-66 ,ELA 2 , MPP11 ,NM 23-H2 A ,NM 23-H2 B ,PPP2R5C ,PRAME ,TELOMERASE and WT1 which plays an important role in immunological response against CML in the body. The three dimensional structure of the antigens are predicted with the help of SWISS MODEL computational tool. We also tried to predict the Cytotoxic T- lymphocyte (CTL) epitopes which play an important role in rational vaccines with the help of computational software netCTL sever 1.2. With the help of this tool we are able to predict the epitopes of the selected antigens in which predictions are takes place according to proteasomal cleavage, TAP transport efficiency, MHC class I affinity and combined score. In this we take threshold energy 1.25 for predicting the epitopes which gives prediction of sensivity 0.54 and specificity 0.993 of the given antigens . The selected antigens are predicted with the reference of supertype antigens such as a1, a2, a3, a24, a26, b7, b8 , b27, b39 , b44 ,b58 , b62 .

In this project we also predict the discontinous B cell epitopes from three dimensional structures of selected antigens with the help of Discotope 2.0 server . In this method we predict the B cell epitopes by calculating the surface accesibility and a novel epitopes propensity amino acid score . The final predicted score are calculated by combining the propensity scores of residues in spatial proximity and the contact numbers.
These are only in-silico technique but it plays a very crucial role in making of vaccination by using computational algorithms as it ease the labour of work and increase the chances to get successful candidate vaccine in less duration of time.

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APPENDIX-I

Ala A Arg R Asn N Asp D Cys C Gln Q Glu E Gly G His H Ile Leu L Lys K Met M Phe F Pro P Ser S Thr T Trp W Tyr Y Val V Alanine
Arginine
Asparagine
Aspartic acid( Aspartate )
Cysteine
Glutamine
Glutamic acid ( Glutamate )
Glycine
Histidine
I Isoleucine
Leucine
Lysine
Methionine
Phenylalanine
Proline
Serine
Threonine
Trptophan
Tyrosine
Valine

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