Open Access

In silico characterization of immunogenic epitopes presented by HLA-Cw*0401

Immunome Research20073:7

https://doi.org/10.1186/1745-7580-3-7

Received: 18 May 2007

Accepted: 20 August 2007

Published: 20 August 2007

Abstract

Background

HLA-C locus products are poorly understood in part due to their low expression at the cell surface. Recent data indicate that these molecules serve as major restriction elements for human immunodeficiency virus type 1 (HIV-1) cytotoxic T lymphocyte (CTL) epitopes. We report here a structure-based technique for the prediction of peptides binding to Cw*0401. The models were rigorously trained, tested and validated using experimentally verified Cw*0401 binding and non-binding peptides obtained from biochemical studies. A new scoring scheme facilitates the identification of immunological hot spots within antigens, based on the sum of predicted binding energies of the top four binders within a window of 30 amino acids.

Results

High predictivity is achieved when tested on the training (r 2 = 0.88, s = 3.56 kJ/mol, q 2 = 0.84, s press = 5.18 kJ/mol) and test (AROC = 0.93) datasets. Characterization of the predicted Cw*0401 binding sequences indicate that amino acids at key anchor positions share common physico-chemical properties which correlate well with existing experimental studies.

Conclusion

The analysis of predicted Cw*0401-binding peptides showed that anchor residues may not be restrictive and the Cw*0401 binding pockets may possibly accommodate a wide variety of peptides with common physico-chemical properties. The potential Cw*0401-specific T-cell epitope repertoires for HIV-1 p24gag and gp160gag glycoproteins are well distributed throughout both glycoproteins, with thirteen and nine immunological hot spots for HIV-1 p24gag and gp160gag glycoproteins respectively. These findings provide new insights into HLA-C peptide selectivity, indicating that pre-selection of candidate HLA-C peptides may occur at the TAP level, prior to peptide loading in the endoplasmic reticulum.

Background

Major histocompatibility complex (MHC) class I molecules, HLA-A, -B, and -C, are cell surface glycoproteins consisting of a polymorphic heavy α chain non-covalently linked to a light chain, β2-microglobulin (β2m). HLA-A and -B molecules play critical roles in cell mediated immune responses by binding short antigenic peptide fragments and presenting them on the surface of antigen-presenting cells for recognition by the CD8+ cytotoxic T lymphocyte (CTL). Although several HLA-C specificities with CTL epitopes have been reported [1, 2], much remains unknown with regards to their role in the immune response against viral antigens in part due to their poor expression at the cell surface [3, 4]. Recent research shows that this group of molecules plays a major role in the control of human immunodeficiency virus type 1 (HIV-1) infection [5]. Improved understanding of peptide binding to this group of molecules is important in the study of HIV-1 disease progression, as well as the design of effective HIV peptide vaccines.

The HLA-C allele, Cw*0401, is of particular interest in the study of HIV-1 disease progression because it is the restriction element for HIV-1 proteins [5]. Two HIV-1 proteins (p24gag and gp160gag) are currently known to be restricted by Cw*0401 [5]. Cw*0401 is present in approximately 10% of the general population [6]. The allele is expressed intracellularly in amounts comparable with HLA-A and -B molecules, but is poorly expressed at the cell surface [7, 8]. Improved understanding of peptide binding to this molecule is important for elucidating its role in HIV-1 disease progression.

Computational strategies for prediction of peptide binding to HLA-A and -B molecules are relatively advanced [9], while sequence-based predictive models for HLA-C molecules have encountered limited success due to the lack of experimental training data [10]. Two matrix-based prediction algorithms for Cw*0401 were reported [11, 12], but a sequence independent approach is still lacking. To overcome these limitations, we have developed a structure-based predictive technique that integrates the strength of Monte Carlo simulations and homology modeling [1315]. This method utilizes a probe or "base fragment" to sample different regions of the receptor binding site, followed by loop closure and refinement of the entire class I peptide. The technique has been successfully applied to analyze peptides binding to a variety of MHC class II alleles [14, 15]. In this work, we now extend our analysis to peptides presented by the class I HLA-C molecule. We investigated the HIV-1 p24gag and gp160gag peptide binding repertoire of Cw*0401 and illustrate that areas with high concentration of T-cell epitopes or "immunological hot spots" are potentially well distributed throughout both HIV-1 p24gag and gp160gag. We also show that Cw*0401 can possibly bind antigenic peptides in amounts comparable to both HLA-A and -B molecules. Characterization of predicted Cw*0401 binding sequences reveal that Cw*0401 may bind a large variety of amino acids at anchor positions with common physico-chemical properties which correlate well with existing experimental studies [11].

Results and discussion

Cw*0401 predictive model

High predictivity (r 2 = 0.88, s = 3.56 kJ/mol, q2 = 0.84, spress = 5.18 kJ/mol) is achieved when tested on the training dataset of 6 Cw*0401 peptide sequences. The Cw*0401 predictive model outperforms the predictive models done by Rognan et al. [16] on training datasets of 5 A*0204 (r 2 = 0.85, s press = 2.40 kJ/mol) and 37 2Kk (r 2 = 0.78, s press = 3.16 kJ/mol) peptide sequences and is comparable with our previous DRB1*0402 (r2 = 0.90, s = 1.20 kJ/mol, q2 = 0.82, spress = 1.61 kJ/mol) and DQB1*0503 (r2 = 0.95, s = 1.20 kJ/mol, q2 = 0.75, spress = 2.15 kJ/mol) prediction models on a training set of 8 peptides [17]. The cross-validation coefficient q 2 and the standard error of prediction s press are stable, with q 2 = 0.84 and s press = 5.18 kJ/mol. This iterative regression procedure validates the internal consistency of the scoring function in the current model, rendering it suitable for predictions on the test dataset obtained from biochemical studies. The predictive performance of our model is further validated using the test dataset of 58 peptides. The external validation results indicate that our Cw*0401 predictive model is suitable for discriminating binding ligands from the background with high accuracy (AROC = 0.93) with sensitivity of 76% (SP = 0.95).

Characterization of Cw*0401 binding peptides

An in-depth analysis was performed to investigate the characteristics of Cw*0401 binding peptides. A panel of 2279 sequences was generated using an overlapping sliding window of size 9 across the entire p24gag [5] and gp160gag [18] glycoproteins and modeled into the binding groove of Cw*0401. From these sequences, a total of 877 binding sequences (predicted binders; SE = 76%, SP = 80%) were selected and a systematic analysis was performed to analyze the number of occurrence of individual amino acid residues and physico-chemical properties [19] at each position of Cw*0401 binding peptides.

Peptide position p2 is characterized by alanine (10%), glycine (13%), leucine (9%), serine (9%). 60% of the predicted residues at this position are hydrophobic in nature, while 93% are neutral. These properties correlate with the physico-chemical properties of existing binding motif (Tyr/Phe) at p2 [11] as well as with the observed conservation in the test data (Table 1: Phe – 58% and Tyr – 26%). The p3 position shows a strong preference for glycine (11%) and threonine (8%). Existing anchor residues at this position are aspartic acid and histidine, which accounts for 9% of the total position-specific composition in the dataset. The p4 position shares similar characteristics as p3 (glycine: 9%; threonine: 8%), with additional preference for leucine (8%). Similar results were obtained at the p5 position (alanine: 8%; glycine: 9%; leucine: 8%). At p6, characteristic residues include glycine (9%) and leucine (8%). This position is favored by neutral (81%) acyclic (89%), medium/large (77%), and hydrophobic (51%) residues. The physico-chemical properties of these residues are in agreement with Val/Ile/Leu as reported in earlier studies [11] and is comparable with the conservation in the test dataset reported in Table 1 (Val – 29%, Ile – 12%, Leu and Pro – 10%). Finally, the p9 position was defined by six amino acids, including alanine (8%), glycine (10%), isoleucine (8%), leucine (8%), threonine (9%), and valine (8%). This position is primarily dominated by neutral (90%) and hydrophobic (58%) residues, and agrees with profiles of Leu/Phe as previously reported [11] and is consistent with the conservation in the test dataset reported in Table 1 (Leu and Phe – 39%,). Collectively, our data indicates that individual binding pockets may not be highly specific as previously reported [11] but can rather accommodate a wide-range of anchor residues with common physico-chemical properties. We attribute the discrepancies to two possibilities: i) the lack of extensive research on Cw*0401 peptides; and ii) natural peptides carrying these residues may be present in small amount and were thus not detected by experimental studies.
Table 1

HLA-Cw4 dataset used in this study.

No.

Category

Source

Peptide

IC50 (nM)

Ref.

1

Training Set

Cw3 consensus

FAMPNFQTL

651

24

2

Training Set

Cw6 consensus

IPFPIVRYL

>30000

24

3

Training Set

Cw7 consensus

KYPDFVDAL

2.4

24

4

Training Set

Cw4 consensus

QYDDAVYKL

18

24

5

Training Set

Histone H3.3

RYRPGTVAL

>30000

24

6

Training Set

Unknown Cw6 natural ligand

YQFTGIKKY

>30000

24

7

Test Set

Transcripton factor SUPT4H 56–64

SFDGIIAMM

Binder

10

8

Test Set

Transducin-like 3 120–128

AFDPTSTLL

Binder

10

9

Test Set

UBE3B variant 1 742–750

VFDPALNLF

Binder

10

10

Test Set

XP_173235 3–11

LFDITGQDF

Binder

10

11

Test Set

8 59–67

VYDTNPAKF

Binder

10

12

Test Set

Acyl-CoA synthetase 4 82–90

LFDHAVSKF

Binder

10

13

Test Set

Adenosylhomocyteinehydrolase 566–574

SFDAHLTEL

Binder

10

14

Test Set

ART-1 Adenocarcinoma antigen 21–29

SFDLLPREF

Binder

10

15

Test Set

ATP-binding cassette, sub-family F, member 3 500–508

YYDPKHVIF

Binder

10

16

Test Set

Block of proliferation 1 639–647

SYDSKLVWF

Binder

10

17

Test Set

BM-015 144–152

HFDPEVVQI

Binder

10

18

Test Set

CDC45 541–549

HFDLSVIEL

Binder

10

19

Test Set

Cholesterol acyltransferase 72–80

HFDDFVTNL

Binder

10

20

Test Set

Chromosome 20 open reading frame 40 318–326

FFDNISSEL

Binder

10

21

Test Set

Elongation factor 2 265–273

YFDPANGKF

Binder

10

22

Test Set

Epithelial cell transforming oncogene 21–29

IFDSKVTEI

Binder

10

23

Test Set

Ethanolamine kinase EKI1 132–141

HWDPQEVTL

Binder

10

24

Test Set

Eukaryotic translation initiation factor 3, Su 6 interacting protein 478–486

FLDLTEGEF

Binder

10

25

Test Set

Fatty acid synthetase 544–552

TFDDIVHSF

Binder

10

26

Test Set

FK506 binding protein 9 303–311

VFDIHVIDF

Binder

10

27

Test Set

Glutamine:fructose-6-phosphate amidotransferase (GFAT) 345–353

NFDDYTVNL

Binder

10

28

Test Set

Tousled-like kinase 456–464

AFDLTEQRY

Binder

10

29

Test Set

Transcription factor IIE 144–152

LFDPMTGTF

Binder

10

30

Test Set

HSP 70 kDa 1A 179–205

IFDLGGGTF

Binder

10

31

Test Set

HSPC198 19–27

SYDLFVNSF

Binder

10

32

Test Set

HSP J2 156–164

SFDTGFTSF

Binder

10

33

Test Set

Hypothetical protein FLJ00365 80–88

YFDAIPVTM

Binder

10

34

Test Set

Hypothetical protein FLJ11220 373–381

YLPDFLDYF

Binder

10

35

Test Set

Hypothetical protein FLJ20343 415–423

RFDEAYIYM

Binder

10

36

Test Set

Insuline degrading enzyme IDE 150–158

YFDVSHEHL

Binder

10

37

Test Set

Integrin alpha-V (Vitronectin) 224–232

KYDPNVYSI

Binder

10

38

Test Set

KCIP-1 186–194

AFDEAIAEL

Binder

10

39

Test Set

KIAA0461 721–729

SMDPLPVFL

Binder

10

40

Test Set

KIAA1463 444–452

FYDERIVVV

Binder

10

41

Test Set

KIAA1921 350–358

VYDGKIYTL

Binder

10

42

Test Set

Metalloproteinase 10 331–339

FWPSLPSYL

Binder

10

43

Test Set

Methionine-tRNA synthetase 2 (mitochondrial) 58–66

YYDEKVVKL

Binder

10

44

Test Set

Mucin-5B 168–176

TFDGTSYTF

Binder

10

45

Test Set

Myosin phosphatase target subunit 1 37–45

KFDDGAVFL

Binder

10

46

Test Set

Nuclear autoantigenic sperm protein 599–607

QYDEAVAQF

Binder

10

47

Test Set

Nucleoporin NUP358 261–269

SFDSALQSV

Binder

10

48

Test Set

P621 70–78

VFDKTLAEL

Binder

10

49

Test Set

Phosphate carrier precursor 331–339

IYDSVKVYF

Binder

10

50

Test Set

PRO2242 61–69

YFDPQYFEF

Binder

10

51

Test Set

Protein phosphatase 6 102–110

KWPDRITLL

Binder

10

52

Test Set

Putative prostate tumor suppressor 165–173

TFDLQRIGF

Binder

10

53

Test Set

Rac1 168–176

VFDEAIRAV

Binder

10

54

Test Set

RNA Helicase A 697–705

VFDPVPVGV

Binder

10

55

Test Set

Similiar to KIAA1911 80–88

FWDGKIVLV

Binder

10

56

Test Set

Topoisomerase I 241–249

YYDGKVMKL

Binder

10

57

Test Set

Tensin 3 143–151

FYDDKVSAL

Binder

10

58

Test Set

HIV-1 (BRU) gp120 350–358

SFNCGGEFF

Binder

18

59

Test Set

HIV-1 (BRU) gag p24 307–315

QASQEVKNW

Binder

21

60

Test Set

Synthetic peptide

AYDDAVYKL

Binder

25

61

Test Set

Cw7 consensus

AYADFVYAY

>30000

24

62

Test Set

CKShs2

KYFDEHYEY

>30000

24

63

Test Set

Unknown Cw6 natural ligand

YRHDGGNVL

>30000

24

64

Test Set

Unknown Cw7 natural ligand

NKADVILKY

>30000

24

Prediction of p24gag and gp160gag immunological hot spots

The potential T-cell epitope repertoire (data not shown) and immunological hot spots (Figure 1) for HIV-1 p24gag and gp160gag are well distributed throughout both glycoproteins. The known p24gag epitope (p24 168–175) and gp160gag epitope (gp160 375–383) were successfully predicted by our model at the threshold of -200 (SE = 76%, SP = 80%) [5, 20]. At this threshold, the number of predicted hot spots for p24gag are fourteen (p24 20–75, 89–148, 143–262, 311–381, 447–506, 526–590, 664–727, 708–791, 873–930, 996–1053, 1054–1157, 1157–1316, 1308–1368, and 1378–1427), with an estimated FN = 3, and FP = 3 for SE = 0.76 and SP = 0.95 (Figure 1 and Table 2). For gp160gag we predict nine hot spots (gp160 44–89, 102–171, 174–289, 341–504, 489–559, 581–639, 667–721, 708–766, and 793–852), with an estimated FN = 2, FP = 2 for SE = 0.76 and SP = 0.95 (Figure 1 and Table 3). The results presented here indicate that Cw*0401 can bind antigenic peptides with specificities comparable to HLA-A and -B molecules, and any variability in antigen expression may be directly related to the loss or reduced cell surface expression of the molecule by mechanisms as yet unknown [8, 21].
Table 2

Predicted Cw*0401-specific immunological hotspots for p24gag.

No.

Position

Sequence

1

20–75

RLRPGGKKKYKLKHIVWASRELERFAVNPGLLETSEGCRQILGQLQPSLQTGSE

EL

2

89–148

YNTVATLYCVHQRIEIKDTKEALDKIEEEQNKSKKKAQQAAADTGHSNQVSQNY

PIVQNIQGQMVHQAIS

3

143–262

PIVQNIQGQMVHQAISPRTLNAWVKVVEEKAFSPEVIPMFSALSEGATPQDLNT

MLNTVGGHQAAMQMLKETINEEAAEWDRVHPVHAGPIAPGQMREPRGSDIAGTT

STLQEQIGWMTNNPPIPVGEIY

4

311–381

QEVKNWMTETLLVQNANPDCKTILKALGPAATLEEMMTACQGVGGPGHKARVLA

EAMSQVTNSATIMMQRG

5

447–506

EFSSEQTRANSPTRRELQVWGRDNNSPSEAGADRQGTVSFNFPQVTLWQRPLVT

IKIGGQ

6

526–590

LPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLT

QIGCTLNFPIS

7

664–727

FRELNKRTQDFWEVQLGIPHPAGLKKKKSVTVLDVGDAYFSVPLDEDFRKYTAF

TIPSINNETP

8

708–791

DEDFRKYTAFTIPSINNETPGIRYQYNVLPQGWKGSPAIFQSSMTKILEPFRKQ

NPDIVIYQYMDDLYVGSDLEIGQHRTKIEE

9

873–930

TKALTEVIPLTEEAELELAENREILKEPVHGVYYDPSKDLIAEIQKQGQGQWTY

QIYQ

10

996–1053

TWIPEWEFVNTPPLVKLWYQLEKEPIVGAETFYVDGAANRETKLGKAGYVTNRG

RQKV

11

1054–1157

VTLTDTTNQKTELQAIYLALQDSGLEVNIVTDSQYALGIIQAQPDQSESELVNQ

IIEQLIKKEKVYLAWVPAHKGIGGNEQVDKLVSAGIRKVLFLDGIDKAQDE

12

1157–1316

DEHEKYHSNWRAMASDFNLPPVVAKEIVASCDKCQLKGEAMHGQVDCSPGIWQL

DCTHLEGKVILVAVHVASGYIEAEVIPAETGQETAYFLLKLAGRWPVKTIHTDN

GSNFTGATVRAACWWAGIKQEFGIPYNPQSQGVVESMNKELKKIIGQVRDQA

13

1308–1368

IIGQVRDQAEHLKTAVQMAVFIHNFKRKGGIGGYSAGERIVDIIATDIQTKELQ

KQITKIQ

14

1378–1427

RNPLWKGPAKLLWKGEGAVVIQDNSDIKVVPRRKAKIIRDYGKQMAGDDC

Table 3

Predicted Cw*0401-specific immunological hotspots for gp160gag.

No.

Position

Sequence

1

44–89

VWKEATTTLFCASDAKAYDTEVHNVWATHACVPTDPNPQEVVLVNV

2

102–171

EQMHEDIISLWDQSLKPCVKLTPLCVSLKCTDLGNATNTNSSNTNSSSGEMM

MEKGEIKNCSFNISTSIR

3

174–289

VQKEYAFFYKLDIIPIDNDTTSYTLTSCNTSVITQACPKVSFEPIPIHYCAP

AGFAILKCNNKTFNGTGPCTNVSTVQCTHGIRPVVSTQLLLNGSLAEEEVVI

RSANFTDNAKTI

4

341–504

AKWNATLKQIASKLREQFGNNKTIIFKQSSGGDPEIVTHSFNCGGEFFYCNS

TQLFNSTWFNSTWSTEGSNNTEGSDTITLPCRIKQFINMWQEVGKAMYAPPI

SGQIRCSSNITGLLLTRDGGNNNNGSEIFRPGGGDMRDNWRSELYKYKVVKI

EPLGVAPT

5

489–559

KYKVVKIEPLGVAPTKAKRRVVQREKRAVGIGALFLGFLGAAGSTMGARSMT

LTVQARQLLSGIVQQQNN

6

581–639

LQARILAVERYLKDQQLLGIWGCSGKLICTTAVPWNASWSNKSLEQIWNNMT

WMEWDRE

7

667–721

ELDKWASLWNWFNITNWLWYIKIFIMIVGGLVGLRIVFAVLSIVNRVRQGYS

PLS

8

708–766

SIVNRVRQGYSPLSFQTHLPTPRGPDRPEGIEEEGGERDRDRSIRLVNGSLA

LIWDDLR

9

793–852

RGWEALKYWWNLLQYWSQELKNSAVSLLNATAIAVAEGTDRVIEVVQGACRA

IRHIPRRI

Figure 1

Predicted start positions of Cw*0401-specific hotspots (sliding window size = 30) along (A) p24gag and (B) gp160gag glycoproteins. Scores are computed based on the sum of predicted binding energies of top four binders within the 30 amino acid sliding window. Predicted hotspots regions are shown in blue boxes with experimentally verified regions shown in red.

Conclusion

Due to the low expression of HLA-C molecules at the cell surface, their role in cell mediated immune responses remain poorly understood. Collectively, the outcome of this analysis provides insights into the binding specificities of Cw*0401. Our data strongly indicate that Cw*0401 can bind antigenic peptides in amounts comparable to both HLA-A and -B molecules, and show the existence of a potentially large number of Cw*0401-specific T-cell epitopes that are evenly distributed throughout both HIV-1 p24gag and gp160gag glycoproteins. It remains to be determined what proportion of these peptides may be expressed at the cell surface and capable of eliciting functional responses. Probably, pre-selection of candidate HLA-C peptides may occur at the TAP level, prior to peptide loading in the ER [8]. Consequently, a higher concentration of peptides is necessary for complexation with HLA-C molecules, resulting in their release from TAP. This provides a possible explanation for the reduced cell surface expression of HLA-C molecules [8].

Methods

Data

Crystallographic data

The coordinates of Cw*0401 were obtained from the Protein Databank (PDB) with PDB code 1QQD [22]. The structure was relaxed by conjugate gradient minimization, using the Internal Coordinate Mechanics (ICM) software [23].

Experimental binding data

The dataset comprises a total of 64 (57 binders and 7 non-binders) 9-mer peptides (Table 1). The available dataset is divided into training and testing datasets. Peptides with experimental IC50 values were selected as training data for optimizing the empirical free energy function (refer Empirical Free Energy Function). Due to the lack of experimental data, only 9 peptides with experimental IC50 values were identified, six (three binders and three non-binders) of which were used for training, while the remainder (four non-binders) were included in the test dataset as true negatives. Therefore, the training dataset contained six peptides with experimentally determined IC50 values (2 high-affinity binders, 1 medium-affinity binder, and 3 non-binders) derived from biochemical studies [24], while the testing dataset comprised the remainder 58 peptides (54 binders and 4 non-binders) [10, 18, 21, 24, 25]. Experimental IC50 values were classified as follows – high-affinity binders: IC50 ≤ 500 nM, medium-affinity binders: 500 nM < IC50 ≤ 1500 nM, low-affinity binders: 1500 < IC50 ≤ 5000 nM and non-binders: 5000 < IC50.

HIV-1 sequence data

The sequences of HIV-1 p24gag and gp160gag glycoproteins were obtained from UniProt [26]. The accession numbers for p24gag and gp160gag glycoproteins used in this study are P04585 and P03377 respectively.

Model

Peptide docking

Docking was performed according to the procedure utilized in previous similar works [1315]: (i) pseudo-Brownian rigid body docking of peptide fragments to the ends of the binding groove, (ii) central loop closure by satisfaction of spatial constraints, and (iii) refinement of the backbone and side-chain atoms of the ligand and receptor contact regions.

Empirical free energy function

The scoring function presented herein is based on the free energy potential in ICM [23]. Computation of the binding free energy was performed according to previous similar work based on the difference between the energy of the solvated complex and the sum of the energy of the solvated receptor and that of the peptide ligand, followed by optimization using experimental IC50 values [15]. This step was followed by 6-fold cross-validation for assessment of quality of the scoring function [15]. In k-fold cross-validation, k random, (approximately) equal-sized, disjoint partitions of the sample data were constructed, and all given models were trained on (k-1) partitions and tested on the excluded partition. The results were averaged after k such experiments, and thus the observed error rate may be taken as an estimate of the error rate expected upon generalization to new data. The predictive power of the models was assessed by the cross-validation coefficient q 2 and the standard error of prediction s press .

Immunological hot spot prediction

In this study, 'immunological hot spots' are defined as antigenic regions of up to 30 amino acids and modeled according to previous similar work based on the sum of predicted binding energies of the top four binders within a window of 30 amino acids [27]. Where available, these predicted hotspots were validated with available experimentally determined sites.

Training, testing and validation

The free energy scoring function was calibrated using 6 peptides with experimental IC50 values and tested on a dataset 58 peptides (54 binders and 4 non-binders) obtained from biochemical studies (Table 1). The predictive performance of our model was assessed using sensitivity (SE), specificity (SP) and receiver operating characteristic (ROC) analysis [28]. SE = TP/(TP+FN) and SP = TN/(TN+FP), indicate percentages of correctly predicted binders and non-binders, respectively. TP (true positives) represents correctly predicted experimental binders and TN (true negatives) for experimental non-binders incorrectly predicted as binders. FN (false negatives) denotes experimental binders predicted as non-binders and FP (false positives) stands for experimental non-binders predicted as binders. The accuracy of our predictions was assessed by the ROC analysis where the ROC curve is generated by plotting SE as a function of (1-SP) for a complete range of classification thresholds. The area under the ROC curve (AROC) provides a measure of overall prediction accuracy, AROC < 70% for poor, AROC > 80% for good and AROC > 90% for excellent predictions [15].

Abbreviations

CTL: 

cytotoxic T lymphocyte

HLA: 

human leukocyte antigen

MHC: 

major histocompatibility complex

ER: 

endoplasmic reticulum

HIV: 

human immunodeficiency virus

ROC: 

receiver operating characteristic

SE: 

sensitivity

SP: 

specificity

TN: 

true negative

TP: 

true positive

FN: 

false negative

FP: 

false positive

Declarations

Acknowledgements

This work was partly funded by the National Institute of Allergy and Infectious Diseases, National Institute of Health, USA (Grant #5 U19 AI56541 & Contract #HHSN266200400085C).

Authors’ Affiliations

(1)
Institute for Infocomm Research
(2)
Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore
(3)
Department of Pharmacology and Molecular Sciences, John Hopkins University School of Medicine
(4)
Cancer Vaccine Center, Dana-Farber Cancer Institute
(5)
Department of Chemistry and Biomolecular Sciences & Biotechnology Research Institute, Macquarie University

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© Tong et al. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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