Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option.
Results
Here, we present an MHC-II binding prediction algorithm aiming at dealing with these challenges. The method is a pan-specific version of the earlier published allele-specific NN-align algorithm and does not require any pre-alignment of the input data. This allows the method to benefit also from information from alleles covered by limited binding data. The method is evaluated on a large and diverse set of benchmark data, and is shown to significantly out-perform state-of-the-art MHC-II prediction methods. In particular, the method is found to boost the performance for alleles characterized by limited binding data where conventional allele-specific methods tend to achieve poor prediction accuracy.
Conclusions
The method thus shows great potential for efficient boosting the accuracy of MHC-II binding prediction, as accurate predictions can be obtained for novel alleles at highly reduced experimental costs. Pan-specific binding predictions can be obtained for all alleles with know protein sequence and the method can benefit by including data in the training from alleles even where only few binders are known. The method and benchmark data are available at http://www.cbs.dtu.dk/services/NetMHCIIpan-2.0
Background
Binding of peptides to MHC II molecules play a major role in governing adaptive immune responses. They allow peptides derived from pathogens in the extracellular compartment to be presented by professional antigen presenting cells (APCs) to T helper cells of the immune system.
These T cells might in turn activate the presenting cell to kill intracellular bacterial infections. Help is also for most antigens needed to activate B cells to produce antibodies that may neutralize the pathogen. Over the last decade a number of different methods for prediction of binding to MHC II molecules have been developed, the most known being the TEPITOPE method [1]. Prediction of binding of peptides to MHC II is complicated by the immense polymorphism of the MHC class II alleles since the many different encoded MHC class II molecules (more than 690 different known HLA-DR alleles are known) bind very different sets of peptides. The TEPITOPE method covers 50 of these HLA-DR alleles. During the last decays several data driven so-called allele-specific methods have been developed for alleles where sufficient numbers of binding peptides are known. These methods cover a very broad range of different bioinformatics training algorithms including Gibbs sampler [2, 3], artificial neural networks [4, 5], support vector machines [6–8], hidden Markov models [9], as well as other (often exotic) motif search algorithms [10–18]. For a detailed review please refer to Nielsen et al. [19].
These methods can interpolate between peptide binding data and create predictions for peptides not present in the training set. Recently, pan-specific methods that in principle can make predictions for all alleles with known amino acid sequence have been developed [20–26]. These methods work by including information about the amino acid sequence of the MHC molecule as input to the method allowing the methods to integrate information across multiple alleles simultaneously thus boosting the predictive performance and potentially extrapolate the predictions to previously un-characterized MHC molecules. Several benchmark calculations have demonstrated the power of such pan-specific methods [27] and have shown how accurate predictions can be obtained also for alleles for which no or very limited binding data have been identified [21, 28].
One of the best performing pan-specific MHC class II prediction method is the NetMHCIIpan method [29]. An important limiting factor for this method lies in the need for a pre-alignment of the input training data identifying the peptide-binding core prior to the training of the method. Such pre-alignments require sufficient data being available for all MHC molecules included in the training data in order to derive accurate allele-specific predictions. It has earlier been shown that this number of peptide binding data for MHC class II is of the order of many hundred [3, 19], which makes it very costly to develop accurate MHC class II predictions. In order to circumvent this, we here propose a less demanding, yet highly efficient method to generate MHC class II predictors. This method is a pan-specific version of the earlier published allele-specific NN-align algorithm [5] and does not require any pre-alignment of the input data. The method hence has the potential to benefit also from information from alleles covered by limited binding data. Here, we demonstrate its predictive power in a series of large-scale benchmark calculations.
Materials and methods
Data
Quantitative peptide binding data covering 24 HLA-DR molecules were obtained from the IEDB database and combined with data from an in-house database containing MHC class II peptide binding affinity data obtained from a high-throughput peptide-binding screening assay described earlier [30]. The peptide coverage in the data set varied from a maximal coverage of 7685 peptide binding measurements for the DRB1*0101 allele to a minimal coverage of only 30 peptide binding measurements for the DRB1*1404 allele (see table 1). The peptide data were split into 5 groups used for cross validation using the approach described by Nielsen et al. [3] minimizing the sequence overlap between the training and test data. Each data set and the corresponding partitions are available online at http://www.cbs.dtu.dk/suppl/immunology/NetMHCIIpan-2.0.
Table 1
Quantitative HLA-DR peptide binding data
Allele
#
# bind
Allele
#
# bind
DRB1*0101
7685
4382
DRB1*1101
1794
778
DRB1*0301
2505
649
DRB1*1201
117
81
DRB1*0302
148
44
DRB1*1202
117
79
DRB1*0401
3116
1039
DRB1*1302
1580
493
DRB1*0404
577
336
DRB1*1402
118
78
DRB1*0405
1582
627
DRB1*1404
30
16
DRB1*0701
1745
849
DRB1*1412
116
63
DRB1*0802
1520
431
DRB1*1501
1769
709
DRB1*0806
118
91
DRB3*0101
1501
281
DRB1*0813
1370
455
DRB3*0301
160
70
DRB1*0819
116
54
DRB4*0101
1521
485
DRB1*0901
1520
622
DRB5*0101
3106
1280
Total
33931
13992
# is the number of peptide binding data for each allele, and #bind is the number of peptides with binding affinity stronger than 500 nM.
A large set of MHC class II ligands from the SYFPEITHI database [15] (November 2009) was used as external evaluation set. Only ligands with at least four digit HLA-DR resolution were used. All ligands included in the training data were excluded from the evaluation set. The SYFPEITHI evaluation data sets consist of 1164 MHC class II ligands, restricted to a total of 28 HLA-DR alleles (see table 2).
Table 2
MHC class II ligands from the SYFPEITHI database
Allele
#
Allele
#
HLA-DRB1*0101
53
HLA-DRB1*1101
35
HLA-DRB1*0102
5
HLA-DRB1*1104
8
HLA-DRB1*0301
88
HLA-DRB1*1201
11
HLA-DRB1*0401
468
HLA-DRB1*1301
16
HLA-DRB1*0402
36
HLA-DRB1*1302
19
HLA-DRB1*0403
1
HLA-DRB1*1401
9
HLA-DRB1*0404
42
HLA-DRB1*1501
22
HLA-DRB1*0405
36
HLA-DRB1*1502
3
HLA-DRB1*0701
47
HLA-DRB1*1601
2
HLA-DRB1*0801
39
HLA-DRB3*0101
2
HLA-DRB1*0802
1
HLA-DRB3*0301
5
HLA-DRB1*0803
1
HLA-DRB4*0101
6
HLA-DRB1*0901
6
HLA-DRB4*0103
2
HLA-DRB1*1001
183
HLA-DRB5*0101
18
Total
1164
A second evaluation set consisted of HLA-DR class II restricted T cell epitopes downloaded from the IEDB database June 28th, 2010 [31]. Also here, only epitopes with four digit HLA-DR resolution were used. As above, all epitopes included in the quantitative training data were excluded. Further, epitopes shorter than 9 or longer than 24 amino acids were excluded, since shorter peptides do not fit the 9 amino acid core of the HLA-DR binding motif, and longer peptide most likely are not experimentally characterized as minimal epitopes. This leaves us with a set of 1325 epitopes covering 42 HLA-DR alleles (see table 3).
Table 3
HLA-DR restriction T cell epitope from the IEDB database
Allele
#
Allele
#
HLA-DRB1*0101
125
HLA-DRB1*1103
3
HLA-DRB1*0102
4
HLA-DRB1*1104
6
HLA-DRB1*0103
5
HLA-DRB1*1201
3
HLA-DRB1*0301
173
HLA-DRB1*1301
15
HLA-DRB1*0401
342
HLA-DRB1*1302
10
HLA-DRB1*0402
33
HLA-DRB1*1303
3
HLA-DRB1*0403
14
HLA-DRB1*1401
16
HLA-DRB1*0404
46
HLA-DRB1*1404
1
HLA-DRB1*0405
21
HLA-DRB1*1405
2
HLA-DRB1*0406
6
HLA-DRB1*1501
193
HLA-DRB1*0407
4
HLA-DRB1*1502
20
HLA-DRB1*0408
2
HLA-DRB1*1503
2
HLA-DRB1*0701
56
HLA-DRB1*1601
5
HLA-DRB1*0703
1
HLA-DRB1*1602
3
HLA-DRB1*0801
4
HLA-DRB3*0101
12
HLA-DRB1*0802
2
HLA-DRB3*0202
10
HLA-DRB1*0803
2
HLA-DRB3*0301
1
HLA-DRB1*0901
13
HLA-DRB4*0101
17
HLA-DRB1*1001
4
HLA-DRB4*0103
1
HLA-DRB1*1101
88
HLA-DRB5*0101
55
HLA-DRB1*1102
1
HLA-DRB5*0102
1
Total
1325
Method
The pan-specific NetMHCIIpan-2.0 method is a hybrid of the earlier published methods for pan-specific for MHC class I and class II binding, NetMHCpan [20, 21], and NetMHCIIpan [29], and the NN-align method recently published for allele-specific MHC class II binding predictions [5]. The overall method architecture is similar to the NN-align method, and the manner in which the MHC polymorphism method is incorporated is similar to that of the NetMHCpan and NetMHCIIpan methods.
The method was implemented as a conventional feed-forward artificial neural network. Like the NN-align method, the method consists of a two-step procedure that simultaneously estimates the optimal peptide binding register (core) and network weight configuration. Initially, all network weights were assigned random values. Given this set of network weights, the core of a given peptide was identified as the highest scoring of all nonamers contained within the peptide. The score of a nonamer peptide was calculated using the conventional feed-forward algorithm. The network weights were updated using gradient descent back-propagation. Given a peptide core alignment, the weights were updated to lower the sum of squared errors between the predicted binding score and the measured binding affinity target value. A peptide core was presented to the network as described for the NN-align method including encoding of peptide flanking regions (PFR), PFR length and the peptide length. The MHC environment defining the peptide binding strength was implemented in terms of the MHC pseudo sequence constructed from 21 polymorphic amino acid positions in potential contact with the bound peptide as described by Nielsen et al. [29]. Two types of sequence encodings (sparse and blosum) were applied for the peptide-core and MHC pseudo sequences as described by Nielsen et al. [32]. For each peptide core, the input to the neural network thus consisted of the peptide core and MHC environment residues ((9+21) × 20 = 600 inputs), the PFRs (2 × 20 = 40 inputs), the peptide length (2 inputs), the length of the C and N terminal PFR's (2 × 2 = 4 inputs) resulting in a total of 646 input values. The peptide binding affinity IC50 values were encoded to the neural network as log-transformed values, using the relation 1-log (aff)/log(15,000), where aff is the measured binding affinity (IC50) in nM units [32]. Note, that we here use 15,000 as the base for the logarithmic transformation. This is in contrast to the 50,000 used in previous works by our group, and is chosen due to a lower sensitivity for weak binding peptides of the high-throughput peptide-binding screening assay described by Justesen et al. [30].
The networks were trained using 5-fold cross-validation. Network ensembles were trained with 40 hidden neurons. The procedure of i) identifying the optimal peptide core, and ii) updating the network weights to lower the predictive error was repeated for 500 cycles. Since the "search landscape" has a large set of local minima each with close to identical performance values, the network training was repeated 10 times, each with different initial configuration values. This led to significantly improved prediction accuracy (data not shown). In total 20 (2 encoding schemes*10 seeds) networks were created for each training/test set configuration. The binding core of a given peptide was assigned by a majority vote of the networks in the ensemble.
Leave-one-out (LOO) network training and benchmark
Leave-one-(allele)-out experiments were conducted to investigate the predictive performance of the method in situations where binding data for a given allele was excluded from the training. Two types of LOO experiments were conducted. In the first type, peptide-binding data for a given allele were excluded from the training of the prediction method, and upon training, the predictive performance was evaluated using the peptide binding affinity values for the HLA-DR molecule in question. This is the LOO approach applied in the NetMHCIIpan-1.0 method [29] and allows for a direct comparison of this method to the method proposed here when trained on similar data sets.
Since many of the peptides in the training data have been measured for binding affinities to multiple alleles, the above LOO experiment can lead to a significant overestimation of the performance for a given prediction method. To reduce this bias, a second type of LOO experiment was conducted where all data representing a given peptide was excluded from the training of the prediction method. That is, if a given peptide was measured against multiple alleles including the allele in question, all these measurements were excluded from the LOO training. To avoid reducing the size of the training data too much, this second type of LOO training was performed as a three-fold cross-validation for alleles characterized by more than 200 data points.
Performance measures
The predictive performance was measured in terms of the area under the ROC curve (AUC) value and Pearson's correlation coefficient (PCC). The receiver operating characteristic (ROC) curve is a graphical plot of the sensitivity versus the false positive rate (1 - specificity) as the discrimination threshold is varied. Through out this work, a binding threshold value of 500 nM was used to classify the peptides. The area under the ROC curve (AUC) gives an indication of the accuracy of a prediction method. An AUC value of 1 corresponds to perfect predictions and a value of 0.5 reflects random predictions. Likewise, PCC is a measure of the accuracy of a prediction method. It is obtained by dividing the covariance of the two variables by the product of their standard deviations. For perfect predictions PCC is 1 (or -1), and for random predictions PCC is 0.
Nearest neighbor distance calculation
The distance between two MHC alleles was estimated as described by Nielsen et al. [29] using the relation , where s(A, B) is the BLOSUM50 similarity score between the pseudo sequences of allele A and B, respectively. Next, the nearest neighbor distance for an allele is defined as the minimal distance to any allele included in the training data set.
Results
Pan-specific versus allele-specific predictions
In contrast to allele-specific MHC class II prediction methods, the pan-specific method outlined here is proposed to benefit from information even from alleles covered by limited binding data. To demonstrate this, we in table 4 show the performance values obtained by the new NetMHCIIpan-2.0 and older NN-align method using 5 fold cross-validation. The NN-align method was trained in an allele-specific manner as described in by Nielsen et al. [5]. As a reference, the performance of the TEPITOPE method is also included in the benchmark study. The predictive performance for each HLA allele was measured in terms of the area under the ROC curve (AUC) value and Pearson's correlation coefficient (PCC).
Table 4
Five-fold cross-validation performance of the pan-specific NetMHCIIpan-2.0 method compared to the allele-specific NN-align and TEPITOPE methods on the quantitative benchmark data set
NN-align
NetMHCIIpan-2.0
TEPITOPE
Allele
#
# bind
PCC
AUC
PCC
AUC
AUC
DRB1*0101
7685
4382
0.675
0.825
0.711
0.846
0.727
DRB1*0301
2505
649
0.690
0.855
0.709
0.864
0.718
DRB1*0302
148
44
0.272
0.659
0.525
0.757
DRB1*0401
3116
1039
0.643
0.833
0.670
0.848
0.762
DRB1*0404
577
336
0.565
0.766
0.630
0.818
0.747
DRB1*0405
1582
627
0.710
0.869
0.698
0.858
0.780
DRB1*0701
1745
849
0.718
0.855
0.740
0.864
0.777
DRB1*0802
1520
431
0.518
0.778
0.526
0.780
0.645
DRB1*0806
118
91
0.744
0.902
0.796
0.924
0.884
DRB1*0813
1370
455
0.729
0.878
0.746
0.885
0.750
DRB1*0819
116
54
0.370
0.706
0.608
0.808
DRB1*0901
1520
622
0.597
0.810
0.634
0.818
DRB1*1101
1794
778
0.756
0.873
0.777
0.883
0.793
DRB1*1201
117
81
0.699
0.860
0.764
0.892
DRB1*1202
117
79
0.695
0.866
0.769
0.900
DRB1*1302
1580
493
0.630
0.819
0.634
0.825
0.596
DRB1*1402
118
78
0.623
0.825
0.694
0.860
DRB1*1404
30
16
0.466
0.661
0.613
0.737
DRB1*1412
116
63
0.680
0.857
0.757
0.894
DRB1*1501
1769
709
0.641
0.815
0.653
0.819
0.731
DRB3*0101
1501
281
0.673
0.843
0.690
0.850
DRB3*0301
160
70
0.604
0.826
0.736
0.853
DRB4*0101
1521
485
0.701
0.854
0.675
0.837
DRB5*0101
3106
1280
0.735
0.865
0.765
0.882
0.760
Ave
33931
13992
0.631
0.821
0.688
0.846
Ave*
0.673
0.841
0.697
0.854
0.744
Ave**
0.580
0.797
0.679
0.837
# gives the number of peptide binding data for each allele, #bind gives the number of peptides with a binding affinity stronger than 500 nM. NN-align is the method described by Nielsen et al. [5], NetMHCIIpan-2.0 is the method described here, and TEPITOPE is the method described by Sturniolo et al. [1]. Ave gives the per allele average, Ave* gives the per allele average of the 13 alleles characterized by the TEPITOPE method, and Ave** gives the per allele average of the 11 allele not characterized by the TEPITOPE method. In bold is highlighted the best performing method for each of the 24 alleles. AUC values were calculated using a binding threshold of 500 nM. Only AUC values are included for the TEPITOPE method since prediction values for this method are not linearly related to the binding affinity.
From the results in table 4, it is apparent that the NetMHCIIpan-2.0 method significantly outperforms both the NN-align and TEPITOPE methods (p < 0.01, binomial test). For 9 of the 9 alleles covered by less than 400 peptide-binding measurements, we find that NetMHCIIpan-2.0 outperforms NN-align. These results strongly indicate that NetMHCIIpan-2.0 is capable of benefiting from information from the multiple alleles included in the benchmark to boost the predictive performance and deliver accurate predictions also for alleles covered by limited binding data. Only for 3 out of the 24 alleles does the NN-align perform better than NetMHCIIpan-2.0. These alleles are all covered by more than 1500 peptide-binding measurements. This hence confirms the results obtained earlier for MHC class I binding predictions namely that pan-specific predictions are particularly beneficial when binding data are scarce or absent [28]. What is also clear from the data in table 4 is that the NetMHCIIpan-2.0 method is capable of maintaining its high performance also for alleles not characterized by the TEPITIOPE method.
NetMHCpanII-1.0 versus NetMHCpanII-2.0
In the pan-specific training algorithm implemented in the NetMHCIIpan-2.0 method, alignment and binding affinity prediction is performed simultaneously. To further demonstrate that this approach does indeed outperform the NetMHCIIpan-1.0 method where the two steps were decoupled, we performed a series of LOO experiments as described in Materials and methods. In these experiments, peptide-binding data for a given allele were excluded from the training of the prediction method, and upon training, the predictive performance was evaluated using the peptide binding affinity values for the HLA-DR molecule in question. This experiment thus simulated prediction of binding to hitherto un-characterized HLA-DR molecules. The first LOO experiment was conducted based on the binding data using in the original NetMHCIIpan paper covering 14 HLA-DR alleles [29]. Details of this analysis are shown in table 5. The average PCC and AUC values for the 14 LOO experiments were 0.541, 0.768 and 0.606, 0.799 for the NetMHCIIpan-1.0 and NetMHCIIpan-2.0 methods, respectively. This difference is statistically highly significant (p < 0.01, binomial test). Only for one allele (DRB1*1302) did the NetMHCIIpan-1.0 method achieve a higher performance than NetMHCIIpan-2.0. These results thus demonstrate, that the training algorithm implemented in the NetMHCIIpan-2.0 method leads to significantly improved prediction accuracy compared to the algorithm employed in the NetMHCIIpan-1.0 method.
Table 5
LOO benchmark comparison of the pan-specific NetMHCIIpan-2.0 and the NetMHCIIpan-1.0 methods
NetMHCIIpan-1.0
NetMHCIIpan-2.0
TEPITOPE
Allele
#
#bind
PCC
AUC
PCC
AUC
AUC
DRB1*0101
5166
3510
0.571
0.778
0.627
0.794
0.720
DRB1*0301
1020
277
0.465
0.746
0.560
0.792
0.664
DRB1*0401
1024
510
0.591
0.775
0.652
0.802
0.716
DRB1*0404
663
386
0.693
0.852
0.731
0.869
0.770
DRB1*0405
630
425
0.594
0.808
0.626
0.823
0.759
DRB1*0701
853
498
0.655
0.825
0.753
0.886
0.761
DRB1*0802
420
148
0.637
0.841
0.700
0.869
0.766
DRB1*0901
530
254
0.406
0.653
0.474
0.684
DRB1*1101
950
429
0.580
0.799
0.721
0.875
0.721
DRB1*1302
498
199
0.323
0.658
0.337
0.648
0.652
DRB1*1501
934
450
0.533
0.738
0.598
0.769
0.686
DRB3*0101
549
75
0.449
0.716
0.474
0.733
DRB4*0101
446
200
0.448
0.724
0.515
0.762
DRB5*0101
924
478
0.627
0.831
0.722
0.879
0.686
Ave
0.541
0.768
0.606
0.799
Ave*
0.570
0.786
0.639
0.819
0.718
The two methods are compared in a leave-one-out experiment on the peptide binding data described in the original NetMHCIIpan publication [29].
# is the number of peptide binding data for each allele, #bind is the number of peptides with a binding affinity stronger than 500 nM. NetMHCIIpan-1.0 is the method by Nielsen et al. [29], NetMHCIIpan-2.0 is the method described here, and TEPITOPE is the method by Sturniolo et al. [1]. Prediction values for NetMHCIIpan-1.0 were taken from [29]. Ave gives the per allele average, Ave* gives the per allele average of the 11 alleles characterized by the TEPITOPE method. In bold is highlighted the best performing method for each of the 14 alleles. AUC values were calculated using a binding threshold of 500 nM. Only AUC values are included for the TEPITOPE method since prediction values for this method are not linearly related to the binding affinity.
Next, we investigated to what extent expanding the peptide data set with broader allelic coverage and more binding data would lead to an improved predictive performance. To do this, we conducted a second series of LOO experiments comparing the LOO predictive performance of the NetMHCIIpan-2.0 method when trained on the old data set covering 14 HLA-DR allele (termed OLD) to its performance when trained on the extended data set covering 24 HLA-DR alleles (termed NEW). The peptide overlap in both datasets is high, and many peptides have been measured for binding affinities against multiple alleles. This peptide overlap can impose a strongly bias in the benchmark evaluation [20], and to lower this bias all peptides used to characterize a given allele were excluded from the training of the prediction method in the second LOO benchmark (for details see Materials and methods). Both the OLD and NEW methods were evaluated using the peptide binding data in the new peptide data set. The results of the extended LOO calculation are shown in table 6.
Table 6
The extended LOO benchmark
OLD
NEW
TEPITOPE
Allele
#
#bind
PCC
AUC
NN
dist
PCC
AUC
NN
dist
AUC
DRB1*0101
7685
4382
0.567
0.767
DRB1*0401
0.352
0.583
0.786
DRB1*1402
0.322
0.727
DRB1*0301
2505
649
0.433
0.727
DRB3*0101
0.277
0.499
0.765
DRB1*0302
0.156
0.718
DRB1*0401
3116
1039
0.563
0.787
DRB1*0405
0.066
0.594
0.804
DRB1*0405
0.066
0.762
DRB1*0404
577
336
0.592
0.806
DRB1*0401
0.091
0.595
0.804
DRB1*0401
0.091
0.747
DRB1*0405
1582
627
0.638
0.826
DRB1*0401
0.066
0.633
0.833
DRB1*0401
0.066
DRB1*0701
1745
849
0.659
0.831
DRB1*0901
0.504
0.648
0.826
DRB1*0901
0.504
0.780
DRB1*0802
1520
431
0.380
0.710
DRB1*1101
0.111
0.369
0.692
DRB1*0813
0.041
0.777
DRB1*0901
1520
622
0.539
0.757
DRB5*0101
0.431
0.517
0.762
DRB5*0101
0.431
0.645
DRB1*1101
1794
778
0.602
0.799
DRB1*1302
0.084
0.460
0.741
DRB1*1302
0.084
DRB1*1302
1580
493
0.338
0.691
DRB1*1101
0.084
0.323
0.671
DRB1*1101
0.084
0.793
DRB1*1501
1769
709
0.568
0.775
DRB1*0404
0.295
0.525
0.756
DRB1*0404
0.295
0.596
DRB3*0101
1501
281
0.339
0.672
DRB1*0301
0.277
0.374
0.702
DRB3*0301
0.223
0.731
DRB4*0101
1521
485
0.506
0.753
DRB1*0404
0.397
0.518
0.766
DRB1*0404
0.397
DRB5*0101
3106
1280
0.547
0.781
DRB1*1101
0.295
0.608
0.813
DRB1*1101
0.295
DRB1*0302
148
44
0.396
0.729
DRB1*0301
0.156
0.542
0.759
DRB1*1402
0.119
0.760
DRB1*0806
118
91
0.670
0.886
DRB1*0802
0.107
0.703
0.902
DRB1*0802
0.107
DRB1*0813
1370
455
0.505
0.735
DRB1*0802
0.041
0.340
0.666
DRB1*0802
0.041
0.884
DRB1*0819
116
54
0.567
0.789
DRB1*0802
0.107
0.566
0.813
DRB1*0813
0.083
0.750
DRB1*1201
117
81
0.626
0.786
DRB1*1101
0.445
0.609
0.798
DRB1*1202
0.045
DRB1*1202
117
79
0.623
0.814
DRB1*1101
0.399
0.713
0.879
DRB1*1201
0.045
DRB1*1402
118
78
0.570
0.793
DRB1*1101
0.148
0.659
0.846
DRB1*0302
0.119
DRB1*1404
30
16
0.393
0.594
DRB1*0404
0.311
0.646
0.679
DRB1*0806
0.240
DRB1*1412
116
63
0.640
0.845
DRB1*0802
0.180
0.738
0.897
DRB1*0813
0.139
DRB3*0301
160
70
0.395
0.738
DRB3*0101
0.223
0.545
0.765
DRB3*0101
0.223
Ave
0.527
0.766
0.554
0.780
Ave*
0.543
0.779
0.529
0.774
0.744
Ave**
0.539
0.771
0.606
0.800
The predictive performance of the pan-specific NN-align method when trained in a leave-one-out experiment and evaluated on the 24 alleles included in the new peptide binding data set.
# is the number of peptide binding data for each allele, #bind is the number of peptides with a binding affinity stronger than 500 nM. OLD is the method described here trained on the old peptide data set, NEW is the method described here trained on the new data set, and TEPITOPE is the method by Sturniolo et al. [1]. NN is the nearest neighbor as defined by the pseudo sequence distance, and dist is the nearest neighbor distance calculated as described in Materials and methods. Ave is the per allele average, Ave* is the per allele average of the 13 alleles characterized by the TEPITOPE method, and Ave** is the per-allele average performance of the 10 alleles included in the new peptide binding data set. In bold is highlighted the best performing method for each of the 24 alleles. AUC values were calculated using a binding threshold of 500 nM. Only AUC values are included for the TEPITOPE method since prediction values for this method are not linearly related to the binding affinity. The double line separates the 10 novel alleles from the original 14 alleles included in the development of the NetMHCIIpan-1.0 method.
The results presented in table 6 clearly demonstrate that the enrichment of novel peptide binding data with a broader allelic coverage leads to an improved predictive performance of the pan-specific prediction method. We can quantify to what degree an allele will benefit from other alleles being present in the training data by calculating its distance to the nearest neighbor in the training data (see Materials and methods). Earlier work has demonstrated that this distance measure correlates strongly with the performance of pan-specific prediction methods [20, 21]. For 10 of the 11 alleles, where including the novel 10 alleles has decreased the nearest neighbor distance, the NEW method has a higher AUC predictive performance compared to the OLD. For the remaining 13 alleles, the 10 novel alleles have not altered the nearest neighbor distance and the performance of the two methods is similar. This strongly underlines the essential prerequisite for accurate pan-specific prediction methods demonstrated earlier for MHC class I, namely a population of the close neighborhood of un-characterized MHC molecules [20, 28].
Lin benchmark
The Lin benchmark consists of binding affinities of 103 overlapping peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). The results of this benchmark are shown in table 7.
Table 7
Predictive performance in terms of the AUC on the Lin benchmark data set
Allele
NetMHCIIpan-2.0
NetMHCIIpan-1.0
TEPITOPE
Multipred_SVM
SVMHC
DRB1*0101
0.883
0.847
0.919
0.860
0.860
DRB1*0301
0.716
0.668
0.718
0.800
0.690
DRB1*0401
0.846
0.815
0.745
0.650
0.750
DRB1*0701
0.878
0.852
0.715
0.700
0.740
DRB1*1101
0.884
0.821
0.824
0.780
0.830
DRB1*1301
0.729
0.715
0.718
0.630
0.720
DRB1*1501
0.838
0.791
0.737
0.620
0.660
Ave
0.825
0.787
0.768
0.720
0.750
The AUC was calculated using the following binding affinity threshold values for each of the 7 alleles: DRB1*0101, 0401, 0701, and 1501 threshold = 100 nM, DRB1*0301, 1101, and 1301, threshold = 1000 nM. The performance values for Multipred_SVM and SVMHC were taken from Nielsen et al. [5]. TEPITOPE is the method described by Sturniolo et al. [1]. NetMHCIIpan-2.0 is the pan-specific method described here, and NetMHCIIpan-1.0 is the pan-specific method by Nielsen et al. [29]. For each allele, the best performing method is highlighted in bold.
The results in table 7 clearly show that NetMHCIIpan-2.0 outperforms both the earlier NetMHCIIpan-1.0 method, as well as the other methods included in the benchmark.
Identifying MHC class II ligands and T cell epitopes
The performance of the NetMHCIIpan-2.0 method on the large set of SYFPEITHI ligand and IEDB T cell epitope data was next investigated. The benchmark was performed as described by Nielsen et al. [29]. The ligand source protein was split into overlapping peptides of the length of the ligand/epitope. All peptides except the annotated HLA ligand/epitope were taken as negatives. This is a very stringent assumption since suboptimal peptides sharing the ligand binding-core are counted as negatives even though they could be presented on the HLA molecule. Thus, this setup is likely to underestimate the predictive performance, but the effect should be equal for all methods compared in the benchmark. For each protein-HLA ligand/epitope pair, the predictive performance was estimated as the AUC value. Table 8 gives a summary of the performance of this benchmark calculation (details can be found in Additional file 1: Table S1).
Table 8
Endogenous HLA-DR ligand benchmark
SYF
#
NetMHCIIpan-1.0
NetMHCIIpan-2.0
TEPITOPE
Ave per ligand
1164
0.800
0.829
Ave per allele
28
0.788
0.797
In TEPITOPE
17
0.768
0.786
0.799
!In TEPITOPE
11
0.819
0.814
IEDB
#
NetMHCIIpan-1.0
NetMHCIIpan-2.0
TEPITOPE
Ave per epitope
1325
0.729
0.751
Ave per allele
42
0.759
0.781
In TEPITOPE
20
0.745
0.747
0.755
!In TEPITOPE
22
0.772
0.812
NetMHCIIpan-1.0 is the method described by Nielsen et al. [29], NetMHCIIpan-2.0 is the pan-specific method described here, and TEPITOPE is the method described by Sturniolo et al. [1]. Ave per ligand/epitope gives the average AUC over the 1164/1325 ligands/epitopes in the benchmark data set. Ave per allele gives the average over the per allele averaged AUC values. In TEPITOPE gives the per allele average of the subset of alleles characterized by the TEPITOPE method, and !In TEPITOPE give the per-allele average performance of the alleles not characterized by the TEPITOPE method. AUC values were calculated as described in the text. For each benchmark subset, the best performing method is highlighted in bold.
Both the SYFPEITI ligand and IEDB epitope benchmarks show that the NetMHCIIpan-2.0 method performs better than the original NetMHCIIpan-1.0 method. On the per ligand basis, the NetMHCIIpan-2.0 method significantly outperforms NetMHCIIpan-1.0 for both data sets (p < 0.01, binomial test excluding ties). In terms of the per-allele performance, the NetMHCIIpan-2.0 also achieved a higher performance than the NetMHCIIpan-1.0 method. This difference is however not statistically significant (p > 0.1 binomial test, in both cases). For the alleles characterized by the TEPITOPE method, the TEPITOPE method achieves the highest performance of the three methods for both data sets. This difference, however, is not statistically significant (p > 0.5 in all cases, binomial test). For alleles not characterized by TEPITOPE, the NetMHCIIpan-2.0 method significantly outperform NetMHCIIpan-1.0 for the IEDB data set (p < 0.01, binomial test), whereas the two methods for this set of alleles achieve a similar predictive performance when evaluated on the SYFPEITHI dataset.
We next investigated how the predictive performance of the NetMHCIIpan-2.0 method depended on the length of the ligand/epitope under investigation. Figure 1 shows a histograms of the average AUC values for the NetMHCIIpan-2.0 (named 2.0) and NetMHCIIpan-1.0 (named 1.0) methods as a function of the ligand/epitope length for the SYFPEITHI and IEDB data sets, respectively.
Figure 1
Histogram of the predictive performance measured in terms of the AUC value for the ligands/epitopes in the SYFPEITHI/IEDB dataset as a function of the peptide length. 2.0 refers to the pan-specific method developed here, and 1.0 refers to the NetMHCIIpan-1.0 method. SYF refers to the SYFPEITHI ligand data set, and IEDB refers to the IEDB T cell epitope data set.
Figure 1 clearly demonstrates that the NetMHCIIpan-2.0 method, for the majority of peptide lengths, outperforms the NetMHCIIpan-1.0 method. Only for very short peptides (length equal to 9 for the SYFPEITHI data set and length equal to 10 for the IEDB data set) does the NetMHCIIpan-1.0 achieve the highest AUC value. What is also clear for the IEDB data set is that both methods achieve their highest predictive performance for peptides of length less than 15 amino acids. The average AUC for epitopes with a length less then 15 amino acids is 0.823. This values is significantly higher than the average AUC for epitopes with a length greater than 15 (0.704, p < 0.005, t-test). This difference is not observed for the SYFPEITHI ligand data set, hence strongly suggesting that the longer epitopes in the IEDB data set are not "true" epitopes in the sense of defining the minimal HLA restriction element.
Discussion
Development of accurate prediction algorithms for MHC class II binding is complicated by the fact that the MHC class II molecule has an open binding cleft, and that peptide binders are accommodated in the binding cleft in a binding register that a priori is unknown. Training of methods for prediction of peptide-MHC class II binding hence rely on either a two step procedure where first the binding register is identified and next the aligned peptides are used to train the binding prediction algorithm or a procedure where these two steps are integrated and performed simultaneously.
We have earlier shown that developing allele-specific prediction methods for MHC class II binding using the latter approach leads to higher prediction accuracy [3, 5]. We have further for MHC class I demonstrated that training the predictors in a pan-specific manner, incorporating all binding data across multiple MHC molecules simultaneously in the training, leads to a significant boost in the predictive performance in particular for MHC molecules characterized by few or no binding data [20–22, 28].
Based on these findings, we have in this paper developed a pan-specific method for prediction of MHC class II binding affinities. The method was trained on binding data covering multiple MHC class II simultaneously, and does not require any prior alignment or binding register-identification. The method was evaluated in several large-scale benchmarks and shown consistently to outperform all other methods investigated, including state-of the-art allele-specific (NN-align [5]) and pan-specific (NetMHCIIpan [29]) methods, as well as and the well-known TEPITOPE method [1]. In particular, it was demonstrated that the proposed method due to its pan-specific nature could boost performance for alleles characterized by limited binding data, and in such cases significantly out-perform allele specific methods. The method thus demonstrates great potential for efficient boosting of the accuracy of MHC class II binding prediction, as accurate predictions can be achieved for novel alleles at a highly reduced experimental cost, and pan-specific binding predictions can be obtained for all alleles with known protein sequence by a method trained using data with limited allelic coverage.
When benchmarked on large data sets of know HLA-DR ligands and epitopes, the method was shown to have a predictive performance comparable to that of TEPITOPE for alleles covered by this method, and maybe more important maintain this high performance also for alleles not described by the TEPITOPE method.
For MHC class I, we have earlier demonstrated that a pan-specific predictor can benefit from being trained on cross-loci (and cross-species) peptide binding data [20]. The development of a cross-loci model for HLA class II is complicated by the fact that the HLA-DRA molecule is close to monomorphic (only two allelic version exists). This is in contrast to HLA-DP and HLA-DQ where both the α and β chains are highly polymorphic. Moreover, the structures of the HLA molecules are less conserved across the three loci for class II compared to class I, and finally very limited peptide binding data have been generated characterizing the different HLA-DP and DQ molecules. As of September 2010, only five HLA-DP and six HLA-DQ alleles have been characterized in the IEDB database with more than 200 peptide-binding measurements [31]. Nonetheless, large amounts of peptide binding data for the HLA-DP and HLA-DQ loci will most likely become available in the near future providing a broader allelic coverage, and future evaluations will demonstrate if also MHC class II binding prediction algorithms using training algorithms like the one outlined in this work, will benefit from pan-specific training across the different loci.
This work was supported by a NIH (National Institute of Health) grant (contract no. HHSN272200900045C) and EU 7th framework grant 222773 (Pep-ChipOmics).
Authors’ Affiliations
(1)
Center A for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark
(2)
Laboratory of Experimental Immunology, Faculty of Health Sciences, University of Copenhagen
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, where s(A, B) is the BLOSUM50 similarity score between the pseudo sequences of allele A and B, respectively. Next, the nearest neighbor distance for an allele is defined as the minimal distance to any allele included in the training data set.
