CITATIONS
For publication of results, please cite:
This version: NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data
Birkir Reynisson, Bruno Alvarez, Sinu Paul, Bjoern Peters and Morten Nielsen
Accepted for publication, NAR Webserver issue 2020
NetMHCpan-4.0: Improved Peptide MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
Vanessa Jurtz, Sinu Paul, Massimo Andreatta, Paolo Marcatili, Bjoern Peters and Morten Nielsen
The Journal of Immunology (2017) ji1700893; DOI: 10.4049/jimmunol.1700893
Full text
[PDF]
NetMHCpan-3.0: improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length data sets
Morten Nielsen and Massimo Andreatta
Genome Medicine (2016) : 8:33
Full text
[PDF]
NetMHCpan, a method for MHC class I binding prediction beyond humans
Ilka Hoof, Bjoern Peters, John Sidney, Lasse Eggers Pedersen, Ole Lund, Soren Buus, and Morten Nielsen
Immunogenetics 61.1 (2009) : 1-13
PMID: 19002680
Full text
DATA RESOURCES
Data resources used to develop this server was obtained from
IEDB database .
Quantitative peptide binding data were obtained
from the IEDB database.
IMGT/HLA database . Robinson J, Malik A, Parham P, Bodmer JG,
Marsh SGE: IMGT/HLA - a sequence database for the human major histocompatibility complex. Tissue Antigens (2000),
55:280-287.
HLA protein sequences were obtained from the IMGT/HLA database (version 3.1.0).
PORTABLE VERSION
Would you prefer to run NetMHCpan at your own site? NetMHCpan v. 4.1
is available as a stand-alone software package, with the same
functionality as the service above. Ready-to-ship packages
exist for the most common UNIX platforms. There is a download tap
for academic users; other users are requested to contact
CBS Software Package Manager at
health-software@dtu.dk .
INPUT DATA
In this section, the user must define the input for the prediction server following these steps:
1) Specify the desired type of input data (FASTA or PEPTIDE) using the drop down menu.
2) Provide the input data by means of pasting the data into the blank field, uploading it using the "Choose File" button or by loading sample data using the "Load Data" button. All the input sequences must be in one-letter amino acid code. The alphabet is as follows (case sensitive):
A C D E F G H I K L M N P Q R S T V W Y and X (unknown)
Any other symbol will be converted to X before processing. At most 5000 sequences are allowed per submission; each sequence must be not more than 20,000 amino acids long and not less than 8 amino acids long.
3) If FASTA was selected as input type, the user must select the peptide length(s) the prediction server is going to work with. NetMHCpan-4.1 will "chop" the input FASTA sequence in overlapping peptides of the provided length(s) and will predict binding against all of them. By default input proteins are digested into 9-mer peptides. Note that, if PEPTIDE was selected as input type, this step is unnecessary and thus the peptide length selector will directly not appear in the interface.
MHC SELECTION
Here, the user must define which MHC(s) molecule(s) the input data is going to be predicted against:
1) First, select the HLA/MHC supertype family.
2) After selecting the MHC family, the user will be able to select a single or multiple MHC molecules from the updated "Select Allele(s)" list. On the other hand, the user may opt to directly type the MHC names in the provided blank field (separated by commas and without blank spaces); if this is the case, there will be no need to select an MHC supertype familiy from the drop-down menu. Click here for a list of MHC molecule names (use the names in the first column). Please note that a maximum of 20 MHC types is allowed per submission.
3) Optionally, the user may choose to paste a full MHC protein sequence in the blank box, or directly upload it by clicking the "Choose file" button. Such sequence must be in FASTA format.
Please note that steps 2) and 3) are mutually exclusive, and are only labeled this way for explanation purposes.
ADDITIONAL CONFIGURATION
In this section, the user may define additional parameters to further customize the run:
1, 2) Specify thresholds for strong and weak binders. They are expressed in terms of %Rank, that is percentile of the predicted binding affinity compared to the distribution of affinities calculated on set of random natural peptides. The peptide will be identified as a strong binder if it is found among the top x% predicted peptides, where x% is the specified threshold for strong binders (by default 0.5%). The peptide will be identified as a weak binder if the % Rank is above the threshold of the strong binders but below the specified threshold for the weak binders (by default 2%).
3) Specify a %Rank threshold to filter out predictions. Only sequences with a predicted %Rank value less than the specified threshold will be printed. To print all predictions, leave this value set to -99.
4) Tick this option to include also Binding Affinity predictions together with Eluted Ligand likelihood.
5) Tick this box to have the output sorted by descending prediction score.
6) Enable this option to export the prediction output to .XLS format (readable by most spreadsheet softwares, like Microsoft Excel).
SUBMISSION
After the user has finished the "INPUT DATA", "MHC SELECTION" and "ADDITIONAL CONFIGURATION" steps, the submission can now be done. To do so, the user can click on "Submit" to submit the job to the processing server, or click on "Clear fields" to clear the page and start over.
The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.
After the server has finished running the corresponding predictions, an
output page will be delivered to the user. A description of the output format can be found at output format
At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; when it terminates you will be notified by e-mail with a URL to your results. They will be stored on the server for 24 hours.
EXAMPLE
For the following FASTA input example:
>Gag_180_209
TPQDLNTMLNTVGGHQAAMQMLKETINEEA
With parameters:
Peptide length: 8, 9, 10, 11, 12
Allele: HLA-A*0301
Sort by prediction score: On
NetMHCpan-4.1 will return the following output (showing the first 10 predicted peptides):
# NetMHCpan version 4.1
# Tmpdir made /usr/opt/www/webface/tmp/server/netmhcpan/5E4EEA2C000053D26D7D1DEF/netMHCpanYdWfgb
# Input is in FSA format
# Peptide length 8,9,10,11,12
# Make Eluted ligand likelihood predictions
HLA-A03:01 : Distance to training data 0.000 (using nearest neighbor HLA-A03:01)
# Rank Threshold for Strong binding peptides 0.500
# Rank Threshold for Weak binding peptides 2.000
------------------------------------------------------------------------------------------------------------
Pos HLA Peptide Core Of Gp Gl Ip Il Icore Identity Score_EL Rnk_EL BindLevel
------------------------------------------------------------------------------------------------------------
15 HLA-A*03:01 HQAAMQMLK HQAAMQMLK 0 0 0 0 0 HQAAMQMLK Gag_180_209 0.6594640 0.259 <= SB
14 HLA-A*03:01 GHQAAMQMLK GQAAMQMLK 0 1 1 0 0 GHQAAMQMLK Gag_180_209 0.2451190 1.084 <= WB
13 HLA-A*03:01 GGHQAAMQMLK GQAAMQMLK 0 1 2 0 0 GGHQAAMQMLK Gag_180_209 0.1045310 1.977 <= WB
12 HLA-A*03:01 VGGHQAAMQMLK VQAAMQMLK 0 1 3 0 0 VGGHQAAMQMLK Gag_180_209 0.0245250 4.089
7 HLA-A*03:01 TMLNTVGGH TMLNTVGGH 0 0 0 0 0 TMLNTVGGH Gag_180_209 0.0194210 4.545
15 HLA-A*03:01 HQAAMQMLKE HQAAMQMLK 0 0 0 0 0 HQAAMQMLK Gag_180_209 0.0083750 6.588
16 HLA-A*03:01 QAAMQMLK QAA-MQMLK 0 0 0 3 1 QAAMQMLK Gag_180_209 0.0042090 8.777
8 HLA-A*03:01 MLNTVGGHQ MLNTVGGHQ 0 0 0 0 0 MLNTVGGHQ Gag_180_209 0.0029890 10.119
21 HLA-A*03:01 MLKETINEE MLKETINEE 0 0 0 0 0 MLKETINEE Gag_180_209 0.0015830 13.034
11 HLA-A*03:01 TVGGHQAAM TVGGHQAAM 0 0 0 0 0 TVGGHQAAM Gag_180_209 0.0013180 14.043
DESCRIPTION
The prediction output for each molecule consists of the following columns:
Pos: Residue number (starting from 0) of the peptide in the protein sequence.
HLA: Specified MHC molecule / Allele name.
Peptide: Amino acid sequence of the potential ligand.
Core: The minimal 9 amino acid binding core directly in contact with the MHC.
Of: The starting position of the Core within the Peptide (if > 0, the method predicts a N-terminal protrusion).
Gp: Position of the deletion, if any.
Gl: Length of the deletion, if any.
Ip: Position of the insertion, if any.
Il: Length of the insertion, if any.
Icore: Interaction core. This is the sequence of the binding core including eventual insertions of deletions.
Identity: Protein identifier, i.e. the name of the FASTA entry.
Score: The raw prediction score.
%Rank: Rank of the predicted binding score compared to a set of random natural peptides. This measure is not affected by inherent bias of certain molecules towards higher or lower mean predicted affinities. Strong binders are defined as having %rank<0.5, and weak binders with %rank<2. We advise to select candidate binders based on %Rank rather than Score
BindLevel: (SB: Strong Binder, WB: Weak Binder). The peptide will be identified as a strong binder if the %Rank is below the specified threshold for the strong binders (by default, 0.5%). The peptide will be identified as a weak binder if the %Rank is above the threshold of the strong binders but below the specified threshold for the weak binders (by default, 2%).
NOTES
Peptide vs. iCore vs. Core
Three amino acid sequences are reported for each row of predictions:
The Peptide is the complete amino acid sequence evaluated by NetMHCpan. Peptides are the
full sequences submitted as a peptide list, or the result of digestion of source proteins (Fasta submission)
The iCore is a substring of Peptide , encompassing
all residues between P1 and P-omega of the MHC. For all intents and purposes, this is the minimal candidate
ligand/epitope that should be considered for further validation.
The Core is always 9 amino acids long,
and is a construction used for sequence aligment and identification of binding anchors.
ARTICLE ABSTRACTS
MAIN REFERENCE
NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data
Birkir Reynisson 1* , Bruno Alvarez 2* , and Morten Nielsen1,3
Accepted for publication, NAR webserver issue 2020
Major Histocompatibility Complex (MHC) molecules are expressed on the cell
surface, where they present peptides to T cells, which gives them a key
role in the development of T cell immune responses. MHC molecules come in
two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I
predominantly present peptides derived from intracellular proteins,
whereas MHC-II predominantly presents peptides from extracellular
proteins. In both cases, the binding between MHC and antigenic peptides
is the most selective step in the antigen presentation pathway. Therefore,
the prediction of peptide binding to MHC is a powerful utility to predict
the possible specificity of a T cell immune response. Commonly MHC binding
prediction tools are trained on binding affinity or mass spectrometry
eluted ligands. Recent studies have however demonstrated how the
integration of both data types can boost predictive performances. Inspired
by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two
web-servers created to predict binding between peptides and MHC-I and
MHC-II, respectively. Both methods exploit tailored machine learning
strategies to integrate different training data types, resulting in
state-of-the-art performance and outperforming their competitors. The
servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/
and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.
EARLIER REFERENCES
NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Dta
Vanessa Jurtz 1 , Sinu Paul 2 , Massimo Andreatta 3 , Paolo Marcatili 1 , Bjoern Peters 2 , and Morten Nielsen1,3
The Journal of Immunology (2017) ji1700893; DOI: 10.4049/jimmunol.1700893
Full text
[PDF]
1
Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark
2
Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, CA92037 La Jolla, USA
3
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina
Cytotoxic T cells are of central importance in the immune system’s response to disease.
They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules.
Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway.
Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention.
In the past, predictors of peptide–MHC interactions have primarily been trained on binding affinity data.
Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about
peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides.
In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data
leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an
increase in predictive performance compared with state-of-the-art methods when it comes to identification of
naturally processed ligands, cancer neoantigens, and T cell epitopes.
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length data sets
Morten Nielsen1,2 and Massimo Andreatta1
Genome Medicine (2016) : 8:33
1 Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina
2 Center for Biological Sequence Analysis,
Technical University of Denmark,
DK-2800 Lyngby, Denmark
Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space.
Full text
[PDF]
NetMHCpan - MHC class I binding prediction beyond humans
Hoof I1 ,
Peter B3 ,
Sidney J3 ,
Pedersen LE2
Lund O1 ,
Buus S2 ,
Nielsen M1
Immunogenetics. (2009) Jan;61(1):1-13.
1 Center for Biological Sequence Analysis,
Technical University of Denmark,
DK-2800 Lyngby, Denmark
2 Division of Experimental Immunology,
Institute of Medical Microbiology and Immunology,
University of Copenhagen, Denmark
3 La Jolla Institute for Allergy and Immunology, San Diego, California, United States of America
Binding of peptides to major histocompatibility complex (MHC) molecules
is the single most selective step in the recognition of pathogens by
the cellular immune system. The human MHC genomic region (called HLA)
is extremely polymorphic comprising several thousand alleles, each
encoding a distinct MHC molecule. The potentially unique specificity
of the majority of HLA alleles that have been identified to date
remains uncharacterized. Likewise, only a limited number of chimpanzee
and rhesus macaque MHC class I molecules have been characterized
experimentally. Here, we present NetMHCpan-2.0, a method that generates
quantitative predictions of the affinity of any peptide-MHC class I
interaction. NetMHCpan-2.0 has been trained on the hitherto largest set
of quantitative MHC binding data available, covering HLA-A and HLA-B,
as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class
I molecules. We show that the NetMHCpan-2.0 method can accurately
predict binding to uncharacterized HLA molecules, including HLA-C and
HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict
peptide binding to chimpanzee and macaque MHC class I molecules. The power
of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune
responses in large out-bred populations is demonstrated. Further, we used
NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class
I molecule SLA-1*0401. Ninety-three percent of the predicted peptides
were demonstrated to bind stronger than 500 nM. The high performance
of NetMHCpan-2.0 for non-human primates documents the method's ability
to provide broad allelic coverage also beyond human MHC molecules. The
method is available at http://www.cbs.dtu.dk/services/NetMHCpan.
PMID: 19002680
Full text
SUBMISSION
Hover the mouse cursor over the symbol for a short description of the options
INPUT TYPE:
Fasta
Peptide
SELECT SPECIES/LOCI:
HLA supertype representative
HLA-A01
HLA-A02
HLA-A03
HLA-A11
HLA-A23
HLA-A24
HLA-A25
HLA-A26
HLA-A29
HLA-A30
HLA-A31
HLA-A32
HLA-A33
HLA-A34
HLA-A36
HLA-A43
HLA-A66
HLA-A68
HLA-A69
HLA-A74
HLA-A80
HLA-B07
HLA-B08
HLA-B13
HLA-B14
HLA-B15
HLA-B18
HLA-B27
HLA-B35
HLA-B37
HLA-B38
HLA-B39
HLA-B40
HLA-B41
HLA-B42
HLA-B44
HLA-B45
HLA-B46
HLA-B47
HLA-B48
HLA-B49
HLA-B50
HLA-B51
HLA-B52
HLA-B53
HLA-B54
HLA-B55
HLA-B56
HLA-B57
HLA-B58
HLA-B59
HLA-B67
HLA-B73
HLA-B78
HLA-B81
HLA-B82
HLA-B83
HLA-C01
HLA-C02
HLA-C03
HLA-C04
HLA-C05
HLA-C06
HLA-C07
HLA-C08
HLA-C12
HLA-C14
HLA-C15
HLA-C16
HLA-C17
HLA-C18
HLA-E01
HLA-G01
Chimpanzee (Patr)
Rhesus macaque (Mamu)
Pig (SLA)
Mouse (H-2)
Gorilla lowlands (Gogo)
BoLA
DLA
EQCA
Select Allele(s) (max 20 per submission)
HLA-A*01:01 (A1)
HLA-A*02:01 (A2)
HLA-A*03:01 (A3)
HLA-A*24:02 (A24)
HLA-A*26:01 (A26)
HLA-B*07:02 (B7)
HLA-B*08:01 (B8)
HLA-B*27:05 (B27)
HLA-B*39:01 (B39)
HLA-B*40:01 (B44)
HLA-B*58:01 (B58)
HLA-B*15:01 (B62)
HLA-A*01:01
HLA-A*01:02
HLA-A*01:03
HLA-A*01:04
HLA-A*01:06
HLA-A*01:07
HLA-A*01:08
HLA-A*01:09
HLA-A*01:10
HLA-A*01:100
HLA-A*01:101
HLA-A*01:102
HLA-A*01:103
HLA-A*01:104
HLA-A*01:105
HLA-A*01:106
HLA-A*01:107
HLA-A*01:108
HLA-A*01:109
HLA-A*01:110
HLA-A*01:111
HLA-A*01:112
HLA-A*01:113
HLA-A*01:114
HLA-A*01:115
HLA-A*01:116
HLA-A*01:117
HLA-A*01:118
HLA-A*01:119
HLA-A*01:12
HLA-A*01:120
HLA-A*01:121
HLA-A*01:122
HLA-A*01:124
HLA-A*01:125
HLA-A*01:126
HLA-A*01:127
HLA-A*01:128
HLA-A*01:129
HLA-A*01:13
HLA-A*01:130
HLA-A*01:131
HLA-A*01:132
HLA-A*01:133
HLA-A*01:134
HLA-A*01:135
HLA-A*01:136
HLA-A*01:137
HLA-A*01:138
HLA-A*01:139
HLA-A*01:14
HLA-A*01:140
HLA-A*01:141
HLA-A*01:142
HLA-A*01:143
HLA-A*01:144
HLA-A*01:145
HLA-A*01:146
HLA-A*01:148
HLA-A*01:149
HLA-A*01:150
HLA-A*01:151
HLA-A*01:152
HLA-A*01:153
HLA-A*01:154
HLA-A*01:155
HLA-A*01:156
HLA-A*01:157
HLA-A*01:158
HLA-A*01:159
HLA-A*01:161
HLA-A*01:163
HLA-A*01:164
HLA-A*01:165
HLA-A*01:166
HLA-A*01:167
HLA-A*01:168
HLA-A*01:169
HLA-A*01:17
HLA-A*01:170
HLA-A*01:171
HLA-A*01:172
HLA-A*01:173
HLA-A*01:174
HLA-A*01:175
HLA-A*01:176
HLA-A*01:177
HLA-A*01:180
HLA-A*01:181
HLA-A*01:182
HLA-A*01:183
HLA-A*01:184
HLA-A*01:185
HLA-A*01:187
HLA-A*01:188
HLA-A*01:189
HLA-A*01:19
HLA-A*01:190
HLA-A*01:191
HLA-A*01:192
HLA-A*01:193
HLA-A*01:194
HLA-A*01:195
HLA-A*01:196
HLA-A*01:197
HLA-A*01:198
HLA-A*01:199
HLA-A*01:20
HLA-A*01:200
HLA-A*01:201
HLA-A*01:202
HLA-A*01:203
HLA-A*01:204
HLA-A*01:205
HLA-A*01:206
HLA-A*01:207
HLA-A*01:209
HLA-A*01:21
HLA-A*01:210
HLA-A*01:211
HLA-A*01:212
HLA-A*01:213
HLA-A*01:214
HLA-A*01:215
HLA-A*01:216
HLA-A*01:217
HLA-A*01:218
HLA-A*01:219
HLA-A*01:220
HLA-A*01:221
HLA-A*01:222
HLA-A*01:223
HLA-A*01:224
HLA-A*01:225
HLA-A*01:226
HLA-A*01:227
HLA-A*01:229
HLA-A*01:23
HLA-A*01:230
HLA-A*01:231
HLA-A*01:232
HLA-A*01:233
HLA-A*01:234
HLA-A*01:235
HLA-A*01:236
HLA-A*01:237
HLA-A*01:238
HLA-A*01:239
HLA-A*01:24
HLA-A*01:241
HLA-A*01:242
HLA-A*01:243
HLA-A*01:244
HLA-A*01:245
HLA-A*01:246
HLA-A*01:249
HLA-A*01:25
HLA-A*01:251
HLA-A*01:252
HLA-A*01:253
HLA-A*01:254
HLA-A*01:255
HLA-A*01:256
HLA-A*01:257
HLA-A*01:259
HLA-A*01:26
HLA-A*01:260
HLA-A*01:261
HLA-A*01:262
HLA-A*01:263
HLA-A*01:264
HLA-A*01:265
HLA-A*01:266
HLA-A*01:267
HLA-A*01:268
HLA-A*01:270
HLA-A*01:271
HLA-A*01:272
HLA-A*01:273
HLA-A*01:274
HLA-A*01:275
HLA-A*01:276
HLA-A*01:277
HLA-A*01:278
HLA-A*01:279
HLA-A*01:28
HLA-A*01:280
HLA-A*01:281
HLA-A*01:282
HLA-A*01:283
HLA-A*01:284
HLA-A*01:286
HLA-A*01:288
HLA-A*01:289
HLA-A*01:29
HLA-A*01:291
HLA-A*01:292
HLA-A*01:294
HLA-A*01:295
HLA-A*01:296
HLA-A*01:297
HLA-A*01:30
HLA-A*01:32
HLA-A*01:33
HLA-A*01:35
HLA-A*01:36
HLA-A*01:37
HLA-A*01:38
HLA-A*01:39
HLA-A*01:40
HLA-A*01:41
HLA-A*01:42
HLA-A*01:43
HLA-A*01:44
HLA-A*01:45
HLA-A*01:46
HLA-A*01:47
HLA-A*01:48
HLA-A*01:49
HLA-A*01:50
HLA-A*01:51
HLA-A*01:54
HLA-A*01:55
HLA-A*01:58
HLA-A*01:59
HLA-A*01:60
HLA-A*01:61
HLA-A*01:62
HLA-A*01:63
HLA-A*01:64
HLA-A*01:65
HLA-A*01:66
HLA-A*01:67
HLA-A*01:68
HLA-A*01:69
HLA-A*01:70
HLA-A*01:71
HLA-A*01:72
HLA-A*01:73
HLA-A*01:74
HLA-A*01:75
HLA-A*01:76
HLA-A*01:77
HLA-A*01:78
HLA-A*01:79
HLA-A*01:80
HLA-A*01:81
HLA-A*01:82
HLA-A*01:83
HLA-A*01:84
HLA-A*01:85
HLA-A*01:86
HLA-A*01:88
HLA-A*01:89
HLA-A*01:90
HLA-A*01:91
HLA-A*01:92
HLA-A*01:93
HLA-A*01:94
HLA-A*01:95
HLA-A*01:96
HLA-A*01:97
HLA-A*01:98
HLA-A*01:99
HLA-A*02:01
HLA-A*02:02
HLA-A*02:03
HLA-A*02:04
HLA-A*02:05
HLA-A*02:06
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HLA-C*15:137
HLA-C*15:138
HLA-C*15:139
HLA-C*15:140
HLA-C*15:141
HLA-C*15:142
HLA-C*15:143
HLA-C*15:144
HLA-C*15:146
HLA-C*15:147
HLA-C*15:148
HLA-C*15:149
HLA-C*15:15
HLA-C*15:150
HLA-C*15:151
HLA-C*15:152
HLA-C*15:153
HLA-C*15:154
HLA-C*15:155
HLA-C*15:157
HLA-C*15:158
HLA-C*15:159
HLA-C*15:16
HLA-C*15:161
HLA-C*15:162
HLA-C*15:163
HLA-C*15:165
HLA-C*15:166
HLA-C*15:167
HLA-C*15:168
HLA-C*15:169
HLA-C*15:17
HLA-C*15:170
HLA-C*15:171
HLA-C*15:172
HLA-C*15:173
HLA-C*15:174
HLA-C*15:175
HLA-C*15:176
HLA-C*15:178
HLA-C*15:179
HLA-C*15:18
HLA-C*15:180
HLA-C*15:181
HLA-C*15:182
HLA-C*15:183
HLA-C*15:19
HLA-C*15:20
HLA-C*15:21
HLA-C*15:22
HLA-C*15:23
HLA-C*15:24
HLA-C*15:25
HLA-C*15:26
HLA-C*15:27
HLA-C*15:28
HLA-C*15:29
HLA-C*15:30
HLA-C*15:31
HLA-C*15:33
HLA-C*15:34
HLA-C*15:35
HLA-C*15:36
HLA-C*15:37
HLA-C*15:38
HLA-C*15:39
HLA-C*15:40
HLA-C*15:41
HLA-C*15:42
HLA-C*15:43
HLA-C*15:44
HLA-C*15:45
HLA-C*15:46
HLA-C*15:47
HLA-C*15:48
HLA-C*15:49
HLA-C*15:50
HLA-C*15:51
HLA-C*15:52
HLA-C*15:53
HLA-C*15:54
HLA-C*15:55
HLA-C*15:56
HLA-C*15:57
HLA-C*15:58
HLA-C*15:59
HLA-C*15:60
HLA-C*15:61
HLA-C*15:62
HLA-C*15:63
HLA-C*15:64
HLA-C*15:65
HLA-C*15:66
HLA-C*15:67
HLA-C*15:68
HLA-C*15:69
HLA-C*15:70
HLA-C*15:71
HLA-C*15:72
HLA-C*15:73
HLA-C*15:74
HLA-C*15:75
HLA-C*15:76
HLA-C*15:77
HLA-C*15:78
HLA-C*15:79
HLA-C*15:80
HLA-C*15:81
HLA-C*15:82
HLA-C*15:83
HLA-C*15:85
HLA-C*15:86
HLA-C*15:87
HLA-C*15:88
HLA-C*15:89
HLA-C*15:90
HLA-C*15:91
HLA-C*15:93
HLA-C*15:94
HLA-C*15:97
HLA-C*15:98
HLA-C*15:99
HLA-C*16:01
HLA-C*16:02
HLA-C*16:04
HLA-C*16:06
HLA-C*16:07
HLA-C*16:08
HLA-C*16:09
HLA-C*16:10
HLA-C*16:100
HLA-C*16:101
HLA-C*16:102
HLA-C*16:103
HLA-C*16:104
HLA-C*16:105
HLA-C*16:106
HLA-C*16:107
HLA-C*16:108
HLA-C*16:109
HLA-C*16:11
HLA-C*16:110
HLA-C*16:111
HLA-C*16:112
HLA-C*16:113
HLA-C*16:114
HLA-C*16:115
HLA-C*16:116
HLA-C*16:117
HLA-C*16:118
HLA-C*16:119
HLA-C*16:12
HLA-C*16:120
HLA-C*16:121
HLA-C*16:122
HLA-C*16:124
HLA-C*16:125
HLA-C*16:126
HLA-C*16:127
HLA-C*16:128
HLA-C*16:129
HLA-C*16:13
HLA-C*16:130
HLA-C*16:131
HLA-C*16:133
HLA-C*16:134
HLA-C*16:135
HLA-C*16:136
HLA-C*16:137
HLA-C*16:138
HLA-C*16:139
HLA-C*16:14
HLA-C*16:140
HLA-C*16:141
HLA-C*16:142
HLA-C*16:143
HLA-C*16:144
HLA-C*16:145
HLA-C*16:146
HLA-C*16:15
HLA-C*16:17
HLA-C*16:18
HLA-C*16:19
HLA-C*16:20
HLA-C*16:21
HLA-C*16:22
HLA-C*16:23
HLA-C*16:24
HLA-C*16:25
HLA-C*16:26
HLA-C*16:27
HLA-C*16:28
HLA-C*16:29
HLA-C*16:31
HLA-C*16:32
HLA-C*16:33
HLA-C*16:34
HLA-C*16:35
HLA-C*16:36
HLA-C*16:37
HLA-C*16:38
HLA-C*16:39
HLA-C*16:40
HLA-C*16:41
HLA-C*16:42
HLA-C*16:43
HLA-C*16:44
HLA-C*16:45
HLA-C*16:46
HLA-C*16:47
HLA-C*16:48
HLA-C*16:49
HLA-C*16:50
HLA-C*16:51
HLA-C*16:52
HLA-C*16:53
HLA-C*16:54
HLA-C*16:55
HLA-C*16:56
HLA-C*16:57
HLA-C*16:58
HLA-C*16:59
HLA-C*16:60
HLA-C*16:61
HLA-C*16:62
HLA-C*16:63
HLA-C*16:64
HLA-C*16:65
HLA-C*16:66
HLA-C*16:67
HLA-C*16:68
HLA-C*16:69
HLA-C*16:70
HLA-C*16:71
HLA-C*16:72
HLA-C*16:73
HLA-C*16:74
HLA-C*16:75
HLA-C*16:76
HLA-C*16:78
HLA-C*16:79
HLA-C*16:80
HLA-C*16:81
HLA-C*16:82
HLA-C*16:83
HLA-C*16:84
HLA-C*16:85
HLA-C*16:86
HLA-C*16:87
HLA-C*16:88
HLA-C*16:90
HLA-C*16:91
HLA-C*16:92
HLA-C*16:93
HLA-C*16:94
HLA-C*16:95
HLA-C*16:96
HLA-C*16:97
HLA-C*16:98
HLA-C*16:99
HLA-C*17:01
HLA-C*17:02
HLA-C*17:03
HLA-C*17:04
HLA-C*17:05
HLA-C*17:06
HLA-C*17:07
HLA-C*17:08
HLA-C*17:09
HLA-C*17:10
HLA-C*17:11
HLA-C*17:12
HLA-C*17:13
HLA-C*17:14
HLA-C*17:15
HLA-C*17:16
HLA-C*17:17
HLA-C*17:18
HLA-C*17:19
HLA-C*17:20
HLA-C*17:21
HLA-C*17:22
HLA-C*17:23
HLA-C*17:24
HLA-C*17:25
HLA-C*17:26
HLA-C*17:28
HLA-C*17:29
HLA-C*17:30
HLA-C*17:31
HLA-C*17:32
HLA-C*17:33
HLA-C*17:34
HLA-C*17:35
HLA-C*17:36
HLA-C*17:37
HLA-C*17:38
HLA-C*17:39
HLA-C*17:40
HLA-C*17:41
HLA-C*18:01
HLA-C*18:02
HLA-C*18:03
HLA-C*18:04
HLA-C*18:05
HLA-C*18:06
HLA-C*18:08
HLA-C*18:09
HLA-C*18:10
HLA-C*18:11
HLA-C*18:12
HLA-E*01:01
HLA-E*01:03
HLA-G*01:01
HLA-G*01:02
HLA-G*01:03
HLA-G*01:04
HLA-G*01:06
HLA-G*01:07
HLA-G*01:08
HLA-G*01:09
Patr-A*0101
Patr-A*0201
Patr-A*0301
Patr-A*0302
Patr-A*0401
Patr-A*0402
Patr-A*0404
Patr-A*0501
Patr-A*0601
Patr-A*0602
Patr-A*0701
Patr-A*0801
Patr-A*0802
Patr-A*0803
Patr-A*0901
Patr-A*0902
Patr-A*1001
Patr-A*1101
Patr-A*1201
Patr-A*1301
Patr-A*1401
Patr-A*1501
Patr-A*1502
Patr-A*1601
Patr-A*1701
Patr-A*1702
Patr-A*1703
Patr-A*1801
Patr-A*2301
Patr-A*2401
Patr-B*0101
Patr-B*0102
Patr-B*0201
Patr-B*0203
Patr-B*0301
Patr-B*0302
Patr-B*0401
Patr-B*0402
Patr-B*0501
Patr-B*0502
Patr-B*0601
Patr-B*0701
Patr-B*0702
Patr-B*0801
Patr-B*0802
Patr-B*0901
Patr-B*1001
Patr-B*1101
Patr-B*1102
Patr-B*1202
Patr-B*1301
Patr-B*1401
Patr-B*1601
Patr-B*1602
Patr-B*1701
Patr-B*1702
Patr-B*1703
Patr-B*1801
Patr-B*1901
Patr-B*2001
Patr-B*2101
Patr-B*2201
Patr-B*2202
Patr-B*2301
Patr-B*2302
Patr-B*2303
Patr-B*2401
Patr-B*2402
Patr-B*2501
Patr-B*2601
Patr-B*2701
Patr-B*2801
Patr-B*2901
Patr-B*3001
Patr-B*3501
Patr-B*3601
Patr-B*3701
Patr-C*0201
Patr-C*0202
Patr-C*0203
Patr-C*0204
Patr-C*0205
Patr-C*0206
Patr-C*0301
Patr-C*0302
Patr-C*0303
Patr-C*0304
Patr-C*0401
Patr-C*0501
Patr-C*0502
Patr-C*0601
Patr-C*0701
Patr-C*0801
Patr-C*0901
Patr-C*0902
Patr-C*0903
Patr-C*0904
Patr-C*0905
Patr-C*1001
Patr-C*1101
Patr-C*1201
Patr-C*1301
Patr-C*1302
Patr-C*1501
Patr-C*1601
Mamu-A1*00101 (Mamu-A*01)
Mamu-A1*00102
Mamu-A1*00103
Mamu-A1*00104
Mamu-A1*00105
Mamu-A1*00201 (Mamu-A*02)
Mamu-A1*00301 (Mamu-A*03)
Mamu-A1*00302
Mamu-A1*00303
Mamu-A1*00304
Mamu-A1*00305
Mamu-A1*00306 (Mamu-A*21)
Mamu-A1*00307
Mamu-A1*00308
Mamu-A1*00310
Mamu-A1*00401 (Mamu-A*04)
Mamu-A1*00402
Mamu-A1*00403
Mamu-A1*00601 (Mamu-A*06)
Mamu-A1*00602 (Mamu-A*0602)
Mamu-A1*00701 (Mamu-A*07)
Mamu-A1*00702
Mamu-A1*00703 (Mamu-A*0703)
Mamu-A1*00704
Mamu-A1*00705
Mamu-A1*00801
Mamu-A1*01001
Mamu-A1*01002
Mamu-A1*01101 (Mamu-A*11)
Mamu-A1*01102
Mamu-A1*01103
Mamu-A1*01104
Mamu-A1*01201
Mamu-A1*01601 (Mamu-A*1602)
Mamu-A1*01801
Mamu-A1*01802
Mamu-A1*01803
Mamu-A1*01804
Mamu-A1*01805
Mamu-A1*01806
Mamu-A1*01807
Mamu-A1*01808
Mamu-A1*01901 (Mamu-A*19)
Mamu-A1*01902
Mamu-A1*01903
Mamu-A1*01904
Mamu-A1*01905
Mamu-A1*01906
Mamu-A1*01907
Mamu-A1*02201 (Mamu-A*2201)
Mamu-A1*02202
Mamu-A1*02203
Mamu-A1*02301 (Mamu-A*23)
Mamu-A1*02302
Mamu-A1*02501 (Mamu-A*25)
Mamu-A1*02502
Mamu-A1*02601 (Mamu-A*26)
Mamu-A1*02602
Mamu-A1*02603
Mamu-A1*02801 (Mamu-A*28)
Mamu-A1*02802
Mamu-A1*02803
Mamu-A1*02804
Mamu-A1*02805
Mamu-A1*02806
Mamu-A1*03201
Mamu-A1*03202
Mamu-A1*03203
Mamu-A1*03301
Mamu-A1*04001
Mamu-A1*04002
Mamu-A1*04003
Mamu-A1*04101
Mamu-A1*04102
Mamu-A1*04201
Mamu-A1*04301
Mamu-A1*04501
Mamu-A1*04801
Mamu-A1*04901
Mamu-A1*04902
Mamu-A1*04903
Mamu-A1*04904
Mamu-A1*05001
Mamu-A1*05101
Mamu-A1*05201
Mamu-A1*05301
Mamu-A1*05302
Mamu-A1*05401
Mamu-A1*05402
Mamu-A1*05501
Mamu-A1*05601
Mamu-A1*05602
Mamu-A1*05603
Mamu-A1*05701
Mamu-A1*05702
Mamu-A1*05901
Mamu-A1*06001
Mamu-A1*06101
Mamu-A1*06301
Mamu-A1*06501
Mamu-A1*06601
Mamu-A1*07301
Mamu-A1*07401
Mamu-A1*07402
Mamu-A1*07403
Mamu-A1*08101
Mamu-A1*08501
Mamu-A1*09101
Mamu-A1*09201
Mamu-A1*10501
Mamu-A1*10502
Mamu-A1*10503
Mamu-A1*10504
Mamu-A1*10601
Mamu-A1*10701
Mamu-A1*10801
Mamu-A1*10901
Mamu-A1*11001
Mamu-A1*11101
Mamu-A1*11201
Mamu-A1*11301
Mamu-A2*0101
Mamu-A2*0102
Mamu-A2*0103
Mamu-A2*0501
Mamu-A2*05020
Mamu-A2*05030
Mamu-A2*05040
Mamu-A2*0505 (Mamu-A*0505)
Mamu-A2*0506 (Mamu-A*0506)
Mamu-A2*0507 (Mamu-A*0507)
Mamu-A2*0509 (Mamu-A*0509)
Mamu-A2*0510 (Mamu-A*0510)
Mamu-A2*0511 (Mamu-A*0511)
Mamu-A2*0512
Mamu-A2*0513
Mamu-A2*0514
Mamu-A2*05150
Mamu-A2*05160
Mamu-A2*0517
Mamu-A2*0518
Mamu-A2*0519
Mamu-A2*0520
Mamu-A2*0521
Mamu-A2*0522
Mamu-A2*0523
Mamu-A2*0524
Mamu-A2*0525
Mamu-A2*0526
Mamu-A2*0527
Mamu-A2*0528
Mamu-A2*0529
Mamu-A2*0531
Mamu-A2*05320
Mamu-A2*0533
Mamu-A2*0534
Mamu-A2*0535
Mamu-A2*0536
Mamu-A2*0537
Mamu-A2*0538
Mamu-A2*0539
Mamu-A2*0540
Mamu-A2*0541
Mamu-A2*0542
Mamu-A2*0543
Mamu-A2*0544
Mamu-A2*0545
Mamu-A2*0546
Mamu-A2*0547
Mamu-A2*2401 (Mamu-A*24)
Mamu-A2*2402
Mamu-A2*2403
Mamu-A3*1301
Mamu-A3*1302
Mamu-A3*1303
Mamu-A3*1304
Mamu-A3*1305 (Mamu-A*1305)
Mamu-A3*1306 (Mamu-A*1506)
Mamu-A3*1307
Mamu-A3*1308
Mamu-A3*1309
Mamu-A3*1310
Mamu-A3*1311
Mamu-A3*1312
Mamu-A3*1313
Mamu-A4*0101
Mamu-A4*01020
Mamu-A4*0103
Mamu-A4*0202
Mamu-A4*0203
Mamu-A4*0205
Mamu-A4*0301
Mamu-A4*0302
Mamu-A4*1402
Mamu-A4*14030
Mamu-A4*1404
Mamu-A4*1405
Mamu-A4*1406
Mamu-A4*1407
Mamu-A4*1408
Mamu-A4*1409
Mamu-A5*30010
Mamu-A5*3002
Mamu-A5*3003
Mamu-A5*3004
Mamu-A5*3005
Mamu-A5*3006
Mamu-A6*0101
Mamu-A6*0102
Mamu-A6*0103
Mamu-A6*0104
Mamu-A6*0105
Mamu-A7*0101
Mamu-A7*0102
Mamu-A7*0103 (Mamu-A70103)
Mamu-A7*0201
Mamu-AG*01
Mamu-AG*02011
Mamu-AG*02012
Mamu-AG*0202
Mamu-AG*03011
Mamu-AG*0302
Mamu-B*00101 (Mamu-B*01)
Mamu-B*00102
Mamu-B*00201 (Mamu-B*02)
Mamu-B*00202
Mamu-B*00301 (Mamu-B*03)
Mamu-B*00302
Mamu-B*00401 (Mamu-B*04)
Mamu-B*00501 (Mamu-B*05)
Mamu-B*00502
Mamu-B*00601
Mamu-B*00602
Mamu-B*00701 (Mamu-B*07)
Mamu-B*00702
Mamu-B*00703
Mamu-B*00704
Mamu-B*00801 (Mamu-B*08)
Mamu-B*01001 (Mamu-B1001)
Mamu-B*01101
Mamu-B*01201 (Mamu-B*12)
Mamu-B*01301
Mamu-B*01401
Mamu-B*01501
Mamu-B*01502
Mamu-B*01601
Mamu-B*01701 (Mamu-B*17)
Mamu-B*01702
Mamu-B*01703
Mamu-B*01801
Mamu-B*01901 (Mamu-B*19)
Mamu-B*01902
Mamu-B*01903
Mamu-B*02001 (Mamu-B*20)
Mamu-B*02101 (Mamu-B*21)
Mamu-B*02102
Mamu-B*02103
Mamu-B*02201 (Mamu-B*22)
Mamu-B*02301
Mamu-B*02401 (Mamu-B*24)
Mamu-B*02501
Mamu-B*02601
Mamu-B*02602
Mamu-B*02701 (Mamu-B*27)
Mamu-B*02702
Mamu-B*02703
Mamu-B*02801 (Mamu-B*28)
Mamu-B*02802
Mamu-B*02803
Mamu-B*02901
Mamu-B*02902
Mamu-B*03001
Mamu-B*03002 (Mamu-B*3002)
Mamu-B*03003
Mamu-B*03004
Mamu-B*03005
Mamu-B*03101
Mamu-B*03102
Mamu-B*03103
Mamu-B*03201
Mamu-B*03301
Mamu-B*03401
Mamu-B*03501
Mamu-B*03601 (Mamu-B*36)
Mamu-B*03602
Mamu-B*03701 (Mamu-B*37)
Mamu-B*03801 (Mamu-B*38)
Mamu-B*03802
Mamu-B*03901 (Mamu-B*39)
Mamu-B*04001 (Mamu-B*40)
Mamu-B*04002
Mamu-B*04101 (Mamu-B*41)
Mamu-B*04201
Mamu-B*04301 (Mamu-B*43)
Mamu-B*04401 (Mamu-B*44)
Mamu-B*04402
Mamu-B*04403
Mamu-B*04404
Mamu-B*04405
Mamu-B*04501 (Mamu-B*45)
Mamu-B*04502
Mamu-B*04503
Mamu-B*04504
Mamu-B*04601 (Mamu-B*46)
Mamu-B*04602
Mamu-B*04603
Mamu-B*04604
Mamu-B*04605
Mamu-B*04607
Mamu-B*04608
Mamu-B*04609
Mamu-B*04610
Mamu-B*04611
Mamu-B*04612
Mamu-B*04613
Mamu-B*04614
Mamu-B*04615
Mamu-B*04616
Mamu-B*04617
Mamu-B*04701 (Mamu-B*47)
Mamu-B*04702
Mamu-B*04703
Mamu-B*04704
Mamu-B*04705
Mamu-B*04801 (Mamu-B*48)
Mamu-B*04802
Mamu-B*04901 (Mamu-B*49)
Mamu-B*05002 (Mamu-B*5001)
Mamu-B*05101
Mamu-B*05102
Mamu-B*05103
Mamu-B*05104
Mamu-B*05105
Mamu-B*05201 (Mamu-B*52)
Mamu-B*05301 (Mamu-B*53)
Mamu-B*05302
Mamu-B*05401 (Mamu-B*54)
Mamu-B*05501 (Mamu-B*55)
Mamu-B*05601
Mamu-B*05602
Mamu-B*05701 (Mamu-B*57)
Mamu-B*05702
Mamu-B*05802 (Mamu-B*5802)
Mamu-B*05901
Mamu-B*06001
Mamu-B*06002 (Mamu-B*6002)
Mamu-B*06003
Mamu-B*06101 (Mamu-B*61)
Mamu-B*06102
Mamu-B*06103
Mamu-B*06301 (Mamu-B*63)
Mamu-B*06302
Mamu-B*06401 (Mamu-B*64)
Mamu-B*06402
Mamu-B*06501 (Mamu-B*65)
Mamu-B*06502
Mamu-B*06503
Mamu-B*06601 (Mamu-B*66)
Mamu-B*06701 (Mamu-B*67)
Mamu-B*06702
Mamu-B*06801
Mamu-B*06802
Mamu-B*06803
Mamu-B*06804
Mamu-B*06805
Mamu-B*06901 (Mamu-B*69)
Mamu-B*06902
Mamu-B*06903
Mamu-B*06904
Mamu-B*07001 (Mamu-B*70)
Mamu-B*07002
Mamu-B*07101 (Mamu-B*71)
Mamu-B*07201
Mamu-B*07202
Mamu-B*07301
Mamu-B*07401
Mamu-B*07402
Mamu-B*07501
Mamu-B*07502
Mamu-B*07601
Mamu-B*07602
Mamu-B*07701
Mamu-B*07702
Mamu-B*07801
Mamu-B*07901
Mamu-B*07902
Mamu-B*07903
Mamu-B*08001
Mamu-B*08101
Mamu-B*08102
Mamu-B*08201
Mamu-B*08202
Mamu-B*08301 (Mamu-B8301)
Mamu-B*08401
Mamu-B*08501
Mamu-B*08502
Mamu-B*08601
Mamu-B*08602
Mamu-B*08603
Mamu-B*08701 (Mamu-B8701)
Mamu-B*08801
Mamu-B*08901
Mamu-B*09001
Mamu-B*09101
Mamu-B*09102
Mamu-B*09201
Mamu-B*09301
Mamu-B*09401
Mamu-B*09501
Mamu-B*09601
Mamu-B*09701
Mamu-B*09801
Mamu-B*09901
Mamu-B*10001
Mamu-B*10101
SLA-1*0101
SLA-1*0201
SLA-1*0202
SLA-1*0401
SLA-1*0501
SLA-1*0601
SLA-1*0701
SLA-1*0702
SLA-1*0801
SLA-1*1101
SLA-1*1201
SLA-1*1301
SLA-2*0101
SLA-2*0102
SLA-2*0201
SLA-2*0202
SLA-2*0301
SLA-2*0302
SLA-2*0401
SLA-2*0402
SLA-2*0501
SLA-2*0502
SLA-2*0601
SLA-2*0701
SLA-2*1001
SLA-2*1002
SLA-2*1101
SLA-2*1201
SLA-3*0101
SLA-3*0301
SLA-3*0302
SLA-3*0303
SLA-3*0304
SLA-3*0401
SLA-3*0501
SLA-3*0502
SLA-3*0503
SLA-3*0601
SLA-3*0602
SLA-3*0701
SLA-6*0101
SLA-6*0102
SLA-6*0103
SLA-6*0104
SLA-6*0105
SLA-2-CDY.AA
SLA-2-HB01
SLA-2-LWH.AA
SLA-2-TPK.AA
SLA-2-YC.AA
SLA-2-YDL.AA
SLA-2-YDY.AA
SLA-2-YTH.AA
SLA-1*es11
SLA-2*es22
SLA-1-CHANGDA
SLA-1-HB01
SLA-1-HB02
SLA-1-HB03
SLA-1-HB04
SLA-1-LWH
SLA-1-TPK
SLA-1-YC
SLA-1-YDL01
SLA-1-YTH
SLA-2-YDL02
SLA-3-CDY
SLA-3-HB01
SLA-3-LWH
SLA-3-TPK
SLA-3-YC
SLA-3-YDL
SLA-3-YDY01
SLA-3-YDY02
SLA-3-YTH
H-2-Db
H-2-Dd
H-2-Kb
H-2-Kd
H-2-Kk
H-2-Ld
H-2-Qa1
H-2-Qa2
Gogo-B*0101
BoLA-HD6
BoLA-JSP.1
BoLA-T2c
BoLA-T2b
BoLA-T2a
BoLa-T7
BoLA-D18.4
BoLA-AW10
BoLA-T5
BoLA-1:00901
BoLA-1:00902
BoLA-1:01901
BoLA-1:02001
BoLA-1:02101
BoLA-1:02301 (D18.4)
BoLA-1:02801
BoLA-1:02901
BoLA-1:03101
BoLA-1:03102
BoLA-1:04201
BoLA-1:04901
BoLA-1:06101
BoLA-1:06701
BoLA-1:07401
BoLA-2:00501
BoLA-2:00601
BoLA-2:00602
BoLA-2:00801
BoLA-2:00802
BoLA-2:01201 (T2a)
BoLA-2:01601
BoLA-2:01602
BoLA-2:01801
BoLA-2:01802
BoLA-2:02201
BoLA-2:02501
BoLA-2:02601
BoLA-2:02602
BoLA-2:02603
BoLA-2:03001
BoLA-2:03202
BoLA-2:04301
BoLA-2:04401
BoLA-2:04402
BoLA-2:04501
BoLA-2:04601
BoLA-2:04701
BoLA-2:04801
BoLA-2:05401
BoLA-2:05501
BoLA-2:05601
BoLA-2:05701
BoLA-2:06001
BoLA-2:06201
BoLA-2:06901
BoLA-2:07001
BoLA-2:07101
BoLA-3:00101 (AW10)
BoLA-3:00102
BoLA-3:00103
BoLA-3:00201 (JSP.1)
BoLA-3:00401
BoLA-3:00402
BoLA-3:00403
BoLA-3:01001
BoLA-3:01101
BoLA-3:01701
BoLA-3:01702
BoLA-3:01703
BoLA-3:02701
BoLA-3:02702
BoLA-3:03501
BoLA-3:03601
BoLA-3:03701
BoLA-3:03801
BoLA-3:05001
BoLA-3:05002
BoLA-3:05101
BoLA-3:05201
BoLA-3:05301
BoLA-3:05801
BoLA-3:05901
BoLA-3:06501
BoLA-3:06601
BoLA-3:06602
BoLA-3:06801
BoLA-3:07301
BoLA-4:02401
BoLA-4:02402
BoLA-4:06301
BoLA-5:00301
BoLA-5:03901
BoLA-5:06401
BoLA-5:07201
BoLA-6:01301 (HD6)
BoLA-6:01302
BoLA-6:01401
BoLA-6:01402
BoLA-6:01501
BoLA-6:01502
BoLA-6:03401
BoLA-6:04001
BoLA-6:04101 (T2b)
BoLA-amani.1
BoLA-gb1.7
DLA-8803401
DLA-8850101
DLA-8850801
Eqca-16:00101
Eqca-1:00101
... or type Allele names (i.e. HLA-A01:01) separated by commas and without spaces (max 20 per submission):
For a list of allowed allele names click here
... or paste a single full length MHC protein sequence in FASTA
format into the field below:
... or load a file containing a full length MHC protein sequence in FASTA
format directly from your local disk:
ADDITIONAL CONFIGURATION:
Threshold for strong binder: % Rank
Threshold for weak binder: % Rank
Filtering threshold for %Rank (leave -99 to print all)
Include BA predictions
Sort by prediction score
Save predictions to XLS file
Restrictions:
At most 5000 sequences per submission;
each sequence not more than 20,000 amino acids and
not less than 8 amino acids. Max 20 MHC alleles per submission.
Confidentiality:
The sequences are kept confidential and will be deleted
after processing.
CITATIONS
For publication of results, please cite:
This version: NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data
Birkir Reynisson, Bruno Alvarez, Sinu Paul, Bjoern Peters and Morten Nielsen
Accepted for publication, NAR Webserver issue 2020
NetMHCpan-4.0: Improved Peptide MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
Vanessa Jurtz, Sinu Paul, Massimo Andreatta, Paolo Marcatili, Bjoern Peters and Morten Nielsen
The Journal of Immunology (2017) ji1700893; DOI: 10.4049/jimmunol.1700893
Full text
[PDF]
NetMHCpan-3.0: improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length data sets
Morten Nielsen and Massimo Andreatta
Genome Medicine (2016) : 8:33
Full text
[PDF]
NetMHCpan, a method for MHC class I binding prediction beyond humans
Ilka Hoof, Bjoern Peters, John Sidney, Lasse Eggers Pedersen, Ole Lund, Soren Buus, and Morten Nielsen
Immunogenetics 61.1 (2009) : 1-13
PMID: 19002680
Full text
DATA RESOURCES
Data resources used to develop this server was obtained from
IEDB database .
Quantitative peptide binding data were obtained
from the IEDB database.
IMGT/HLA database . Robinson J, Malik A, Parham P, Bodmer JG,
Marsh SGE: IMGT/HLA - a sequence database for the human major histocompatibility complex. Tissue Antigens (2000),
55:280-287.
HLA protein sequences were obtained from the IMGT/HLA database (version 3.1.0).
PORTABLE VERSION
Would you prefer to run NetMHCpan at your own site? NetMHCpan v. 4.1
is available as a stand-alone software package, with the same
functionality as the service above. Ready-to-ship packages
exist for the most common UNIX platforms. There is a download tap
for academic users; other users are requested to contact
CBS Software Package Manager at
health-software@dtu.dk .