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Overview

The web-server performs assessment of your T cell therapy target (epitope/peptide antigen) for potential cross-reactivity (CR) in human tissues, using protein abundance database.

After submitting an epitope sequence and selecting its associated allele type(s), on the results page you will obtain the summary of the results in the form of a table (called CR-profile), containing the sums of abundances of the matching natural epitopes in the tissues. The profile is a 4x22 table, which is used for the visualization map (PNG image). the CR-index is calculated from the profile as described in the paper using formulae (see Methods section of the publication). The calculation usually takes about 1 min, depending on number of matches - between the sequences of given epitope and all human proteins.

Input

Amino acid sequence (1 char, case ignored). Selection of the allele types (included are several dozens of HLA types, default is HLA-0201). Selection of the combined score threshold (Q), the suggested value of 0.02 gives top 10% of the HLA binders.

The example: suggests the sequence EVDPASNTY and allele HLA-A0101, Q-threshold=0.0002 (Press ‘Example’ Button).

Press Button “Submit” for job submission. The page is automatically refreshed, and results will be displayed after calculation. The updated URL can be used for results retrieval at any time.

Notes: the amino acid sequence has to have a length of at least seven positions in order to avoid excessive database matches. The number of mismatches cannot exceed a half of the size of the provided epitope for the same reason. Furthermore, MHC binding scores can only be provided for epitopes with a length between 8 and 14 positions, due to the implementation of the underlying software.

Output

After the jobs is calculated, its status changes from ‘waiting’ and ‘running’ to ‘Done’. The resulting page shows the CR-profile table and its visualization, and also gives a CR-index value below.

Details of text files

At the bottom of the page there is link to download file with raw output of the program, for reference purpose, plus the files with a text version of the table and visualization image.

In the text lines for each Natural Peptide (NP) contain the following columns:

‘refseqID’ (RefSeq ID),

‘epitope’ (aa sequence),

‘index’ (start position in protein sequence),

'mismatch’ (number of mismatches).

‘cleavageScore’ - score of proteasomal cleavage,

‘tapScore’ - score of binding to the TAP transporter,

‘mhcScore’ - the score of its affinity to MHC class I alleles; it is given after HLA type and “:”, e.g. 5 in “HLA-A02:01:5”.

‘combinedScore’ - the probability that the epitope will be created by cells, it is calculated Q = PCL/(ATAP*AMHC).

‘stringID’ - STRING ID (composed of 9606. and ENSP),

‘transcriptID’ - ENST Ensembl transcript ID,

‘geneID’ - ENSG Ensembl gene ID,

‘geneName’ (gene name).

After that the abundance values from PaxDB V.4.0 are listed for the 22 Tissues in ppm (ppm - parts per million, this means that the abundance values are normalized, so that the sums of all protein abundances in the PaxDB database for a tissue is equal to 1 million).

For example:

refseqID epitope index mismatch cleavageScore tapScore mhcScore combinedScore stringID transcriptID geneID geneName Brain Heart Lung Liver Kidney Prostate gland Pancreas Gall bladder Colon Esophagus Rectum Uterus Female gonad Testis Placenta Skin Plasma Platelet Saliva Urine Whole organism Cell line

NP_001011548.1 EVDPASNTY 168 0 2.354E-1 4.853E-3 HLA-A0101:115, 4.219E-1 9606.ENSP00000276344 ENST00000276344 ENSG00000147381 MAGEA4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 37.4 35.3 0.252 0.00 0.00 0.00 0.00 0.00 34.4 238

NP_005357.2 EVDPTSHSY 280 3 2.126E-1 4.853E-3 HLA-A0101:23, 1.904E0 9606.ENSP00000347358 ENST00000355220 ENSG00000185247 MAGEA11 0.00 0.00 0.00 7.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.166 0.290 0.123 0.00 0.00 0.00 0.00 0.00 0.313 8.41

NP_005355.2 EVDPAGHSY 170 3 2.227E-1 4.853E-3 HLA-A0101:81, 5.665E-1 9606.ENSP00000286482 ENST00000286482 ENSG00000156009 MAGEA8 0.00100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.86 0.168 0.00 0.00 0.00 0.00 0.00 1.02 18.7

XP_005262392.1 EVDPAGHSY 166 3 2.192E-1 4.853E-3 HLA-A0101:81, 5.576E-1 9606.ENSP00000243314 ENST00000243314 ENSG00000123584 MAGEA9 0.00 0.00 0.00 0.00 0.00 0.164 0.214 0.00 0.00 0.00 0.00 0.00 4.52 6.44 0.00 0.00 0.00 0.00 0.00 0.00 2.49 12.0

References

Webserver iCrossR

Assessment of cancer and virus antigens for cross-reactivity in human tissues.
Jaravine V., Raffegerst S., Schendel D.J., Frishman D. Bioinformatics. 2017 Jan 1;33(1):104-111. PMID:27614350
DOI: https://doi.org/10.1093/bioinformatics/btw567 Epub 2016 Sep 10.

Abundance data

Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell-lines. Wang M, Herrmann CJ, Simonovic M, Szklarczyk D, von Mering C. Proteomics. Feb 2015. PMID:25656970

Immunology

Correlation Between the Number of T Cell Receptors Required for T Cell Activation and TCR–Ligand Affinity.
B.A. Schodin, T.J. Tsomides, D.M. Kranz. Immunity, Vol. 5, 137–146, 1996.

NetChop

The role of the proteasome in generating cytotoxic T cell epitopes: Insights obtained from improved predictions of proteasomal cleavage.
M. Nielsen, C. Lundegaard, O. Lund, and C. Keşmir. Immunogenetics, 57(1-2):33-41, 2005. PMID:15744535

Prediction of proteasome cleavage motifs by neural networks.
C. Keşmir, A. Nussbaum, H. Schild, V. Detours, and S. Brunak. Prot. Eng., 15(4): 287-296, 2002. PMID:11983929

TAP affinity

Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors.
B. Peters, S. Bulik, R. Tampe, P. M. van Endert, H-G. Holzhütter. J Immunol, 171:1741-1749, 2003. PMID:12902473

NetMHC

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, Brunak S, Lund O. Protein Sci., 12:1007-17, 2003. PMID:12717023

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11
Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. Nucleic Acids Res. 1;36(Web Server issue):W509-12. 2008. PMID:18463140

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.
Lundegaard C, Lund O, Nielsen M. Bioinformatics, 24(11):1397-98, 2008. PMID:18413329

Expression data

An integrated encyclopedia of DNA elements in the human genome.
The ENCODE Project Consortium. Nature, 489(7414):57-74, 2012. PMID:22955616

Alternative isoform regulation in human tissue transcriptomes.
Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. Nature 456:470-476, 2008. PMID:18978772

Maintenance

This web site is updated and maintained by Victor Zharavin. If you encounter any problems or have comments regarding iCrossR, please contact ga42joh(a)mytum.de.

Collaboration

The iCrossR web server was developed in collaboration with Medigene Immunotherapies GmbH a subsidiary of Medigene AG.

Working Group

This service is hosted by the Wissenschaftszentrum Weihenstephan, Department of Genome-Oriented Bioinformatics of the Technical University Munich.

iCrossR was designed and created by members of the Frishman group.

Disclaimer

No warranties or guaranties concerning this web based tool "iCrossR", express or implied, including but not limited to a warranty of merchantability or fitness for a particular purpose is given. This web based tool "iCrossR" is solely intended to be an initial source of information in TCR selection based on certain assumptions that may or may not align with your specific conditions, without the warranty or guaranty of generated data to be complete, reliable and externally verified. This web based tool "iCrossR" does not provide any warranty or guaranty regarding toxicology data and information in animals or humans and does not provide any data and information regarding protein sequences.

No warranties or guaranties are given that the generated data and information will meet your requirements or operate under your specific conditions of use. In addition, no warranties or guaranties are given that use of this web based tool "iCrossR" will be secure, error free, or free from interruption. The user shall determine upon its sole responsibility whether generated data and information sufficiently meets any needs and requirements. The user is solely responsible and liable for any loss incurred due to failure of this web based tool "iCrossR" to meet the user’s requirements. No liability is given to the user or any other third party for indirect, consequential, special or punitive damages resulting from the use of the web based tool "iCrossR".