ArrayPitope - 1.0

Residue-level epitope mapping of antigens based on peptide microarray data

ArrayPitope performs residue-level epitope mapping of antigens of interests based on peptide microarray data.

The platform algorithm involves statistical hypothesis testing on each native-mapped synthetic peptide and produce as output a report of the antigen residues identified to be statistically significant for the preservation of binding to the antibody (i.e. high signal intensity).

Submission

1. Array File

The array file should be a comma or tab separated file containing the following column names (capitalization doesn't matter):

Column type	Synonyms accepted
Signal^Required	signal, sig, raw, pm, intensity
Sequence^Required	seq, sequence, peptide, oligomer, coresequence, probe_sequence
Sector	sec, sector, well
Wavelength	wavelength, nm

The file should be gzip compressed and should not exceed 50 Mb in size. For an example of array file, see the Sample data section.

Upload file:

Restriction: The input array files cannot be more than 50 MB of total size. The files should be compressed with gzip.

2. Antigen Sequences

Paste a single sequence or several sequences in FASTA format into the field below:

or submit a file in FASTA format directly from your local disk:

Restriction: At most 200 protein sequences per submission; each sequence not more than 10,000 amino acids.

Download Sample Data

A sample data set is available for testing of the server functionality. The data originates from Hansen et al.[1] and contain peptide microarray measurements of Human Serum Albumin-deriving peptides probed with polyclonal anti-HSA antibodies.

Use the download links below to download the microarray and protein sequence data files and submit them to the server for testing.

Microarray File:	Download
Fasta File:	Download

[1] Hansen LB, Buus S, Schafer-Nielsen C (2013) Data from: Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.3003f

3. Submit

Additional options

Sectors:		Sectors to include; e.g. all/1-6/A01,A04,A07,A08. Default: All
Significance Threshold Alpha:
Peptide length:		Force algorithm to perform analysis on peptides of this length only. Default: The algorithm uses the data to find the most frequent length.

Confidentiality:
Sequences and array data are kept confidential and will be deleted after processing!

Instructions

ArrayPitope performs residue-level epitope mapping of antigens of interests based on peptide microarray data.

This tool has been designed with peptide microarray studies in mind that aims to characterize linear antibody epitopes using an exhaustive amino acid substitution analysis of peptides originating from target antigens.

Usage

Submission is divided into four steps

Upload array file
Upload or paste antigen sequences
(optional) Tweak additional parameters
Submit the job

1. Array file

The array file should be a comma or tab separated file containing rows of peptide sequences and corresponding signal value. The rows may also contain a sector name (e.g. array well "A01") or a wavelength value (e.g. "552" or "IgE").

A header row should state clearly what the different columns contain. The following column terminologies can be used (capitalization doesn't matter):

Column type	Synonyms accepted
Signal^Required	signal, sig, raw, pm, intensity
Sequence^Required	seq, sequence, peptide, oligomer, coresequence, probe_sequence
Sector	sec, sector, well
Wavelength	wavelength, nm

The file should be gzip compressed and should not exceed 50 Mb in size. For an example of array file, see the Sample data section.

Note: Any peptide sequences not derived (including single amino acid substitution) from the supporting antigen sequence file will not be processed.

2. Antigen sequences

These sequences are used for guiding the mapping of array peptides back to its original antigen sequence and to identify substituted peptides.

The sequences must be in FASTA format

The sequences can be pasted into the input field in FASTA format instead of uploading a file.

Note: Antigen sequences with no derived array peptides will not be reported in the output.

3. Additional options

The following default algorithm parameters can be customized by the user:

Choose sectors to include
In case that the processing of all array wells in the uploaded data is undesired, specify which wells should be processed by entering the names of the wells in a comma separated list, e.g. A01,A03,A04. In case of numeric wells (0-99) you can also specify a range, e.g. 1-6.

Choose how to handle array layout
If the microarray is designed with physically separated sectors (wells), tick the Handle sectors individually checkbox. This will ensure that peptides in one well is not normalized against deriving peptides in another well, subjected to a different treatment.

Choose significance threshold
The statistical inference in the algorithm uses a default significance threshold of 0.0001, which means that there is less than 0.01% chance that the null hypothesis is rejected incorrectly (false positive). Other thresholds are available from the dropdown menu under Significance Threshold Alpha.

Specify peptide length manually
The algorithm cannot process multiple peptide lengths. In case of multiple peptide lengths in the array data, you can specify the peptide length that you wish to have processed. By default the algorithm uses the data to find the most frequent length.

4. Submit the job

Click on the "Submit" button. 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.i

At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.

Output format

Article abstracts

Main reference:

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.

¹Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
²Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark
³La 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

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