Limits...
An open-source computational and data resource to analyze digital maps of immunopeptidomes.

Caron E, Espona L, Kowalewski DJ, Schuster H, Ternette N, Alpízar A, Schittenhelm RB, Ramarathinam SH, Lindestam Arlehamn CS, Chiek Koh C, Gillet LC, Rabsteyn A, Navarro P, Kim S, Lam H, Sturm T, Marcilla M, Sette A, Campbell DS, Deutsch EW, Moritz RL, Purcell AW, Rammensee HG, Stevanovic S, Aebersold R - Elife (2015)

Bottom Line: We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides.Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS).This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland.

ABSTRACT
We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

No MeSH data available.


Related in: MedlinePlus

Automated NetMHC-based method for annotating and visualizing HLA allele-specific peptides.PBMC#2 was typed positive for HLA-A02, -A03, -B35, -B39, and is shown here as a representative sample. (A) The stand-alone software package of the HLA binding prediction algorithm NetMHC 3.4 was used to predict the binding affinity of all identified peptides to HLA-A02, -A03, -B35 and -B39 (four peptides are shown for simplicity). For each peptide, an annotation score was calculated by dividing the second lowest IC50 value (second best predicted allele) by the lowest IC50 value (best predicted allele). Peptides with a score ≥3 were annotated to the HLA allele predicted to bind best. Peptides with a score below 3 were considered as non-annotated. Non-annotated peptides were curated in the output files in Figure 2—source data 2 and correspond to 1) non-HLA peptides/contaminants, 2) peptides predicted to strongly bind more than one HLA allele (supertype peptides), 3) peptides predicted to bind HLA-C alleles, 4) exceptional HLA peptides with no known binding motifs. Annotation scores of all eluted peptides are shown in Figure 2—source data 2. Additional information is provided in Supplementary file 1. (B) Curves showing the distribution of the predicted HLA binding affinities for all HLA-A03-annotated peptides with a score ≥3. Overall, 91% of all HLA-A03-annotated peptides are predicted to have a binding affinity below 500 nM for the HLA-A03 molecule (see also Figure 2—figure supplement 4 and Figure 2—figure supplement 5). The same peptides are predicted to be non-binders for the other alleles – i.e., HLA-A02, -B35 and -B39. (C) Heat map visualization following clustering of predicted HLA binding affinity values. The white box highlights HLA-A03-annotated peptides. The four peptides in the table in (a) are indicated by arrows and their respective predicted binding affinity for the HLA-A03 molecule is indicated in parenthesis.DOI:http://dx.doi.org/10.7554/eLife.07661.014
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4507788&req=5

fig2s1: Automated NetMHC-based method for annotating and visualizing HLA allele-specific peptides.PBMC#2 was typed positive for HLA-A02, -A03, -B35, -B39, and is shown here as a representative sample. (A) The stand-alone software package of the HLA binding prediction algorithm NetMHC 3.4 was used to predict the binding affinity of all identified peptides to HLA-A02, -A03, -B35 and -B39 (four peptides are shown for simplicity). For each peptide, an annotation score was calculated by dividing the second lowest IC50 value (second best predicted allele) by the lowest IC50 value (best predicted allele). Peptides with a score ≥3 were annotated to the HLA allele predicted to bind best. Peptides with a score below 3 were considered as non-annotated. Non-annotated peptides were curated in the output files in Figure 2—source data 2 and correspond to 1) non-HLA peptides/contaminants, 2) peptides predicted to strongly bind more than one HLA allele (supertype peptides), 3) peptides predicted to bind HLA-C alleles, 4) exceptional HLA peptides with no known binding motifs. Annotation scores of all eluted peptides are shown in Figure 2—source data 2. Additional information is provided in Supplementary file 1. (B) Curves showing the distribution of the predicted HLA binding affinities for all HLA-A03-annotated peptides with a score ≥3. Overall, 91% of all HLA-A03-annotated peptides are predicted to have a binding affinity below 500 nM for the HLA-A03 molecule (see also Figure 2—figure supplement 4 and Figure 2—figure supplement 5). The same peptides are predicted to be non-binders for the other alleles – i.e., HLA-A02, -B35 and -B39. (C) Heat map visualization following clustering of predicted HLA binding affinity values. The white box highlights HLA-A03-annotated peptides. The four peptides in the table in (a) are indicated by arrows and their respective predicted binding affinity for the HLA-A03 molecule is indicated in parenthesis.DOI:http://dx.doi.org/10.7554/eLife.07661.014

Mentions: Large-scale DDA-based identification of immunoaffinity purified HLA class I peptides is supported by several software tools (e.g., MaxQuant, Perseus or X-PRESIDENT) and results in thousands of unclassified peptides of various lengths. Since large HLA peptidomic datasets are generated at an increasing pace, additional computational frameworks facilitating the HLA annotation and storage of such datasets need to be developed. Here, we first created a computational workflow to support the identification, classification/annotation, visualization and storage of HLA peptidomic data in an allele-dependent manner. The software tools described in the section below enable (1) systematic annotation of peptides to their respective HLA allele, (2) visualization of HLA peptidomic datasets, and (3) generation of HLA class I allele-specific peptide spectral libraries, which can be converted into high quality assay libraries for the processing of SWATH-data (Figure 2, Figure 2—figure supplement 1, Figure 2—source data 2 and Supplementary file 1).10.7554/eLife.07661.008Figure 2.Content and analysis of the pilot repository.


An open-source computational and data resource to analyze digital maps of immunopeptidomes.

Caron E, Espona L, Kowalewski DJ, Schuster H, Ternette N, Alpízar A, Schittenhelm RB, Ramarathinam SH, Lindestam Arlehamn CS, Chiek Koh C, Gillet LC, Rabsteyn A, Navarro P, Kim S, Lam H, Sturm T, Marcilla M, Sette A, Campbell DS, Deutsch EW, Moritz RL, Purcell AW, Rammensee HG, Stevanovic S, Aebersold R - Elife (2015)

Automated NetMHC-based method for annotating and visualizing HLA allele-specific peptides.PBMC#2 was typed positive for HLA-A02, -A03, -B35, -B39, and is shown here as a representative sample. (A) The stand-alone software package of the HLA binding prediction algorithm NetMHC 3.4 was used to predict the binding affinity of all identified peptides to HLA-A02, -A03, -B35 and -B39 (four peptides are shown for simplicity). For each peptide, an annotation score was calculated by dividing the second lowest IC50 value (second best predicted allele) by the lowest IC50 value (best predicted allele). Peptides with a score ≥3 were annotated to the HLA allele predicted to bind best. Peptides with a score below 3 were considered as non-annotated. Non-annotated peptides were curated in the output files in Figure 2—source data 2 and correspond to 1) non-HLA peptides/contaminants, 2) peptides predicted to strongly bind more than one HLA allele (supertype peptides), 3) peptides predicted to bind HLA-C alleles, 4) exceptional HLA peptides with no known binding motifs. Annotation scores of all eluted peptides are shown in Figure 2—source data 2. Additional information is provided in Supplementary file 1. (B) Curves showing the distribution of the predicted HLA binding affinities for all HLA-A03-annotated peptides with a score ≥3. Overall, 91% of all HLA-A03-annotated peptides are predicted to have a binding affinity below 500 nM for the HLA-A03 molecule (see also Figure 2—figure supplement 4 and Figure 2—figure supplement 5). The same peptides are predicted to be non-binders for the other alleles – i.e., HLA-A02, -B35 and -B39. (C) Heat map visualization following clustering of predicted HLA binding affinity values. The white box highlights HLA-A03-annotated peptides. The four peptides in the table in (a) are indicated by arrows and their respective predicted binding affinity for the HLA-A03 molecule is indicated in parenthesis.DOI:http://dx.doi.org/10.7554/eLife.07661.014
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507788&req=5

fig2s1: Automated NetMHC-based method for annotating and visualizing HLA allele-specific peptides.PBMC#2 was typed positive for HLA-A02, -A03, -B35, -B39, and is shown here as a representative sample. (A) The stand-alone software package of the HLA binding prediction algorithm NetMHC 3.4 was used to predict the binding affinity of all identified peptides to HLA-A02, -A03, -B35 and -B39 (four peptides are shown for simplicity). For each peptide, an annotation score was calculated by dividing the second lowest IC50 value (second best predicted allele) by the lowest IC50 value (best predicted allele). Peptides with a score ≥3 were annotated to the HLA allele predicted to bind best. Peptides with a score below 3 were considered as non-annotated. Non-annotated peptides were curated in the output files in Figure 2—source data 2 and correspond to 1) non-HLA peptides/contaminants, 2) peptides predicted to strongly bind more than one HLA allele (supertype peptides), 3) peptides predicted to bind HLA-C alleles, 4) exceptional HLA peptides with no known binding motifs. Annotation scores of all eluted peptides are shown in Figure 2—source data 2. Additional information is provided in Supplementary file 1. (B) Curves showing the distribution of the predicted HLA binding affinities for all HLA-A03-annotated peptides with a score ≥3. Overall, 91% of all HLA-A03-annotated peptides are predicted to have a binding affinity below 500 nM for the HLA-A03 molecule (see also Figure 2—figure supplement 4 and Figure 2—figure supplement 5). The same peptides are predicted to be non-binders for the other alleles – i.e., HLA-A02, -B35 and -B39. (C) Heat map visualization following clustering of predicted HLA binding affinity values. The white box highlights HLA-A03-annotated peptides. The four peptides in the table in (a) are indicated by arrows and their respective predicted binding affinity for the HLA-A03 molecule is indicated in parenthesis.DOI:http://dx.doi.org/10.7554/eLife.07661.014
Mentions: Large-scale DDA-based identification of immunoaffinity purified HLA class I peptides is supported by several software tools (e.g., MaxQuant, Perseus or X-PRESIDENT) and results in thousands of unclassified peptides of various lengths. Since large HLA peptidomic datasets are generated at an increasing pace, additional computational frameworks facilitating the HLA annotation and storage of such datasets need to be developed. Here, we first created a computational workflow to support the identification, classification/annotation, visualization and storage of HLA peptidomic data in an allele-dependent manner. The software tools described in the section below enable (1) systematic annotation of peptides to their respective HLA allele, (2) visualization of HLA peptidomic datasets, and (3) generation of HLA class I allele-specific peptide spectral libraries, which can be converted into high quality assay libraries for the processing of SWATH-data (Figure 2, Figure 2—figure supplement 1, Figure 2—source data 2 and Supplementary file 1).10.7554/eLife.07661.008Figure 2.Content and analysis of the pilot repository.

Bottom Line: We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides.Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS).This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland.

ABSTRACT
We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

No MeSH data available.


Related in: MedlinePlus