gpcf Two pipelines for the identification of tumor specific peptides from Next Generation Sequencing data originating from display analysis Jasmin Schlotthauer , Agnes Hotz-Wagenblatt , Karl-Heinz Glatting , Sabine Weiss , Annette Altmann 1 1 1 2 2 1 Bioinformatics (Husar, W180), Core Facility Genomics&Proteomics, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany 2 KKE Nuclearmedicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 350, 69120 Heidelberg, Germany home: http://genome.dkfz-heidelberg.de, [email protected] Problem: Finding few interesting peptides out of millions Specific peptides can help in directing tumor specific drugs or radioactive isotopes to the tumor. The identification of these peptides by phage and ribosome display is done with large libraries presenting scaffold peptides with a variable region of 6-10 amino acids by exposure of the libraries to tumor cells and/or purified target proteins for several rounds. Unbound peptides are washed off and will not be multiplied and sequenced in the next rounds (Zoller et al, 2012). Phagemid vector Min-23 Secretory signal SFTI-1library promoter Min23/ SFTI-1 His Pelf tag Example: Binding Peptides to Head and Neck Tumors Starting with 1014 different synthetic peptides, 10 AS long, cloned into the vector for ribosome display. Running 4 display rounds against head and neck tumor cells with sequencing after each round. Analysis of the NGS data with PeptideDisplayAnalysis and PepEnrich. Reduction of Diversity Starting with 1014 different peptides, 10 AS long. After 2 rounds there are about 106 different peptides with read counts, after 4 rounds 105 peptides. Comparing the different rounds with PepEnrich leaves about 102 peptides for further analysis. Phage Display M13 protein pIII Sequencing NGS pSEX81 Peptide Peptides 322 ori Ampr Amplification in Bacteria f1 ori Selection Phage isolation Extracellular Domains tumor tissue Proteins Cell culture After sequencing the resulting peptide clones the library inserts have to be extracted, clustered and translated into proteins, and several rounds have to be analyzed together. The pipeline PeptideDisplayAnalysis was created for the analysis of a single experiment and PepEnrich combines the results of the different rounds to extract the peptides showing the highest enrichment. Output PeptideDisplayAnalysis Analysis Steps with the Pipelines PeptideDisplayAnalysis PeptideDisplayAnalysis filters the input reads and finally arranges the inserts according to their frequency. If paired-end data is provided the pipeline uses Pear to merge the paired-end data. Primer Removal: First of all the pipeline uses Cutadapt to remove the PCR primer. Then the complementary primer is computed and removed as well. Collapsing equal DNA and peptide sequences: All identical DNA sequences are collapsed, the frequency of a sequence is stored in the header line of the representative sequence. Then the reads are translated into amino acid sequences and equal peptides are collapsed. Search Insert: searches for the left and right flank in every peptide and extracts the inserts inbetween if they have the correct length. The inserts are then collapsed as well and their frequency is stored. PepEnrich For each insert PepEnrich computes the total frequencies over all panning rounds. Output PepEnrich (different exp. with 3 rounds) Inserts with frequency lower than the specified minimum frequency threshold are discarded. Resulting inserts are sorted according to their total frequency. Conclusion PeptideDisplayAnalysis and PepEnrich are useful tools to identify peptides enriched by Phage Display or Ribosome Display analysis using Next Generation Sequencing. PepDisplayAnalysis and PepEnrich have been implemented in the W3H task framework in the DKFZ (Ernst et al., 2003). A stand alone version can be requested by email ([email protected]). References 1. Zoller F, Markert A, Barthe P, Zhao W, Askoxylakis V, Altmann A, Mier W, Haberkorn U. Combination of Phage Display and Molecular Grafting Generates Highly Specific Tumor-targeting Miniproteins. Angewandte Chemie Int Edition: 2012;51(52):13136-9. doi: 10.1002/anie.201203857 2. P.Ernst, K.-H. Glatting, S.Suhai. A task framework for the Web interface W2H. Bioinformatics: 2003;19(2):278-282. 3 Peptides choosen for further analysis one shows promising binding Part of PepEnrich Result - count data of the different rounds: PEP-1 1.Rd 0 PEP-2 1.Rd 0 PEP-3 1.Rd 0 2.Rd 0 2.Rd 0 2.Rd 0 3.Rd 75 4.Rd 3630 3.Rd 0 4.Rd 1246 3.Rd 39 4.Rd 49 Binding Test: Binding was measured using Iodine-125 labeled peptide which was incubated with tumour and normal cells. PEP-1 showed 10% binding to 1 million tumour cells, PEP-2 and PEP-3 less than 1%.