PepSeq
Overview
PepSeq is an In vitro method for simultaneously assessing the binding specificity of target proteins against hundreds of thousands of peptides. The method was developed by a team of researchers at the Translational Genomics Research Institute (TGen) and Arizona University.[1] The method involves building a peptide library, and tagging each peptide by linking it with a unique DNA barcode. The library is incubated with a sample containing target proteins following which the peptides bound to the targets are retrieved. High-throughput Next-Generation Sequencing (NGS) is used to sequence the DNA barcodes of the peptides that were able to bind to the target proteins.
History
The emergence of PepSeq derives from a decade-long effort to achieve highly multiplexed serology and improve upon the limited scalability of previous serological assays.[1] Traditional methods such as ELISA are limited to one antibody target at a time and scalable methods such as peptide microarrays are cost prohibitive. With the advent of NGS, it became possible to do high-throughput quantification of peptides through linking peptides to a DNA probe. PhIP-Seq is the first successful embodiment of this approach.[2][3] This method involves synthesizing an oligonucleotide library based on proteins of choice and then displaying this proteomic-scale library on a bacteriophage vector.[2][4] Immunoprecipitation followed by NGS is used to quantify peptides and determine antibody reactivity with targets. PepSeq improves on this method by replacing the phage display construct with a simpler covalent adduct between target peptides and DNA probes.[1] PepSeq is a significant advancement in large-scale proteomic assays and paves the way for further innovation.[5]
Design of Peptide-Encoding Oligonucleotide Library
Informatic approaches are used to segment full-length target proteins into short peptides (between 15 and 64 amino acids in length).[1] Two different informatic algorithms used for this purpose are a sliding window algorithm (which selects equally spaced peptide sequences across target proteins), and a set cover approach, which minimizes the number of peptides needed to cover all potential epitopes in a protein.[6] Peptides are encoded as a sequence of DNA oligonucleotides based on codon frequency usage and GC-content.[1] Adapters (19 base-pair long sequences) are added to both ends of the DNA molecule. These adapters act as primers for amplification via Polymerase Chain Reaction (PCR), and as a unique barcode by which each peptide is identified after sequencing. A commercial provider can then be used to sequence oligonucleotides.
Synthesis of DNA-barcoded peptide libraries
DNA oligonucleotides encoding peptides from a set of target proteins are amplified by PCR and converted to messenger RNA by in vitro transcription.[1] A puromycin adapter linked to a polyethylene glycol (PEG) linker is added to the mRNA.[7] Peptides produced by in cell-free protein synthesis are able to bind back to the mRNA via the PEG linker, resulting in mRNA:peptide complexes.[1] The mRNA in these complexes undergoes reverse transcription and is then degraded to generate the final DNA-barcoded peptides, which can be used for multiplexed serology assays.[8]
Highly multiplexed serology
Selective capture of antibody-bound DNA-barcoded peptides
The PepSeq library is incubated with an antibody-containing sample.[1] Members of the library interact with the sample antibodies, and the DNA-barcoded peptides which successfully bind to antibodies in the solution are enriched by using magnetic beads coated with a capture protein.[9] DNA-barcoded peptides that are not attached to antibodies are washed off. The remaining beads are resuspended in water and heated to elute the DNA-barcoded peptides. PCR amplification is then used to amplify the DNA tags in preparation for high-throughput next-generation sequencing.
High-Throughput Sequencing Analysis
Oligonucleotide sequencing analysis is done through the PepSIRF package and begins with demultiplexing sequence data.[10] Reads are then matched to peptides and the read count for each peptide is normalized to reads per million (RPM).[1] The RPM of buffer-only negative controls is then subtracted from the peptide RPM count. Enrichment Z-scores for peptides are determined based on the peptide's deviation from the distribution of negative controls. A subset of peptides across replicates can then be selected via user-defined thresholds. PepSIRF has a built-in QIIME2[11] package to visualize peptide Z-score distributions and aid the user in threshold choice.[12]
Applications in current research
PepSeq has been used to study the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).[6] By generating high-throughput libraries of peptides from all human coronavirus proteomes, researchers were able to identify frequently targeted epitopes by the immune system following SARS-CoV-2 exposure. This epitope-resolved profiling showed the antibody response to SARS-CoV-2 cross-recognizes both SARS-CoV-2 epitopes and other endemic corona viruses. PepSeq has also been used to assess the antibody specificity of the immune response elicited by COVID-19 vaccination.[13] A peptide library was generated from the SARS-CoV-2 proteome as well as the proteome of endemic coronaviruses. Two separate classes of responses to vaccination were identified: one against minimally conserved regions of the SARS-CoV-2 spike protein, and one against conserved regions of the S2 subunit.
Benefits
- PepSeq can be utilized for massively parallelized peptide synthesis, enabling large-scale proteomic assays.[1] Other multiplex (assay) serological assays are limited to the range of 5-500 simultaneous antigens whereas PepSeq can profile antibody reactivity against thousands or even hundreds of thousands of antigenic peptides.[14]
- PepSeq is not limited to immunological assays but could also be used to deeply profile the substrate specificity of both kinases and proteases.[8] PepSeq could access peptide specificity to a wide array of targets such as proteins, toxins, enzymes and biomarkers.
- PepSeq has an in-house bioinformatic package PepSIRF to ease high-throughput sequencing analysis and peptide quantification.
Limitations
- PepSeq cannot profile protein binding interactions that are dependent on post-translational modifications as well as secondary, tertiary, or quaternary protein structure.[1]
- PepSeq peptides are both linear and small (~30 amino acids which limits the number of identifiable protein-protein interactions).
- PepSeq cannot assess the functional implications of enriched peptides.
- The cost of DNA sequencing and synthesis restricts the scalability of this assay.[15]
Similar methods
Smaller scale methods, which screen for sensitivity to few select antigens, include enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassays (CLIA), lateral flow assays (LFA), and flow cytometry[16]. These methods can be scaled-up by using encoded beads, which allows for assessing binding to 5 to 500 peptides at the same time.[14] Methods for building high-density peptide arrays do exist, but the requirement to build an array for each sample has limited their uptake.[17] PepSeq builds on a similar concept as a multiplex approach where phages are made to express libraries of DNA-tagged peptides, and the binding specificity of each peptide to antibodies of interest is quantified by sequencing.[4][18]
References
- ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 Henson, Sierra N.; Elko, Evan A.; Swiderski, Piotr M.; Liang, Yong; Engelbrektson, Anna L.; Piña, Alejandra; Boyle, Annalee S.; Fink, Zane; Facista, Salvatore J.; Martinez, Vidal; Rahee, Fatima; Brown, Annabelle; Kelley, Erin J.; Nelson, Georgia A.; Raspet, Isaiah (February 2023). "PepSeq: a fully in vitro platform for highly multiplexed serology using customizable DNA-barcoded peptide libraries". Nature Protocols. 18 (2): 396–423. doi:10.1038/s41596-022-00766-8. ISSN 1750-2799. PMID 36385198 Check
|pmid=value (help). Unknown parameter|s2cid=ignored (help) - ↑ 2.0 2.1 Mohan, Divya; Wansley, Daniel L.; Sie, Brandon M.; Noon, Muhammad S.; Baer, Alan N.; Laserson, Uri; Larman, H. Benjamin (2018). "PhIP-Seq characterization of serum antibodies using oligonucleotide-encoded peptidomes". Nature Protocols. 13 (9): 1958–1978. doi:10.1038/s41596-018-0025-6. PMC 6568263 Check
|pmc=value (help). PMID 30190553. - ↑ Tiu, Charles Kevin; Zhu, Feng; Wang, Lin-Fa; De Alwis, Ruklanthi (2022). "Phage Immuno Precipitation Sequencing (PhIP-Seq): The Promise of High Throughput Serology". Pathogens. 11 (5): 568. doi:10.3390/pathogens11050568. PMC 9143919 Check
|pmc=value (help). PMID 35631089 Check|pmid=value (help). - ↑ 4.0 4.1 Xu, George J.; Kula, Tomasz; Xu, Qikai; Li, Mamie Z.; Vernon, Suzanne D.; Ndung'u, Thumbi; Ruxrungtham, Kiat; Sanchez, Jorge; Brander, Christian; Chung, Raymond T.; o'Connor, Kevin C.; Walker, Bruce; Larman, H. Benjamin; Elledge, Stephen J. (2015). "Comprehensive serological profiling of human populations using a synthetic human virome". Science. 348 (6239): aaa0698. doi:10.1126/science.aaa0698. PMC 4844011. PMID 26045439.
- ↑ Ham, Becky. "PEPSEQ PROVIDES MASSIVE PLATFORM FOR ANTIBODY ANALYSES". The Translational Genomics Research Institute (TGen). TGen. Retrieved 7 March 2023.
- ↑ 6.0 6.1 Ladner, Jason T.; Henson, Sierra N.; Boyle, Annalee S.; Engelbrektson, Anna L.; Fink, Zane W.; Rahee, Fatima; d'Ambrozio, Jonathan; Schaecher, Kurt E.; Stone, Mars; Dong, Wenjuan; Dadwal, Sanjeet; Yu, Jianhua; Caligiuri, Michael A.; Cieplak, Piotr; Bjørås, Magnar; Fenstad, Mona H.; Nordbø, Svein A.; Kainov, Denis E.; Muranaka, Norihito; Chee, Mark S.; Shiryaev, Sergey A.; Altin, John A. (2021). "Epitope-resolved profiling of the SARS-CoV-2 antibody response identifies cross-reactivity with endemic human coronaviruses". Cell Reports Medicine. 2 (1): 100189. doi:10.1016/j.xcrm.2020.100189. PMC 7816965 Check
|pmc=value (help). PMID 33495758 Check|pmid=value (help). - ↑ Roberts, Richard W.; Szostak, Jack W. (1997). "RNA-peptide fusions for the in vitro selection of peptides and proteins". Proceedings of the National Academy of Sciences. 94 (23): 12297–12302. Bibcode:1997PNAS...9412297R. doi:10.1073/pnas.94.23.12297. PMC 24913. PMID 9356443.
- ↑ 8.0 8.1 Kozlov, Igor A.; Thomsen, Elliot R.; Munchel, Sarah E.; Villegas, Patricia; Capek, Petr; Gower, Austin J.; k. Pond, Stephanie J.; Chudin, Eugene; Chee, Mark S. (2012). "A Highly Scalable Peptide-Based Assay System for Proteomics". PLOS ONE. 7 (6): e37441. doi:10.1371/journal.pone.0037441. PMC 3373263. PMID 22701568.
- ↑ Shiryaev, Sergey A.; Aleshin, Alexander E.; Muranaka, Norihito; Kukreja, Muskan; Routenberg, David A.; Remacle, Albert G.; Liddington, Robert C.; Cieplak, Piotr; Kozlov, Igor A.; Strongin, Alex Y. (2014). "Structural and functional diversity of metalloproteinases encoded by the Bacteroides fragilispathogenicity island". FEBS Journal. 281 (11): 2487–2502. doi:10.1111/febs.12804. PMC 4047133. PMID 24698179.
- ↑ Fink, Zane W.; Martinez, Vidal; Altin, John; Ladner, Jason T. (2020). "PepSIRF: A flexible and comprehensive tool for the analysis of data from highly-multiplexed DNA-barcoded peptide assays". arXiv:2007.05050 [q-bio.QM].
- ↑ Bolyen, Evan; et al. (2019). "Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2". Nature Biotechnology. 37 (8): 852–857. doi:10.1038/s41587-019-0209-9. PMC 7015180 Check
|pmc=value (help). PMID 31341288. - ↑ Brown, Annabelle M.; Bolyen, Evan; Raspet, Isaiah; Altin, John A.; Ladner, Jason T. (2022). "PepSIRF + QIIME 2: Software tools for automated, reproducible analysis of highly-multiplexed serology data". arXiv:2207.11509 [q-bio.QM].
- ↑ Elko, Evan A.; Nelson, Georgia A.; Mead, Heather L.; Kelley, Erin J.; Carvalho, Sophia T.; Sarbo, Nathan G.; Harms, Caroline E.; Le Verche, Virginia; Cardoso, Angelo A.; Ely, Jennifer L.; Boyle, Annalee S.; Piña, Alejandra; Henson, Sierra N.; Rahee, Fatima; Keim, Paul S.; Celona, Kimberly R.; Yi, Jinhee; Settles, Erik W.; Bota, Daniela A.; Yu, George C.; Morris, Sheldon R.; Zaia, John A.; Ladner, Jason T.; Altin, John A. (2022). "COVID-19 vaccination elicits an evolving, cross-reactive antibody response to epitopes conserved with endemic coronavirus spike proteins". Cell Reports. 40 (1): 111022. doi:10.1016/j.celrep.2022.111022. PMC 9188999 Check
|pmc=value (help). PMID 35753310 Check|pmid=value (help). Unknown parameter|s2cid=ignored (help) - ↑ 14.0 14.1 Graham, Hilary; Chandler, Don J.; Dunbar, Sherry A. (2019). "The genesis and evolution of bead-based multiplexing". Methods. 158: 2–11. doi:10.1016/j.ymeth.2019.01.007. PMID 30659874. Unknown parameter
|s2cid=ignored (help) - ↑ Dewey, Frederick E.; Pan, Stephen; Wheeler, Matthew T.; Quake, Stephen R.; Ashley, Euan A. (2012). "DNA Sequencing". Circulation. 125 (7): 931–944. doi:10.1161/CIRCULATIONAHA.110.972828. PMC 3364518. PMID 22354974. Unknown parameter
|s2cid=ignored (help) - ↑ Michel, Moïse; Bouam, Amar; Edouard, Sophie; Fenollar, Florence; Di Pinto, Fabrizio; Mège, Jean-Louis; Drancourt, Michel; Vitte, Joana (2020). "Evaluating ELISA, Immunofluorescence, and Lateral Flow Assay for SARS-CoV-2 Serologic Assays". Frontiers in Microbiology. 11: 597529. doi:10.3389/fmicb.2020.597529. PMC 7759487 Check
|pmc=value (help). PMID 33362745 Check|pmid=value (help). - ↑ Buus, Søren; Rockberg, Johan; Forsström, Björn; Nilsson, Peter; Uhlen, Mathias; Schafer-Nielsen, Claus (2012). "High-resolution Mapping of Linear Antibody Epitopes Using Ultrahigh-density Peptide Microarrays". Molecular & Cellular Proteomics. 11 (12): 1790–1800. doi:10.1074/mcp.M112.020800. PMC 3518105. PMID 22984286.
- ↑ Mohan, Divya; Wansley, Daniel L.; Sie, Brandon M.; Noon, Muhammad S.; Baer, Alan N.; Laserson, Uri; Larman, H. Benjamin (2018). "PhIP-Seq characterization of serum antibodies using oligonucleotide-encoded peptidomes". Nature Protocols. 13 (9): 1958–1978. doi:10.1038/s41596-018-0025-6. PMC 6568263 Check
|pmc=value (help). PMID 30190553.
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