Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A rapid, robust, and refined solution Enhance Accuracy and Throughput in Mass Spectrometry-Based De Novo Sequencing of Monoclonal Antibodies

ABSTRACT: Elucidating antibody sequences is crucial for understanding antigen binding and advancing therapeutic and research applications. However, current de novo sequencing technology is hindered by high costs, low throughput, missing ion information, and assembly limitations in accuracy and robustness. Here, we developed a refined sequencing solution by integrating Single-Pot Multi-Enzymatic Gradient Digestion (SP-MEGD) with a beam search-based assembler (Fusion) to decipher complete sequences. This solution achieved high accuracy (over 99%) in antibody reconstruction. Biological validation of multiple commercial antibodies with unavailable sequences further supports its effectiveness. Additionally, our solution can discriminate and achieve over 99% accurate sequence coverage of mixtures containing two or three COVID-19 neutralizing antibodies. To our knowledge, this is the first study to enable simultaneous sequencing of multiple antibodies using only bottom-up mass spectrometry, improving throughput without increasing duration or expense. In summary, our method advances antibody research by enhancing the accuracy and efficiency of complex antibody sequencing.

ORGANISM(S): Homo Sapiens Mus Musculus

SUBMITTER: Ningshao Xia

PROVIDER: PXD060500 | iProX | Wed Feb 05 00:00:00 GMT 2025

REPOSITORIES: iProX

ACCESS DATA

Json Xml

Similar Datasets

MS-based sequencing of the anti-FLAG-M2 antibody

Project description:We demonstrate a method for direct de novo sequencing of monoclonal IgG from the purified antibody products. The method uses a panel of multiple complementary proteases to generate suitable peptides for de novo sequencing by LC-MS/MS in a bottom-up fashion. Furthermore, we apply a dual fragmentation scheme, using both stepped high-energy collision dissociation (stepped HCD) and electron transfer high-energy collision dissociation (EThcD) on all peptide precursors. The method achieves full sequence coverage of the monoclonal antibody Herceptin, with an accuracy of 98% in the variable regions. We applied the method to sequence the widely used anti-FLAG-M2 mouse monoclonal antibody, which we successfully validated by remodeling a high-resolution crystal structure of the Fab and demonstrating binding to a FLAG-tagged target protein in Western blot analysis. The method thus offers robust and reliable sequences of monoclonal antibodies.

2021-01-11 | PXD023419 | Pride

Comparison of different hashing methods on human PBMCs and primary mice tissues

Project description:the aim 1 was to evaluate hashing (sample multiplexing) accuracy of TotalSeq-B antibodies and CellPlex (a multiplexing solution from 10x Genomics) on 2 pools of PBMCs from 3 different donors. Thus the genetic difference between donors was used as a ground truth for sample demultiplexing based on antibody or lipid hashing hashing. the aim 2 was to evaluate TotalSeq antibody and lipid hashing on different mice primary tissues using different hashing protocols.

2022-02-12 | E-MTAB-11401 | biostudies-arrayexpress

De Novo Sequencing of SARS-CoV-2 and influenza monoclonal antibodies by mass spectrometry using HCD and EThcD fragmentation and Supernovo software

Project description:In this study, we used electron-transfer/higher energy collision dissociation (EThcD) fragmentation to develop a mass-spectrometric (MS) method for distinguishing between Ile and Leu in Bovine Serum Albumin (BSA) and a SARS-CoV-1 anti-receptor binding domain (RBD) Spike monoclonal antibody (mAb). Supplemental activation normalized collision energy (SA-NCE) values were optimized. Combinations of multiple enzyme digestions, coupled with higher-energy collisional dissociation (HCD) and EThcD fragmentation, were used to increase coverage of all amino acid residues of the antibody. The data was searched with Supernovo™, which generated a complete de novo sequence for the test mAb and two additional SARS-CoV-2 human mAbs, giving correct sequences for the variable regions and selection between Ile and Leu residues. We then used the method on an additional set of twenty-five anti-hemagglutinin (HA) influenza antibodies of unknown sequence and determined high confidence sequences for >99% of the complementarity determining regions (CDRs). Importantly, the recombinant expression of these mAbs verified their binding and specificity to the HA influenza antigen. Unique glycosylation sites were also identified for eight antibodies. Our findings indicate this methodology results in almost complete antibody sequence coverage with >90% of residues determined with high confidence. Ile/Leu residues can be differentiated and glycosylation sites can be reliably identified.

2022-06-03 | PXD030094 | Pride

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Project description:Antibodies are immune glycoproteins that represent a crucial part of the adaptive immune system as they mediate broad protection against viruses, bacteria, and cancer. Moreover, antibodies may serve as a unique source of information on past and current physiological and pathological events in the human body. The dysregulation of the antibody-dependent response can lead to a variety of abnormalities and diseases. Antibodies are found in the secreted form in the blood (antibody phenotype) and membrane-bound on B cells as B-cell receptor (antibody genotype). Thus, investigating the relationship between genomic and phenotypic antibody diversity is of decisive importance for understanding antibody-driven immune protection and disease. In this project, we aim to establish a robust high-throughput framework for single-cell (SC) genomic and proteomic characterization of antibody repertoires using high-throughput genomic sequencing and mass spectrometry. The generated data will be analyzed using a variety of bioinformatics tools.

2024-06-04 | PXD046237 | Pride

Structural stability based deep sequencing of 5 protein targets

Project description:We developed an efficient, cost-effective assay for Structural Inference By Structure-stability-based selection of deep mutation variants and high throughput sequencing (Sibs-Seq). We demonstrated that unlike naturally occurring homologous sequences evolved over billions of years, these stability-selected, artificial homologs in a single-round 24 or 36 hours of mutation selections is often sufficient for producing high-accuracy structure prediction with < 2 angstrom root-mean-squared deviation (RMSD) to the native structure.

2023-12-02 | GSE248664 | GEO

Upload for 2012 MCP manuscript - "Shotgun protein sequencing with meta-contig assembly."

Project description:Shotgun protein sequencing with meta-contig assembly. Full-length de novo sequencing from tandem mass (MS/MS) spectra of unknown proteins such as antibodies or proteins from organisms with unsequenced genomes remains a challenging open problem. Conventional algorithms designed to individually sequence each MS/MS spectrum are limited by incomplete peptide fragmentation or low signal to noise ratios and tend to result in short de novo sequences at low sequencing accuracy. Our shotgun protein sequencing (SPS) approach was developed to ameliorate these limitations by first finding groups of unidentified spectra from the same peptides (contigs) and then deriving a consensus de novo sequence for each assembled set of spectra (contig sequences). But whereas SPS enables much more accurate reconstruction of de novo sequences longer than can be recovered from individual MS/MS spectra, it still requires error-tolerant matching to homologous proteins to group smaller contig sequences into full-length protein sequences, thus limiting its effectiveness on sequences from poorly annotated proteins. Using low and high resolution CID and high resolution HCD MS/MS spectra, we address this limitation with a Meta-SPS algorithm designed to overlap and further assemble SPS contigs into Meta-SPS de novo contig sequences extending as long as 100 amino acids at over 97% accuracy without requiring any knowledge of homologous protein sequences. We demonstrate Meta-SPS using distinct MS/MS data sets obtained with separate enzymatic digestions and discuss how the remaining de novo sequencing limitations relate to MS/MS acquisition settings.

2014-01-16 | MSV000078530 | MassIVE

Digital proteomics by DNA sequencing

Project description:The simultaneous measurement of many different proteins presents a major challenge to the fields of surgical and clinical pathology, and there is a genuine need for the development of a more direct, quantitative approach to multiplex analysis of proteins in tissues and clinical assays. Here we present a novel, high-throughput immunoassay, driven by the hypothesis that highly sensitive, multiplex protein characterization can be obtained by coupling the unique specificity of antibodies with massively parallel sequencing to detect and quantitate antigens from formalin-fixed, paraffin-embedded tissue (FFPE) and in solution. The method involves conjugating antibodies with a universal anchor oligonucleotide that is hybridized with a second oligonucleotide specifying the sample, antibody, and a unique molecule identifier, akin to “make, model and serial” number. Recovery of the data-rich oligonucleotide from mixtures of antibodies from samples such as histological tissue sections, (herein breast carcinoma) or extracts in an ELISA-type format, enabling error free-amplification that increases the sensitivity and expands the range of analysis.

2025-07-01 | GSE71486 | GEO

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [RNA-seq]

Project description:Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes sequences? that are critical for the removal of PCR amplification biases within both bulk and single-cell sequencing experiments. However, the impact that PCR and sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We demonstrate that PCR errors and not sequencing errors are the main source of inaccuracy in sequencing data and that the use of UMIs synthesized with homotrimeric nucleoside building blocks provides a solution to pinpoint and remove errors, allowing absolute counting of sequenced molecules.

2023-06-01 | GSE218899 | GEO

Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [scRNA-Seq]

2023-06-01 | GSE218901 | GEO

High-throughput profiling of antigen-specific antibody secreting cells using an antibody secretion trap (TRAPnSeq)

Project description:Following activation by cognate antigen, B cells undergo fine-tuning of their antigen receptors and may ultimately differentiate into antibody-secreting cells (ASCs). While antigen-specific antibodies from B cell receptor (BCR) expressing B cells can be readily cloned and sequenced following flow sorting, antigen-specific plasma cells that lack surface BCR cannot be easily profiled in a high-throughput way. Here, we report an approach, TRAPnSeq (antigen specificity mapping through Ig secretionTRAPandSequencing), that allows capture of secreted antibodies on the surface of ASCs, which in turn enables high-throughput screening of single ASCs against large antigen panels and recovery of paired VH:VL antibody sequences. This approach incorporates flow cytometry, standard microfluidic platforms and DNA barcoding technologies to isolate and characterize antigen-specific ASCs through single cell V(D)J, RNA and antigen barcode sequencing. We show the utility of TRAPnSeq by profiling antigen-specific IgG and IgE plasma cells from mouse and humans and validate antigen binding by ELISA. TRAPnSeq can easily be combined with existing B cell platforms to accelerate antibody discovery from ASCs and can further be expanded to any protein secreting-cells.

2023-07-07 | GSE233507 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data