Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Sequencing of monoclonal antibody 1D3 (mAb1D3)

ABSTRACT: De novo sequencing and expression of recombinant 1D3 antibody (R1D3). To achieve maximum coverage for antibody sequencing, 5 µg of the antibody original 1D3 was digested in parallel with five different proteases: trypsin, elastase, chymotrypsin, and Asp-N. For each digestion mixture, peptides were loaded onto a nanoflow C18 HPLC column, and peptides were resolved using an aqueous to organic gradient over the course of 90 minutes. As they eluted from the column, peptides were directly ionized on a Thermo Fisher Orbitrap orbitrap Velos mass spectrometer. In a data-dependent manner, both high-resolution full mass measurements and multiple different tandem mass fragmentation (MS/MS) modalities were collected to give the greatest likelihood of correct sequence interpretation. These include standard collision-induced dissociation (CID), higher-energy dissociation (HCD), and electron transfer dissociation (ETD). After acquisition, data were transferred to Abterra Bioscience for analysis using their proprietary Valens platform. Briefly, an analysis of bottom-up mass spectra generated by the Vanderbilt University Proteomics facility using multiple enzymes was conducted. The framework sequence was identified by performing a database search of the spectra against the germline immunoglobulin gene sequences.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Dr. Hayes McDonald

PROVIDER: PXD036958 | JPOST Repository | Sun Sep 25 00:00:00 BST 2022

REPOSITORIES: jPOST

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

Using RNA-Seq to create sample-specific proteomic databases that enable mass spectrometric discovery of splice junction peptides

Project description:Many new alternative splice forms have been detected at the transcript level using next generation sequencing (NGS) methods, especially RNA-Seq, but it is not known how many of these transcripts are being translated. Leveraging the unprecedented capabilities of NGS, we collected RNA-Seq and proteomics data from the same cell population (Jurkat cells) and created a bioinformatics pipeline that builds customized databases for the discovery of novel splice-junction peptides. Results: Eighty million paired-end Illumina reads and ~500,000 tandem mass spectra were used to identify 12,873 transcripts (19,320 including isoforms) and 6,810 proteins. We developed a bioinformatics workflow to retrieve high-confidence, novel splice junction sequences from the RNA data, translate these sequences into the analogous polypeptide sequence, and create a customized splice junction database for MS searching. Jurkat T-cell mRNA was analyzed on an Illumina HiSeq2000. ~80 million paired end reads (2x200bp, ~350bp lengths) were collected.

2013-05-17 | E-GEOD-45428 | biostudies-arrayexpress

Using RNA-Seq to create sample-specific proteomic databases that enable mass spectrometric discovery of splice junction peptides

2013-05-17 | GSE45428 | GEO

Immunogenic epitopes of the meningioma-associated antigen TXNDC16

Project description:We used customized peptide arrays of 163 TXNDC16 peptides to identify immunogenic TXNDC16 epitopes and asked whether discrimination of meningioma and control sera is possible with a specific subset selection of all immunogenic epitopes. CelluSpotsM-bM-^DM-" peptide arrays were manufactured by Intavis (Germany) with 163 TXNDC16 spanning peptides spotted as two replicate subarrays. 15-mer peptides overlapping by 10 amino acids were covalently bound to cellulose membranes with their C-termini. Please note that the non_normalized.txt contains Ch1 Mean values for all samples (each sequence represented in duplicates) and the identifiers in the 'index' column corresponds to those in the 'index' column in raw gpr files; sample data table contains classification values (for each sequence) described in the sample data processing field.

2014-07-01 | E-GEOD-58949 | biostudies-arrayexpress

Top-Down Characterization of an Intact Monoclonal Antibody using Activated Ion-Electron Transfer Dissociation

Project description:Here, we examined the utility of AI-ETD for sequencing of the intact NIST monoclonal antibody on a modified Fusion Lumos Orbitrap platform using direct infusion. We also tested other dissociation techniques including HCD, ETD, and EThcD.

2020-10-19 | MSV000086331 | MassIVE

Chromosome 11-Centric Human Proteome Project

Project description:For the analysis of chromosome 11-centric human proteome, we analyzed the human brain hippocampal proteome. The samples from brain tissues were extensively off-gel fractionated by 12 fractions and analyzed by a high resolution mass spectrometer, Orbitrap XL ETD with collision induced dissociation, after LC separation using C18 column.

2016-10-10 | PXD000395 | Pride

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

De novo nine-species benchmark peptide identifications for Casanovo and other methods

Project description:Predicted peptides for the 9-species de novo sequencing benchmark MSV000090982 as described in Yilmaz et al. [Yilmaz2023]. FTP directory contains outputs of 5 de novo peptide sequencing methods on the 9-species benchmark: Casanovo, Casanovo_bm (benchmark), PointNovo, DeepNovo and Novor. Output files for Casanovo contain scan numbers and run names to allow matching to spectra files. [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux*, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

2024-03-28 | MSV000094434 | MassIVE

De novo nine-species benchmark peptide identifications for Casanovo and other methods

Project description:Predicted peptides for the 9-species de novo sequencing benchmark MSV000090982 as described in Yilmaz et al. [Yilmaz2023]. FTP directory contains outputs of 5 de novo peptide sequencing methods on the 9-species benchmark: Casanovo, Casanovo_bm (benchmark), PointNovo, DeepNovo and Novor. [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux*, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

2024-02-01 | MSV000093979 | MassIVE

Application of de novo sequencing to large-scale complex proteomics datasets

Project description:Dependent on concise, pre-defined protein sequence databases, traditional search algorithms perform poorly when analyzing mass spectra derived from wholly uncharacterized protein products. Conversely, de novo peptide sequencing algorithms can interpret mass spectra without relying on reference databases. However, such algorithms have been difficult to apply to complex protein mixtures, in part due to a lack of methods for automatically validating de novo sequencing results. Here, we present novel metrics for benchmarking de novo sequencing algorithm performance on large scale proteomics datasets, and present a method for accurately calibrating false discovery rates on de novo results. We also present a novel algorithm (LADS) which leverages experimentally disambiguated fragmentation spectra to boost sequencing accuracy and sensitivity. LADS improves sequencing accuracy on longer peptides relative to other algorithms and improves discriminability of correct and incorrect sequences. Using these advancements, we demonstrate accurate de novo identification of peptide sequences not identifiable using database search-based approaches.

2016-01-12 | PXD003317 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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