Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

De Novo MS/MS Sequencing of Native Human Antibodies

ABSTRACT: MS/MS data upload for 2016 JPR submission "De Novo MS/MS Sequencing of Native Human Antibodies".

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Nuno Bandeira

PROVIDER: MSV000080039 | MassIVE | Fri Aug 12 12:46:00 BST 2016

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Similar Datasets

De Novo MS/MS Sequencing of Native Human Antibodies.

Project description: Not available

| S-EPMC5574256 | biostudies-literature

De novo sequencing of DIA data

Project description:Testing datasets and pre-trained model for DeepNovo, a deep learning-based tool for de novo sequencing of DIA data.

2018-05-16 | MSV000082368 | MassIVE

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Project description:Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systematically varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

2023-01-16 | PXD037803 | Pride

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

Native single molecule sequencing and de novo assembly of Hepatitis B Virus to detect 5mCpG

Project description:Methylation of Hepatitis B Virus (HBV) DNA in a CpG context (5mCpG) can alter the expression patterns of viral genes related to infection and cellular transformation. Moreover, it may also provide clues to why certain infections are cleared, or persist with or without progression to cancer. The detection of 5mCpG often requires techniques that damage DNA or introduce bias through a myriad of limitations. Therefore, we developed a method for the detection of 5mCpG on the HBV genome that does not rely on bisulfite conversion or PCR. Moreover, using the developed technique, we have provided the first de novo assembly of native HBV DNA, as well as the first landscape of 5mCpG from native HBV sequences

2021-01-05 | GSE162518 | GEO

Sequencing-grade de novo analysis of MS/MS triplets (CID/HCD/ETD) from overlapping peptides.

Project description: Not available

| S-EPMC4591044 | biostudies-literature

Straightforward and de novo peptide sequencing by MALDI-MS/MS using a Lys-N metalloendopeptidase.

Project description: Not available

| S-EPMC2667348 | biostudies-literature

Assessing protein sequence database suitability using de novo sequencing

Project description:The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extant organisms.

2019-11-20 | PXD015083 | Pride

Quick and clean: Cracking E.coli’s code by LC-MS/MS, de novo sequencing, and dictionary search

Project description:In this study, we faced the challenge of deciphering a protein that has been designed and expressed by E.coli in such a way that the amino acid sequence encodes two concatenated English sentences. The sequence carried unknown modifications and cannot be found online. The letters ‘O’ and ‘U’ are both replaced by ‘K’ in the protein. To solve the challenge, we developped a workflow consisting of shotgun proteomics, de novo sequencing and a bioinformatic tool to search for words from the identified sequences. By using this workflow, we assembled the first complete English sentence and validated by searching against a customized sequence database.

2020-05-26 | PXD012015 | Pride

Combining phage display with de novo protein sequencing for reverse engineering of monoclonal antibodies.

Project description: Not available

| S-EPMC4966834 | biostudies-other