Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes.

ABSTRACT: Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines - that is, Mascot, Mascot+Percolator, and PEAKS DB - as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods' performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.

SUBMITTER: Mishto M

PROVIDER: S-EPMC9286349 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

Project description:Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines – i.e. Mascot, Mascot+Percolator and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by mass spectrometry to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated. Head of the research group Molecular Immunology at King’s College London and the Francis Crick Institute, London (UK). Email: michele.mishto@kcl.ac.uk,

2022-04-06 | PXD031709 | Pride

SPI-ART and the features of cis-spliced peptides in HLA class I immunopeptidomes

Project description:The identification of peptides presented by HLA class I molecules to CD8+ T cells – i.e. the HLA-I immunopeptidome - is a useful tool to fish epitopes suitable for vaccinations and immunotherapies against cancer, infection and autoimmunity. These peptides are mainly generated by proteasomes through peptide hydrolysis and peptide splicing. Post-translationally spliced epitopes can enlarge the antigenic landscape and thus immunotherapy efficacy. Their identification is challenging, and the few methods proposed so far hinted toward a controversial broad spectrum of cis-spliced peptide frequencies in HLA-I immunopeptidomes. We here developed a new method named SPI-ART, which can identify cis-spliced peptides in HLA-I immunopeptidomes with precision and recall outperforming all other methods. The benchmark of the methods’ performances provided the proper analytical framework to explain the discordant results published so far. It also allowed to estimate the frequency of cis-spliced peptides in HLA-I immunopeptidomes, and to shed light on the biochemistry of these unconventional peptides and their potential antigenicity.

2022-08-12 | PXD025346 | Pride

MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.

Project description:Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.

| S-EPMC3128686 | biostudies-literature

The HLA class-II immunopeptidomes of AAV capsids proteins.

Project description:IntroductionGene therapies are using Adeno-associated viruses (AAVs) as vectors, but immune responses against the capsids pose challenges to their efficiency and safety. Helper T cell recognition of capsid-derived peptides bound to human leukocyte antigen (HLA) class II molecules is an essential step in the AAV-specific adaptive immunity.MethodsUsing MHC-associated peptide proteomics, we identified the HLA-DR and HLA-DQ immunopeptidomes of the capsid proteins of three different AAV serotypes (AAV2, AAV6, and AAV9) from a panel of healthy donors selected to represent a majority of allele usage.ResultsThe identified sequences span the capsids of all serotypes, with AAV2 having the highest peptide count. For all the serotypes, multiple promiscuous peptides were identified and displayed by both HLA-DR and -DQ. However, despite high sequence homology, there were few identical peptides among AAV2, AAV6, and AAV9 immunopeptidomes, and none were promiscuous.DiscussionResults from this work represent a comprehensive immunopeptidomics research of potential CD4+ T cell epitopes and provide the basis for immunosurveillance efforts for safer and more efficient AAV-based gene therapies.

| S-EPMC9807805 | biostudies-literature

Combining results of multiple search engines in proteomics.

Project description:A crucial component of the analysis of shotgun proteomics datasets is the search engine, an algorithm that attempts to identify the peptide sequence from the parent molecular ion that produced each fragment ion spectrum in the dataset. There are many different search engines, both commercial and open source, each employing a somewhat different technique for spectrum identification. The set of high-scoring peptide-spectrum matches for a defined set of input spectra differs markedly among the various search engine results; individual engines each provide unique correct identifications among a core set of correlative identifications. This has led to the approach of combining the results from multiple search engines to achieve improved analysis of each dataset. Here we review the techniques and available software for combining the results of multiple search engines and briefly compare the relative performance of these techniques.

| S-EPMC3769318 | biostudies-literature

Global Identification of Post-Translationally Spliced Peptides with Neo-Fusion.

Project description:Post-translationally spliced peptides have recently garnered significant interest as potential targets for cancer immunotherapy and as contributors to autoimmune diseases such as type 1 diabetes, yet feasible identification methods for spliced peptides have yet to be developed. Here we present Neo-Fusion, a search program for discovering spliced peptides in tandem mass spectrometry data. Neo-Fusion utilizes two separated ion database searches to identify the two halves of each spliced peptide, and then it infers the full spliced sequence. This strategy allows for the identification of spliced peptides without peptide length constraints, providing a broadly applicable tool suitable for identification of spliced peptides in a variety of systems, such as the HLA-I and HLA-II immunopeptidomes and in vitro digested protein samples obtained from organelles, cells, or tissues of interest. Using simulated spliced peptides to benchmark Neo-Fusion, 25% of all simulated spliced peptides were identified at a measured false-discovery rate of 5% for HLA-I. Neo-Fusion provides the research community with a powerful new tool to aid in the study of the prevalence and biological significance of post-translationally spliced peptides.

| S-EPMC6465104 | biostudies-literature

Characterizing interdisciplinarity of researchers and research topics using web search engines.

Project description:Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers' interdisciplinarity at the individual level (diversity of topics related to the researcher) and at the social level (his/her centrality in the researchers' network). We found that visibility boosts by certain research topics were positively correlated with researchers' individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers' social-level interdisciplinarity. Research topics' correlations with researchers' individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of "interdisciplinarity" of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.

| S-EPMC3374816 | biostudies-other

Comment on "A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands".

Project description:There is still no convincing evidence for the frequent occurrence of posttranslationally spliced HLA-I peptides.

| S-EPMC7039048 | biostudies-literature

Evaluation of proteomic search engines for the analysis of histone modifications.

Project description:Identification of histone post-translational modifications (PTMs) is challenging for proteomics search engines. Including many histone PTMs in one search increases the number of candidate peptides dramatically, leading to low search speed and fewer identified spectra. To evaluate database search engines on identifying histone PTMs, we present a method in which one kind of modification is searched each time, for example, unmodified, individually modified, and multimodified, each search result is filtered with false discovery rate less than 1%, and the identifications of multiple search engines are combined to obtain confident results. We apply this method for eight search engines on histone data sets. We find that two search engines, pFind and Mascot, identify most of the confident results at a reasonable speed, so we recommend using them to identify histone modifications. During the evaluation, we also find some important aspects for the analysis of histone modifications. Our evaluation of different search engines on identifying histone modifications will hopefully help those who are hoping to enter the histone proteomics field. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD001118.

| S-EPMC4184451 | biostudies-literature

ASDB: database of alternatively spliced genes.

Project description:Version 2.1 of ASDB (Alternative Splicing Data Base) contains 1922 protein and 2486 DNA sequences. The protein entries from SWISS-PROT are joined into clusters corresponding to alternatively spliced variants of one gene. The DNA division consists of complete genes with alternative splicing mentioned or annotated in GenBank. The search engine allows one to search over SWISS-PROT and GenBank fields and then follow the links to all variants. The database can be assessed at the URL http://cbcg.nersc.gov/asdb

| S-EPMC102426 | biostudies-literature

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