Biology subjects

ElAbd, H.

Publications and source records attributed to ElAbd, H..

3 recordsLinked to original sources

bioRxiv2024

Seq2MAIT: A Novel Deep Learning Framework for Identifying Mucosal Associated Invariant T (MAIT) Cells

Mucosal-associated invariant T (MAIT) cells are a group of unconventional T cells that mainly recognize bacterial vitamin B metabolites presented on MHC-related protein 1 (MR1). MAIT cells have been shown to play an important role in controlling bacterial infection and in responding to viral infections. Furthermore, MAIT cells have been implicated in different chronic inflammatory diseases such as inflammatory bowel disease and multiple sclerosis. Despite their involvement in different physiological and pathological processes, a deeper understanding of MAIT cells is still lacking. Arguably, this can be attributed to the difficulty of quantifying and measuring MAIT cells in different biological samples which is commonly done using flow cytometry-based methods and single-cell-based RNA sequencing techniques. These methods mostly require fresh samples which are difficult to obtain, especially from tissues, have low to medium throughput, and are costly and labor-intensive. To address these limitations, we developed sequence-to-MAIT (Seq2MAIT) which is a transformer-based deep neural network capable of identifying MAIT cells in bulk TCR-sequencing datasets, enabling the quantification of MAIT cells from any biological materials where human DNA is available. Benchmarking Seq2MAIT across different test datasets showed an average area-under-the-receiver-operator-curve (AU[ROC]) >0.80. In conclusion, Seq2MAIT is a novel, economical, and scalable method for identifying and quantifying MAIT cells in virtually any biological sample.

bioinformatics↗

bioRxiv2022

Predicting Peptide HLA-II Presentation Using Immunopeptidomics, Transcriptomics and Deep Multimodal Learning

The human leukocyte antigen (HLA) class II proteins present peptides to CD4+ T cells through an interaction with T cell receptors (TCRs). Thus, HLA proteins are key players in shaping immunogenicity and immunodominance. Nevertheless, factors governing peptide presentation by HLA-II proteins are still poorly understood. To address this problem, we profiled the blood transcriptome and immunopeptidome of 20 healthy individuals and integrated the profiles with publicly available immunopeptidomics datasets. In depth multi-omics analysis identified expression levels and subcellular locations as import sequence-independent features governing presentation. Levering this knowledge, we developed the Peptide Immune Annotator Multimodal (PIA-M) tool, as a novel pan multimodal transformer-based framework that utilises sequence-dependent along with sequence-independent features to model presentation by HLA-II proteins. PIA-M illustrated a consistently superior performance relative to existing tools across two independent test datasets (area under the curve: 0.93 vs. 0.84 and 0.95 vs. 0.86), respectively. Besides achieving a higher predictive accuracy, PIA-M with its Rust-based pre-processing engine, had significantly shorter runtimes. PIA-M is freely available with a permissive licence as a standalone pipeline and as a webserver (https://hybridcomputing.ikmb.uni-kiel.de/pia). In conclusion, PIA-M enables a new state-of-the-art accuracy in predicting peptide presentation by HLA-II proteins in vivo.

bioinformatics↗

bioRxiv2022

VCF2Prot: An Efficient and Parallel Tool for Generating Personalized Proteomes from VCF Files

MotivationThe ability to generate sample-specific protein sequences is a crucial step in neo-antigen discovery, cancer vaccine development, and proteogenomics. The revolutionary increase in the throughput of sequencers has fueled large-scale genomic and transcriptomic studies, holding great promises for the emerging field of personalized medicine. However, most sequencing projects store their sequencing data in an abbreviated variant calling format (VCF) that is not immediately amenable to subsequent proteomic and peptidomic analyses. Furthermore, data processing of such increasingly massive genome-scale datasets calls for parallel and concurrent programming, and consequently refactoring of existing algorithms and/or the development of new parallel algorithms. ResultsHere, we introduce sequence intermediate representation (SIR), a novel and generic algorithm for generating personalized or sample-specific protein sequences from a consequence-called VCF file and the corresponding reference proteome. An implementation of SIR, named VCF2Prot, was developed to aid personalized medicine and proteogenomics by generating personalized proteomes in FASTA format from a collection of consequence-called genomic alterations stored in a VCF file. Benchmarking VCF2Prot against the recently published PrecisionProDB showed an ~1000-fold improvement in runtime (depending on the input size). Furthermore, in a scale-up study VCF2Prot processed a VCF file containing 99,254 variants observed across 8,192 patients in ~ 11 minutes, demonstrating the massive improvement in the execution speed and the utility of SIR and VCF2prot in bridging large-scale genomic and proteomic studies. Availability and ImplementationVCF2Prot comes with a permissive MIT-license, enabling the commercial and non-commercial utilization of the tool. The source code along with precompiled versions for Linux/Mac OS are available at https://github.com/ikmb/vcf2prot. The modular units used for building VCF2Prot are available as a Rust crate at https://crates.io/crates/ppgg with documentations and examples at https://docs.rs/ppgg/0.1.4/ppgg/ under the same MIT-license.

genomics↗