Biology subjects

Alvarez, B.

Publications and source records attributed to Alvarez, B..

9 recordsLinked to original sources

bioRxiv2025

Substrate recognition and cleavage by mucin degrading O-glycopeptidases from the gut microbe Bacteroides caccae.

O-glycopeptidases are enzymes that hydrolyze the peptide bonds in glycoproteins by a mechanism that involves specific recognition of O-linked glycans on the substrate. Bacteroides caccae, an accomplished mucin degrader, is a member of the human gut microbiota with sixteen genes encoding putative O-glycopeptidases in the peptidase_M60 family. At present, the diversity of substrate selective in O- glycopeptidases is not well-understood nor is the rationale behind their expansion in bacteria such as B. caccae. Here we reveal the activity and diversity of the peptidase_M60 O-glycopeptidases encoded in the B. caccae genome. At least thirteen of the sixteen peptidase_M60 genes are active mucinases. Targeted functional studies by a high-throughput FRET screen combined with detailed kinetic analyses reveal that five examples in an uncharacterized clade of peptidase_M60 proteins have different substrate selectivities despite their relatively high degree of relatedness. Structural analyses of these enzymes, including bound complexes, reveal new insight into the molecular underpinnings of O-glycopeptidase diversity. This highlights the larger context of how varied the selectivity of peptidase_M60 O-glycopeptidases can be for the glycan moiety and/or the peptide portion of the substrates, and why mucin degraders like B. caccae diversify O-glycopeptidase substrate repertoires to potentially maximize breakdown of this extraordinarily complex polymer.

biochemistry↗

bioRxiv2024

Deep Learning-Based Motif Discovery in MajorHistocompatibility Complex: A Primer

In this work, we present a deep learning-based approach for motif discovery in the Major Histo-compatibility Complex (MHC) system, which plays a key role in the immune response. We explore the use of convolutional neural networks (CNNs) to identify peptide binding motifs for different MHC alleles. By training models on data from specific MHC Class I and II molecules, we demonstrate how 1-dimensional convolutional filters can effectively capture motifs and binding preferences. Our study introduces a method for extracting motif logos directly from the trained models, providing insights into how internal neural network representations align with known biological motifs. The results show significant alignment with experimental binding motifs, underscoring the utility of deep learning in immunological research and the potential for improving vaccine design and immunotherapy.

bioinformatics↗

bioRxiv2024

Synthetic bacteria with programmed cell targeting and protein injection suppress tumor growth in vivo

Bacterial living therapeutics (BLTs) hold promise for treating cancer and other human diseases because they can be engineered and transported into the microbiota (e.g., of tumors, gastrointestinal tract) to deliver therapeutic payloads. Current approaches rely on the natural tropism of the bacterial chassis used and trigger the local release of protein cargoes, typically through active extracellular secretion or bacterial lysis. BLTs capable of targeting specific cellular subsets and delivering payloads intracellularly might provide new therapeutic opportunities and improve efficacy while reducing off-target effects. We used synthetic biology to develop BLTs that can deliver defined cargo proteins into the cytoplasm of target cells. We designed a modular synthetic bacterium with programmed adhesion to cells by targeting defined cell surface antigen and armed with an inducible type III secretion system (T3SS) for injection of a protein cargo of interest. As a proof of principle, we programmed synthetic bacteria to recognize the epidermal growth factor receptor (EGFR) and inject the catalytic fragments of the potent adenosine diphosphate-ribosyltransferase toxins ExoA and TccC3. These BLTs demonstrated the ability to trigger robust tumor cell death in vitro. Intratumoral administration of these synthetic bacteria suppressed tumor growth in vivo and prolonged the survival of treated animals when the tumor cells were recognized by the engineered bacteria. These results demonstrate the potential of programming cell targeting and controlled protein injection for the development of effective and specific BLTs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/590337v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@eb769dorg.highwire.dtl.DTLVardef@b84b39org.highwire.dtl.DTLVardef@152b8b0org.highwire.dtl.DTLVardef@1db4108_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology and next-generation vaccine development. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that accurately deal with these challenges is currently a major bottleneck for the large-scale application of this technique. Here, we present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA allele while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets.

immunology↗

bioRxiv2021

Molecular basis of unidirectional information transmission in two-component systems: lessons from the DesK-DesR thermosensor

Cellular signaling systems transmit information over long distances using allosteric transitions and/or post-translational modifications. In two-component systems the sensor histidine kinase and response regulator are wired through phosphoryl-transfer reactions, using either a uni- or bi-directional transmission mode, allowing to build rich regulatory networks. Using the thermosensor DesK-DesR two-component system from Bacillus subtilis and combining crystal structures, QM/MM calculations and integrative kinetic modeling, we uncover that: i) longer or shorter distances between the phosphoryl-acceptor and -donor residues can shift the phosphoryl-transfer equilibrium; ii) the phosphorylation-dependent dimerization of the regulator acts as a sequestering mechanism by preventing the interaction with the histidine kinase; and iii) the kinases intrinsic conformational equilibrium makes the phosphotransferase state unlikely in the absence of histidine phosphorylation, minimizing backwards transmission. These mechanisms allow the system to control the direction of signal transmission in a very efficient way, showcasing the key role that structure-encoded allostery plays in signaling proteins to store and transmit information.

biophysics↗

bioRxiv2019

In vivo diversification of target genomic sites using processive T7 RNA polymerase-base deaminase fusions blocked by RNA-guided dCas9

Diversification of specific DNA segments typically involve in vitro generation of large sequence libraries and their introduction in cells for selection. Alternative in vivo mutagenesis systems on cells often show deleterious offsite mutations and restricted capabilities. To overcome these limitations, we have developed an in vivo platform to diversify specific DNA segments based on protein fusions between various base deaminases (BD) and the T7 RNA polymerase (T7RNAP) that recognizes a cognate promoter oriented towards the target sequence. The transcriptional elongation of these fusions generates transitions C to T or A to G on both DNA strands and in long DNA segments. To delimit the boundaries of the diversified DNA, the catalytically dead Cas9 (dCas9) is tethered with custom-designed crRNAs as a "roadblock" for BD-T7RNAP elongation. While the efficiency of this platform is demonstrated in E. coli, the system can be adapted to a variety of bacterial and eukaryotic hosts.

bioengineering↗

bioRxiv2019

NNAlign_MA; semi-supervised MHC peptidome deconvolution for accurate characterization of MHC binding motifs and improved T cell epitope prediction

Antigen presentation by Major Histocompatibility Complex (MHC) is a cornerstone of the cellular immune system, and characterization of the rules defining which peptides are suitable for MHC presentation is of key interest for understanding T cell immunogenicity and the development of T cell driven therapeutics. The collective set of peptides presented on a cell surface by MHC molecules is known as the immunopeptidome. Due to the vast MHC polymorphism, this peptidome is unique between individuals. Current state-of-the-art liquid chromatography mass spectrometry (LC-MS) technologies allow the identification of large peptidomes specific for a given host or cell line, and numerous studies have proven this data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. The data obtained with MS techniques is usually poly-specific - i.e. it contains multiple sequence motifs matching the different MHC molecules expressed in the system under investigation. Deconvolution of this poly-specificity has proven a challenge, and imposes a substantial limitation for learning MHC specific rules for antigen presentation from immunopeptidome data. This since each ligand first must be unambiguously associated to its presenting MHC molecule(s) within the haplotype of the cell line. Here, we describe NNAlign_MA, a method that is able to address this challenge. NNAlign_MA is capable of taking a mixed training set composed of single-allele (SA) data (peptides assigned to single MHCs) and multi-allele (MA) data (peptides with multiple options for MHCs assignments) as input, and in a fully automated manner deconvolute the individual MHC restriction of all MA sequences while learning the binding specificities of all the MHCs present in the training set. The NNAlign_MA method was benchmarked on a large and diverse dataset, covering human MHC class I and class II, and bovine class I (BoLA) data. For all cases, the method was demonstrated to have unprecedented performance compared to state-of-the-art methods, achieving a complete deconvolution of binding motifs contained within poly-specificity MS eluted ligand data and an improved predictive performance for identification of both eluted ligands and T cell epitopes. Given its very high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules for MHC antigen presentation and guide the development of novel T cell-based therapeutics.

bioinformatics↗

bioRxiv2018

Footprints of antigen processing boost MHC class II natural ligand binding predictions

Major Histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide:MHC-II binding are trained on binding affinity data, generated in-vitro and therefore lacking information about antigen processing. For the first time, we here describe prediction models of peptide:MHC-II binding trained directly on naturally eluted peptides, and show that these, in addition to peptide binding to the MHC, incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the peptides. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the epitope presented. The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands, and foreshadow a new generation of improved peptide:MHC-II prediction tools of considerable importance for understanding and manipulating immune responses.

immunology↗

bioRxiv2017

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

Recent advances in proteomics and mass-spectrometry have widely expanded the detectable peptide repertoire presented by major histocompatibility complex (MHC) molecules on the cell surface, collectively known as the immunopeptidome. Finely characterizing the immunopeptidome brings about important basic insights into the mechanisms of antigen presentation, but can also reveal promising targets for vaccine development and cancer immunotherapy. In this report, we describe a number of practical and efficient approaches to analyze immunopeptidomics data, discussing the identification of meaningful sequence motifs in various scenarios and considering current limitations. We address the issue of filtering false hits and contaminants, and the problem of motif deconvolution in cell lines expressing multiple MHC alleles, both for the MHC class I and class II systems. Finally, we demonstrate how machine learning can be readily employed by non-expert users to generate accurate prediction models directly from mass-spectrometry eluted ligand data sets.

bioinformatics↗