Biology subjects

Greiff, V.

Publications and source records attributed to Greiff, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Influenza H1N1 Imprinting Uncouples Serological and B-Cell Responses in Mice

Antigenic drift reduces the protective potential of antibodies generated against earlier influenza variants. Unfortunately, a new infection or vaccination often preferentially recalls antibodies targeting previously encountered strains, thereby limiting responses to newly emerged variants. Although this phenomenon, known as immunological imprinting, has been well documented at the serological level, its underlying cellular mechanisms remain poorly understood. To resolve imprinting at both the serological and cellular level, we combined serum antibody profiling with targeted B-cell characterization in mice, including single-cell B-cell receptor (BCR) sequencing of germinal center (GC) B cells following secondary H1N1 challenge and bulk BCR sequencing of bone marrow plasma cells. We found that a primary infection with the 2009 pandemic H1N1 ("swine flu") strain largely prevented the generation of new serum antibodies following secondary exposure to the antigenically distinct H1N1 strain A/Puerto Rico/8/1934 (PR8), whereas the reciprocal infection sequence updated antibody specificities towards the last encountered virus. GC B cells did not mirror serological imprinting and instead generated updated BCR repertoires directed against the heterologous challenge strain. Likewise, BCR diversity in draining lymph node B cells failed to mirror the serological imprinting observed in the antibody response. Together, these findings reveal a striking uncoupling of cellular and serological immune responses during influenza imprinting, highlighting the need to study both compartments to better understand immune memory and guide the development of improved seasonal influenza vaccines across a diverse population.

immunology↗

bioRxiv2026

What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study

Generative models are increasingly used to model adaptive immune receptor repertoire (AIRR) sequence distributions, promising to decode the sequence diversity shaping immune responses and accelerate the design of therapeutic antibodies and T-cell receptors. Yet it remains unclear whether these models produce biologically meaningful outputs or merely capture surface-level sequence statistics while missing features driven by receptor generation and selection. Rigorous evaluation is needed, but the field lacks established standards, as existing machine learning metrics do not all translate directly to the AIRR domain, given the complex structure of the data and the lack of biological ground truth. Consequently, researchers face difficulties in evaluating the models and selecting appropriate ones, which can critically affect downstream clinical applications. Here, we apply a suite of evaluation metrics tailored to AIRR sequence data and present a systematic comparison of popular generative model families proposed for the AIRR field, including variational autoencoders, long short-term memory networks, antibody language models, selection models, and simple statistical baselines. We focus specifically on the task of learning individual-specific immune receptor repertoires, a clinically relevant challenge with direct implications for personalized immunotherapy, disease monitoring, and vaccine response studies. By analyzing the sequences generated by each model, we identify memorization risks, innovation capabilities, and sensitivity to hyperparameter tuning. Taken together, these results advance the understanding of how current generative models reproduce the biology of individual immune repertoires and lay the groundwork for more principled model development and evaluation.

immunology↗

bioRxiv2026

Structure-guided computational design and mechanistic understanding of the p95HER2-targeting NAZ-mAb antibody and its variants

Human epidermal growth factor receptor 2 (HER2) is an oncogenic receptor tyrosine kinase in breast cancer and other malignancies. A subset of HER2-positive tumours expresses 611-CTF-p95HER2, a tumour-specific, hyperactive truncated isoform associated with metastasis and treatment resistance that lacks most of the extracellular domain targeted by conventional HER2-directed antibodies. We previously developed NAZ-mAb (formerly known as Oslo-2), a monoclonal antibody against 611-CTF-p95HER2. Here, we describe a computational antibody-engineering workflow for designing variants of NAZ-mAb. Starting from the sequence alone, we modeled the NAZ-mAb-611-CTF-p95HER2 complex, generated a combinatorial mutational landscape using FoldX 5.0, and prioritized candidate variants using predicted interaction energy and developability criteria. Two variants representing distinct design strategies were selected for validation: an aromatic double mutant, NAZ-mAb v1 (L:S31W/L:H107W), and a conservative single mutant, NAZ-mAb v2 (L:S31M). Both variants were successfully expressed as recombinant IgGs; NAZ-mAb v2 achieved a five-fold higher recombinant expression yield than parental NAZ-mAb, while both variants retained antigen binding with a higher apparent signal than the parental antibody in indirect ELISA. However, Biacore two-state kinetic analysis revealed weaker affinities than the parental antibody (KD NAZ-mAb v1: 32.6 nM, NAZ-mAb v2: 9.45 nM vs. parental NAZ-mAb: 5.33 nM). These findings show that the computational workflow can generate experimentally tractable, antigen-engaging NAZ-mAb variants, while also highlighting the limitations of fixed-backbone interaction-energy ranking as a predictor of binding affinity and yield. This study provides a practical framework for computationally driven, developability-aware antibody optimization in the absence of experimental structural data.

bioinformatics↗

bioRxiv2026

Amylo-Pipe: an integrated web server for mechanistic and kinetic prediction of protein and peptide aggregation

Protein aggregation is central to amyloid-related disorders and remains a major developability challenge for protein therapeutics. Over the past two decades, significant advances have been made to predict aggregation-prone regions (APRs) and estimate aggregation propensity in proteins and peptides. In contrast, the prediction of aggregation kinetics has received relatively less attention due to the limited availability and heterogeneity of experimental data. Consequently, aggregation propensities from APR prediction algorithms were widely accepted as a means to predict relative changes in the aggregation kinetics of proteins and mutants. Previous studies have demonstrated, using large-scale datasets, that aggregation propensity shows a weak or inconsistent correlation with aggregation kinetics. In the present study, we have integrated complementary state-of-the-art mechanistic and kinetic prediction tools for protein aggregation into a unified, user-friendly web framework entitled "Amylo-Pipe". Amylo-Pipe also implements practical features that are especially useful for protein engineering, such as gatekeeper-residue mutational scanning to support the design of aggregation-resistant variants. By consolidating multiple prediction tasks in a single interface, Amylo-Pipe enables a more comprehensive assessment of aggregation behavior than APR-only workflows. The web server is freely accessible at: https://web.iitm.ac.in/bioinfo2/amylopipe/.

bioinformatics↗

bioRxiv2026

Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML

Machine learning (ML) enables adaptive immune receptor repertoires (AIRRs) analyses for biomarker identification and therapeutic development. With the majority of AIRR data partially or imperfectly labeled, unsupervised ML is essential for motif discovery, biologically meaningful clustering, and generation of novel receptor sequences. However, no unified framework for unsupervised ML exists in the AIRR field, hindering the assessment of model robustness and generalizability. Here, we present an immuneML release advancing unsupervised ML in the AIRR field through unified clustering workflows, interpretable generative modeling, integration with protein language model embeddings, dimensionality reduction, and visualization. We demonstrate immuneMLs utility in three use cases: (i) benchmarking generative models for epitope-specific sequence generation, assessing specificity and novelty, (ii) systematic evaluation of clustering approaches on experimental receptor sequences against biological properties, such as epitope specificity and MHC, and (iii) unsupervised analysis of an experimental AIRR dataset to examine potential confounding, a practice widespread in related fields but unexplored in AIRR analyses.

bioinformatics↗

bioRxiv2026

Effects of protein interface mutations on protein quality and affinity

Accurately modeling antibody-antigen interactions requires distinguishing intrinsic binding affinity ("protein-interaction") from protein biophysical properties ("protein-quality"), including folding, stability, and expression. However, high-throughput mutational measurements commonly used to train and benchmark computational models often conflate these effects, obscuring the true determinants of molecular recognition. Here, we present an experimental and analytical framework to disentangle protein-interaction effects from protein-quality effects in single-domain antibody (VHH)-antigen binding. Using a large-scale deep mutational scanning (DMS) dataset spanning four VHH-antigen complexes, with single and double mutations in both partners, we introduce control binders to quantify protein-quality changes independently of protein-interaction. This enables decomposition of experimentally measured affinity into protein-interaction and protein-quality components at scale. Leveraging the disentangled dataset, we evaluated state-of-the-art structure- and sequence-based models for protein-quality and protein-interaction prediction and show that their performance largely reflects protein-quality rather than protein-interaction effects. Our results highlight a major confounder in current datasets and suggest that accounting for protein-quality will be essential for training next-generation affinity-prediction models. Nomenclature Antibody related termsO_LIPrimary VHH: The VHH of a VHH-antigen complex for which the paratope and the epitope weremutated. C_LIO_LIControl VHH: A second VHH that binds to the same antigen as the primary VHH but has non-overlapping epitope positions and therefore does not bind to any of the mutated antigen positions. C_LI Affinity-related termsO_LIReal Affinity: "The strength of the interaction between two [...] molecules that bind reversibly (interact)" 1. In the context of antibody-antigen binding, it quantifies interactions between active proteins (which are expressed and correctly folded 2 and are therefore functionally and biologically active (see below). It is commonly quantified by the equilibrium dissociation constant, KD. C_LIO_LIObserved affinity ({degrees}KD): The interaction strength experimentally measured between two molecules. Unlike real affinity, this value is confounded by the biophysical properties of the individual binding partners, specifically their folding, stability, and expression levels. Consequently, the observed affinity often differs from the real/intrinsic affinity if a significant fraction of the protein population is inactive 3. NOTE: Unless otherwise specified, {degrees}KD is reported in - log10 space. For example, a {degrees}KD of -9 corresponds to 10-9M or 1nM. C_LIO_LIChange in observed affinity ({Delta}{degrees}KD): The shift in the observed affinity between two proteins upon mutation, reported as the log10-transformed fold change. A value of 1 reflects a 10-fold difference, a value of 2 a 100-fold difference, etc. This aggregate change resolves into two distinct biophysical components 2, 4: O_LIProtein-interaction change: The change in the intrinsic thermodynamic affinity between the two binding partners, each in its active state (i.e., the specific change in interface Gibbs free energy because both enthalpy and entropy are considered). C_LIO_LIProtein-quality change: The change in the fraction of the mutated protein population that is biologically active - meaning it is expressed, correctly folded, and stable 2, 5. O_LIFolding: The process that guides the polypeptide chain toward its native conformation, which is a prerequisite for forming a functional binding site. C_LIO_LIStability: The thermodynamic capacity to maintain the folded structure over time and under physiological conditions. Stability (decrease in Gibbs free energy from the unfolded to the folded state) ensures the binding interface remains intact and prevents competing processes such as aggregation 6. C_LIO_LIExpression: The steady-state abundance of the protein. This is largely dependent on proper folding and stability, as cellular quality control mechanisms degrade proteins that fail to fold or remain stable at functional concentrations. C_LI C_LI C_LIO_LIChange in relative affinity ({Delta}{Delta}{degrees}KD): the difference between the {Delta}{degrees}KD of the primary VHH compared to the control VHH for a given epitope mutation. C_LI Model-related termsO_LIESM-IF1 sc: Single-chain (sc) structure-conditioned inverse folding model (ESM-IF1), using the isolated monomer structure of the mutated protein: either the VHH or the antigen 7. C_LIO_LIESM-IF1 mc: Multi-chain (mc) structure-conditioned model (ESM-IF1), using the full complex structure (both antibody and antigen) 7. C_LIO_LIStability prediction score: Score that represents the predicted change in stability based on a single mutation, normally represented as {Delta}{Delta}G. C_LI

molecular biology↗

bioRxiv2026

MS-BCR-DB: an integrated BCR repertoire database to mine humoral multiple sclerosis signatures

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) in which B cells play a critical role. While B-cell receptor (BCR) sequencing studies in MS are increasing, progress in understanding MS-associated BCR repertoire features and convergent patterns across patients has been limited by small cohorts, heterogeneous experimental methodologies, and fragmented data storage. To overcome these challenges, we developed the MS-BCR-Database, the first publicly accessible and uniformly processed collection of human MS BCR sequencing datasets. We harmonized raw BCR-sequencing data into an AIRR-compliant database incorporating clinical and technical metadata, enabling coherent cross-study analyses. Using this resource, we identified putative disease-associated BCR-sequence features, including CNS-biased V-gene usage, marked oligoclonal expansion in cerebrospinal fluid, and convergent clonotype clusters shared exclusively among MS patients. Integration with antigen-annotated BCR databases revealed matches to antibodies recognizing both viral antigens, including Epstein-Barr virus, and CNS self-proteins. The MS-BCR-Database provides a scalable foundation for mechanistic discovery and biomarker development in MS, while establishing a broadly applicable resource for integrative analyses of BCR repertoires.

bioinformatics↗

bioRxiv2026

Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores

Antibody-antigen binding prediction remains a central challenge for AI-driven therapeutic discovery, particularly in discriminating cognate interactions from structurally plausible but incorrect pairings. We present a controlled, AI-method- and antibody-format-agnostic evaluation framework that measures binding specificity under realistic conditions. Using 106 experimentally determined single-chain antibody (nanobody)-antigen complexes and 11,342 shuffled non-cognate pairings, we benchmarked publicly-available state-of-the-art structure prediction methods (AlphaFold3, Boltz-2, Chai-1). Although the methods tested often generated geometrically plausible complexes, internal confidence metrics (ipTM) frequently failed to discriminate correct from incorrect pairings. Increased sampling improved structural refinement but not pairing discrimination, indicating that computational resources are better allocated across independent seeds and explicit negative controls. We conclude that internal confidence scores are not inherently calibrated to binding specificity and require validation against realistic decoys. To enable community benchmarking and method development, we release [~]1.8 million AI-generated complex structures and guidance for the benchmarks ahead.

bioinformatics↗

bioRxiv2026

Disulphide and sequence-encoded conformational priors guide nanobody structure prediction

Nanobody binding is largely governed by the HCDR3 loop, which adopts distinct placement regimes relative to the framework: compact, framework-contacting (kinked blueprint) and solvent-exposed (extended blueprint). Many nanobodies also contain additional cysteines that form non-canonical disulphide bonds, imposing covalent constraints on binding-loop conformations. Current structure predictors are typically trained and benchmarked with smooth coordinate-based objectives, so models may appear reasonable under root-mean-square deviation (RMSD), while adopting an incorrect HCDR3 blueprint or failing to recover the native disulphide connectivity, impacting paratope geometry and functional interpretation. Here, we show that the HCDR3 blueprint is predictable from sequence alone, allowing for explicit constraints during modelling. We implement these principles into NbForge, a lightweight nanobody folding model that incorporates blueprint- and disulphide-aware inductive biases and is trained with filtered self-distillation. NbForge improves recovery of HCDR3 blueprint and non-canonical disulphide formation over previous lightweight models and achieves coordinate accuracy at par to state-of-the-art, large, resource-intensive predictors, while running at sub-second inference speed. We show that using NbForge monomer models as templates further improves the success rate of predicting nanobody-antigen complexes. Together, these results motivate blueprint- and disulphide-aware benchmarks for nanobody modelling beyond RMSD, and show that appropriate inductive biases can close the performance gap to heavyweight predictors. We make the sequence classifier (NbFrame) and NbForge available for download and via a user-friendly web server.

bioinformatics↗

bioRxiv2026

Putative target antigens of the stereotyped intrathecal B cell response in multiple sclerosis

Intrathecal antibody production is a hallmark of multiple sclerosis (MS), yet the antigenic targets of these antibodies remain elusive. Intrathecal antibody-secreting cells in MS patients characteristically produce IgG1 antibodies carrying the G1m1 allotype and preferentially use IGHV4 heavy-chain genes paired with IGKV1 or IGKV3 light chains. This stereotyped pattern points to a common antigen-driven selection process. To test this hypothesis, we generated monoclonal antibodies from intrathecal B-lineage cells bearing this stereotyped B-cell receptor configuration and screened them against a comprehensive library of human and Epstein-Barr virus proteins. In parallel, we developed and validated an immunoprecipitation-tandem mass-spectrometry (IPMS/MS) workflow for fresh-frozen brain tissue that preserves conformational epitopes. Several proteins, like CLDN11 and PLD1, were enriched by antibodies from multiple patients. Other targets, like MED21 and GIPC2, were unique to individual patients. Our study delivers a scalable pipeline for antigen discovery in MS and nominates candidate antigens for future validation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/697481v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@b522eaorg.highwire.dtl.DTLVardef@76240org.highwire.dtl.DTLVardef@c36205org.highwire.dtl.DTLVardef@1587019_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Quantitative mapping of antigen specificity in adaptive immune repertoire embedding spaces

The adaptive immune receptor repertoire (AIRR) encompasses an immense diversity of antibody and T-cell receptor sequences, whose collective organization - how receptors are distributed, clustered, and interrelated across sequence and functional (e.g., antigen-binding) dimensions - remains poorly characterized. Representing AIRRs in continuous representation spaces that capture sequence, biochemical, and structural similarity between receptors may enable comparisons beyond discrete sequence features. Using both one-hot encodings and protein language model (PLM) embeddings, we developed a quantitative framework to map immune receptor organization at global (sequence-set-level) and local (single-sequence-level) scales. Applying the geometry-aware Wasserstein-2 distance, we show that the global structure of the AIRR space can be recovered from as few as [~]105 sequence embeddings, at least 10 orders of magnitude smaller than the theoretical immune receptor diversity. We found that immune receptor sequences annotated with different antigen specificities occupy distinct regions of representation space. To resolve local relationships, we introduce a spatial homogeneity metric that quantifies the extent of functional clustering. We found higher spatial homogeneity in embedding spaces than in sequence space for diverse antigen-specific datasets. Our framework establishes a foundation for quantitative mapping of adaptive immune repertoire organization.

immunology↗

bioRxiv2025

Improving nanobody structure prediction with self-distillation

Nanobodies are increasingly attractive therapeutic and biotechnological molecules, yet accurate structure prediction of their highly variable H-CDR3 loops remains a central challenge for machine learning models. Here, we investigate whether nanobody-specific structure prediction can be improved through curated synthetic data strategies. We systematically evaluate different data augmentation regimes, including self-distillation from unlabelled VHH sequences. To ensure structural plausibility of synthetic training samples, we develop NanoKink, the first sequence-based classifier of kinked versus extended H-CDR3 conformations, and apply stringent filtering criteria for non-canonical disulfide bond placement and confor-mational accuracy. On a curated benchmark enriched for challenging nanobody features, we show that, for a fixed training compute budget, a nanobody-specific model trained with filtered synthetic data significantly improves over baseline models and NanobodyBuilder2, achieving lower mean H-CDR3 RMSD and fewer structural violations, while remaining competitive with AlphaFold3 at approximately two orders of magnitude lower per-structure inference time. Our results highlight promising directions in synthetic data generation for nanobody structure modelling and provide a practical framework for optimisation of VHH structure prediction models.

bioengineering↗

bioRxiv2025

PEPE: Scalable extraction of multi-modal protein language model representations

MotivationProtein language models (PLMs) capture intricate amino-acid dependencies, producing embeddings that encode rich structural, functional, and evolutionary information. Despite their potential, current extraction workflows rely on arbitrary choices, with respect to embedding layer, pooling, and padding, that frequently yield suboptimal representations for feature extraction and downstream analyses. Large-scale embedding generation is further limited by inefficiencies in computation and memory: (i) accumulating all model outputs in memory before writing to disk causes severe bottlenecks, and (ii) repeatedly embedding identical sequences to extract different modes introduces redundant computation and drastically reduces throughput and scalability. ResultsWe introduce PEPE (Parallel Extraction for Protein Embeddings), a command-line tool and Python library that enables efficient, high-throughput, and multimodal extraction from protein language models. PEPEs parallelized and streaming-based architecture achieves runtimes several orders of magnitude faster than sequential approaches. Unlike conventional methods--whose peak memory usage scales linearly with output size and fails when memory capacity is exceeded--PEPE maintains stable, low memory consumption, enabling multimodal embedding extraction even beyond available RAM. PEPE supports a wide range of state-of-the-art and custom PLMs through a simple, flexible interface. By combining scalability, robustness, and ease of use, PEPE allows researchers to generate massive, information-rich embedding datasets efficiently, and facilitate the discovery of optimal representations for structural, functional, and evolutionary downstream tasks. By streamlining the generation of diverse embedding configurations, PEPE provides researchers with the necessary data to identify high-performing latent states for specific biological contexts without requiring additional computational resources. Availability and ImplementationPEPE is a command-line tool written in Python and published under MIT license. The source code and documentation are available at https://github.com/csi-greifflab/pepe-cli. PEPE is also available for installation from PyPI under https://pypi.org/project/pepe-cli and deposited on Zenodo at https://zenodo.org/records/15912054.

bioinformatics↗

bioRxiv2025

TCR2HLA: calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant metaclonotypes

T cell receptors (TCRs) recognize peptides presented by polymorphic human leukocyte antigen (HLA) molecules, but HLA genotype data are often missing from TCR repertoire sequencing studies. To address this, we developed TCR2HLA, an open-source tool that infers HLA genotypes from TCR{beta} repertoires. Expanding on work linking public TRBV-CDR3 sequences to HLA genotypes, we incorporated "quasi-public" metaclonotypes - composed of rarer TCR{beta} sequences with shared amino acid features - enriched by HLA genotypes. Using four TCR{beta}seq datasets from 3,150 individuals, we applied TRBV gene partitioning and locality-sensitive hashing to identify [~]96,000 TCR{beta} features strongly associated with specific HLA alleles from 71M input TCRs. Binary HLA classifiers built with these features achieved high balanced accuracy (>0.9) across common HLA-A (9/12), B (9/12), C (6/13), DRB1 (11/11) alleles and prevalent DPA1/DPB1 (6/10), DQA1/DQB1 (8/17) heterodimers. We also introduced a high-sensitivity calibration to support predictions in samples with as few as 5,000 unique clonotypes. Calibrated predictions with confidence filtering improved reliability. Beyond genotype imputation, TCR2HLA enables the discovery of novel HLA- and exposure-associated TCRs, as shown by the identification of SARS-CoV-2 related TCRs in a large COVID-19 dataset lacking HLA data. TCR2HLA provides a scalable framework for bridging the gap between TCRseq data and HLA genotype for biomarker discovery.

immunology↗

bioRxiv2025

Active learning for improving out-of-distribution lab-in-the-loop experimental design

The accurate prediction of antibody-antigen binding is crucial for developing antibody-based therapeutics and advancing immunological research. Library-on-library approaches, where many antigens are probed against many antibodies, can identify specific interacting pairs. Machine learning models can predict target binding by analyzing many-to-many relationships between antibodies and antigens. However, these models face challenges when predicting interactions when test antibodies and antigens are not represented in the training data, a scenario known as out-of-distribution prediction. Generating experimental binding data is costly, limiting the availability of comprehensive datasets. Active learning can reduce costs by starting with a small labeled subset of data and iteratively expanding the labeled dataset. Few active learning approaches are available to handle data with many-to-many relationships as, for example, obtained from library-on-library screening approaches. In this study, we developed and evaluated fourteen novel active learning strategies for antibody-antigen binding prediction in a library-on-library setting and evaluated their out-of-distribution performance using the Absolut! simulation framework. We found that three of the fourteen algorithms tested significantly outperformed the baseline where random data are iteratively labeled. The best algorithm reduced the number of required antigen mutant variants by up to 35%, and sped up the learning process by 28 steps compared to the random baseline. These findings demonstrate that active learning can improve experimental efficiency in a library-on-library setting and advance antibody-antigen binding prediction.

bioinformatics↗

bioRxiv2025

YAbS: The Antibody Society's antibody therapeutics database

Therapeutic antibodies have gained prominence in recent years due to their precision in targeting specific diseases. As these molecules become increasingly essential in modern medicine, comprehensive data tracking and analysis are critical for advancing research and ensuring successful clinical outcomes. YAbS, The Antibody Societys Antibody Therapeutics Database, serves as a vital resource for monitoring the development and clinical progress of therapeutic antibodies. The database catalogues detailed information on over 2,900 commercially sponsored investigational antibody candidates that have entered clinical study since 2000, as well as all approved antibody therapeutics. Data for the late-stage clinical pipeline and antibody therapeutics in regulatory review or approved (over 450 molecules) are openly accessible (https://db.antibodysociety.org). Antibody-related information includes molecular format, targeted antigen, current development status, indications studied, and the clinical development timeline of the antibodies, as well as the geographical region of company sponsors. Furthermore, the database supports in-depth industry trends analysis, facilitating the identification of innovative developments and the assessment of success rates within the field. This resource is continually updated and refined, providing invaluable insights to researchers, clinicians, and industry professionals engaged in antibody therapeutics development.

scientific communication and education↗

bioRxiv2024

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

immunology↗

bioRxiv2024

Revealing the hidden sequence distribution of epitope-specific TCR repertoires and its influence on machine learning model performance

Numerous efforts have been made to decipher the epitope-T cell receptor (TCR) recognition code. Both simple machine learning techniques and deep learning strategies have been used to train models to predict the binding of epitopes by TCR sequences. A good training data set rests at the basis of every accurate prediction model, yet little attention has been given to the composition of these data sets. In this paper, we studied the natural distribution of TCR sequences within epitope-specific TCR repertoires, i.e. a set of TCRs binding the same epitope, and its impact on the predictability of TCR-epitope interactions. We found that the observed diversity of these repertoires can result from a smaller set of core binding motifs constrained by TCR generation. Moreover, a clear relationship was found between the sequence distribution of the training data and performance metrics, emphasizing the importance of the used ground-truth data when using machine learning models in this domain. Taken together, these findings inform data set composition to help push epitope-TCR prediction models to the next level.

bioinformatics↗