mAbs

The binding of transmembrane (TM) ligands to their cognate TM receptors on neighboring cells governs intercellular adhesion and direct cell-cell communication. However, these interactions are difficult to study in vitro because they depend on membrane presentation, ligand orientation, receptor clustering, and avidity, features often not captured by soluble recombinant ligands or cell-free assays. Here, we describe a flow cytometry-based assay using fluorescent, lentiviral-derived virus-like particles (VLPs) displaying TM ligands to quantify binding to their receptors on target cells. Fluorescent VLPs are generated in-house by plasmid transfection in HEK293T cells and enable direct fluorescent detection without fluorochrome-conjugated secondary antibodies. The system is modular and readily accommodates engineered ligand constructs, including patient-derived variants. We applied this platform to generate ICAM-1-displaying fluorescent VLPs and to study human LFA-1 function in patient-derived leukocytes. This protocol provides a detailed workflow for VLP production and in vitro binding assays, offering a simple, quantitative, and cost-effective approach for studying TM ligand-receptor interactions in a membrane context. The system is well suited for mechanistic studies, functional assessment of patient-derived variants, and direct binding assays using patient-derived cells. Integrating the assay into multicolor flow cytometry panels enables simultaneous immunophenotyping and quantification of up to four ligand-receptor interactions at single-cell resolution. Key featuresO_LIQuantifies TM ligand-receptor binding in a membrane context using fluorescent VLPs and flow cytometry. C_LIO_LIFully in-house, modular system based on plasmid transfection in HEK293T cells, without reliance on recombinant ligands or fluorochrome-conjugated secondary antibodies. C_LIO_LISupports testing of engineered ligand variants, including patient-derived alleles, and direct functional studies on patient-derived cells. C_LIO_LICompatible with multicolor flow cytometry panels, enabling simultaneous immunophenotyping and quantification of up to four ligand-receptor interactions at single-cell resolution. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/725198v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@a43069org.highwire.dtl.DTLVardef@166491borg.highwire.dtl.DTLVardef@49c7d4org.highwire.dtl.DTLVardef@1de36a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundCombinatorial fragment antigen-binding (Fab) libraries encode an immense heavy-light chain pairing space, often exceeding 10{superscript 1} possible combinations, which far surpasses the diversity that can be experimentally constructed and screened in display systems. As a result, direct Fab screening samples only a small fraction of the theoretical search space, creating a practical bottleneck for functional binder discovery. ResultsHere, we frame Fab discovery as a staged search problem by decoupling heavy-chain (HC) and light-chain (LC) exploration. We implemented a sequential HC preselection-remating workflow in yeast surface display, in which antigen-reactive HC variants are first enriched and subsequently recombined with a diverse LC repertoire to reconstruct a focused Fab library. In a SARS-CoV-2 spike-targeted campaign, HC and LC libraries of 2.05 x 10 and 2.33 x 10 members corresponded to a theoretical pairing space of approximately 4.8 x 10{superscript 1} combinations. Sequential HC enrichment followed by LC remating allowed recovery of multiple functional Fab clones from a tractable library scale of approximately 10, including clones that shared a common HC scaffold but carried distinct LC partners. A representative recombinant IgG output showed broad but heterogeneous spike/RBD binding, measurable pseudovirus neutralization activity (EC = 11.1 nM), and compatibility with standard early biophysical characterization after full-length IgG reformatting. ConclusionsThese results provide proof of principle that combinatorial Fab discovery can be approached as a staged exploration problem under realistic library-size constraints. By focusing downstream Fab reconstruction on an antigen-compatible HC subspace, sequential HC preselection followed by LC remating offers a practical strategy for exploring otherwise intractable antibody pairing landscapes in eukaryotic display systems.

Epitope mapping is central to rational antibody drug design, affinity optimization and the anticipation of therapeutic resistance mechanisms. Here, we demonstrate the use of Sensor Integrated Proteome on Chip (SPOC) technology for single amino acid resolution epitope mapping. By generating high throughput (HTP) binding kinetics data, we identify important residues within the target epitope whose mutations alter drug-target interactions. The SPOC platform integrates simultaneous HTP cell-free production of folded proteins in nanowells from immobilized plasmid DNAs or linear expression cassettes and capture onto biosensor chips for subsequent label-free binding kinetic analysis using surface plasmon resonance (SPR). The model system comprised the extracellular domain (ECD) of CD20, a membrane-spanning 4-domain family protein, screened against its FDA-approved therapeutic monoclonal antibodies (thAbs) - rituximab and ocrelizumab. Using our proprietary POC protein nanofactory system, a partial deep mutationally scanned (DMS) CD20 ECD mutant library of 79 variants was produced on SPOC biosensor chips via rational single amino acid substitutions of the epitope and surrounding residues with alanine, aspartic acid, lysine, and serine, collectively representing four broad classes of amino acid side chain chemistries: nonpolar, acidic, basic, and polar neutral. The SPOC protein biosensor chip was then screened with both thAbs using SPOC SPR to generate kinetic affinity data, evaluate mutations that led to affinity loss or gain, and ultimately identify critical epitope residues that interface with the antibodies. Most mutations within the rituximab and ocrelizumab epitopes - EPANPSEK and YNCEPANPSEKNSPST, respectively - resulted in complete loss of binding or >25% increase in apparent KD. Notably, N171, P172, and S173 mutations, irrespective of side chain substitution, resulted in complete loss of rituximab binding while at least three diverse side chain substitutions at E168, P169, N171, P172, S173, E174, K175, and T180, led to complete loss of binding for ocrelizumab. These outcomes identify the listed residues as the most critical contact points for their respective antibodies. Interestingly, we also found that functional side-chain substitutions at some residues flanking the epitope increased affinity. This indicates that these non-epitope residues contribute to antibody contact, and that polarity at these sites is a tractable lever for affinity modulation by targeting the corresponding contact residues on the antibody CDRs. The proposed SPOC approach of screening drug candidates against on-chip library of mutationally-scanned therapeutic targets is relevant in the early phase of drug development to resolve epitopes at the residue-level to support more informed down-selection of candidates. It facilitates cost-effective improvement of thAbs, enhancing therapeutic efficacy across a wide array of therapeutic targets, including rare variants that might otherwise lead to therapeutic resistance.

Antibody-dependent cellular cytotoxicity (ADCC) is a major mechanism of action for many FDA-approved therapeutic antibodies that is driven by interactions between the antibody Fc and Fc{gamma} receptors (Fc{gamma}Rs) on immune effector cells. Murine models used for preclinical antibody evaluation currently have limited predictive value for clinical ADCC performance due to interspecies differences in Fc-Fc{gamma}R interactions. The molecular determinants governing Fc-Fc{gamma}R engagement in mice remain poorly defined, complicating the interpretation of murine ADCC data and its clinical relevance. To address this, we present the high-resolution crystal structure of the receptor that regulates Fc-mediated cytotoxicity in mice, mouse Fc{gamma}RIV, alone and in complex with mouse IgG2a Fc. This complex preserves key features of the human IgG1 Fc-human Fc{gamma}RIIIa interface which mediates ADCC in humans including salt bridges, hydrogen bonds, and a proline sandwich. However, subtle variations in receptor orientation, Fc-Fc{gamma}R electrostatics, and glycan positions reduce human IgG1 Fc- mouse Fc{gamma}RIV binding affinity, resulting in species-restricted Fc-Fc{gamma}R mediated immune responses. Modeling of human IgG1 Fc interactions with mouse Fc{gamma}RIV predicted steric clashes, suggesting opportunities to modulate the interaction. One structure-guided substitution variant of human IgG1, Fchumo, maintains comparable human Fc{gamma}RIIIa engagement with enhanced binding to and activation of mouse Fc{gamma}RIV, relative to human IgG1 Fc. This study provides proof-of-concept for engineering human Fc domains for cross-species Fc{gamma}R recognition and provides a strategic framework to improve the predictive power of in vivo preclinical models.

The development of clinically effective CAR-T cell therapies for solid tumors requires careful optimization of receptor design, functional fitness, and manufacturability. While advancing low-affinity HER2-targeting CAR-T cells toward clinical application, we found that the candidate with the strongest in vivo antitumor activity--comprising a CD8 hinge and transmembrane region and a 4-1BB co-stimulatory domain--exhibited measurable tonic signaling. This basal antigen-independent signaling, likely driven by high CAR surface expression, was associated with increased apoptosis and reduced ex vivo expansion under research-grade manufacturing conditions. Modification of the transmembrane domain reduced CAR surface expression but did not alleviate tonic signaling and instead impaired antitumor activity. By contrast, transient pharmacologic inhibition of CAR signaling with dasatinib rescued expansion and reduced apoptosis in small-scale research cultures. Notably, these tonic-signaling-associated defects were largely absent during large-scale, GMP-compliant manufacturing, which enabled robust CAR-T cell expansion without additional benefit from dasatinib supplementation. Together, these findings show that tonic signaling is not inherently detrimental to CAR-T cell performance and that its functional consequences are highly dependent on manufacturing context. Our study underscores the importance of evaluating CAR candidates within clinically relevant production platforms and supports the advancement of this 4-1BB-based HER2-specific CAR-T cell product toward clinical testing.

The precise de novo design of antibodies remains a therapeutic challenge. The AI platform, GaluxDesign, was evaluated in a high-efficiency Precision-Scale Workflow by synthesizing and testing only 50 full-length IgG candidates per epitope across eight distinct epitopes from six therapeutic targets. This campaign yielded a 10.5% binder rate (estimated EC50 < 100 nM), identifying target-specific binders for seven of eight epitopes, with multiple candidates exhibiting sub-nanomolar to single-digit nanomolar dissociation constants (Kd). We further assessed the same workflow on nine shared benchmark targets selected for external comparison, where GaluxDesign identified target-specific binders for eight of nine targets, demonstrating strong target-level performance relative to previously reported de novo antibody design approaches. Together, these results establish a high-efficiency, precision-scale workflow for generating novel, high-affinity therapeutic antibodies.

Monoclonal antibody (mAb) glycosylation is a critical quality attribute that is difficult to rationally engineer and rapidly assess during cell line development. Here, we investigate whether cell-surface glycosylation can serve as a predictive indicator of mAb product glycosylation following targeted glycogene engineering in CHO cells. Five key glycogenes (COSMC, FUT8, B4GALT1, ST3GAL4, ST6GAL1) were investigated in two mAb-producing CHO cell lines. Product glycan analysis revealed consistent, gene-specific effects across hosts, including loss of core fucosylation, and tuneable galactosylation and sialylation. Lectin-based surface profiling reliably reflected product outcomes for COSMC and FUT8 modifications but showed limited predictive power for galactosylation and 2,3-sialylation, highlighting glycosylation pathway redundancy and context dependence. This study provides the first systematic, cross-cell line evaluation of lectin-based cell-surface glycan profiling as a predictor of mAb product glycosylation, establishing its practical utility and inherent limitations for CHO glycoengineering workflows. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/724788v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@6d5cfborg.highwire.dtl.DTLVardef@1f38e0aorg.highwire.dtl.DTLVardef@f25fa2org.highwire.dtl.DTLVardef@64a0dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

{beta}4-galactosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs), enhancing complement-dependent cytotoxicity, antibody-dependent cytotoxicity, and antibody-dependent cellular phagocytosis. Despite its therapeutic importance, galactosylation remains the most variable glycosylation motif due to its sensitivity to cell culture conditions. Here, we describe a dual genetic engineering strategy applied to two mAb-producing CHO cell lines, DP12 and VRC01, to simultaneously overcome the cellular machinery and metabolic bottlenecks that limit {beta}4-galactosylation. The first engineering event knocks out COSMC, the chaperone required for core 1 {beta}-1,3-galactosyltransferase 1 activity, to redirect UDP-Gal consumption from O-linked {beta}3-galactosylation towards mAb Fc N-linked {beta}4-galactosylation. The second event overexpresses {beta}-1,4-galactosyltransferase 1 ({beta}4GalT1) to augment cellular galactosylation machinery. Each modification was characterised individually (COSMC- and GalT+) and in combination (C-/GT+) across both cell lines in batch and fed batch cultures. The combined C-/GT+ strategy consistently achieved greater than 90% mAb Fc {beta}4-galactosylation, irrespective of host cell line or culture mode. Metabolic characterisation confirmed that both engineering events alleviate their respective bottlenecks: COSMC knockout redirects UDP-Gal flux and {beta}4GalT1 overexpression increases N-galactosylation capacity. The C-/GT+ strategy also reduced production of Man5 glycans, which accelerate serum clearance and pose immunogenicity risks. Metabolic profiling suggests that the COSMC knockout attenuates UTP consumption and contributes to reduced Man5 production. C-/GT+ glycoengineering had no negative impact on mAb titre. Our results establish the C-/GT+ dual glycoengineering strategy as a robust approach for consistently achieving high mAb galactosylation across diverse cell culture conditions, with the additional benefit of reduced Man5 glycans. HighlightsO_LIDual COSMC KO and {beta}4GalT1 overexpression achieves >90% mAb Fc galactosylation. C_LIO_LICOSMC KO redirects UDP-Gal from O-glycans to mAb Fc without impacting cell growth. C_LIO_LIDual glycoengineering reduces production of undesired Man5 glycans. C_LI

1.Prediction of conformational B-cell epitopes is critical for vaccine design, immunotherapy, and antibody engineering. To date, several host-independent computational methods have been developed for predicting antibody-interacting residues in antigen structures. However, it is well established that antigen-antibody (Ag-Ab) interactions vary depending on the host immune system indicating the importance of developing host-specific prediction models. In this study, we present, for the first time, a human host-specific method, HAIRpred2, that predicts antibody-interacting residues in an antigen from its tertiary structure. The dataset was derived from HAIRpred and comprises 277 human Ag-Ab complexes, with 221 structures used for training and 56 for independent testing. Preliminary analysis revealed that residues with a relative surface accessibility (RSA) below 0.05, corresponding to buried regions, are highly likely to be non-interacting, underscoring the importance of structural accessibility in antibody recognition. To identify the most informative features, we evaluated multiple feature representations, including RSA, large language model (LLM)-based embeddings, distance-based features, and physicochemical properties. A model trained on single-residue RSA features achieved an AUC of 0.72. Incorporating a sliding window of 15 residues to capture local structural context improved performance to an AUC of 0.75. The best performance (AUC = 0.78 on the independent test set) was achieved by integrating RSA with physicochemical descriptors. Benchmarking against existing antibody-interaction prediction methods on the same independent dataset demonstrated that HAIRpred2 outperforms current tools, further highlighting the advantage of host-specific modeling. HAIRpred2 is freely available as a web server at https://webs.iiitd.edu.in/raghava/hairpred2/. HighlightsO_LIDevelopment of HAIRpred2, the first human host-specific method for predicting antibody-interacting residues. C_LIO_LIAnalysis of 277 human antigen-antibody complexes to capture host-dependent interaction patterns. C_LIO_LIRelative surface accessibility (RSA) identified as a key determinant, with buried residues rarely participating in interactions. C_LIO_LIIntegration of RSA with physicochemical features achieved the best performance (AUC = 0.78) on an independent dataset. C_LIO_LIHAIRpred2 outperforms existing methods and is available as a web server for epitope prediction. C_LI

Protein structure prediction models (such as AlphaFold, Chai, Boltz) have transformed structural biology and are increasingly explored for drug discovery; however, their utility for large-scale screening of antibody-antigen (AB-AG) interactions remains unclear, particularly for distinguishing true binding from non-binding pairs at scale. To our knowledge, there has not been an exhaustive exploration of Boltz-2 inference settings on this high impact problem, and in this paper we set out to describe and implement a novel benchmarking framework that can accelerate progress in the field. We evaluated Boltz-2 (NVIDIA NIM implementation) on 519 therapeutic monoclonal antibodies from Thera-SAbDab, pairing each antibody with its cognate target and a randomly assigned non-cognate antigen. We developed a novel evaluation framework that systematically captures variability across stochastic seeds while benchmarking different inference settings, including datasets with and without crystallographically resolved antibody structures. Across settings, Boltz-2-derived confidence metrics showed weak, though above-chance, discrimination (0.5 < ROC-AUC < 0.60). Among evaluated metrics, the minimum value of the interface predicted TM-score (ipTM-min) across seed-samples, captured the strongest signal. Interestingly, additional feature aggregation and multivariate modelling provided little to no improvement. Increasing the number of stochastic predictions yielded front-loaded gains, with diminishing returns beyond [~]15-20 seed-samples, suggesting limited value of extensive sampling in practical workflows. Notably, inference without multiple sequence alignments (MSAs) slightly improved performance on non-crystallized antibodies ({Delta}AUROC {approx} +0.027) while reducing runtime by [~]8 seconds per prediction compared to shallow MSA settings. Overall, these results indicate that off-the-shelf confidence metrics from general-purpose structure prediction models may be insufficient for reliable target-antibody screening and highlight the need for task-specific optimization, while confirming that modest amounts of sampling can be helpful, but not in itself sufficient to improve performance significantly as gains plateau relatively quickly.

Generative models are increasingly used for protein design, but the lack of standardized evaluation frameworks limits comparison across model classes and hinders translation to experimental success. Here, we introduce a unified sampling and benchmarking framework that enables controlled sequence generation across alignment, protein language, and structure-based models, and apply it to Tobacco etch virus (TEV) protease. Across hundreds of thousands of designed sequences, different models explore distinct regions of sequence space with no clear computational selection metrics to assess enzymatic function. Experimental evaluation reveals large differences in functional outcomes, ranging from non-functional variants to sequences with 9-fold higher activity than wildtype. Machine learning-designed libraries achieve a 39.32% hit rate (percentage of variants matching or exceeding wildtype activity) compared to 6.06% for an error-prone PCR baseline. Structure-based models perform best overall, with hit rates of 74.4% and 66.8% for ESM-IF1 and ProteinMPNN, respectively. Commonly used selection metrics do not strongly correlate with experimental activity, highlighting a gap between in silico evaluation and enzyme function. Together, these results establish a generalizable framework for benchmarking generative protein models and demonstrate the necessity of experimental validation for guiding model development and sequence prioritization.

Chimeric antigen receptor (CAR) T-cell therapy has demonstrated transformative efficacy in hematologic malignancies, but its broader use remains constrained by complex ex vivo manufacturing, prolonged production timelines, high cost, and dependence on lymphodepleting chemotherapy. Emerging in vivo CAR-T generation strategies aim to address these limitations, but they introduce additional safety concerns associated with systemic delivery of gene-modifying vectors, including off-target transduction and insertional mutagenesis. This paper describes a novel antigen-driven CAR T-cell expansion platform (adCAR-T) based on co-culture of CAR T cells with engineered target cells expressing defined antigen density and lacking the inhibitory checkpoint ligand PD-L1. This system induces immediate activation, rapid proliferation, and sustained cytotoxic differentiation of CAR T cells without reliance on artificial CD3/CD28 bead stimulation or exogenous cytokine-driven expansion. In contrast to conventional methods, the platform eliminates the lag phase of CAR T-cell expansion and enables rapid scaling to clinically relevant doses (108-109 cells) within several days, depending on the initial cell input. Mechanistically, antigen-driven CAR engagement and target-cell lysis trigger cytokine release and amplification of CAR T cells in a physiologically relevant manner. This process promotes coordinated expansion of both directly antigen-engaged and non-engaged CAR T cells. The platform preserves "functional fitness", minimizes exhaustion, and avoids systemic exposure to gene-delivery vectors. Taken together, this strategy defines a hybrid manufacturing paradigm that bridges the control of ex vivo production with the physiological logic of in vivo activation. Proposed method has a potential to reduce manufacturing complexity, improve safety, and possibly decrease or eliminate the need for lymphodepleting conditioning. This work presents a potential alternative to both standard ex vivo manufacturing and emerging in vivo CAR-T generation approaches, with important implications for improving the accessibility, safety, and cost-effectiveness of CAR T-cell therapies.

Cetuximab is a chimeric IgG1 monoclonal antibody that has been a cornerstone therapy for EGFR-driven malignancies for nearly two decades. Its therapeutic activity is governed by competitive displacement of endogenous EGFR ligands, making binding affinity a direct determinant of clinical efficacy. We applied ConvergeAB, a target-aware antibody design platform, in a fully zero-shot configuration to generate a biobetter version of cetuximab. The lead Converge-designed antibody binds EGFR with a mean KD of 315 pM -- approximately 2.1-fold tighter than cetuximab (673 pM) and 4.4-fold tighter than a recently published, computationally designed anti-EGFR antibody from Cradle Bio (1.38 nM). The affinity gain arises from six substitutions that leave the global paratope architecture intact (C RMSD 0.15 [A] vs cetuximab) and instead optimize the binding interface through localized packing and electrostatic adjustments. A panel of biophysical and developability assays -- HIC, DLS, DSF, and PSR ELISA -- shows that the Converge variant matches or exceeds cetuximab on monomericity, monodispersity, polyspecificity, and thermal stability, while remaining within a developable hydrophobicity envelope. Together, these data demonstrate that a single zero-shot ConvergeAB campaign can deliver a biobetter molecule with significantly improved affinity and a clean developability profile, without compromising the parental antibodys drug-like properties.

Neoantigens are cancer-specific antigens arising from genomic alterations. Single Amino Acid Variants (SAAVs) represent a primary class of these neoantigens. To evaluate the therapeutic potential of Neurofibromin 1 (NF1)-derived SAAVs - given that NF1 is frequently mutated in malignant brain tumors - we prioritized the 40 NF1 SAAVs determined to be HLA-A*02:01 binders using computational prediction coupled with experimental validation. To validate these predicted neoepitopes, we employed a two-tiered experimental approach in HLA-A*02:01 homozygous U87-MG cells. We first synthesized minigene constructs encoding the predicted neoepitopes, introduced them via lentiviral transfection and confirmed their expression by mass spectrometry (MS). Subsequently, we performed endogenous validation using pan-HLA immunoprecipitation mass spectrometry (IP-MS), confirming 4 (10 neoepitopes) of the 40 candidate SAAVs. We observed a discrepancy between in silico predictions and the observed sequences. Our endogenous peptidomics further revealed conserved peptide motifs and demonstrated that peptide selection for HLA presentation is transient. While our study substantiates the therapeutic feasibility of T-cell immunotherapies targeting NF1 mutations, these results underscore a limitation in current computational prediction. Our study highlights the necessity of experimental validation to refine neoantigen prioritization strategies.

Antibody design campaigns increasingly generate many candidates before only a small subset can be tested experimentally, making candidate filtering a central bottleneck. We study whether an autoresearch loop can discover better training-free filters for antibody binder classification by iteratively proposing rule variants, evaluating them under a fixed Leave-One-System-Out protocol, recording each experiment in version control, and using the results to guide the next iteration. Across 75 unique logged filter variants on seven antibody-antigen systems, the loop improves average ROC-AUC from 0.6371 for the initial baseline to 0.8060 for a compact final rule that we call the RMSD-Tuned Triad rule, an absolute gain of 0.1689 and a relative improvement of 26.5%. The discovered filter is competitive with supervised machine learning baselines and prompted LLM baselines evaluated on the same systems: it exceeds logistic regression (0.7144), feature-selected balanced logistic regression (0.7536), and GPT-4o tabular few-shot prompting (0.7640), and it comes within 0.0044 ROC-AUC of the strongest GPT-5 tabular few-shot result (0.8104). Unlike the LLM baseline, the final rule requires no prompted examples and no LLM inference once the numeric structure-derived features are available. These results show that systematic autoresearch can turn simple structural-confidence signals into compact, interpretable filters that are useful when target-specific training data are scarce.

Antibodies originate from germline templates and are diversified by somatic hypermutation, producing sequences in which conserved germline residues scaffold structure while rare non-germline (NGL) substitutions refine antigen binding. Current antibody language models (ALMs) treat all residues equivalently and inherit a germline bias that systematically down-weights functionally critical NGL mutations as statistical noise. We introduce PRISM, a germline-aware ALM that explicitly represents germline and nongermline residues as distinct token types over a factorized 53-token vocabulary. PRISM achieves state-of-the-art pseudo-perplexity in hypervariable CDRs and is uniquely positively correlated with experimental binding affinity across three deep mutational scanning landscapes on which all compared ALMs anti-correlate. The dual-vocabulary further enables property-specific controllable generation previously unattainable with entangled ALMs. NGL-directed sampling improves physics-based binding scores while GL-directed sampling preserves stability and solubility. These results establish disentangled germline/non-germline representation as a substantive advance in antibody language modeling.

Peptide mapping is a critical tool for characterizing biotherapeutic proteins and is essential for the development of monoclonal antibody drugs. Here we describe a new direct infusion technology that streamlines peptide mapping data collection and analysis, accelerating the method by up to 100-fold. This method, which we term RaPiD-mAb-MS, combines high-throughput plate-based sample preparation with direct infusion mass spectrometry analysis. RaPiD-mAb-MS allows analysis of 96 samples within [~] 1.5 to 2 hours, routinely achieves >95% sequence coverage, and has been successfully applied to 28 unique antibodies and over 2,000 samples. Here we demonstrate that RaPiD-mAb-MS detects and quantifies oxidation, deamidation, isomerization, glycosylation, and sequence variants with results comparable to conventional LC-MS based methods in a fraction of the time. Further, by eliminating chromatography, data analysis is greatly streamlined and simplified. By allowing for the collection of [~] 1,000 peptide maps per day, RaPiD-mAb-MS is positioned to accelerate all phases of antibody-based drug discovery & development and sets the stage for collection of massive datasets that would allow artificial intelligent prediction of optimal antibody variants and formulations.

Machine learning continues to accelerate peptide and protein design through the rapid prediction and generation of sequences with desired characteristics. Many applications focus on predicting properties, functions, and structures, as well as generating point mutations and de novo designs. Nevertheless, many models prove less generalizable than initially claimed. Most predictors and generators are trained on sequential datasets, where imbalances can be addressed during preprocessing. In contrast, structural bias, a subtype of algorithmic bias arising from uneven representation of structural classes in training datasets, and the limitations of early protein structure predictors have frequently remained undetected and uncorrected. The recent surge in powerful protein structure prediction tools, such as the AlphaFold and RosettaFold series and their variants, now presents opportunities to mitigate this issue. We hypothesize that such structural sampling biases influence the downstream performance of ML models. Using antimicrobial peptides as a case study, we audited the structural biases in 16 state-of-the-art predictors for antimicrobial activity and tested whether structural information constrains their predictions. Our analysis revealed that models explicitly trained on sequential data still produce predictions biased by uneven fold representations and data leakage. These findings highlight the importance of integrating balanced structural data or implementing bias-mitigating strategies to develop agnostic models that maximize bioactive protein discovery and multi-objective optimization.

Cytokine release syndrome (CRS) is a major dose-limiting toxicity of T cell-engaging immunotherapies. Existing CRS models are drug-class-specific and have not addressed whether a single mechanistic cytokine network can capture severity differences across mechanistically distinct drug classes. Here, we developed a PK-driven quantitative systems pharmacology (QSP) model linking drug exposure, T cell activation dynamics, and a macrophage-amplified cytokine network to clinical CRS severity. The 17-parameter downstream amplification network with macrophage-gated STAT3 positive feedback was developed iteratively. The network was calibrated on blinatumomab, structurally refined using TGN1412 as a transparently disclosed development case, then locked and tested blind on OKT3. The same locked network was used to evaluate cross-drug transferability across three antibody-based T cell engager classes: bispecific, CD28 superagonist, and anti-CD3 with activation-induced cell death. The locked network reproduced the clinically observed CRS severity ordering across all three drugs without re-fitting any shared parameter. The OKT3 blind prediction passed eight qualitative plausibility checks and three of three quantitative cytokine peaks within published clinical ranges. Tocilizumab rescue simulation reproduced five clinically validated phenomena. A mechanistic parameter swap test reversing the T cell exhaustion rate between OKT3 and TGN1412 reversed CRS severity in the expected direction, supporting a mechanistic rather than parameter-fitted interpretation. Local robustness analysis (ABC-style accepted ensemble: 692 of 5,000 parameter sets accepted, 13.8%) and a 2D stability map over the two threshold-setting parameters (0 of 900 wrong-order combinations) confirmed that the cross-drug severity ordering is a property of a feasible parameter region rather than a single tuned point. Profile likelihood analysis of the IL-6 feedback and clearance rates revealed complementary asymmetric profiles consistent with practical identifiability as a ratio. The same locked model predicted three qualitatively distinct dose-response shapes without re-fitting. Findings should be interpreted as a mechanistic proof-of-concept; prospective clinical validation remains pending.

BackgroundSingle-cell multi-omics profiling of hematopoietic malignancies frequently involves pooling of patient samples before library preparation to reduce costs. Demultiplexing and quality control of the resulting sequencing data depend on experimental design, sequencing depth, and computational methods. Existing approaches benchmark individual tools, auto-select a single best method, or apply majority voting. However, none systematically exploit disagreement patterns among orthogonal strategies as a diagnostic signal for cell quality. ResultsWe introduce Split-flow, a modular Nextflow pipeline that runs hashing-based and SNP-based demultiplexing, and transcriptome-based doublet detection in parallel. It classifies cells into quality strata through a concordance-based decision framework. Validation on multiplexed CITE-seq data from 14 multiple myeloma patients across eight Chromium channels demonstrates high reproducibility and shows that discordant cells cluster within specific cell types and quality strata. TCR clonotype cross-referencing against VDJdb confirms that concordance-based classification enriches for biologically genuine immune receptor sequences, with a 5.3-fold enrichment of confirmed public TCR sequences in the high-confidence stratum. Downsampling analysis reveals that SNP-based methods are more depth-sensitive than hash-based approaches, supporting the recommendation to combine both strategies. The framework transfers to AML samples across three assay types (snMultiome-seq, scRNA-seq, scATAC-seq), where ATAC-based demultiplexing resolves donor assignment discordance under low hashing efficiency. ConclusionsSplit-flow demonstrates that combining of orthogonal preprocessing methods yields structured information about cell quality and offers a concordance-based framework that transforms this disagreement into a diagnostic signal. It introduces a preprocessing approach that can be exploited beyond hematopoietic malignancies in multiplexed single-cell applications. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/724135v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f36dbcorg.highwire.dtl.DTLVardef@a9799forg.highwire.dtl.DTLVardef@6fca94org.highwire.dtl.DTLVardef@15cc1f3_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights and main findingsO_LIIntroduces Split-flow, a modular Nextflow DSL2 pipeline for preprocessing of multiplexed single-cell multi-omics sequencing data from hematopoietic malignancy samples via a post hoc concordance-based decision framework. C_LIO_LIProvides practical guidance for the experimental design of multiplexed single-cell multi-omics experiments, including the recommendation to combine antibody-based hashing with a SNP genotype reference for orthogonal demultiplexing. C_LIO_LIReveals that SNP-based demultiplexing is more sensitive to sequencing depth than hash-based approaches, and that the combined strategy mitigates depth-dependent biases in cell-type recovery. C_LIO_LIDemonstrates that disagreement between demultiplexing methods contains structured diagnostic information about cell quality, with concordance categories reflecting genuine quality gradients in multiple myeloma CITE-seq samples. C_LIO_LIValidates the concordance framework using T cell receptor sequences as an orthogonal biological readout, with a 5.3-fold enrichment of confirmed public TCR sequences in the high-confidence stratum. C_LIO_LIApplies the preprocessing framework to AML patient samples across three assay types (snMultiome-seq, scRNA-seq, and scATAC-seq) and demonstrates that ATAC-based demultiplexing can resolve donor-assignment discordance. C_LI