mAbs最新文献

Many complex models for antibody affinity prediction have been developed and successfully deployed. Recent results for T-cell receptor epitope prediction have shown, that even simple distance-based models can achieve a similar performance while requiring less parameters, being more easily interpretable and faster to compute. Encouraged by these results AbDist, a new distance-based model, was developed for antibody affinity prediction. It uses fragments around mutation sites to calculate distances between antibody sequences, demonstrating that a local environment alone suffices as an effective featurization. AbDist was used to perform classification and regression tasks on multiple disjunct public datasets. Its performance matches state-of-the-art machine-learning (ML) models. AbDist is interpretable, computationally efficient, and well suited for data-sparse, early-stage antibody engineering workflows, while sharing the limited out-of-distribution generalization common to current models. AbDist is available as an open-source, publicly accessible tool.

Antibody-drug conjugates (ADCs) and other biopharmaceuticals require robust analytical methods to assess biotransformation in biological matrices. Current approaches often require off-line enrichment and extensive chromatographic separation, limiting throughput and complicating data processing. We developed a native affinity liquid chromatography-mass spectrometry (aLC-MS) method using POROS CaptureSelect FcXL columns combined with optimized solvents and MS parameters for direct analysis (1D aLC-MS) of ADCs and other antibody-derived formats in complex sample matrices, such as serum. The method was evaluated using stability studies and concentration series in mouse serum. Direct analysis enabled accurate determination of drug-antibody ratio (DAR), drug-load distribution (DLD) and relative drug abundance across samples without chromatographic peak integration. Stability studies revealed distinct ADC biotransformation profiles in serum versus PBS, including maleimide hydrolysis and disulfide exchange at under-conjugated cysteine sites. The aLC-MS method achieved excellent linearity (R² = 0.99) over 125-2000 µg/mL in serum and demonstrated sensitivity to 31.25 µg/mL. This rapid, selective aLC-MS method enables high-throughput monitoring of ADC quality attributes in complex matrices with minimal sample preparation, supporting biopharmaceutical product development and bioanalysis applications. The method is exclusively based on MS results, which makes data processing and reporting fast and easy to automate.

Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns; however, the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed alpseq, an optimized, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. alpseq is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The alpseq software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. alpseq also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. alpseq thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. alspeq is available at https://github.com/kzeglinski/alpseq.

Controlling glycosylation, a critical quality attribute of biopharmaceuticals such as monoclonal antibodies, is essential, as it significantly influences biological activity and therapeutic efficacy. Although numerous studies have examined the impact of process parameters (PP, e.g. temperature, pH, dissolved oxygen) on glycosylation, the lack of standardized reporting makes cross-study comparisons challenging and prevents clear conclusions. Here, we systematically reviewed the literature and applied a normalized quantitative framework, the Glycan Indices approach, as a standardized quantitative criterion to evaluate the impact of process parameters on glycoform distribution in IgG-producing CHO cell systems objectively. This methodology enabled the integration and reinterpretation of large, heterogeneous datasets, validating some well-known patterns while providing novel perspectives about process parameters. Our analysis revealed that PP manipulations of pH, dissolved oxygen or CO₂ partial pressure rarely resulted in meaningful shifts in glycosylation, with changes <5% observed for galactose, fucose, or N-acetylneuraminic acid content. In contrast, for several cases temperature and osmolality changes notably affected galactosylation (>10%) and fucosylation (1-10%), variations that may have significant biological consequences. To our knowledge, this is the first comprehensive quantitative assessment of process parameters effects on glycosylation, showing that such influences are consistently limited, independent of CHO cell line or culture mode. Based in our observations we strongly recommend reporting both glycan distribution and glycan indices when performing glycan analysis. Dual reporting facilitates inter-study comparisons and prevents subtle shifts in sugar moieties from being masked by glycan redistribution.

Antibody therapeutics are a rapidly growing class of biopharmaceuticals, but concerns regarding potential developability issues persist. While complementarity-determining region (CDR) loops are imperative for antigen specificity and mutations are challenging, the framework regions can be exchanged to align with developability attributes such as aggregation, clearance, and viscosity, all governed by physicochemical characteristics. In this study, we systematically analyze the electrostatic and hydrophobic surface properties of germline-encoded antibody frameworks to assess their role in modulating Fv developability. Using structure prediction and surface patch analysis, we identify differences between kappa and lambda light-chain frameworks, characterize outlier germlines with extreme surface properties, and demonstrate using hydrophobic interaction chromatography and a heparin column that framework selection can compensate for CDR loop physicochemical characteristics. Our findings reveal that rational framework selection can serve as a systematic tool for optimizing antibody developability. This study provides a toolbox for antibody design, enhancing therapeutic candidate selection by leveraging inherent germline properties.

Computational prediction of the viscosity of therapeutic monoclonal antibodies (mAbs) at high concentration is highly desirable in the early discovery and development phases where the material needed for experimental determination is typically limited. Here, we present a unique coarse-grained (CG) simulation method that enables residue-level simulation of full-length antibodies with an elastic network, under simulated shearing force, to de novo predict viscosities of solutions of two distinct mAbs (an IgG1 and an IgG4), in the absence and presence of six excipients. Our results suggest the method can properly distinguish the viscosity profile of the two model mAbs, and directionally forecast viscosity change in response to added excipients. Furthermore, this CG modeling approach provides detailed protein-protein interaction mapping down to residue-level contacts, including contact lifetimes and nature of interactions, illuminating microscopic insights into the underlying molecular interactions. It serves as a valuable tool for viscosity prediction, mechanistic insights, and mitigation strategies.

Studying the interactions between antibodies and antigens is fundamental to the development of novel therapeutic biologics. Predictions of such interactions start with data collection. Though there exist reliable resources to identify antibody structures in the Protein Data Bank (PDB), such data still requires substantial processing to be usable in predictive tasks. Redundancy in sequences needs to be removed to avoid data leakages between train, test, and validation sets. Descriptors such as surface accessibility, secondary structure, and antibody region information need to be additionally annotated. Information on inter- and intra-molecular contacts, which is crucial to studying paratope/epitope information, needs to be collected. The specialized immunoglobulin format of Nanobodies® requires a separate dataset mirroring that of antibodies, given that their structure contains only a single VHH chain. Because antibody-antigen structures account for a small amount of all protein-protein contacts, having a molecular contact reference from other proteins is also desired. To address these issues, we introduce NAStructuralDB (https://naturalantibody.com/na-structural/), a dataset of processed structures of antibodies, Nanobodies®, proteins, and their complexes with molecular contact information and associated annotations. We use the opportunity of having collected the contact data to provide a reference of binding propensities of different residues across distinct contact types.

Protein language models (PLMs) provide a powerful framework for learning sequence - property relationships in antibodies. However, their performance and reliability in real-world industrial antibody discovery pipelines remain underexplored. Here, we systematically evaluate several state-of-the-art PLMs using internal datasets comprising antibody sequences and developability assay measurements from 33 historical therapeutic programs. The assays span three critical developability dimensions: polyspecificity reagent (PSR), hydrophobic interaction chromatography (HIC), and affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS). Across all assays, domain-adaptive fine-tuning of PLMs on internal antibody sequence data consistently improves predictive performance relative to pretrained representations alone. In addition, we assess sequence likelihoods derived from pretrained PLMs as unsupervised indicators of developability risk and analyze their strengths and limitations across assay types. Together, these results demonstrate that PLMs can provide robust and complementary signals for antibody developability assessment, supporting their practical use in early-stage candidate optimization and selection.

Chimeric antigen receptor (CAR)-modified T cells have garnered substantial attention due to their clinical success, culminating in six Food and Drug Administration-approved therapies for hematological malignancies. Notably, CD19-specific CAR T cell therapies have achieved remarkable clinical efficacy in treating B-cell malignancies, but these profound and durable responses are not observed in CAR T therapies targeting other indications, particularly solid tumors. Key design elements of CAR constructs - namely, antigen binding affinity and spacer length - play critical roles in determining T cell effector function and overall therapeutic effectiveness. Refining CAR designs may enhance T cell functionality, extend clinical application, and potentially apply CAR T cell therapies across a wider array of malignancies. In this study, affinity variant and spacer variant CARs targeting BCMA and DLL3 tumor antigens were evaluated using in vitro measurements of antigen-binding properties and effector function. Each panel of CARs spanned 2-3 logs of antigen binding affinity (BCMA: 181 pM KD to 74 nM KD, DLL3: 417 pM to 407 nM). Additionally, CAR T cells were challenged with tumor spheroids composed of BCMA⁺ H929 and DLL3⁺ SHP77 tumor cells. We show that for both tumor models, higher affinity CARs (KD stronger than approximately 100 nM) paired with an intermediate length spacer (IgG1 Fc, CH2-CH3, 230AA) elicited the strongest levels of tumor killing, CAR⁺ T cell expansion, and proinflammatory cytokine production. These CARs displayed the strongest cellular affinity when measured in a conjugation assay, suggesting a relationship between cellular affinity and T cell functional performance. This study highlights the critical role of CAR design in enhancing T cell functionality, demonstrating that high-affinity CARs combined with intermediate-length spacers yield superior performance in targeting BCMA and DLL3 antigens. This study provides a framework for rational CAR design, informing strategies to broaden the clinical utility of CAR T-cell therapies beyond hematologic cancers.