mAbs最新文献

{"title":"Mimic antibodies: leveraging ligand mimicry for epitope-targeted antibody discovery.","authors":"Brennan Abanades, Joanan Lopez-Morales, Ivan Tanasijevic, Cornelia Wagner, Janina Speck, Sebastian Fenn, Hubert Kettenberger, Joachim Butzer, Sarah Mundigl, Karolis Martinkus, Andreas Loukas, Wing Ki Wong, Homa MohammadiPeyhani, Bruno E Correia, Anna Vangone","doi":"10.1080/19420862.2026.2719289","DOIUrl":"10.1080/19420862.2026.2719289","url":null,"abstract":"<p><p>Antibodies are renowned for their ability to bind diverse targets with high affinity and specificity, yet identifying binders with predefined epitope specificity remains a major challenge. In this study, we investigate the concept of mimic antibodies-antibodies that recapitulate the binding mode of a target's cognate ligand. Through a systematic analysis of the Protein Data Bank (PDB), we show that such mimicry is widespread and arises through diverse structural mechanisms, such as single-loop, multi-loop and scattered interaction mimicry. These findings indicate that protein interfaces impose strong constraints on binding, leading to convergent interaction solutions that can be independently discovered by antibodies. Building on these findings, we developed a ligand-guided strategy to mine immune repertoire data by selecting antibodies whose predicted binding interfaces mimic the interaction motif of a cognate ligand. Applied to the interaction between interleukin-18 (IL-18) and its receptor alpha (IL-18RA), mimicry-guided screening of a 20,000-sequence repertoire yielded 31 candidates, 11 of which (35% hit rate) bound the IL-18RA D3 domain, with eight reaching sub-nanomolar affinities that surpass the cognate ligand. Our findings establish mimic antibodies as a promising strategy for rational antibody selection, engineering, and design, with broad implications for therapeutic antibody development and drug discovery.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2719289"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13502006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148794983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing the extremes for antibody developability: hydrophobic and electrostatic germline framework signatures for CDR-loop compensation.","authors":"Vera A Spanke, Valentin J Egger-Hoerschinger, Clarissa A Seidler, Katharina B Kroell, Vincent Wieser, Sabine Imhof-Jung, Benjamin Weiche, Alexander Bujotzek, Guy Georges, Klaus R Liedl","doi":"10.1080/19420862.2026.2627669","DOIUrl":"10.1080/19420862.2026.2627669","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2627669"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12915817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146180714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NAStructuralDB : structural database to facilitate computational studies of molecular modeling and recognition of proteins with special focus on antibody-antigen interactions.","authors":"Dawid Chomicz, Paweł Dudzic, Sonia Wrobel, Tomasz Gawlowski, Samuel Demharter, Roberto Spreafico, Hervé Minoux, Andrew Phillips, Konrad Krawczyk","doi":"10.1080/19420862.2026.2630438","DOIUrl":"10.1080/19420862.2026.2630438","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2630438"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147378094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing human FcRn mouse models to improve pharmacokinetic evaluation of antibody drug candidates.","authors":"Gregory Christianson, Zachary M Howard, Samantha Kenney, Emily Lowell, Nijaguna Bethur, Derry Roopenian, Elena Gonzalo-Gil","doi":"10.1080/19420862.2026.2649990","DOIUrl":"10.1080/19420862.2026.2649990","url":null,"abstract":"<p><p>The use of animal models that can reliably predict drug performance in human patients is critical to antibody therapeutic development. Along with assessing toxicity and efficacy, determining the pharmacokinetic (PK) properties of therapeutics in Tg32 and Tg276 mice is essential to preclinical characterization. While Tg32 mice have been well established as indispensable in their ability to model the PK properties of antibody therapeutics, their intact immunity leaves them capable of mounting anti-drug antibody responses that interfere with PK interpretation. Here, we demonstrate the negative impact anti-drug responses can have on PK parameters derived from Tg32 mice, and provide strong evidence to support the use of immunodeficient Tg32 SCID mice as an equivalent means to model human PK. In addition, we investigate one possible cause for reduced FcRn function observed in Tg276 mice when compared to Tg32 mice in spite of evidence that their FcRn protein levels are actually higher. We also introduce NSG Tg32 mice and our attempts to block off-target binding of human IgGs to their high-affinity Fc gamma receptors, which failed to recover FcRn function similar to that observed from Tg32 mouse controls, dramatically limiting their utility for PK analysis. Taken together, our results provide a comparison of these preclinical animal models, so they can be used to improve human PK predictions of antibody therapeutic candidates in development.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2649990"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147513308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Catching enhanced flexibility in antibody-antigen recognition: critical evaluation of physics-based and AI-driven methods.","authors":"Honglin Xu, Ruyue Mi, Ganggang Bai, Yue Kang, Chuance Sun, Yanjing Wang, Changjun Chen, Buyong Ma","doi":"10.1080/19420862.2026.2743404","DOIUrl":"10.1080/19420862.2026.2743404","url":null,"abstract":"<p><p>Protein conformational dynamics often govern protein-protein interaction mechanisms and function, as flexibility enables adaptation to different environments. Yet, the extent to which current physics‑based and emerging AI‑driven methods can predict flexibility‑dependent physicochemical properties remains to be determined. Here, we studied the HyHEL10 (Hy10) antibody lineage binding to lysozymes with different conformational flexibility. Contrary to traditional models, the affinity-matured Hy10^4X antibody achieves high affinity by actively destabilizing its CDRH1 loop, enabling the capture of mobile antigen variants via entropy-buffered conformational selection. This structural plasticity presents a significant challenge to standard computational protocols. We found that the polarization effect is enhanced with increasing protein flexibility. Our benchmarking of MM/PBSA and AI-driven structure predictors reveals that both approaches initially fail to accurately rank affinities in more flexible systems, primarily due to sampling limitations and inaccurate structure prediction. However, the inclusion of structural templates in Chai-1 effectively mitigated these limitations, leading to enhanced correlation with experimental affinities. In the future, by coupling AI's quick screening with robust physics-based evaluations, we may have a potential pathway for in silico engineering of therapeutic antibodies against the most elusive, shapeshifting targets in the proteome.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2743404"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of protein language models for antibody developability prediction.","authors":"Samad Amini, Yimin Huang, Mark Julian, Christina Palmer, Simone Sciabola, Ye Wang","doi":"10.1080/19420862.2026.2647489","DOIUrl":"10.1080/19420862.2026.2647489","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2647489"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13007433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147486577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning predictions of IgG1 and IgG4 self-association and high-concentration solution properties.","authors":"Na-Young Kwon, Chloe N Brown, Hsin-Ting Chen, Steven R Cottle, Ronan M Kelly, Bryan E Jones, William F Weiss, Peter M Tessier","doi":"10.1080/19420862.2026.2663641","DOIUrl":"10.1080/19420862.2026.2663641","url":null,"abstract":"<p><p>To meet the widespread demand for subcutaneous delivery of antibody therapeutics, candidates with low viscosity, high solubility, and/or low aggregation propensity in concentrated formulations must be identified. Moreover, early identification of candidates with low self-association increases the likelihood of success at later stages of the development process. Here, we experimentally profile the self-association behavior of a panel of clinical-stage antibodies as a function of pH, excipient content, and antibody isotype. We find that acidic formulations (pH 5) with proline (200 mM) are most effective at suppressing self-association for both IgG1 and IgG4 variants. Moreover, our self-association measurements are correlated with antibody viscosity measurements and inversely correlated with antibody recovery after their concentration using membrane filters. Notably, we developed interpretable machine learning-based classifier and regressor models for predicting IgG1 and IgG4 self-association and demonstrated that they identify antibodies with favorable high-concentration properties. These findings are expected to improve the antibody development process by facilitating the identification of drug-like molecules during their discovery and optimization.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2663641"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13166204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of antibody non-specificity using protein language models and biophysical parameters.","authors":"Laila I Sakhnini, Ludovica Beltrame, Simone Fulle, Pietro Sormanni, Anette Henriksen, Nikolai Lorenzen, Michele Vendruscolo, Daniele Granata","doi":"10.1080/19420862.2026.2678000","DOIUrl":"10.1080/19420862.2026.2678000","url":null,"abstract":"<p><p>The development of therapeutic antibodies requires optimizing target binding affinity and pharmacodynamics, while ensuring high developability potential, including minimizing non-specific binding. In this study, we address this problem by predicting antibody non-specificity by two complementary approaches: (1) antibody sequence embeddings by protein language models (PLMs) and (2) a comprehensive set of sequence-based biophysical descriptors. We benchmark the PLM embeddings against interpretable, sequence-derived biophysical descriptors and use fragment-specific models (variable heavy (VH) and variable light (VL) chains, concatenated regions, and individual complementary-determining regions (CDRs) to identify region-level determinants of non-specificity in functional antibodies that bind defined targets. These models were trained on previously published human and mouse antibody data and tested on three public datasets. We show that non-specificity is best predicted from the VH domain and heavy-chain CDRs. These region-level analyses show that heavy-chain features, especially H-CDR3, dominate the predictive signal. The top performing PLM, a VH domain-based Evolutionary Scale Modeling 1 v LogisticReg model, resulted in 10-fold cross-validation accuracy of up to 71%. While predictive accuracy is comparable to classical machine-learning baselines, PLM-based embeddings provide sequence-context representations that yield consistent region-level attribution and provide complementarity to the classical models by enhancing the reliability of predicted non‑specificity scores when used in combination. Our biophysical descriptor-based analysis identified the isoelectric point as a key driver of non-specificity, consistent with previous reports. Our findings highlight the importance of biophysical properties in predicting antibody non-specificity and highlight the potential of PLMs for the development of antibody-based therapeutics. These conclusions are robust to alternative class definitions and are not driven by VH-VL mutational bias. We illustrate the generalizability and practical use of the PLM approach by extending it to therapeutic antibodies and nanobodies, providing a tool for early-stage developability assessment, and we make the models publicly available.</p>","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2678000"},"PeriodicalIF":7.9,"publicationDate":"2026-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13217913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148031930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1080/19420862.2026.2644040","DOIUrl":"10.1080/19420862.2026.2644040","url":null,"abstract":"","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2644040"},"PeriodicalIF":7.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12987510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147444112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1080/19420862.2026.2686538","DOIUrl":"10.1080/19420862.2026.2686538","url":null,"abstract":"","PeriodicalId":18206,"journal":{"name":"mAbs","volume":"18 1","pages":"2686538"},"PeriodicalIF":7.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13271264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148211849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}