Monoclonal and Polyclonal Antibodies Research

This study determines the structural basis of antigen recognition by two mAbs targeting PfCSP, revealing conserved and distinct binding modes and their implications for antibody function.

PANCS-spec-Binders rapidly identifies binders with defined specificity by combining selection and counterselection to target specific isoforms or epitopes.

This study uses deep sequencing and machine learning to optimize antibody discovery by analyzing training data and search space design for high-affinity VHHs.

This paper predicts antibody non-specificity using protein language models and biophysical parameters, showing that heavy variable domains and isoelectric point are key predictors.

This paper introduces an integer linear programming method for optimizing antibody libraries by balancing diversity and affinity, showing improved performance over baseline designs.

This paper develops multivalent nanobodies that neutralize multiple S. aureus toxins, showing potent activity in murine models of sepsis and pneumonia.

The paper introduces AbDirect, a proteomics-based method for antibody discovery that identifies potent neutralizing antibodies missed by single-cell BCR sequencing in COVID-19 plasma.

H3BERTa is a transformer model trained on CDR-H3 sequences to analyze antibody repertoires, capturing immunological signatures and supporting classifier development for broadly neutralizing antibodies.

The paper uses an autoresearch loop to develop an interpretable filter rule for antibody binder classification, achieving high ROC-AUC without training data.

This paper develops COX-2-targeted nanobodies for enhanced endoscopic imaging of colorectal adenomas, showing tumor-specific accumulation in vivo.

This paper determines the structure of mouse FcγRIV and uses it to engineer a human Fc variant that enhances cross-species FcγR recognition.

This paper develops an ELISA-based pulldown method to isolate functional antigen-specific antibodies from serum samples, demonstrating high specificity and sensitivity for detecting neutralising antibodies against influenza haemagglutinin.

This study engineers B cells to express fully customizable antibodies with enhanced Fc functions through genome editing of the immunoglobulin Heavy chain locus.

This paper develops IL-4-specific affibodies to control cytokine delivery and bioactivity, showing reduced IL-4 release and partial signaling inhibition in macrophages.

This paper describes the engineering of a bispecific fusion protein combining CTLA-4 and IL-10 to modulate immune checkpoints and cytokine receptors, showing T cell-inhibitory effects in vitro.

The paper identifies multi-factorial regulatory networks governing placental antibody transfer, showing how Fc receptor expression, IgG subclass, and clinical covariates interact to influence transfer efficiency.

PRISM improves antibody language modeling by distinguishing germline and non-germline residues, achieving better binding affinity correlation and controllable generation.

This paper describes a nanobody-based method for targeting diverse Acinetobacter baumannii isolates and related species, demonstrating its effectiveness in capturing bacteria from complex mixtures.

The paper designs a biobetter version of cetuximab with enhanced EGFR affinity using ConvergeAB, showing improved binding without compromising developability.

This paper develops MINDS, a multispecific nanobody-Fc platform that co-depletes membrane receptors and delivers diverse payloads, showing improved antitumor activity in cancer models.

The paper identifies structurally promiscuous IgA1 clones in human serum that exist as both monomers and J-chain-coupled dimers, challenging the view that these forms are clonally distinct.

This paper introduces a method to map epitopes at the single amino acid level by screening antibody drugs against a mutationally-scanned target protein library on a biosensor chip, identifying critical residues for binding.

Origin-1 generates high-confidence, specific antibodies against zero-prior epitopes through generative AI, validated with biophysical assays and cryo-EM.

Aiki-GeNano improves nanobody design by integrating developability signals into training, enhancing thermal stability and reducing liabilities while preserving humanness.

This study identifies tumor-binding autoantibodies in ovarian cancer patients that fail to engage Fc{gamma}RIIIa, impairing antibody-dependent cellular cytotoxicity.

This study uses AI to design high-affinity anti-GIPR antibodies that induce sustained weight loss in obese mice without rebound.

The paper uses high-throughput antibody variant analysis and AI models to optimize antibody productivity by navigating fitness landscapes and identifying structurally compatible CDR variants.

This paper develops a humanized anti-fibrotic antibody with a collagen-binding peptide to enhance targeting of fibrotic tissues and reduce post-traumatic scarring.

This study evaluates MoE architectures for antibody language models, finding token-choice routing improves performance by specializing in CDRH3 residues and enabling efficient pre-training.

This paper uses AI to design protein masks that control TNF-family ligand assembly and activity, enabling conditional activation and improved therapeutic applications.