Monoclonal and Polyclonal Antibodies Research

DoggifAI translates non-canine antibody sequences into canine ones using a transformer model, generating species-appropriate framework regions and preserving binding specificity.

HyperBind2 improves antibody discovery through multi-shot learning, achieving higher accuracy with iterative lab-AI feedback cycles and reducing experimental burden.

This study shows that IgG's anti-inflammatory activity depends on the interaction between Fc{gamma}RIIB and DC-SIGN, and engineering sialylated IgG1 enhances its potency in treating autoimmune diseases.

This study develops DARPin T cell engagers that specifically target HLA/peptide complexes on tumor cells, showing high affinity and specificity through in vitro selection and structural analysis.

The paper presents the cryo-EM structure of Xenopus IgX, revealing a hexameric assembly that informs strategies for engineering human IgM and IgG hexamers with therapeutic applications.

This paper designs antibodies de novo with atomic precision using RFdiffusion and yeast display, validated by cryo-EM and biophysical methods.

This paper evaluates two bispecific CD20xCD40 antibodies, showing they modulate multiple immune cell types and achieve deep immunomodulation without safety issues.

The paper develops a computational platform to stabilize human VH domains for creating multispecific biologics with conditional activity.

This paper evaluates a novel Fc-optimized antibody drug conjugate targeting CD7, showing potent anti-leukemia activity in preclinical T-ALL models.

This paper introduces TFDesign-sdAb, a deep learning framework for designing therapeutic single-domain antibodies with tailored functionalities, achieving high success rates in generating Protein A-binding sdAbs.

This paper shows that BLOSUM similarity scores correlate with antibody binding affinity, suggesting generative models implicitly learn evolutionary priors.

ALLM-Ab uses fine-tuned protein language models and active learning to optimize antibody sequences for both high binding affinity and developability.

This study assesses the effects of deglycosylation on the stability and function of an anti-cocaine mAb using DSF and fluorescence labeling.

This study uses deep learning to design anti-VEGF nanobodies with improved affinity and stability through structure-guided mutational modeling and a sequence-based melting temperature predictor.

ANABAG provides a curated dataset of antibody-antigen complexes with structural and physicochemical features to improve Ab-Ag modeling and antibody engineering.

This paper introduces peleke-1, a suite of protein language models fine-tuned for generating targeted antibody sequences from antigen inputs.

This paper describes a method to discover protein-small molecule hybrids using pharmacophore-driven antibody discovery on yeast, showing that screening outcomes are pharmacophore-dependent.

The paper describes a system for producing glycosylated anti-mouse nanobodies in Pichia pastoris, showing they retain high affinity and functionality with posttranslational modifications.

The paper curates a database of therapeutic antibodies linked to clinical trial outcomes, identifies poor performance of existing tools in predicting immunogenicity, and highlights the need for systems immunology approaches.

This paper presents an AI framework to design common light chains for bispecific antibodies, reducing experimental screening and enabling production-ready antibodies for 7 out of 10 targets.

Property Enhancer optimizes antibody properties using a data-efficient framework, achieving significant affinity gains in both in silico and in vitro tests.

This paper introduces a yeast mating-based platform for generating ultra-large antibody libraries and demonstrates its ability to discover high-affinity binders.

This study isolates human monoclonal antibodies against hypervirulent, pandrug-resistant Klebsiella pneumoniae and identifies capsule-specific antibodies as protective against bloodstream infection.

This study develops antibody-drug conjugates targeting CD97, an aGPCR overexpressed in cancer, showing efficacy against AML and GBM cells with minimal off-target toxicity.

The paper develops a pharmacokinetic/pharmacodynamic model to evaluate dosing strategies for trimer-based bispecific antibodies, finding that the current recommended dose is optimal for a broad population and that patient stratification based on biomarkers can improve efficacy.

This study maps the paratope residues of tilvestamab, an anti-AXL antibody, identifying twelve critical residues and Glu2 on AXL essential for binding.

This study designs factorizable antibody libraries using neural networks, finding they outperform random and enumerated libraries in pMHC binding.

The paper reports cryo-EM structures of avian IgA and compares them to mammalian IgA, revealing structural differences that may affect mucosal immune responses.

Germinal is a generative pipeline that designs functional antibodies with nanomolar binding affinities using minimal experimental testing.

This study shows that rabbit kappa light chain allotypes affect chimeric antibody expression and function, with K1-b9 maintaining robust performance due to a stable disulfide bond.