AIMC Topic: Complementarity Determining Regions

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An iterative strategy to design 4-1BB agonist nanobodies de novo with generative AI models.

Scientific reports
The 4-1BB receptor, a key member of the tumor necrosis factor receptor (TNFR) family, represents a highly promising target for cancer immunotherapy. In this study, we developed a novel in silico pipeline to design VHH domain antibodies targeting 4-1B...

Tuning antibody stability and function by rational designs of framework mutations.

mAbs
Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches, however, often focus on the antibody complementarity-determining region (CDR) whilst ignoring the i...

Epi4Ab: a data-driven prediction model of conformational epitopes for specific antibody VH/VL families and CDRs sequences.

mAbs
Antibodies recognize antigens via complementary and structurally dependent mechanisms. Therefore, inclusion of antibody inputs is crucial for accurate epitope prediction. Given the limited availability of antibody-antigen complex structures, any epit...

High-fidelity in silico generation and augmentation of TCR repertoire data using generative adversarial networks.

Scientific reports
Engineered T-cell receptor (eTCR) systems rely on accurately generated T-cell receptor (TCR) sequences to enhance immunotherapy predictability and efficacy. The most variable and crucial part of the TCR receptor is the CDR3 sequence region. Current m...

Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.

PLoS computational biology
The design of proteins capable effectively binding to specific protein targets is crucial for developing therapies, diagnostics, and vaccine candidates for viral infections. Here, we introduce a complementarity-determining region (CDR) grafting appro...

T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.

Frontiers in immunology
BACKGROUND: T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their variations across digestive system cancers are not well understood. Characterizing TCR differences between colorectal cancer (CRC) and gastric cancer (GC)...

ParaAntiProt provides paratope prediction using antibody and protein language models.

Scientific reports
Efficiently predicting the paratope holds immense potential for enhancing antibody design, treating cancers and other serious diseases, and advancing personalized medicine. Although traditional methods are highly accurate, they are often time-consumi...

AI-driven antibody design with generative diffusion models: current insights and future directions.

Acta pharmacologica Sinica
Therapeutic antibodies are at the forefront of biotherapeutics, valued for their high target specificity and binding affinity. Despite their potential, optimizing antibodies for superior efficacy presents significant challenges in both monetary and t...

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

eLife
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D st...

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

PLoS computational biology
As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in pro...