AIMC Topic: Complementarity Determining Regions

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

Scientific reports May 26, 2025
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...
Humans Receptors, Antigen, T-Cell Complementarity Determining Regions Generative Adversarial Networks Neural Networks, Computer Computer Simulation
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Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.

PLoS computational biology Apr 21, 2025
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...
Humans Machine Learning SARS-CoV-2 Spike Glycoprotein, Coronavirus COVID-19 Epitopes Single-Domain Antibodies Molecular Dynamics Simulation Complementarity Determining Regions
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T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.

Frontiers in immunology Apr 8, 2025
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)...
Female Aged Humans Machine Learning Male Middle Aged Receptors, Antigen, T-Cell Stomach Neoplasms Colorectal Neoplasms Complementarity Determining Regions Neoplasm Staging Digestive System Neoplasms High-Throughput Nucleotide Sequencing
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ParaAntiProt provides paratope prediction using antibody and protein language models.

Scientific reports Nov 25, 2024
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...
Models, Molecular Antibodies Single-Domain Antibodies Complementarity Determining Regions Amino Acid Sequence Deep Learning Computational Biology Humans
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AI-driven antibody design with generative diffusion models: current insights and future directions.

Acta pharmacologica Sinica Sep 30, 2024
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...
Complementarity Determining Regions Humans Artificial Intelligence Drug Design Antibodies Animals
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Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

eLife Jun 26, 2024
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...
Single-Domain Antibodies Complementarity Determining Regions Humans Models, Molecular Protein Conformation Deep Learning Antibodies, Monoclonal
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Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

PLoS computational biology Jun 24, 2024
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...
Humans Neural Networks, Computer Deep Learning Computational Biology Algorithms Databases, Protein Models, Molecular Protein Conformation Complementarity Determining Regions Antibodies
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Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms.

Frontiers in immunology Feb 28, 2024
Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conforma...
Models, Molecular Antibodies Deep Learning Protein Conformation Complementarity Determining Regions
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.

Nature communications Apr 25, 2023
Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic rec...
Complementarity Determining Regions Deep Learning Protein Conformation Antigens Antibodies
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Computational and artificial intelligence-based methods for antibody development.

Trends in pharmacological sciences Jan 18, 2023
Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has signi...
Machine Learning Complementarity Determining Regions Humans Antibodies Artificial Intelligence
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