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Complementarity Determining Regions

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Single T Cell Sequencing Demonstrates the Functional Role of TCR Pairing in Cell Lineage and Antigen Specificity.

Frontiers in immunology
Although structural studies of individual T cell receptors (TCRs) have revealed important roles for both the α and β chain in directing MHC and antigen recognition, repertoire-level immunogenomic analyses have historically examined the β chain alone....

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Advances in experimental medicine and biology
Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from g...

Antibody complementarity determining region design using high-capacity machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomize...

Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Bioinformatics (Oxford, England)
MOTIVATION: Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining l...

Use of machine learning to identify a T cell response to SARS-CoV-2.

Cell reports. Medicine
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease ...

NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.

Frontiers in immunology
Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing ne...

Predicting antibody binders and generating synthetic antibodies using deep learning.

mAbs
The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning...

Heavy chain sequence-based classifier for the specificity of human antibodies.

Briefings in bioinformatics
Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune rep...

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

Nature communications
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impede...