AIMC Topic: Complementarity Determining Regions

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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
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...

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.

Nature communications
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...

Computational and artificial intelligence-based methods for antibody development.

Trends in pharmacological sciences
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...

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...

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...

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 ...

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....

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method.

mAbs
Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the ...

AI-augmented physics-based docking for antibody-antigen complex prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high...