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Antibodies

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AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Frontiers in immunology
INTRODUCTION: Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in a...

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...

BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.

mAbs
Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert k...

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...

Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation.

Analytica chimica acta
Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh a...

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.

Briefings in bioinformatics
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays,...

Challenges in antibody structure prediction.

mAbs
Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. I...

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...

Paragraph-antibody paratope prediction using graph neural networks with minimal feature vectors.

Bioinformatics (Oxford, England)
SUMMARY: The development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by impr...