AIMC Topic: Antigens

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Leveraging artificial intelligence in vaccine development: A narrative review.

Journal of microbiological methods
Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficie...

Machine-learning-based structural analysis of interactions between antibodies and antigens.

Bio Systems
Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many dise...

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

International journal of molecular sciences
Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such ...

Accurate structure prediction of biomolecular interactions with AlphaFold 3.

Nature
The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. Here we describe our AlphaFold 3 model with a substantially...

Beyond Natural Immune Repertoires: Synthetic Antibodies.

Cold Spring Harbor protocols
Synthetic antibody libraries, in which the antigen-binding sites are precisely designed, offer unparalleled precision in antibody engineering, exceeding the potential of natural immune repertoires and constituting a novel generation of research tools...

AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge.

Proteins
Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitatio...

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Science advances
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for pr...

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...

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...

T cell immune responses deciphered.

Science (New York, N.Y.)
A machine-learning approach reveals antigen encoding that predicts T cell responses.