Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Computational and artificial intelligence-based methods for antibody development
Due to their high target specificity and binding affinity, therapeutic antibodies are currently
the largest class of biotherapeutics. The traditional largely empirical antibody development …
the largest class of biotherapeutics. The traditional largely empirical antibody development …
Efficient evolution of human antibodies from general protein language models
Natural evolution must explore a vast landscape of possible sequences for desirable yet
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
Deep-learning-enabled protein–protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution
Host–pathogen interactions and pathogen evolution are underpinned by protein–protein
interactions between viral and host proteins. An understanding of how viral variants affect …
interactions between viral and host proteins. An understanding of how viral variants affect …
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model
Artificial Intelligence (AI) techniques have made great advances in assisting antibody
design. However, antibody design still heavily relies on isolating antigen-specific antibodies …
design. However, antibody design still heavily relies on isolating antigen-specific antibodies …
Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
Therapeutic antibodies are an important and rapidly growing drug modality. However, the
design and discovery of early-stage antibody therapeutics remain a time and cost-intensive …
design and discovery of early-stage antibody therapeutics remain a time and cost-intensive …
Conditional antibody design as 3d equivariant graph translation
Antibody design is valuable for therapeutic usage and biological research. Existing deep-
learning-based methods encounter several key issues: 1) incomplete context for …
learning-based methods encounter several key issues: 1) incomplete context for …
Recent advances in predicting and modeling protein–protein interactions
Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can
cause disease. With recent breakthroughs in structure prediction and a deluge of genomic …
cause disease. With recent breakthroughs in structure prediction and a deluge of genomic …
Proximal exploration for model-guided protein sequence design
Designing protein sequences with a particular biological function is a long-lasting challenge
for protein engineering. Recent advances in machine-learning-guided approaches focus on …
for protein engineering. Recent advances in machine-learning-guided approaches focus on …