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 …
Machine learning-enabled retrobiosynthesis of molecules
Retrobiosynthesis provides an effective and sustainable approach to producing functional
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …
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 …
Learning protein fitness models from evolutionary and assay-labeled data
Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …
unlabeled, evolutionarily related sequences or variant sequences with experimentally …
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Abstract Machine learning has been increasingly used for protein engineering. However,
because the general sequence contexts they capture are not specific to the protein being …
because the general sequence contexts they capture are not specific to the protein being …
Self-play reinforcement learning guides protein engineering
Y Wang, H Tang, L Huang, L Pan, L Yang… - Nature Machine …, 2023 - nature.com
Designing protein sequences towards desired properties is a fundamental goal of protein
engineering, with applications in drug discovery and enzymatic engineering. Machine …
engineering, with applications in drug discovery and enzymatic engineering. Machine …
Combining chemistry and protein engineering for new-to-nature biocatalysis
Biocatalysis, the application of enzymes to solve synthetic problems of human import, has
blossomed into a powerful technology for chemical innovation. In the past decade, a …
blossomed into a powerful technology for chemical innovation. In the past decade, a …
Unsupervised evolution of protein and antibody complexes with a structure-informed language model
Large language models trained on sequence information alone can learn high-level
principles of protein design. However, beyond sequence, the three-dimensional structures of …
principles of protein design. However, beyond sequence, the three-dimensional structures of …
Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering
The effective design of combinatorial libraries to balance fitness and diversity facilitates the
engineering of useful enzyme functions, particularly those that are poorly characterized or …
engineering of useful enzyme functions, particularly those that are poorly characterized or …
Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments
Novel and improved biocatalysts are increasingly sourced from libraries via experimental
screening. The success of such campaigns is crucially dependent on the number of …
screening. The success of such campaigns is crucially dependent on the number of …