Machine-learning-guided directed evolution for protein engineering

By Kevin Kaichuang Yang, Zachary Wu, Frances H. Arnold. 2018

993 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Generating functional protein variants with variational autoencoders (2020, methodology): “More generally, a model capable of guiding exploration 357 of distant regions of functional sequence space could be used to significantly improve the efficiency 358 of existing design approaches [50], or to constrain the search of sequence space for proteins with 359 desirable properties [33, 18].”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “To reduce the experimental effort, machine learning (ML) methods have been proposed to assist directed evolution and successfully engineered multiple important functional proteins [15–20] .”
Learning inverse folding from millions of predicted structures (2022, methodology): “Additionally, as an alternative to sequence generation models, model-guided algorithms design sequences based on predictive models as oracles (Yang et al., 2019; Angermueller et al., 2019; Brookes et al., 2019; Sinai et al., 2020).”
Multi-Scale Representation Learning on Proteins (2022, background): “predict properties of a candidate protein (Yang et al., 2019).”
Energy-based models for atomic-resolution protein conformations (2020, background): “In parallel, recent work proposes that generative models pre-trained on protein sequences can transfer knowledge to downstream supervised tasks (Bepler & Berger, 2019; Alley et al., 2019; Yang et al., 2019; Rives et al., 2019).”
Utilizing graph machine learning within drug discovery and development (2021, methodology): “coupled to ML systems for greater speed, affinity and further in silico selection [197, 198].”
Guided Generative Protein Design using Regularized Transformers (2022, methodology): “The ﬁrst of which is in-silico directed evolution (DE) approach taken from Yang et al. [2019], where a subset of positions are chosen to be iteratively optimized.”
Generative Models for Graph-Based Protein Design (2019, background): “Generative models for protein sequence and structure A number of works have explored the use of generative models for protein engineering and design [13].”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Machine learning methods can not only synthesize existing data into statistical models that generate novel proteins but also iteratively integrate experimental data to guide the protein design (157).”