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We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence.
Design by directed evolution
Frances H Arnold · 1998
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Exploring protein fitness landscapes by directed evolution
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Local fitness landscape of the green fluorescent protein
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Adaptation in protein fitness landscapes is facilitated by indirect paths
Nicholas C Wu, Lei Dai, C Anders Olson, James O Lloyd-Smith, and Ren Sun · 2016
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Mutation effects predicted from sequence co-variation
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Conditioning by adaptive sampling for robust design
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Machine-learning-guided directed evolution for protein engineering
Kevin K Yang, Zachary Wu, and Frances H Arnold · 2019
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A combinatorially complete epistatic fitness landscape in an enzyme active site
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Protein design by directed evolution guided by large language models
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Efficient evolutionary search over chemical space with large language models
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Neil Thomas, Atish Agarwala, David Belanger, Yun S Song, and Lucy J Colwell · 2022
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Improving protein optimization with smoothed fitness landscapes
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Evolutionary-scale prediction of atomic-level protein structure with a language model
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Active learning-assisted directed evolution
Jason Yang, Ravi G Lal, James C Bowden, Raul Astudillo, Mikhail A Hameedi, Sukhvinder Kaur, Matthew Hill, Yisong Yue, and Frances H Arnold · 2024
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Protein language models learn evolutionary statistics of interacting sequence motifs
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