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Computational scori...
Computational scoring and experimental evaluation of enzymes generated by neural networks
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Johnson, Sean R. (author)
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- Fu, Xiaozhi, 1990 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Viknander, Sandra, 1990 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Goldin, Clara, 1996 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Monaco, Sarah (author)
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- Zelezniak, Aleksej, 1984 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Vilniaus universitetas,Vilnius University
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- Yang, Kevin K. (author)
- Microsoft Research
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Nature Biotechnology. - 1087-0156 .- 1546-1696. ; In Press
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Abstract
Subject headings
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- In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model. Focusing on two enzyme families, we expressed and purified over 500 natural and generated sequences with 70–90% identity to the most similar natural sequences to benchmark computational metrics for predicting in vitro enzyme activity. Over three rounds of experiments, we developed a computational filter that improved the rate of experimental success by 50–150%. The proposed metrics and models will drive protein engineering research by serving as a benchmark for generative protein sequence models and helping to select active variants for experimental testing.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Publication and Content Type
- art (subject category)
- ref (subject category)
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