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ThermalProGAN :
ThermalProGAN : a sequence-based thermally stable protein generator trained using unpaired data
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- Huang, Hui-Ling (författare)
- International Program of Health Informatics and Management, College of Management, Chang Gung University, Taoyuan City, Taiwan
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- Weng, Chong-Heng (författare)
- Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan
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- Nordling, Torbjörn E. M. (författare)
- Umeå universitet,Institutionen för tillämpad fysik och elektronik,Department of Mechanical Engineering, National Cheng Kung University, Taiwan
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- Liou, Yi-Fan (författare)
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Road, Taiwan; Department of Virtual-Reality Interaction with Artificial Intelligence Technology, Coretronic Reality Incorporation, Hsinchu County, Taiwan
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(creator_code:org_t)
- World Scientific, 2023
- 2023
- Engelska.
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Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific. - 0219-7200 .- 1757-6334. ; 21:1
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Motivation: The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI) to build ThermalProGAN for generating thermally stable proteins based on sequence information.Results: The ThermalProGAN can strongly mutate the input sequence with a median number of 32 residues. A known normal protein, 1RG0, was used to generate a thermally stable form by mutating 51 residues. After superimposing the two structures, high similarity is shown, indicating that the basic function would be conserved. Eighty four molecular dynamics simulation results of 1RG0 and the COVID-19 vaccine candidates with a total simulation time of 840ns indicate that the thermal stability increased.Conclusion: This proof of concept demonstrated that transfer of a desired protein property from one set of proteins is feasible.
Ämnesord
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- CycleGAN
- generative adversarial neural network
- Protein synthesis
- thermal stability
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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