Research PaperNo Access

ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data

International Program of Health Informatics and Management, College of Management, Chang Gung University, No. 259, Wenhua 1st Road Guishan District, Taoyuan City 33302, Taiwan

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Chong-Heng Weng

Department of Computer Science and Information Engineering, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan City 320317, Taiwan

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Torbjörn E. M. Nordling

Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Road, Tainan City 701, Taiwan

Department of Applied Physics and Electronics, Umeå University 90187 Umeå, Sweden

E-mail Address: torbjorn.nordling@nordlinglab.org

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, and

Yi-Fan Liou

Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Road, Tainan City 701, Taiwan

Department of Virtual-Reality Interaction with Artificial Intelligence Technology, Coretronic Reality Incorporation, No. 5, Wenhua Rd., Hukou Township, Hsinchu County 303, Taiwan

E-mail Address: yifanliou@gmail.com

Corresponding authors.

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https://doi.org/10.1142/S0219720023500087Cited by:0 (Source: Crossref)

This article is part of the issue:

Selected Papers from 31st International Conference on Genome Informatics and 5th ISCB-Asia Conference (GIW XXXI/ISCB-Asia V)

Abstract

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.

Availability and implementation: The source code of ThermalProGAN can be freely accessed at https://github.com/markliou/ThermalProGAN/ with an MIT license.

The website is https://thermalprogan.markliou.tw:433.

Supplementary information: Supplementary data are available on Github.

Keywords:

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