Publications

@misc{Huang2022ThermalProGAN,

title = {ThermalProGAN: a sequence-based thermally stable protein generator trained using un-paired data},

author = {Hui-Ling Huang and Chong-Heng Weng and Torbjörn E. M. Nordling and Yi-Fan Liou},

url = {https://www.iscb.org/giw-iscb-asia2022},

year  = {2022},

date = {2022-12-01},

booktitle = {GIW XXXI/ISCB-Asia V},

publisher = {International Society for Computational Biology},

address = {Tainan, Taiwan},

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. 84 molecular dynamics simulation results of 1RG0 and the Covid-19 vaccine candidates with a total simulation time of 840 ns indicate that the thermal stability increased. Conclusion: This proof of concept demonstrated that the 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.},

howpublished = {31st International Conference on Genome Informatics (GIW) and 5th International Society for Computational Biology (ISCB) Asia conference (GIW XXXI/ISCB-Asia V)},

keywords = {Generative adversial network, Protein, synthetic biology},

pubstate = {published},

tppubtype = {misc}

}