Publications

@article{Huang2023ThermalProGAN,

title = {ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data},

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

url = {https://www.worldscientific.com/doi/10.1142/S0219720023500087},

doi = {10.1142/S0219720023500087},

issn = {0219-7200},

year  = {2023},

date = {2023-02-01},

journal = {Journal of Bioinformatics and Computational Biology},

volume = {21},

number = {01},

pages = {2350008},

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 840[Formula: see text]ns 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.},

keywords = {CycleGAN, Generative adversarial neural network, Protein synthesis, Thermal stability},

pubstate = {published},

tppubtype = {article}

}