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

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. 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.

Conceptualizing the Internet Compulsive-Buying Tendency: What We Know and Need to Know in the Context of the COVID-19 Pandemic

Over the past decade, new innovative products and services have been introduced into the marketplace using advanced technology. The enticement of new products lures consumers to buy compulsively. Because of the convenience and the characteristics of online shopping, it will increase the incidence of compulsive-buying behavior. Meanwhile, due to the fact that the COVID-19 pandemic is continuing to spread around the world, consumers may change their decision-making and behavior to shop online more frequently and intensively. The repetitive shopping online means more cardboard, delivery transportations, and vehicles and that more goods will be produced. It will result in an unfriendly result for the environment. Given the critical role of compulsive buying in the emerging Internet retail environment, it is necessary to develop and validate an instrument to measure the Internet compulsive-buying tendency (ICBT). Therefore, a rigorous measurement-scale-development procedure was applied to evaluate the initial 31 items. After two rounds of data collection and assessment, the final instrument contained 18 items that fall into four subconstructs: the tendency to spend online, feelings about online shopping and spending, dysfunctional online spending, and online post-purchase guilt. These factors can provide a basis for predicting tendencies toward Internet compulsive shopping and can be used to evaluate consumers’abnormal behavior in online-shopping circumstances.