2018 年 11 巻 p. 14-23
Protein production in plants is a hot topic because there are many benefits relative to bacteria, yeasts, and animals, but the amount of protein expression in plants is less. It is argued that editing 5'UTRs increases the amount of translated proteins. However, obtaining such 5'UTRs is difficult due to the cost, time and effort required in experiments. To solve this, we predict the amount of translated proteins by machine learning. In this paper, we propose a method, named “R-STEINER, ” that generates 5'UTRs that increase the amount of proteins of a given gene. The proposed process involves building a model for predicting the amount of translated proteins, generating 5'UTRs, selecting them and increasing the proteins according to the model. This method enables us to obtain 5'UTRs that increase the amount of translated proteins without real synthesis experiments, resulting in reduced cost, time and effort. In our study, we built a prediction model for Oryza sativa and synthesized the 5'UTRs generated by R-STEINER. We confirmed that the model can predict the amount of translated proteins with a correlation coefficient of 0.89.