Proof of Concept

HEATMAP aims to enhance the production efficiency of spinosad by optimizing the metabolic pathways within \( Saccharopolyspora\:spinosa \). Our project intends to develop a deep learning model to predict the optimal temperatures for enzymes within \( Saccharopolyspora\:spinosa \) and employs ecGEM to identify key enzymes that impact yield, thereby pinpointing those restricted by temperature factors. And for these key enzymes, the project will use etcGEM model to propose optimization strategies and validate these through experimental verification.

For the exact input of each step in our project, we would suggest visiting our Human Practices section.

Studying the factors that affect the yield of bacterial products can effectively help us transform the strains to obtain the desired products. From our perspective, this kind of project has the most direct practical significance. At the same time, we believe that temperature is one of the potential influencing factors. Therefore, we think that we can utilize the data of Kcat and the optimal temperature of proteins to predict the enzyme activities at different temperatures and identify the impact of temperature on enzymes through deep learning, which ultimately helps us transform the strains. With such goals, we searched for appropriate prediction and modification objects, and found that \( Saccharopolyspora\:spinosa \), a kind of actinomycetes used in many research projects in our university, is able to produce valuable products. It is convenient for us to conduct experiments and obtain data, so we decided to carry out our project on this bacterium.

Our project intends to develop a deep learning model to predict the optimal temperatures for enzymes within \( Saccharopolyspora\:spinosa \) and employs ecGEM to identify key enzymes that impact yield, thereby pinpointing those restricted by temperature factors. Then we aim to enhance the production efficiency of spinosad by optimizing the metabolic pathways within \( Saccharopolyspora\:spinosa \).