  Enzyme Commission (EC) numbers, which associate a protein sequence with the
biochemical reactions it catalyzes, are essential for the accurate
understanding of enzyme functions and cellular metabolism. Many ab-initio
computational approaches were proposed to predict EC numbers for given input
sequences directly. However, the prediction performance (accuracy, recall,
precision), usability, and efficiency of existing methods still have much room
to be improved. Here, we report ECRECer, a cloud platform for accurately
predicting EC numbers based on novel deep learning techniques. To build
ECRECer, we evaluate different protein representation methods and adopt a
protein language model for protein sequence embedding. After embedding, we
propose a multi-agent hierarchy deep learning-based framework to learn the
proposed tasks in a multi-task manner. Specifically, we used an extreme
multi-label classifier to perform the EC prediction and employed a greedy
strategy to integrate and fine-tune the final model. Comparative analyses
against four representative methods demonstrate that ECRECer delivers the
highest performance, which improves accuracy and F1 score by 70% and 20% over
the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous
enzymes in the Swiss-Prot database with incomplete EC numbers to their full
fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-),
ECRECer annotated it with "1.14.11.38", which supported by further protein
structure analysis based on AlphaFold2. Finally, we established a webserver
(https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve
usability.
