Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework  

作　　者：Zhenkun Shi Rui Deng Qianqian Yuan Zhitao Mao Ruoyu Wang Haoran Li Xiaoping Liao Hongwu Ma 

机构地区：[1]Biodesign Center,Key Laboratory of Engineering Biology for Low-carbon Manufacturing,Tianjin Institute of Industrial Biotechnology,Chinese Academy of Sciences,300308,Tianjin,China [2]National Center of Technology Innovation for Synthetic Biology,300308,Tianjin,China [3]College of Biotechnology,Tianjin University of Science&Technology,Tianjin,China [4]Haihe Laboratory of Synthetic Biology,300308.Tianjin,China

出　　处：《Research》2023年第3期117-128,共12页研究（英文）

基　　金：the National Key Research and Development Program of China(2020YFA0908300);the National Natural Science Foundation of China(32201242);the Youth Innovation Promotion Association CAS,Innovation fund of Haihe Laboratory of Synthetic Biology(22HHSWSS00021);Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(TSBICIP-PTJS-001,TSBICIP-CXRC-018,and TSBICIP-PTJJ-007);the China Postdoctoral Science Foundation(2022M713328).

摘　　要：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 protein sequences.However,the prediction performance(accuracy,recall,and precision),usability,and efficiency of existing methods decreased seriously when dealing with recently discovered proteins,thus still having much room to be improved.Here,we report HDMLF,a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques.HDMLF is composed of an embedding core and a learning core;the embedding core adopts the latest protein language model for protein sequence embedding,and the learning core conducts the EC number prediction.Specifically,HDMLF is designed on the basis of a gated recurrent unit framework to perform EC number prediction in the multi-objective hierarchy,multitasking manner.Additionally,we introduced an attention layer to optimize the EC prediction and employed a greedy strategy to integrate and fine-tune the final model.Comparative analyses against 4 representative methods demonstrate that HDMLF stably delivers the highest performance,which improves accuracy and F1 score by 60%and 40%over the state of the art,respectively.An additional case study of tyrB predicted to compensate for the loss of aspartate aminotransferase aspC,as reported in a previous experimental study,shows that our model can also be used to uncover the enzyme promiscuity.Finally,we established a web platform,namely,ECRECer(https://ecrecer.biodesign.ac.cn),using an entirely could-based serverless architecture and provided an offline bundle to improve usability.

关 键 词：server PREDICTION entirely 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]