NetGO 3.0:Protein Language Model Improves Large-scale Functional Annotations  

作　　者：Shaojun Wang Ronghui You Yunjia Liu Yi Xiong Shanfeng Zhu 

机构地区：[1]Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science,Fudan University,Shanghai 200433,China [2]School of Life Sciences,Fudan University,Shanghai 200433,China [3]Department of Bioinformatics and Biostatistics,Shanghai Jiao Tong University,Shanghai 200240,China [4]Shanghai Artificial Intelligence Laboratory,Shanghai 200232,China [5]Shanghai Qi Zhi Institute,Shanghai 200030,China [6]MOEKey Laboratoryof Computational Neuroscience and Brain-Inspired Intelligence Fudan University,Shanghai 200433,China [7]Shanghai Key Laboratory of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm,Fudan University,Shanghai 200433,China [8]Zhangjiang Fudan International Innovation Center,Shanghai 200433,China

出　　处：《Genomics, Proteomics & Bioinformatics》2023年第2期349-358,共10页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.61872094 and 62272105);the Shanghai Municipal Science and Technology Major Project(Grant No.2018SHZDZX01);the ZJ Lab,and the Shanghai Research Center for Brain Science and Brain-Inspired Intelligence Technology.Shaojun Wang and Ronghui You have been supported by the lll Project(Grant No.B18015);the Shanghai Municipal Science and Technology Major Project(Grant No.2017SHZDZX01);the Information Technology Facility,CAS-MPG Partner Institute for Computational Biology,Shanghai Institute for Biological Sciences,Chinese Academy of Sciences.Yi Xiong has been supported by the National Natural Science Foundation of China(Grant Nos.61832019 and 62172274).

摘　　要：As one of the state-of-the-art automated function prediction(AFP)methods,NetGO 2.0 integrates multi-source information to improve the performance.However,it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins.Recently,protein language models have been proposed to learn informative representations[e.g.,Evolutionary Scale Modeling(ESM)-1b embedding] from protein sequences based on self-supervision.Here,we represented each protein by ESM-1b and used logistic regression(LR)to train a new model,LR-ESM,for AFP.The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0.Therefore,by incorporating LR-ESM into NetGO 2.0,we developed NetGO 3.0 to improve the performance of AFP extensively.

关 键 词：Protein function prediction Web service Protein language model Learning to rank Large-scale multi-label learning 

分 类 号：Q811.4[生物学—生物工程]