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

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

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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 infor-mation 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 embed-ding]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 compo-nent 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.NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.

Protein function predictionWeb serviceProtein language modelLearning to rankLarge-scale multi-label learning

Shaojun Wang、Ronghui You、Yunjia Liu、Yi Xiong、Shanfeng Zhu

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Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science,Fudan University,Shanghai 200433,China

School of Life Sciences,Fudan University,Shanghai 200433,China

Department of Bioinformatics and Biostatistics,Shanghai Jiao Tong University,Shanghai 200240,China

Shanghai Artificial Intelligence Laboratory,Shanghai 200232,China

Shanghai Qi Zhi Institute,Shanghai 200030,China

MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,Fudan University,Shanghai 200433,China

Shanghai Key Laboratory of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm,Fudan University,Shanghai 200433,China

Zhangjiang Fudan International In

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShanghai Municipal Science and Technology Major ProjectZJ LabShanghai Research Center for Brain Science and Brain-Inspired Intelligence Technology111 ProjectShanghai Municipal Science and Technology Major ProjectInformation Technology Facility,CAS-MPG Partner Institute for Computational BiologyShanghai Institute for Biological Sciences,Chinese Academy of SciencesNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaBeijing Academy of Artificial Intelligence(BAAI)

61872094622721052018SHZDZX01B180152017SHZDZX016183201962172274

2023

10.1016/j.gpb.2023.04.001

基因组蛋白质组与生物信息学报(英文版)
中国科学院北京基因组研究所

基因组蛋白质组与生物信息学报(英文版)

CSTPCDCSCD
影响因子：0.495
ISSN：1672-0229
年,卷(期)：2023.21(2)
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