학술논문
AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding
Document Type
Academic Journal
Author
Source
Genome Biology (Online Edition). February 1, 2024, Vol. 25 Issue 1
Subject
Language
English
Abstract
Author(s): Lingyan Zheng[sup.1,2], Shuiyang Shi[sup.1], Mingkun Lu[sup.1], Pan Fang[sup.2,3], Ziqi Pan[sup.1], Hongning Zhang[sup.1], Zhimeng Zhou[sup.1], Hanyu Zhang[sup.1], Minjie Mou[sup.1], Shijie Huang[sup.1], Lin Tao[sup.4], Weiqi Xia[sup.5], Honglin Li[sup.6], Zhenyu Zeng[sup.2,3], Shun [...]
Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO families containing few annotated proteins. Herein, an innovative strategy named AnnoPRO was therefore constructed by enabling sequence-based multi-scale protein representation, dual-path protein encoding using pre-training, and function annotation by long short-term memory-based decoding. A variety of case studies based on different benchmarks were conducted, which confirmed the superior performance of AnnoPRO among available methods. Source code and models have been made freely available at: https://github.com/idrblab/AnnoPRO and https://zenodo.org/records/10012272 Keywords: Protein function annotation, Long-tail problem, Protein representation, Pre-training, LSTM
Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO families containing few annotated proteins. Herein, an innovative strategy named AnnoPRO was therefore constructed by enabling sequence-based multi-scale protein representation, dual-path protein encoding using pre-training, and function annotation by long short-term memory-based decoding. A variety of case studies based on different benchmarks were conducted, which confirmed the superior performance of AnnoPRO among available methods. Source code and models have been made freely available at: https://github.com/idrblab/AnnoPRO and https://zenodo.org/records/10012272 Keywords: Protein function annotation, Long-tail problem, Protein representation, Pre-training, LSTM