1
|
Chen Z, Luo Q. DualNetGO: a dual network model for protein function prediction via effective feature selection. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae437. [PMID: 38963311 DOI: 10.1093/bioinformatics/btae437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 06/05/2024] [Accepted: 07/03/2024] [Indexed: 07/05/2024]
Abstract
MOTIVATION Protein-protein interaction (PPI) networks are crucial for automatically annotating protein functions. As multiple PPI networks exist for the same set of proteins that capture properties from different aspects, it is a challenging task to effectively utilize these heterogeneous networks. Recently, several deep learning models have combined PPI networks from all evidence, or concatenated all graph embeddings for protein function prediction. However, the lack of a judicious selection procedure prevents the effective harness of information from different PPI networks, as these networks vary in densities, structures, and noise levels. Consequently, combining protein features indiscriminately could increase the noise level, leading to decreased model performance. RESULTS We develop DualNetGO, a dual-network model comprised of a Classifier and a Selector, to predict protein functions by effectively selecting features from different sources including graph embeddings of PPI networks, protein domain, and subcellular location information. Evaluation of DualNetGO on human and mouse datasets in comparison with other network-based models shows at least 4.5%, 6.2%, and 14.2% improvement on Fmax in BP, MF, and CC gene ontology categories, respectively, for human, and 3.3%, 10.6%, and 7.7% improvement on Fmax for mouse. We demonstrate the generalization capability of our model by training and testing on the CAFA3 data, and show its versatility by incorporating Esm2 embeddings. We further show that our model is insensitive to the choice of graph embedding method and is time- and memory-saving. These results demonstrate that combining a subset of features including PPI networks and protein attributes selected by our model is more effective in utilizing PPI network information than only using one kind of or concatenating graph embeddings from all kinds of PPI networks. AVAILABILITY AND IMPLEMENTATION The source code of DualNetGO and some of the experiment data are available at: https://github.com/georgedashen/DualNetGO.
Collapse
Affiliation(s)
- Zhuoyang Chen
- Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, 511400, China
| | - Qiong Luo
- Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, 511400, China
- HKUST, Hong Kong SAR, China
| |
Collapse
|
2
|
Lin B, Luo X, Liu Y, Jin X. A comprehensive review and comparison of existing computational methods for protein function prediction. Brief Bioinform 2024; 25:bbae289. [PMID: 39003530 PMCID: PMC11246557 DOI: 10.1093/bib/bbae289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/18/2024] [Indexed: 07/15/2024] Open
Abstract
Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.
Collapse
Affiliation(s)
- Baohui Lin
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong 518118, China
| | - Xiaoling Luo
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, Guangdong, China
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518061, China
| | - Yumeng Liu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong 518118, China
| | - Xiaopeng Jin
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong 518118, China
| |
Collapse
|
3
|
Zheng L, Shi S, Lu M, Fang P, Pan Z, Zhang H, Zhou Z, Zhang H, Mou M, Huang S, Tao L, Xia W, Li H, Zeng Z, Zhang S, Chen Y, Li Z, Zhu F. AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding. Genome Biol 2024; 25:41. [PMID: 38303023 PMCID: PMC10832132 DOI: 10.1186/s13059-024-03166-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Pan Fang
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Zhimeng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Weiqi Xia
- Pharmaceutical Department, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zhenyu Zeng
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shun Zhang
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
| | - Zhaorong Li
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
| |
Collapse
|
4
|
Yan K, Feng J, Huang J, Wu H. iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers. Brief Bioinform 2023:bbad251. [PMID: 37405873 DOI: 10.1093/bib/bbad251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 07/07/2023] Open
Abstract
Nucleic acid-binding proteins are proteins that interact with DNA and RNA to regulate gene expression and transcriptional control. The pathogenesis of many human diseases is related to abnormal gene expression. Therefore, recognizing nucleic acid-binding proteins accurately and efficiently has important implications for disease research. To address this question, some scientists have proposed the method of using sequence information to identify nucleic acid-binding proteins. However, different types of nucleic acid-binding proteins have different subfunctions, and these methods ignore their internal differences, so the performance of the predictor can be further improved. In this study, we proposed a new method, called iDRPro-SC, to predict the type of nucleic acid-binding proteins based on the sequence information. iDRPro-SC considers the internal differences of nucleic acid-binding proteins and combines their subfunctions to build a complete dataset. Additionally, we used an ensemble learning to characterize and predict nucleic acid-binding proteins. The results of the test dataset showed that iDRPro-SC achieved the best prediction performance and was superior to the other existing nucleic acid-binding protein prediction methods. We have established a web server that can be accessed online: http://bliulab.net/iDRPro-SC.
Collapse
Affiliation(s)
- Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Jiawei Feng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Jing Huang
- Huajian Yutong Technology (Beijing) Co., Ltd
- State Key Laboratory of Media Convergence Production Technology and Systems, Beijing China,100803
- Xinhua New Media Culture Communication Co., Ltd
| | - Hao Wu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| |
Collapse
|
5
|
Lee M. Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review. Molecules 2023; 28:5169. [PMID: 37446831 DOI: 10.3390/molecules28135169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
Collapse
Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| |
Collapse
|
6
|
Li J, Wu Z, Lin W, Luo J, Zhang J, Chen Q, Chen J. iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models. BIOINFORMATICS ADVANCES 2023; 3:vbad043. [PMID: 37113248 PMCID: PMC10125906 DOI: 10.1093/bioadv/vbad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/04/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023]
Abstract
Motivation Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences. Results In this article, we propose a novel enhancer identification method (iEnhancer-ELM) based on BERT-like enhancer language models. iEnhancer-ELM tokenizes DNA sequences with multi-scale k-mers and extracts contextual information of different scale k-mers related with their positions via an multi-head attention mechanism. We first evaluate the performance of different scale k-mers, then ensemble them to improve the performance of enhancer identification. The experimental results on two popular benchmark datasets show that our model outperforms state-of-the-art methods. We further illustrate the interpretability of iEnhancer-ELM. For a case study, we discover 30 enhancer motifs via a 3-mer-based model, where 12 of motifs are verified by STREME and JASPAR, demonstrating our model has a potential ability to unveil the biological mechanism of enhancer. Availability and implementation The models and associated code are available at https://github.com/chen-bioinfo/iEnhancer-ELM. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Collapse
Affiliation(s)
| | | | - Wenhao Lin
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Jiawei Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Qingcai Chen
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | | |
Collapse
|