Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features

LI Weihua; LIU Wenyang; GUO Yanbu; WANG Bingyi; QING Hua

doi:10.23919/cje.2022.00.053

Volume 32 Issue 4

Jul. 2023

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LI Weihua, LIU Wenyang, GUO Yanbu, et al., “Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 868-881, 2023, doi: 10.23919/cje.2022.00.053

Citation:

LI Weihua, LIU Wenyang, GUO Yanbu, et al., “Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 868-881, 2023, doi: 10.23919/cje.2022.00.053

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Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features

doi: 10.23919/cje.2022.00.053

LI Weihua^1
,,
LIU Wenyang^1
,,
GUO Yanbu^2
,,
WANG Bingyi^3
,,
QING Hua^2
,

1.
School of Information Science and Engineering, Yunnan University, Kunming 650500, China
2.
College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
3.
Institute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650224, China

Funds: This work was supported by the National Natural Science Foundation of China (32060151, 62062066, 61762090) and the Doctor Scientific Research Fund of the Zhengzhou University of Light Industry (2021BSJJ032).

More Information

Author Bio:
Weihua LI received the Ph.D. degree from Yunnan University, Kunming, China. She is currently an Associate Professor in the School of Information Science and Engineering at Yunnan University, Kunming, China. Her research interests include bioinformatics, data mining and knowledge engineering. (Email: liweihua@ynu.edu.cn)

Wenyang LIU received the B.S. degree from Henan Polytechnic University, Jiaozuo, China. He received the M.S. degree in the School of Information Science and Engineering at Yunnan University, Kunming, China. His research interests include neural networks, intelligent computing, and bioinformatics.(Email: wyl20180901@163.com)

Yanbu GUO (corresponding author) received the Ph.D. degree from Yunnan University, Kunming, China. He is currently a Lecturer in the College of Software Engineering at Zhengzhou University of Light Industry, Zhengzhou, China. His current interests include neural networks, biomedical, and health informatics. (Email: guoyanbu@zzuli.edu.cn)

Bingyi WANG received the Ph.D. from Chinese Academy of Forestry, Kunming, China. He is currently an Associate Research Fellow with the Institute of Highland Forest Science, Chinese Academy of Forestry, Kunming, China. His research interests include bioinformatics and molecular regulation. (Email: wangbykm@163.com)

Hua QING received the Ph.D. degree from South China University of Technology, Guangzhou, China. She is currently a Lecturer in the College of Software Engineering at Zhengzhou University of Light Industry, Zhengzhou, China. Her research interests include machine learning and signal processing. (Email: huaqing@zzuli.edu.cn)
Received Date: 2022-03-18
Accepted Date: 2022-05-30

Available Online: 2022-07-19

Publish Date: 2023-07-05

Abstract

Abstract

Essential proteins with biological functions are necessary for the survival of organisms. Computational recognition methods of essential proteins can reduce the workload and provide candidate proteins for biologists. However, existing methods fail to efficiently identify essential proteins, and generally do not fully use amino acid sequence information to improve the performance of essential protein recognition. In this work, we propose an end-to-end deep contextual representation learning framework called DeepIEP to automatically learn biological discriminative features without prior knowledge based on protein network heterogeneous information. Specifically, the model attaches amino acid sequences as the attributes of each protein node in the protein interaction network, and then automatically learns topological features from protein interaction networks by graph embedding algorithms. Next, multi-scale convolutions and gated recurrent unit networks are used to extract contextual features from gene expression profiles. The extensive experiments confirm that our DeepIEP is an effective and efficient feature learning framework for identifying essential proteins and contextual features of protein sequences can improve the recognition performance of essential proteins.
- Essential proteins,
- Protein interaction networks,
- Gene expression profile,
- Deep neural networks

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References(55)

References

[1]	W. He, L. Zhang, O. D. Villarreal, et al., “De novo identification of essential protein domains from CRISPR-Cas9 tiling-sgRNA knockout screens,” Nature Communications, vol.10, article no.articleno.4541, 2019. doi: 10.1038/s41467-019-12489-8
[2]	P. Y. Zhang, M. T. Zhang, H. Liu, et al., “Prediction of protein subcellular localization based on microscopic images via multi-task multi-instance learning,” Chinese Journal of Electronics, vol.31, no.5, pp.888–896, 2022. doi: 10.1049/cje.2020.00.330
[3]	X. Q. Yang, X. J. Lei, and J. Zhao, “Essential protein prediction based on shuffled frog-leaping algorithm,” Chinese Journal of Electronics, vol.30, no.4, pp.704–711, 2021. doi: 10.1049/cje.2021.05.012
[4]	M. R. Fan, M. Li, Z. F. Liu, et al., “Crystal structures of the PsbS protein essential for photoprotection in plants,” Nature Structural & Molecular Biology, vol.22, no.9, pp.729–735, 2015. doi: 10.1038/nsmb.3068
[5]	M. Li, R. Q. Zheng, H. H. Zhang, et al., “Effective identification of essential proteins based on priori knowledge, network topology and gene expressions,” Methods, vol.67, no.3, pp.325–333, 2014. doi: 10.1016/j.ymeth.2014.02.016
[6]	X. Y. Li, W. K. Li, M. Zeng, et al., “Network-based methods for predicting essential genes or proteins: A survey,” Briefings in Bioinformatics, vol.21, no.2, pp.566–583, 2020. doi: 10.1093/bib/bbz017
[7]	L. M. Cullen and G. M. Arndt, “Genome-wide screening for gene function using RNAi in mammalian cells,” Immunology & Cell Biology, vol.83, no.3, pp.217–223, 2005. doi: 10.1111/j.1440-1711.2005.01332.x
[8]	T. Roemer, B. Jiang, J. Davison, et al., “Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery,” Molecular Microbiology, vol.50, no.1, pp.167–181, 2003. doi: 10.1046/j.1365-2958.2003.03697.x
[9]	H. Jeong, S. P. Mason, A. L. Barabási, et al., “Lethality and centrality in protein networks,” Nature, vol.411, no.6833, pp.41–42, 2001. doi: 10.1038/35075138
[10]	M. W. Hahn and A. D. Kern, “Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks,” Molecular Biology and Evolution, vol.22, no.4, pp.803–806, 2005. doi: 10.1093/molbev/msi072
[11]	M. P. Joy, A. Brock, D. E. Ingber, et al., “High-betweenness proteins in the yeast protein interaction network,” Journal of Biomedicine and Biotechnology, vol.2005, no.2, article no.594674, 2005. doi: 10.1155/JBB.2005.96
[12]	S. Wuchty and P. F. Stadler, “Centers of complex networks,” Journal of Theoretical Biology, vol.223, no.1, pp.45–53, 2003. doi: 10.1016/S0022-5193(03)00071-7
[13]	E. Estrada and J. A. Rodriguez-Velazquez, “Subgraph centrality in complex networks,” Physical Review E, vol.71, no.5, article no.056103, 2005. doi: 10.1103/PhysRevE.71.056103
[14]	P. Bonacich, “Power and centrality: A family of measures,” American Journal of Sociology, vol.92, no.5, pp.1170–1182, 1987. doi: 10.1086/228631
[15]	K. Stephenson and M. Zelen, “Rethinking centrality: Methods and examples,” Social Networks, vol.11, no.1, pp.1–37, 1989. doi: 10.1016/0378-8733(89)90016-6
[16]	M. Zeng, M. Li, Z. H. Fei, et al., “A deep learning framework for identifying essential proteins by integrating multiple types of biological information,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.18, no.1, pp.296–305, 2021. doi: 10.1109/TCBB.2019.2897679
[17]	E. Nasiri, K. Berahmand, M. Rostami, et al., “A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding,” Computers in Biology and Medicine, vol.137, article no.104772, 2021. doi: 10.1016/j.compbiomed.2021.104772
[18]	G. S. Li, M. Li, J. X. Wang, J. et al., “Predicting essential proteins based on subcellular localization, orthology and PPI networks,” BMC Bioinformatics, vol.17, no.S8, article no.279, 2016. doi: 10.1186/s12859-016-1115-5
[19]	A. M. Gustafson, E. S. Snitkin, S. C. J. Parker, et al., “Towards the identification of essential genes using targeted genome sequencing and comparative analysis,” BMC Genomics, vol.7, article no.265, 2006. doi: 10.1186/1471-2164-7-265
[20]	J. C. Zhong, J. X. Wang, W. Peng, et al., “Prediction of essential proteins based on gene expression programming,” BMC Genomics, vol.14, no.S4, article no.S7, 2013. doi: 10.1186/1471-2164-14-S4-S7
[21]	X. Y. Zhu, Y. C. Zhu, Y. H. Tan, et al., “An iterative method for predicting essential proteins based on multifeature fusion and linear neighborhood similarity,” Frontiers in Aging Neuroscience, vol.13, article no.799500, 2022. doi: 10.3389/FNAGI.2021.799500
[22]	L. Wang, J. X. Peng, L. N. Kuang, et al., “Identification of essential proteins based on local random walk and adaptive multi-view multi-label learning,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.19, no.6, pp.3507–3516, 2022. doi: 10.1109/TCBB.2021.3128638
[23]	Y. C. Hwang, C. C. Lin, J. Y. Chang, et al., “Predicting essential genes based on network and sequence analysis,” Molecular BioSystems, vol.5, no.12, pp.1672–1678, 2009. doi: 10.1039/b900611g
[24]	J. Y. Deng, L. Deng, S. C. Su, et al., “Investigating the predictability of essential genes across distantly related organisms using an integrative approach,” Nucleic Acids Research, vol.39, no.3, pp.795–807, 2011. doi: 10.1093/nar/gkq784
[25]	A. K. Payra and A. Ghosh, “Identifying essential proteins using modified-monkey algorithm (MMA),” Computational Biology and Chemistry, vol.88, article no.107324, 2020. doi: 10.1016/j.compbiolchem.2020.107324
[26]	M. Zeng, M. Li, F. X. Wu, et al., “DeepEP: A deep learning framework for identifying essential proteins,” BMC Bioinformatics, vol.20, no.S16, article no.506, 2019. doi: 10.1186/s12859-019-3076-y
[27]	X. Zhang, W. X. Xiao, and W. J. Xiao, “DeepHE: Accurately predicting human essential genes based on deep learning,” PLoS Computational Biology, vol.16, no.9, article no.e1008229, 2020. doi: 10.1371/journal.pcbi.1008229
[28]	M. H. Chen, C. J. T. Ju, G. Y. Zhou, et al., “Multifaceted protein-protein interaction prediction based on Siamese residual RCNN,” Bioinformatics, vol.35, no.14, pp.i305–i314, 2019. doi: 10.1093/bioinformatics/btz328
[29]	Y. B. Guo, B. Y. Wang, W. H. Li, et al., “Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks,” Journal of Bioinformatics and Computational Biology, vol.16, no.5, article no.1850021, 2018. doi: 10.1142/S021972001850021X
[30]	Y. B. Guo, D. M. Zhou, R. C. Nie, et al., “DeepANF: A deep attentive neural framework with distributed representation for chromatin accessibility prediction,” Neurocomputing, vol.379, pp.305–318, 2020. doi: 10.1016/j.neucom.2019.10.091
[31]	M. Ghandi, D. Lee, M. Mohammad-Noori, et al., “Enhanced regulatory sequence prediction using gapped k-mer features,” PLoS Computational Biology, vol.10, no.7, article no.e1003711, 2014. doi: 10.1371/journal.pcbi.1003711
[32]	A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, pp.855–864, 2016.
[33]	H. C. Gao and H. Huang, “Deep attributed network embedding”, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp.3364–3370, 2018.
[34]	X. W. Tang, J. X. Wang, J. C. Zhong, et al., “Predicting essential proteins based on weighted degree centrality,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.11, no.2, pp.407–418, 2014. doi: 10.1109/TCBB.2013.2295318
[35]	M. Li, H. H. Zhang, J. X. Wang, et al., “A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data,” BMC Systems Biology, vol.6, article no.15, 2012. doi: 10.1186/1752-0509-6-15
[36]	K. Plaimas, R. Eils, and R. König, “Identifying essential genes in bacterial metabolic networks with machine learning methods,” BMC Systems Biology, vol.4, article no.56, 2010. doi: 10.1186/1752-0509-4-56
[37]	D. S. Huang and C. H. Zheng, “Independent component analysis-based penalized discriminant method for tumor classification using gene expression data,” Bioinformatics, vol.22, no.15, pp.1855–1862, 2006. doi: 10.1093/bioinformatics/btl190
[38]	R. Fakoor, F. Ladhak, A. Nazi, et al., “Using deep learning to enhance cancer diagnosis and classification,” in Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA, pp.3937–3949, 2013.
[39]	Y. B. Guo, W. H. Li, B. Y. Wang, et al., “DeepACLSTM: Deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction,” BMC Bioinformatics, vol.20, article no.341, 2009. doi: 10.1186/s12859-019-2940-0
[40]	N. Altwaijry and I. Al-Turaiki, “Arabic handwriting recognition system using convolutional neural network,” Neural Computing and Applications, vol.33, no.7, pp.2249–2261, 2021. doi: 10.1007/s00521-020-05070-8
[41]	Z. Cheng, L. Liu, G. L. Lin, et al., “ReHiC: Enhancing Hi-C data resolution via residual convolutional network,” Journal of Bioinformatics and Computational Biology, vol.19, no.2, article no.2150001, 2021. doi: 10.1142/S0219720021500013
[42]	M. Alkhodari and L. Fraiwan, “Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings,” Computer Methods and Programs in Biomedicine, vol.200, article no.105940, 2021. doi: 10.1016/j.cmpb.2021.105940
[43]	J. B. Wang, “Automated detection of premature ventricular contraction based on the improved gated recurrent unit network,” Computer Methods and Programs in Biomedicine, vol.208, article no.106284, 2021. doi: 10.1016/J.CMPB.2021.106284
[44]	The PLOS Computational Biology Staff, “Correction: Enhanced regulatory sequence prediction using gapped k-mer features,” PLoS Computational Biology, vol.10, no.7, article no.e1004035, 2014. doi: 10.1371/journal.pcbi.1004035
[45]	V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, pp.807–814, 2010.
[46]	D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA, 2015.
[47]	C. Stark, B. J. Breitkreutz, T. Reguly, et al., “BioGRID: A general repository for interaction datasets,” Nucleic Acids Research, vol.34, no.S1, pp.D535–D539, 2006. doi: 10.1093/nar/gkj109
[48]	I. Xenarios, L. Salwinski, X. J. Duan, et al., “DIP, the Database of Interacting Proteins: A research tool for studying cellular networks of protein interactions,” Nucleic Acids Research, vol.30, no.1, pp.303–305, 2002. doi: 10.1093/nar/30.1.303
[49]	B. P. Tu, A. Kudlicki, M. Rowicka, et al., “Logic of the yeast metabolic cycle: Temporal compartmentalization of cellular processes,” Science, vol.310, no.5751, pp.1152–1158, 2005. doi: 10.1126/science.1120499
[50]	J. X. Wang, M. Li, H. Wang, et al., “Identification of essential proteins based on edge clustering coefficient,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no.4, pp.1070–1080, 2012. doi: 10.1109/TCBB.2011.147
[51]	M. Li, J. X. Wang, X. Chen, et al., “A local average connectivity-based method for identifying essential proteins from the network level,” Computational Biology and Chemistry, vol.35, no.3, pp.143–150, 2011. doi: 10.1016/j.compbiolchem.2011.04.002
[52]	Z. Zhang, H. X. Yang, J. J. Bu, et al., “ANRL: attributed network representation learning via deep neural networks”, in Proceedings of the Twenty Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp.3155–3161, 2018.
[53]	H. Öztürk, A. Özgür, and E. Ozkirimli, “DeepDTA: Deep drug-target binding affinity prediction,” Bioinformatics, vol.34, no.17, pp.i821–i829, 2018. doi: 10.1093/bioinformatics/bty593
[54]	L. Wang, H. Li, Y. Q. Wang, et al., “MDADP: A webserver integrating database and prediction tools for microbe-disease associations,” IEEE Journal of Biomedical and Health Informatics, vol.26, no.7, pp.3427–3434, 2022. doi: 10.1109/JBHI.2022.3156166
[55]	P. Y. Ping, L. Wang, L. N. Kuang, et al., “A novel method for LncRNA-Disease association prediction based on an lncRNA-Disease association network,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.16, no.2, pp.688–693, 2019. doi: 10.1109/TCBB.2018.2827373