Abstract
As the basis and key of cell activities, protein plays an important role in many life activities. Protein usually does not work alone. Under normal circumstances, most proteins perform specific functions by interacting with other proteins, and play the greatest role in life activity. The prediction of protein-protein interaction (PPI) is a very basic and important research in bioinformatics. PPI controls a large number of cell activities and is the basis of most cell activities. It provides a very important theoretical basis and support for disease prevention and treatment, and drug development. Because experimental methods are slow and expensive, methods based on machine learning are gradually being applied to PPI problems. We propose a new model called BiLSTM-RF, which can effectively predict PPI.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Brohee, S., Van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinform. 7(1), 1–19 (2006)
Sugaya, N., Ikeda, K.: Assessing the druggability of protein-protein interactions by a supervised machine-learning method. BMC Bioinform. 10(1), 1–13 (2009)
Shen, J., et al.: Predicting protein–protein interactions based only on sequences information. Proc. Natl. Acad. Sci. 104(11), 4337–4341 (2007)
Zhang, Q.C., et al.: Structure-based prediction of protein–protein interactions on a genome-wide scale. Nature 490(7421), 556–560 (2012)
Wu, J., Vallenius, T., Ovaska, K., Westermarck, J., Mäkelä, T.P., Hautaniemi, S.: Integrated network analysis platform for protein-protein interactions. Nat. Methods 6(1), 75–77 (2009)
De Las Rivas, J., Fontanillo, C.: Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput. Biol. 6(6), e1000807 (2010)
Zhang, Y.P., Zou, Q.: PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning. Bioinformatics 36(13), 3982–3987 (2020)
Shen, Z., Lin, Y., Zou, Q.: Transcription factors–DNA interactions in rice: identification and verification. Brief Bioinform. 21(3), 946–956 (2020)
Liu, G.H., Shen, H.B., Yu, D.J.: Prediction of protein–protein interaction sites with machine-learning-based data-cleaning and post-filtering procedures. J. Membr. Biol. 249(1), 141–153 (2016)
Sato, T., et al.: Interactions among members of the BCL-2 protein family analyzed with a yeast two-hybrid system. Proc. Natl. Acad. Sci. 91(20), 9238–9242 (1994)
Schwikowski, B., Uetz, P., Fields, S.: A network of protein–protein interactions in yeast. Nat. Biotechnol. 18(12), 1257–1261 (2000)
Coates, P.J., Hall, P.A.: The yeast two-hybrid system for identifying protein–protein interactions. J. Pathol.: A J. Pathol. Soc. Great Br. Ireland 199(1), 4–7 (2003)
Free, R.B., Hazelwood, L.A., Sibley, D.R.: Identifying novel protein-protein interactions using co-immunoprecipitation and mass spectroscopy. Curr. Protoc. Neurosci. 46(1), 5–28 (2009)
Kim, Y., Subramaniam, S.: Locally defined protein phylogenetic profiles reveal previously missed protein interactions and functional relationships. Proteins: Struct. Funct. Bioinform. 62(4), 1115–1124 (2006)
Zhang, S.W., Hao, L.Y., Zhang, T.H.: Prediction of protein–protein interaction with pairwise kernel support vector machine. Int. J. Mol. Sci. 15(2), 3220–3233 (2014)
Burger, L., Van Nimwegen, E.: Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method. Mol. Syst. Biol. 4(1), 165 (2008)
You, Z.H., Zhu, L., Zheng, C.H., Yu, H.J., Deng, S.P., Ji, Z.: Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinform. 15(15), 1–9 (2014)
Cui, G., Fang, C., Han, K.: Prediction of protein-protein interactions between viruses and human by an SVM model. BMC Bioinform. 13(7), 1–10 (2012)
Bradford, J.R., Westhead, D.R.: Improved prediction of protein–protein binding sites using a support vector machines approach. Bioinformatics 21(8), 1487–1494 (2005)
Guo, Y., Yu, L., Wen, Z., Li, M.: sing support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences. Nucleic Acids Res. 36(9), 3025–3030 (2008)
Koike, A., Takagi, T.: Prediction of protein–protein interaction sites using support vector machines. Protein Eng. Des. Sel. 17(2), 165–173 (2004)
Yi, H.C., You, Z.H., Wang, M.N., Guo, Z.H., Wang, Y.B., Zhou, J.R.: RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information. BMC Bioinform. 21(1), 1–10 (2020)
Du, X., Sun, S., Hu, C., Yao, Y., Yan, Y., Zhang, Y.: DeepPPI: boosting prediction of protein–protein interactions with deep neural networks. J. Chem. Inf. Model. 57(6), 1499–1510 (2017)
Sun, T., Zhou, B., Lai, L., Pei, J.: Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinform. 18(1), 1–8 (2017)
Zhang, L., Yu, G., Xia, D., Wang, J.: Protein–protein interactions prediction based on ensemble deep neural networks. Neurocomputing 324, 10–19 (2019)
Kong, M., Zhang, Y., Xu, D., Chen, W., Dehmer, M.: FCTP-WSRC: protein–protein interactions prediction via weighted sparse representation based classification. Front. Genet. 11, 18 (2020)
Ma, W., Cao, Y., Bao, W., Yang, B., Chen, Y.: ACT-SVM: prediction of protein-protein interactions based on support vector basis model. Sci. Program. 2020 (2020)
Acknowledgement
This work is supported by the fundamental Research Funds for the Central Universities, 2020QN89, Xuzhou science and technology plan project (KC19142), the talent project of ‘Qingtan scholar’ of Zaozhuang University, Jiangsu Provincial Natural Science Foundation, China (SBK2019040953), Youth Innovation Team of Scientific Research Foundation of the Higher Education Institutions of Shandong Province, China (2019KJM006), the Key Research Program of the Science Foundation of Shandong Province (ZR2020KE001), the PhD research startup foundation of Zaozhuang University (2014BS13) and Zaozhuang University Foundation (2015YY02), the Natural Science Foundation of China (61902337), Natural Science Fund for Colleges and Universities in Jiangsu Province (19KJB520016), Xuzhou Natural Science Foundation KC21047 and Young talents of science and technology in Jiangsu.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Tao, Z. et al. (2022). Prediction Protein-Protein Interactions with LSTM. In: Jiang, D., Song, H. (eds) Simulation Tools and Techniques. SIMUtools 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-97124-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-97124-3_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97123-6
Online ISBN: 978-3-030-97124-3
eBook Packages: Computer ScienceComputer Science (R0)