1. Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the stateof-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.
R Matthiesen. Methods, algorithms and tools in computational proteomics: a practical point of view. Proteomics, 2010, 7(16): 2815–2832 https://doi.org/10.1002/pmic.200700116
3
S Jones, J M Thornton. Principles of protein-protein interactions. Proceedings of the National Academy of Sciences of the United States of America, 1996, 93(1): 13–20 https://doi.org/10.1073/pnas.93.1.13
4
E M Phizicky, S Fields. Protein-protein interactions: methods for detection and analysis. Microbiological Reviews, 1995, 59(1): 94–123 https://doi.org/10.1128/MMBR.59.1.94-123.1995
5
R Jansen, H Yu, D Greenbaum, Y Kluger, N J Krogan, S Chung, A Emili, M Snyder, J F Greenblatt, M Gerstein. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 2003, 302(5644): 449–453 https://doi.org/10.1126/science.1087361
6
D R Rhodes, S A Tomlins, S Varambally, V Mahavisno, T Barrette, S Kalyanasundaram, D Ghosh, A Pandey, A M Chinnaiyan. Probabilistic model of the human protein-protein interaction network. Nature Biotechnology, 2005, 23(8): 951–959 https://doi.org/10.1038/nbt1103
7
M Oti, B Snel, M A Huynen, H G Brunner. Predicting disease genes using protein-protein interactions. Journal of Medical Genetics, 2006, 43(8): 691–698 https://doi.org/10.1136/jmg.2006.041376
8
E Sprinzak, S Sattath, H Margalit. How reliable are experimental protein-protein interaction data? Journal of Molecular Biology, 2003, 327(5): 919–923 https://doi.org/10.1016/S0022-2836(03)00239-0
9
S Letovsky, S Kasif. Predicting protein function from protein-protein interaction data: a probabilistic approach. Intelligent Systems in Molecular Biology, 2003, 19: 197–204 https://doi.org/10.1093/bioinformatics/btg1026
10
I Xenarios, L Salwinski, X J Duan, P Higney, S Kim, D Eisenberg. DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research, 2002, 30(1): 303–305 https://doi.org/10.1093/nar/30.1.303
11
A Chatr-Aryamontri, B J Breitkreutz, S Heinicke, L Boucher, A Winter, C Stark, J Nixon, L Ramage, N Kolas, L Odonnell. The BioGRID interaction database. Nucleic Acids Research, 2013, 41: D816–D823 https://doi.org/10.1093/nar/gks1158
12
G D Bader, D Betel, CWV Hogue. BIND: the biomolecular interaction network database. Nucleic Acids Research, 2001, 31(1): 248–250 https://doi.org/10.1093/nar/gkg056
13
J M Cherry, C Adler, C A Ball, S A Chervitz, S S Dwight, E T Hester, Y Jia, G Juvik, T Roe, M Schroeder. SGD: saccharomyces genome database. Nucleic Acids Research, 1998, 26(1): 73–79 https://doi.org/10.1093/nar/26.1.73
14
S Peri, J D Navarro, R Amanchy, T Z Kristiansen, C K Jonnalagadda, V Surendranath, V Niranjan, B Muthusamy, T K Gandhi, M Gronborg. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Research, 2003, 13(10): 2363–2371 https://doi.org/10.1101/gr.1680803
15
P Pagel, S Kovac, M Oesterheld, B Brauner, I Dungerkaltenbach, G Frishman, C Montrone, P Mark, V Stumpflen, H Mewes. The MIPS mammalian protein–protein interaction database. Bioinformatics, 2005, 21(6): 832–834 https://doi.org/10.1093/bioinformatics/bti115
16
K Samuel, A Bruno, B Lionel, B Alan, B C Fiona, C Carol, D Margaret, D Marine, F Marc, H Ursula. The IntAct molecular interaction database in 2012. Nucleic Acids Research, 2012, 40(Database issue): 841–846
17
L Wei, P Xing, J Zeng, J Chen, R Su, F Guo. Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier. Artificial Intelligence in Medicine, 2017, 83: 67–74 https://doi.org/10.1016/j.artmed.2017.03.001
18
Y Ding, J Tang, F Guo. Predicting protein-protein interactions via multivariate mutual information of protein sequences. BMC Bioinformatics, 2016, 17(1): 398 https://doi.org/10.1186/s12859-016-1253-9
19
T Wang, L Li, Y Huang, H Zhang, Y Ma, X Zhou. Prediction of proteinprotein interactions from amino acid sequences based on continuous and discrete wavelet transform features. Molecules, 2018, 23(4): 823 https://doi.org/10.3390/molecules23040823
20
Y Wang, Z You, L Li, Y Huang, H Yi. Detection of interactions between proteins by using legendre moments descriptor to extract discriminatory information embedded in PSSM. Molecules, 2017, 22(8): 1366 https://doi.org/10.3390/molecules22081366
21
J Shen, J Zhang, X Luo, W Zhu, K Yu, K Chen, Y Li, H Jiang. Predicting protein–protein interactions based only on sequences information. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(11): 4337–4341 https://doi.org/10.1073/pnas.0607879104
22
Y Guo, L Yu, Z Wen, M Li. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Research, 2008, 36(9): 3025–3030 https://doi.org/10.1093/nar/gkn159
23
I Cosic, M T Hearn. Studies on protein-DNA interactions using the resonant recognition model: application to repressors and transforming proteins. FEBS Journal, 2010, 205(2): 613–619 https://doi.org/10.1111/j.1432-1033.1992.tb16819.x
24
L Yang, J F Xia, J Gui. Prediction of protein-protein interactions from protein sequence using local descriptors. Protein & Peptide Letters, 2010, 17(9): 1085–1090 https://doi.org/10.2174/092986610791760306
25
L Hu, K C C Chan. Extracting coevolutionary features from protein sequences for predicting protein-protein interactions. IEEE/ACM Transactions Computational Biology and Bioinformatics, 2017, 14(1): 155–166 https://doi.org/10.1109/TCBB.2016.2520923
26
Z S Wei, J Y Yang, D J Yu. Predicting protein-protein interactions with weighted PSSM histogram and random forests. In: Proceedings of International Conference on Intelligent Science and Big Data Engineering. 2015, 326–335 https://doi.org/10.1007/978-3-319-23862-3_32
27
J Zahiri, O Yaghoubi, M Mohammad-Noori, R Ebrahimpour, A Masoudi-Nejad. PPIevo: protein-protein interaction prediction from PSSM based evolutionary information. Genomics, 2013, 102(4): 237–242 https://doi.org/10.1016/j.ygeno.2013.05.006
28
C Y Lin, Y C Chen, Y S Lo, J M Yang. Inferring homologous proteinprotein interactions through pair position specific scoring matrix. BMC Bioinformatics, 2013, 14(S2): S11 https://doi.org/10.1186/1471-2105-14-S2-S11
29
Y Wang, Z You, X Li, X Chen, T Jiang, J Zhang. PCVMZM: using the probabilistic classification vector machines model combined with a zernike moments descriptor to predict protein-protein interactions from protein sequences. International Journal of Molecular Sciences, 2017, 18(5): 1029 https://doi.org/10.3390/ijms18051029
30
L P Li, Y B Wang, Z H You, Y Li, J Y An. PCLPred: a bioinformatics ethod for predicting protein-protein interactions by combining relevance vector machine model with low-rank matrix approximation. International Journal of Molecular Sciences, 2018, 19(4): 1029 https://doi.org/10.3390/ijms19041029
31
X Y Song, Z H Chen, X Y Sun, Z H You, L P Li, Y Zhao. An ensemble classifier with random projection for predicting protein–protein interactions using sequence and evolutionary information. Applied Sciences, 2018, 8(1): 89 https://doi.org/10.3390/app8010089
32
J Y An, F R Meng, Z H You, Y H Fang, Y J Zhao, M Zhang. Using the relevance vector machine model combined with local phase quantization to predict protein-protein interactions from protein sequences. BioMed Research International, 2016, 2016: 1–9 https://doi.org/10.1155/2016/4783801
33
W Cheung, G Hamarneh. n-SIFT: n-dimensional scale invariant feature transform. IEEE Transactions on Image Processing, 2009, 18(9): 2012 https://doi.org/10.1109/TIP.2009.2024578
34
H Bay, A Ess, T Tuytelaars, L Van Gool. Speeded-up robust features. Computer Vision & Image Understanding, 2008, 110(3): 404–417 https://doi.org/10.1016/j.cviu.2007.09.014
A Khotanzad, Y H Hong. Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis &Machine Intelligence, 1990, 12(5): 489–497 https://doi.org/10.1109/34.55109
37
F Zhang, S Q Liu, D B Wang, W Guan. Aircraft recognition in infrared image using wavelet moment invariants. Image & Vision Computing, 2009, 27(4): 313–318 https://doi.org/10.1016/j.imavis.2008.08.007
38
N Dalal, B Triggs. Histograms of oriented gradients for human detection. In: Proceedings of International Conference on Computer Vision and Pattern Recognition. 2005, 886–893
39
J Whitehill, C W Omlin. Haar features for FACS AU recognition. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. 2006, 97–101
40
T Ojala, M Pietikainen, D Harwood. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th International Conference on Pattern Recognition. 1994, 582–585
S Qian, D Chen. Discrete gabor transform. IEEE Transactions on Signal Processing, 1993, 41(7): 2429–2438 https://doi.org/10.1109/78.224251
43
J Zeng, D Li, Y Wu, Q Zou, X Liu. An empirical study of features fusion techniques for protein-protein interaction prediction. Current Bioinformatics, 2016, 11(1): 4–12 https://doi.org/10.2174/1574893611666151119221435
44
M E Tipping. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 2001, 1(3): 211–244
45
M E Tipping. The relevance vector machine. In: Proceedings of the 12th International Conference on Neural Information Processing Systems. 2000, 652–658
46
L Wei, Y Yang, R M Nishikawa, M N Wernick, A Edwards. Relevance vector machine for automatic detection of clustered microcalci fications. IEEE Transactions on Medical Imaging, 2005, 24(10): 1278 https://doi.org/10.1109/TMI.2005.855435
W Rong, B Peng, Y Ouyang, C Li, Z Xiong. Structural information aware deep semi-supervised recurrent neural network for sentiment analysis. Frontiers of Computer Science, 2015, 9(2): 171–184 https://doi.org/10.1007/s11704-014-4085-7
49
T Mikolov, M Karafiat, L Burget, J Cernocký, S Khudanpur. Recurrent neural network based language model. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association. 2010, 1045–1048
50
K Gregor, I Danihelka, A Graves, D J Rezende, D Wierstra. DRAW: a recurrent neural network for image generation. In: Proceedings of International Conference of Machine Learning. 2015, 1462–1471
51
T N Sainath, O Vinyals, A Senior, H Sak. Convolutional, long shortterm memory, fully connected deep neural networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2015, 4580–4584 https://doi.org/10.1109/ICASSP.2015.7178838
52
C Dyer, M Ballesteros, W Ling, A Matthews, N A Smith. Transitionbased dependency parsing with stack long short-term memory. Computer Science, 2015, 37(2): 321–332
53
H Sak, A Senior, F Beaufays. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. In: Proceedings of the 15 Annual Conference of the International Speech Communication Association. 2014
L Lazib, B Qin, Y Zhao, W Zhang, T Liu. A syntactic path-based hybrid neural network for negation scope detection. Frontiers of Computer Science, 2020, 14(1): 84–94 https://doi.org/10.1007/s11704-018-7368-6
56
E Sprinzak, H Margalit. Correlated sequence-signatures as markers of protein-protein interaction. Journal of Molecular Biology, 2001, 11(4): 681–692 https://doi.org/10.1006/jmbi.2001.4920
A Benhur, W S Noble. Kernel methods for predicting protein–protein interactions. Intelligent Systems in Molecular Biology, 2005, 21(1): 38–46 https://doi.org/10.1093/bioinformatics/bti1016
60
K Chou, Y Cai. Predicting protein-protein interactions from sequences in a hybridization space. Journal of Proteome Research, 2006, 5(2): 316–322 https://doi.org/10.1021/pr050331g
61
Y Wang, Z You, L Li, L Cheng, X Zhou, L Zhang, X Li, T Jiang. Predicting protein interactions using a deep learning method-stacked sparse autoencoder combined with a probabilistic classification vector machine. Complexity, 2018, 2018: 1–12 https://doi.org/10.1155/2018/4216813
62
T Sun, B Zhou, L Lai, J Pei. Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinformatics, 2017, 18(1): 277 https://doi.org/10.1186/s12859-017-1700-2
63
J J Almagro Armenteros, C K Sønderby, S K Sønderby, H Nielsen, O Winther. DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics, 2017, 33(21): 3387–3395 https://doi.org/10.1093/bioinformatics/btx431
64
H C Yi, Z H You, D S Huang, X Li, T H Jiang, L P Li. A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information. Molecular Therapy Nucleic Acids, 2018, 11: 337–344 https://doi.org/10.1016/j.omtn.2018.03.001
65
Y B Wang, Z H You, X Li, T H Jiang, X Chen, X Zhou, L Wang. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Molecular Biosystems, 2017, 13(7): 1336–1344 https://doi.org/10.1039/C7MB00188F
66
R Sennrich, B Haddow, A Birch. Neural machine translation of rare words with subword units. In: Proceedings of the 54th AnnualMeeting of the Association for Computational Linguistics. 2016, 1715–1725 https://doi.org/10.18653/v1/P16-1162
67
T Kudo. Subword regularization: improving neural network translation models with multiple subword candidates. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018, 66–75 https://doi.org/10.18653/v1/P18-1007
68
T Kudo, J Richardson. SentencePiece: a simple and language independent subword tokenizer and detokenizer for Neural Text Processing. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2018, 66–71 https://doi.org/10.18653/v1/D18-2012
69
P Rebentrost, M Mohseni, S Lloyd. Quantum support vector machine for big data classification. Physical Review Letters, 2013, 113(13): 130503 https://doi.org/10.1103/PhysRevLett.113.130503
70
D Crawford, A Levit, N Ghadermarzy, J S Oberoi, P Ronagh. Reinforcement learning using quantum boltzmann machines. 2016, arXiv preprint arXiv:1612.05695
71
D Qiu, L Li. An overview of quantum computation models: quantum automata. Frontiers of Computer Science, 2008, 2(2): 193–207 https://doi.org/10.1007/s11704-008-0022-y
