基于深度神经网络和局部描述符的大规模蛋白质互作预测方法
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摘要
蛋白质相互作用PPI(Protein-Protein Interaction)是生物体中众多生命活动过程的重要组成部分,蛋白质互作预测是研究蛋白质互作的重要途径。为了提高蛋白质互作预测性能,构建一个用于预测蛋白质互作的深度神经网络模型DPPI。采用局部描述符将氨基酸序列编码成具鉴别性的特定维数向量;使用训练集训练DPPI模型,并使用测试集对DPPI模型进行测试和评价;根据测试和评价的结果调整各参数,优化DPPI模型;使用优化后的DPPI模型,来对蛋白质互作进行预测。结果表明,DPPI模型编码简单、代码简洁,实验获得的较高的准确率,可以作为大规模蛋白质互作预测的有益补充。
Protein-protein interaction(PPI) plays an important role in many biological processes. PPI prediction is an important way to study protein interaction. In order to improve the prediction performance of protein interaction, a DPPI model was constructed to protein-protein interactions. The local descriptor was used to encode the amino acid sequence into a discriminative specific dimension vector. Then, the training set was used to train DPPI model, and the test set was used for testing and evaluation. We adjusted the parameters according to the results of the test and evaluation to optimize the DPPI model. The optimized DPPI model was used to predict protein interactions. The results find that the DPPI model has simple encoding, simple code, and higher accuracy. It can be used as a useful supplement for large-scale protein interaction prediction.
Protein-protein interaction(PPI) plays an important role in many biological processes. PPI prediction is an important way to study protein interaction. In order to improve the prediction performance of protein interaction, a DPPI model was constructed to protein-protein interactions. The local descriptor was used to encode the amino acid sequence into a discriminative specific dimension vector. Then, the training set was used to train DPPI model, and the test set was used for testing and evaluation. We adjusted the parameters according to the results of the test and evaluation to optimize the DPPI model. The optimized DPPI model was used to predict protein interactions. The results find that the DPPI model has simple encoding, simple code, and higher accuracy. It can be used as a useful supplement for large-scale protein interaction prediction.
引文
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[37] Yang L, Xia J F, Gui J. Prediction of Protein-Protein Interactions from Protein Sequence Using Local Descriptors[J]. Protein & Peptide Letters, 2010, 17(9):1085-1090.
[38] Wang J, Zhang L, Jia L Y, et al. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences[J]. International Journal of Molecular Sciences, 2017, 18(11):2373.
[2] Ho Y, Gruhler A, Herlbut A, et al. Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry[J]. Nature,2002,415(6868):180-183.
[3] Gavin A C, Bosche M, Krause R, et al. Functional Organization of the Yeast Proteome by Systematic Analysis of Protein Complexes[J]. Nature, 2002, 415(6868): 141-147.
[4] Heng Z, Metin B, Rhonda B, et al. Global Analysis of Protein Activities Using Proteome Chips[J]. Science, 2001, 293(5537):2101-2105.
[5] Chatterjee P, Basu S, Kundu M, et al. PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables[J]. Cellular & Molecular Biology Letters, 2011, 16(2):264-278.
[6] Fariselli P, Pazos F, Valencia A, et al. Prediction of protein—protein interaction sites in heterocomplexes with neural networks[J]. European Journal of Biochemistry, 2002, 269(5): 1356-1361.
[7] Lin X, Chen X W. Heterogeneous Data Integration by Tree Augmented Naive Bayes for Protein-Protein Interactions Prediction[J]. Proteomics, 2013, 13(2): 261-268.
[8] Browne F, Wang H, Zheng H, et al. Supervised Statistical and Machine Learning Approaches to Inferring Pairwise and Module Based Protein Interaction Networks[C]//Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, New York, USA: IEEE, 2007: 1365-1369.
[9] Valente G T, Acencio M L, Martins C, et al. The Development of a Universal in Silico Predictor of Protein-Protein Interactions[J]. PLoS One, 2013, 8(5): e65587.
[10] Chen X W, Liu M. Prediction of Protein-Protein Interactions Using Random Decision Forest Framework[J]. Bioinformatics, 2005, 21(24): 4394-4400.
[11] Saha I, Zubek J, Klingstr?m T, et al. Ensemble Learning Prediction of Protein-Protein Interactions Using Proteins Functional Annotations[J]. Molecular Biosystems, 2014, 10(4): 820-830.
[12] Mohanty S P, Hughes D P, Salathé M. Using Deep Learning for Image-Based Plant Disease Detection[J]. Frontiers in Plant Science, 2016, 7:1419.
[13] Collobert G, Weston J. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning[C]//International Conference on Machine Learning, 2008:160-167.
[14] Silver D, Huang A, Maddison C J, et al. Mastering the Game of Go with Deep Neural Networks and Tree Search[J]. Nature, 2016, 529(7587):484-489.
[15] Du X Q, Sun S W, Hu CL, et al. DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks[J]. Journal of Chemical Information & Modeling, 2017, 57(6):1499-1510.
[16] Leung M K, Xiong H Y, Lee L J, et al. Deep Learning of the Tissue-Regulated Splicing Code[J]. Bioinformatics, 2014, 30(12): i121-i129.
[17] Zhou J, Troyanskaya O G. Predicting Effects of Noncoding Variants with Deep Learning-Based Sequence Model[J]. Nature Methods, 2015, 12(10): 931-934.
[18] Bengio Y, Courville A, Vincent P. Representation Learning: A Review and New Perspectives[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 35(8):1798-1828.
[19] Alipanahi B, Delong A, Weirauch M T, et al. Predicting the sequence specificities of DNA—and RNA-binding proteins by deep learning[J]. Nature Biotechnology, 2015, 33(8): 831-838.
[20] Krizhevsky A, Sutskever I, Hinton G E. Imagenet Classification with Deep Convolutional Neural Networks[J]. International Conference on Neural Information Processing Systems, 2012, 60(2): 1097-1105.
[21] Tian K, Shao M Y, Wang Y, et al. Boosting Compound-Protein Interaction Prediction by Deep Learning[J]. Methods, 2016, 110: 64-72.
[22] Yang L, Xia J F, Gui F. Prediction of Protein-Protein Interactions from Protein Sequence Using Local Descriptors[J]. Protein & Peptide Letters, 2010, 17(19):1085-1090.
[23] Davies M N, Secker A, Freitas A A, et al. Optimizing Amino Acid Groupings for GPCR Classification[J]. Bioinformatics, 2008, 24(18):1980-1986.
[24] Hinton G E, Salakhutdinov R R. Reducing the Dimensionality of Data with Neural Networks[J]. Science, 2006, 313(5786): 504-507.
[25] Hinton G E, Osindero S, Teh Y W. A Fast Learning Algorithm for Deep Belief Nets[J]. Neural Computation, 2014, 18(7): 1527-1554.
[26] Glorot X, Bordes A, Bengio Y. Deep Sparse Rectifier Neural Networks[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics(AISTATS), 2010: 315-323.
[27] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A Simple Way to Prevent Neural Networks from Over Fitting. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
[28] Kingma D, Ba J. Adam: A Method for Stochastic Optimization[DB]. arXiv:1412.6980V8, 2014.
[29] Bengio Y. Practical Recommendations for Gradient-Based Training of Deep Architectures[J]. Springer Berlin Heidelberg, 2012, 7700(1-3): 437-478.
[30] Shen J, Zhang J, Luo X, et al. Predicting protein-protein interactions based only on sequences information[J]. Proceedings of the National Academy of Sciences, 2007, 104(11):4337-4341.
[31] You Z H, Li S, Gao X, et al. Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model[J]. Biomed Research International, 2014, 2014(2):598129.
[32] Guo Y, Li M, Pu X, et al. PRED_PPI: A Server for Predicting Protein-Protein Interactions Based on Sequence Data with Probability Assignment[J]. Bmc Research Notes, 2010, 3(1): 145-152.
[33] Pan X Y, Zhang Y N, Shen H B. Large-scale Prediction of Human Protein-Protein Interactions from Amino Acid Sequence Based on Latent Topic Features[J]. Journal of Proteome Research 2010, 9(10):4992-5001.
[34] Sun T, Zhou B, Lai H, et al. Sequence-Based Prediction of Protein Protein Interaction Using a Deep-Learning Algorithm[J]. BMC Bioinformatics, 2017, 18(1): 277-285.
[35] You Z H, Lei Y K, Zhu L, et al. Prediction of Protein-Protein Interactions from Amino Acid Sequences with Ensemble Extreme Learning Machines and Principal Component Analysis[J]. BMC Bioinformatics, 2013, 14(S8): S10.
[36] Zhou Y Z, Gao Y, Zheng Y Y. Prediction of Protein-Protein Interactions Using Local Description of Amino Acid Sequence[J]. Communications in Computer & Information Science, 2011, 202:254-262.
[37] Yang L, Xia J F, Gui J. Prediction of Protein-Protein Interactions from Protein Sequence Using Local Descriptors[J]. Protein & Peptide Letters, 2010, 17(9):1085-1090.
[38] Wang J, Zhang L, Jia L Y, et al. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences[J]. International Journal of Molecular Sciences, 2017, 18(11):2373.