Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder


Cite item

Full Text

Abstract

Background:Protein lysine crotonylation (Kcr), a newly discovered important posttranslational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmental defects and malignant transformation.

Objective:Identifying Kcr sites is advantageous for the discovery of its biological mechanism and the development of new drugs for related diseases. However, traditional experimental methods for identifying Kcr sites are expensive and inefficient, necessitating the development of new computational techniques.

Methods:In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained.

Results:The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of 84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art tools. Additionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior to the current model.

Conclusion:These outcomes are additional evidence that Stacking-Kcr has strong application potential and generalization performance.

About the authors

Ying Liang

College of Computer and Information Engineering, Jiangxi Agricultural University

Email: info@benthamscience.net

Suhui Li

College of Computer and Information Engineering, Jiangxi Agricultural University

Email: info@benthamscience.net

Xiya You

College of Computer and Information Engineering, Jiangxi Agricultural University

Email: info@benthamscience.net

You Guo

First Affiliated Hospital, Gannan Medical University

Author for correspondence.
Email: info@benthamscience.net

Jianjun Tang

College of Computer and Information Engineering, Jiangxi Agricultural University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Soffer RL. Post-translational modification of proteins catalyzed by aminoacyl-tRNA-protein transferases. Mol Cell Biochem 1973; 2(1): 3-14. doi: 10.1007/BF01738673 PMID: 4587539
  2. Kouzarides T. Chromatin modifications and their function. Cell 2007; 128(4): 693-705. doi: 10.1016/j.cell.2007.02.005 PMID: 17320507
  3. Verdin E, Ott M. 50 years of protein acetylation: From gene regulation to epigenetics, metabolism and beyond. Nat Rev Mol Cell Biol 2015; 16(4): 258-64. doi: 10.1038/nrm3931 PMID: 25549891
  4. Wan J, Liu H, Chu J, Zhang H. Functions and mechanisms of lysine crotonylation. J Cell Mol Med 2019; 23(11): 7163-9. doi: 10.1111/jcmm.14650 PMID: 31475443
  5. Yu H, Bu C, Liu Y, et al. Global crotonylome reveals CDYL-regulated RPA1 crotonylation in homologous recombination–mediated DNA repair. Sci Adv 2020; 6(11): eaay4697. doi: 10.1126/sciadv.aay4697 PMID: 32201722
  6. Tan M, Luo H, Lee S, et al. Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell 2011; 146(6): 1016-28. doi: 10.1016/j.cell.2011.08.008 PMID: 21925322
  7. Fellows R, Denizot J, Stellato C, et al. Microbiota derived short chain fatty acids promote histone crotonylation in the colon through histone deacetylases. Nat Commun 2018; 9(1): 105. doi: 10.1038/s41467-017-02651-5 PMID: 29317660
  8. Huang H, Zhang D, Wang Y, et al. Lysine benzoylation is a histone mark regulated by SIRT2. Nat Commun 2018; 9(1): 3374. doi: 10.1038/s41467-018-05567-w PMID: 30154464
  9. Jiang G, Nguyen D, Archin NM, et al. HIV latency is reversed by ACSS2-driven histone crotonylation. J Clin Invest 2018; 128(3): 1190-8. doi: 10.1172/JCI98071 PMID: 29457784
  10. Liu S, Yu H, Liu Y, et al. Chromodomain protein CDYL acts as a crotonyl-CoA hydratase to regulate histone crotonylation and spermatogenesis. Mol Cell 2017; 67(5): 853-866.e5. doi: 10.1016/j.molcel.2017.07.011 PMID: 28803779
  11. Ruiz-Andres O, Sanchez-Niño MD, Cannata-Ortiz P, et al. Histone lysine crotonylation during acute kidney injury in mice. Dis Model Mech 2016; 9(6): 633-45. PMID: 27125278
  12. Qiao Y, Zhu X, Gong H. BERT-Kcr: Prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models. Bioinformatics 2022; 38(3): 648-54. doi: 10.1093/bioinformatics/btab712 PMID: 34643684
  13. Huang GH, Zeng WFA. Discrete hidden Markov model for detecting histone crotonyllysine sites, match-communications in mathematical and in computer. Chemistry 2016; 75: 717-30.
  14. Qiu WR, Sun BQ, Tang H, Huang J, Lin H. Identify and analysis crotonylation sites in histone by using support vector machines. Artif Intell Med 2017; 83: 75-81. doi: 10.1016/j.artmed.2017.02.007 PMID: 28283358
  15. Ju Z, He JJ. Prediction of lysine crotonylation sites by incorporating the composition of k -spaced amino acid pairs into Chou’s general PseAAC. J Mol Graph Model 2017; 77: 200-4. doi: 10.1016/j.jmgm.2017.08.020 PMID: 28886434
  16. Qiu WR, Sun BQ, Xiao X, et al. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics 2017; S0888754317301386. doi: 10.1016/j.ygeno.2017.10.008 PMID: 29107015
  17. Liu Y, Yu Z, Chen C, Han Y, Yu B. Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net. Anal Biochem 2020; 609: 113903. doi: 10.1016/j.ab.2020.113903 PMID: 32805274
  18. Jeon YJ, Hasan MM, Park HW, Lee KW, Manavalan B. TACOS: A novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization. Brief Bioinform 2022; 23(4): bbac243. doi: 10.1093/bib/bbac243 PMID: 35753698
  19. Manavalan B, Patra MC. An updated cell-penetrating peptides and their uptake efficiency predictor. J Mol Biol 2022; 434(11): 167604. doi: 10.1016/j.jmb.2022.167604
  20. Shoombuatong W, Basith S, Pitti T, Lee G, Manavalan B. THRONE: A new approach for accurate prediction of human rna n7-methylguanosine sites. J Mol Biol 2022; 434(11): 167549. doi: 10.1016/j.jmb.2022.167549 PMID: 35662472
  21. Liang Y, Wu Y, Zhang Z, Liu N, Peng J, Tang J. Hyb4mC: A hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction. BMC Bioinformatics 2022; 23(1): 258. doi: 10.1186/s12859-022-04789-6 PMID: 35768759
  22. Lv H, Dao FY, Guan ZX, Yang H, Li YW, Lin H. Deep-Kcr: Accurate detection of lysine crotonylation sites using deep learning method. Brief Bioinform 2021; 22(4): bbaa255. doi: 10.1093/bib/bbaa255 PMID: 33099604
  23. Lv H, Dao FY, Zulfiqar H, et al. A sequence-based deep learning approach to predict CTCF-mediated chromatin loop. Brief Bioinform 2021; 22(5): bbab031. doi: 10.1093/bib/bbab031 PMID: 33634313
  24. Zeng M, Wu Y, Lu C, et al. DeepLncLoc: A deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding. Bioinformatics 2021. PMID: 34498677
  25. Gunasekaran H, Ramalakshmi K, Rex Macedo Arokiaraj A, Deepa Kanmani S, Venkatesan C, Suresh Gnana Dhas C. Analysis of DNA sequence classification using CNN and hybrid models. Comput Math Methods Med 2021; 2021: 1-12. doi: 10.1155/2021/1835056 PMID: 34306171
  26. Alipanahi B. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015; 33: 831.
  27. Zeng H, Edwards MD, Liu G, Gifford DK. Convolutional neural network architectures for predicting DNA–protein binding. Bioinformatics 2016; 32(12): i121-7. doi: 10.1093/bioinformatics/btw255 PMID: 27307608
  28. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods 2015; 12(10): 931-4. doi: 10.1038/nmeth.3547 PMID: 26301843
  29. Khanal J, Tayara H, Zou Q, To Chong K. DeepCap-Kcr: Accurate identification and investigation of protein lysine crotonylation sites based on capsule network. Brief Bioinform 2022; 23(1): bbab492. doi: 10.1093/bib/bbab492 PMID: 34882222
  30. Zhang Z, Xu J, Wu Y, Liu N, Wang Y, Liang Y. CapsNet-LDA: Predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data. Brief Bioinform 2023; 24(1): bbac531. doi: 10.1093/bib/bbac531 PMID: 36511221
  31. Ramachandram D, Taylor GW. Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Process Mag 2017; 34(6): 96-108. doi: 10.1109/MSP.2017.2738401
  32. Lv H, Dao FY, Zulfiqar H, Lin H. DeepIPs: Comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach. Brief Bioinform 2021; 22(6): bbab244. doi: 10.1093/bib/bbab244 PMID: 34184738
  33. UniProt Consortium Ongoing and future developments at the universal protein resource. Nucleic Acids Res 2011; 39(Database issue): D214-9. PMID: 21051339
  34. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics 2010; 26(5): 680-2. doi: 10.1093/bioinformatics/btq003 PMID: 20053844
  35. Chen YZ, Wang ZZ, Wang Y, Ying G, Chen Z, Song J. nhKcr: A new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning. Brief Bioinform 2021; 22(6): bbab146. doi: 10.1093/bib/bbab146 PMID: 34002774
  36. Chen Z, Zhao P, Li F, et al. PROSPECT: A web server for predicting protein histidine phosphorylation sites. J Bioinform Comput Biol 2020; 18(4): 2050018. doi: 10.1142/S0219720020500183 PMID: 32501138
  37. Sandberg M, Eriksson L, Jonsson J, Sjöström M, Wold S. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem 1998; 41(14): 2481-91. doi: 10.1021/jm9700575 PMID: 9651153
  38. Chen YZ, Tang YR, Sheng ZY, Zhang Z. Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs. BMC Bioinformatics 2008; 9(1): 101. doi: 10.1186/1471-2105-9-101 PMID: 18282281
  39. Chen Z, He N, Huang Y, Qin WT, Liu X, Li L. Integration of a deep learning classifier with a random forest approach for predicting malonylation sites. Genomics Proteomics Bioinformatics 2018; 16(6): 451-9. doi: 10.1016/j.gpb.2018.08.004 PMID: 30639696
  40. Li A, Wang L, Shi Y, Wang M, Jiang Z, Feng H. Phosphorylation site prediction with a modified k-nearest neighbor algorithm and BLOSUM62 matrix. Conf Proc IEEE Eng Med Biol Soc 2005; 2005: 6075-8. PMID: 17281648
  41. Chen Z, Zhou Y, Song J, Zhang Z. hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties. Biochim Biophys Acta Proteins Proteomics 2013; 1834(8): 1461-7. doi: 10.1016/j.bbapap.2013.04.006 PMID: 23603789
  42. Chen Z, Chen YZ, Wang XF, Wang C, Yan RX, Zhang Z. Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs. PLoS One 2011; 6(7): e22930. doi: 10.1371/journal.pone.0022930 PMID: 21829559
  43. Mosharaf MP, Hassan MM, Ahmed FF, Khatun MS, Moni MA, Mollah MNH. Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana. Comput Biol Chem 2020; 85: 107238. doi: 10.1016/j.compbiolchem.2020.107238 PMID: 32114285
  44. Kao HJ, Nguyen VN, Huang KY, Chang WC, Lee TY. SuccSite: Incorporating amino acid composition and informative k-spaced amino acid pairs to identify protein succinylation sites. Genomics Proteomics Bioinformatics 2020; 18(2): 208-19. doi: 10.1016/j.gpb.2018.10.010 PMID: 32592791
  45. Wen YT, Lei HJ, You ZH, Lei BY, Chen X, Li LP. Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network. J Theor Biol 2017; 430: 9-20. doi: 10.1016/j.jtbi.2017.06.003 PMID: 28625475
  46. Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: A review. Brief Bioinform 2022; 23(2): bbab569. doi: 10.1093/bib/bbab569 PMID: 35089332
  47. Ballard DH. Modular learning in neural networks. Proc Sixth Nat Conf Artif Intell 1987; 279-84.
  48. Hasan MM, Tsukiyama S, Cho JY, et al. Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy. Mol Ther 2022; 30(8): 2856-67. doi: 10.1016/j.ymthe.2022.05.001
  49. Basith S, Lee G, Manavalan B. STALLION: A stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction. Brief Bioinform 2022; 23(1): bbab376. doi: 10.1093/bib/bbab376 PMID: 34532736
  50. Bupi N, Sangaraju V K, Phan L T, et al. An effective integrated machine learning framework for identifying severity of tomato yellow leaf curl virus and their experimental validation. Research 2023; 6(0016)
  51. Liang Y, Zhang ZQ, Liu NN, Wu YN, Gu CL, Wang YL. MAGCNSE: Predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model. BMC Bioinformatics 2022; 23(1): 189. doi: 10.1186/s12859-022-04715-w PMID: 35590258
  52. Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32. doi: 10.1023/A:1010933404324
  53. Wang X, Yu B, Ma A, Chen C, Liu B, Ma Q. Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique. Bioinformatics 2019; 35(14): 2395-402. doi: 10.1093/bioinformatics/bty995 PMID: 30520961
  54. Wang J, Zhou S, Yi Y, Kong J. An improved feature selection based on effective range for classification. ScientificWorldJournal 2014; 2014: 1-8. doi: 10.1155/2014/972125 PMID: 24688449
  55. Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal 2002; 38(4): 367-78. doi: 10.1016/S0167-9473(01)00065-2
  56. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 785-94. doi: 10.1145/2939672.2939785
  57. Sharma A, Singh B. AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM. Comput Biol Med 2020; 125: 103964. doi: 10.1016/j.compbiomed.2020.103964 PMID: 32911276
  58. Deng L, Pan J, Xu X, Yang W, Liu C, Liu H. PDRLGB: Precise DNA-binding residue prediction using a light gradient boosting machine. BMC Bioinformatics 2018; 19(S19): 522. doi: 10.1186/s12859-018-2527-1 PMID: 30598073
  59. Yang S, Fu C, Lian X, Dong X, Zhang Z. Understanding humanvirus protein-protein interactions using a human protein complex-based analysis framework. mSystems 2019; 4(2): e00303-18. doi: 10.1128/mSystems.00303-18 PMID: 30984872
  60. Kiranyaz S, Ince T, Pulkkinen J, et al. Classification and retrieval on macroinvertebrate image databases. Comput Biol Med 2011; 41(7): 463-72. doi: 10.1016/j.compbiomed.2011.04.008 PMID: 21601841
  61. Shi Q, Chen W, Huang S, Wang Y, Xue Z. Deep learning for mining protein data. Brief Bioinform 2021; 22(1): 194-218. doi: 10.1093/bib/bbz156 PMID: 31867611
  62. Wang B, Mei C, Wang Y, et al. Imbalance data processing strategy for protein interaction sites prediction. IEEE/ACM Trans Comput Biol Bioinformatics 2019.
  63. Deng A, Zhang H, Wang W, et al. Developing computational model to predict protein-protein interaction sites based on the XGBoost algorithm. Int J Mol Sci 2020; 21(7): 2274. doi: 10.3390/ijms21072274 PMID: 32218345
  64. Yue Z, Chu X, Xia J. PredCID: Prediction of driver frameshift indels in human cancer. Brief Bioinform 2021; 22(3): bbaa119. doi: 10.1093/bib/bbaa119 PMID: 32591774
  65. Basith S, Manavalan B, Hwan Shin T, Lee G. Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening. Med Res Rev 2020; 40(4): 1276-314. doi: 10.1002/med.21658 PMID: 31922268
  66. Shoombuatong W, Schaduangrat N, Pratiwi R, Nantasenamat C. THPep: A machine learning-based approach for predicting tumor homing peptides. Comput Biol Chem 2019; 80: 441-51. doi: 10.1016/j.compbiolchem.2019.05.008 PMID: 31151025
  67. Su R, Hu J, Zou Q, Manavalan B, Wei L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform 2020; 21(2): 408-20. doi: 10.1093/bib/bby124 PMID: 30649170
  68. Jiang M, Zhao B, Luo S, et al. NeuroPpred-Fuse: An interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods. Brief Bioinform 2021; 22(6): bbab310. doi: 10.1093/bib/bbab310 PMID: 34396388
  69. Liu N, Zhang Z, Wu Y, Wang Y, Liang Y. CRBSP:Prediction of CircRNA-RBP binding sites based on multimodal intermediate fusion. EEE/ACM Trans Comput Biol Bioinform 2023; 20(5): 2898-906. doi: 10.1109/TCBB.2023.3272400
  70. Dang TH, Le HQ, Nguyen TM, Vu ST. D3NER: Biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information. Bioinformatics 2018; 34(20): 3539-46. doi: 10.1093/bioinformatics/bty356 PMID: 29718118
  71. Yang H, Wang M, Liu X, Zhao XM, Li A. PhosIDN: An integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information. Bioinformatics 2021; 37(24): 4668-76. doi: 10.1093/bioinformatics/btab551 PMID: 34320631
  72. Tang Z, Zhang T, Yang B, Su J, Song Q. spaCI: Deciphering spatial cellular communications through adaptive graph model. Brief Bioinform 2023; 24(1): bbac563. doi: 10.1093/bib/bbac563 PMID: 36545790
  73. Song Q, Su J, Miller LD, Zhang W. scLM: Automatic detection of consensus gene clusters across multiple single-cell datasets. Genomics Proteomics Bioinformatics 2021; 19(2): 330-41. doi: 10.1016/j.gpb.2020.09.002 PMID: 33359676
  74. Stukalov A, Girault V, Grass V, et al. Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV. Nature 2021; 594(7862): 246-52. doi: 10.1038/s41586-021-03493-4 PMID: 33845483

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers