Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture

Zutan Li; Jingya Fang; Shining Wang; Liangyun Zhang; Yuanyuan Chen; Cong Pian

doi:10.1093/bib/bbac037

journal article Feb 21, 2022

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture

Zutan Li Jingya Fang Shining Wang Liangyun Zhang Yuanyuan Chen

Cong Pian

Briefings in Bioinformatics Vol. 23 No. 2 · Oxford University Press (OUP)

View at Publisher Save 10.1093/bib/bbac037

Abstract

AbstractProtein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. However, the existing experimental techniques for detecting Kcr sites are cost-ineffective, to a great need for new computational methods to address this problem. We here describe Adapt-Kcr, an advanced deep learning model that utilizes adaptive embedding and is based on a convolutional neural network together with a bidirectional long short-term memory network and attention architecture. On the independent testing set, Adapt-Kcr outperformed the current state-of-the-art Kcr prediction model, with an improvement of 3.2% in accuracy and 1.9% in the area under the receiver operating characteristic curve. Compared to other Kcr models, Adapt-Kcr additionally had a more robust ability to distinguish between crotonylation and other lysine modifications. Another model (Adapt-ST) was trained to predict phosphorylation sites in SARS-CoV-2, and outperformed the equivalent state-of-the-art phosphorylation site prediction model. These results indicate that self-adaptive embedding features perform better than handcrafted features in capturing discriminative information; when used in attention architecture, this could be an effective way of identifying protein Kcr sites. Together, our Adapt framework (including learning embedding features and attention architecture) has a strong potential for prediction of other protein posttranslational modification sites.

Topics

No keywords indexed for this article. Browse by subject →

References

36

[1]

Soffer "Post-translational modification of proteins catalyzed by aminoacyl-tRNA-protein transferases" Mol Cell Biochem (1973) 10.1007/bf01738673

[2]

Wold "In vivo chemical modification of proteins (post-translational modification)" Annu Rev Biochem (1981) 10.1146/annurev.bi.50.070181.004031

[3]

Fu "Proteomic approaches beyond expression profiling and PTM analysis" Anal Bioanal Chem (2018) 10.1007/s00216-018-1021-y

[4]

Huang "Snap shot: histone modifications" Cell (2014) 10.1016/j.cell.2014.09.037

[5]

Wang "Identification of the YEATS domain of GAS41 as a pH-dependent reader of histone succinylation" Proc Natl Acad Sci U S A (2018) 10.1073/pnas.1717664115

[6]

Ramazi "Evaluation of post-translational modifications in histone proteins: a review on histone modification defects in developmental and neurological disorders" J Biosci (2020) 10.1007/s12038-020-00099-2

[7]

Krishna "Post-translational modification of proteins" Adv Enzymol Relat Areas Mol Biol (1993)

[8]

Lee "dbPTM: an information repository of protein post-translational modification" Nucleic Acids Res (2006) 10.1093/nar/gkj083

[9]

Tan "Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification" Cell (2011) 10.1016/j.cell.2011.08.008

[10]

Fellows "Microbiota derived short chain fatty acids promote histone crotonylation in the colon through histone deacetylases" Nat Commun (2018) 10.1038/s41467-017-02651-5

[11]

Huang "Lysine benzoylation is a histone mark regulated by SIRT2" Nat Commun (2018) 10.1038/s41467-018-05567-w

[12]

Jiang "HIV latency is reversed by ACSS2-driven histone crotonylation" J Clin Invest (2018) 10.1172/jci98071

[13]

Liu "Chromodomain protein CDYL acts as a crotonyl-CoA hydratase to regulate histone crotonylation and spermatogenesis" Mol Cell (2017) 10.1016/j.molcel.2017.07.011

[14]

Ruiz-Andres "Histone lysine crotonylation during acute kidney injury in mice" Dis Model Mech (2016)

[15]

Yu "Global crotonylome reveals CDYL-regulated RPA1 crotonylation in homologous recombination-mediated DNA repair" Sci Adv (2020) 10.1126/sciadv.aay4697

[16]

Qiu "Identify and analysis crotonylation sites in histone by using support vector machines" Artif Intell Med (2017) 10.1016/j.artmed.2017.02.007

[17]

Ju "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) 10.1016/j.jmgm.2017.08.020

[18]

Liu "Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net" Anal Biochem (2020) 10.1016/j.ab.2020.113903

[19]

Lv "Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method" Brief Bioinform (2020) 10.1093/bib/bbaa255

[20]

Qiao "BERT-Kcr: prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models" Bioinformatics (2021) 10.1093/bioinformatics/btab712

[21]

CD-HIT Suite: a web server for clustering and comparing biological sequences

Ying Huang, Beifang Niu, Ying Gao et al.

Bioinformatics 2010 10.1093/bioinformatics/btq003

[22]

Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV

Alexey Stukalov, Virginie Girault, Vincent Grass et al.

Nature 2021 10.1038/s41586-021-03493-4

[23]

LSTM: A Search Space Odyssey

Klaus Greff, Rupesh K. Srivastava, Jan Koutnik et al.

IEEE Transactions on Neural Networks and Learning... 2017 10.1109/tnnls.2016.2582924

[24]

Vaswani "Attention is all you need"

[25]

Lin "A structured self-attentive sentence embedding" (2017)

[26]

Zhong "Fine-grained sentiment analysis with faithful attention" (2019)

[27]

Attention is not not Explanation

Sarah Wiegreffe, Yuval Pinter

Proceedings of the 2019 Conference on Empirical Me... 10.18653/v1/d19-1002

[28]

Clark "What does BERT look at? An analysis of BERT’s attention" 10.18653/v1/w19-4828

[29]

Htut "Do attention heads in BERT track syntactic dependencies?" (2019)

[30]

Li "Modern deep learning in bioinformatics" J Mol Cell Biol (2021) 10.1093/jmcb/mjaa030

[31]

Park "Enhancing the interpretability of transcription factor binding site prediction using attention mechanism" Sci Rep (2020) 10.1038/s41598-020-70218-4

[32]

mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning

Zhenzhen Zou, Shuye Tian, Xin Gao et al.

Frontiers in Genetics 2019 10.3389/fgene.2018.00714

[33]

Hong "Identifying enhancer-promoter interactions with neural network based on pretrained DNA vectors and attention mechanism" Bioinformatics (2020) 10.1093/bioinformatics/btz694

[34]

Kingma "Adam: a method for stochastic optimization" In: International Conference on Learning Representations, San Diego, CA, USA, (2015)

[35]

Srivastava "Dropout: a simple way to prevent neural networks from overfitting" J Mach Learn Res (2014)

[36]

Chen "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data" Brief Bioinform (2020) 10.1093/bib/bbz041

Metrics

40

Citations

36

References

Details

Published: Feb 21, 2022
Vol/Issue: 23(2)
License: View

Authors

Z

Zutan Li

College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China

J

Jingya Fang

College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China

S

Shining Wang

Department of Mathematics, College of Science, Nanjing Agricultural University, China

L

Liangyun Zhang

Department of Mathematics, College of Science, Nanjing Agricultural University, China

Y

Yuanyuan Chen

Department of Mathematics, College of Science, Nanjing Agricultural University, China

C

Cong Pian

Department of Mathematics, College of Science, Nanjing Agricultural University, China; The State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing, China

Funding

Fundamental Research Funds for the Central Universities Award: JCQY202108

Startup Foundation for Advanced Talents at Nanjing Agricultural University Award: 050/804009

Cite This Article

Zutan Li, Jingya Fang, Shining Wang, et al. (2022). Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture. Briefings in Bioinformatics, 23(2). https://doi.org/10.1093/bib/bbac037

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture

You May Also Like