Skip to main content
SpringerLink
Log in

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information

  • Original research article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological function mechanisms. In this document, we have constructed a predictor, named PPSNO, forecasting protein SNO sites using stacked integrated learning. PPSNO integrates multiple machine learning techniques into an ensemble model, enhancing its predictive accuracy. First, we established benchmark datasets by collecting SNO sites from various sources, including literature, databases, and other predictors. Second, various techniques for feature extraction are applied to derive characteristics from protein sequences, which are subsequently amalgamated into the PPSNO predictor for training. Five-fold cross-validation experiments show that PPSNO outperformed existing predictors, such as PSNO, PreSNO, pCysMod, DeepNitro, RecSNO, and Mul-SNO. The PPSNO predictor achieved an impressive accuracy of 92.8%, an area under the curve (AUC) of 96.1%, a Matthews correlation coefficient (MCC) of 81.3%, an F1-score of 85.6%, an SN of 79.3%, an SP of 97.7%, and an average precision (AP) of 92.2%. We also employed ROC curves, PR curves, and radar plots to show the superior performance of PPSNO. Our study shows that fused protein sequence features and two-layer stacked ensemble models can improve the accuracy of predicting SNO sites, which can aid in comprehending cellular processes and disease mechanisms. The codes and data are available at https://github.com/serendipity-wly/PPSNO.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

Publicly available datasets were analyzed in this study. Codes and data are available at https://github.com/serendipity-wly/PPSNO.

References

  1. Jia J, Arif A, Terenzi F et al (2014) Target-selective protein S-nitrosylation by sequence motif recognition. Cell 159:623–634. https://doi.org/10.1016/j.cell.2014.09.032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Fernando V, Zheng X, Walia Y et al (2019) S-nitrosylation: an emerging paradigm of redox signaling. Antioxidants 8:404. https://doi.org/10.3390/antiox8090404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hayashi H, Hess DT, Zhang R et al (2018) S-nitrosylation of β-arrestins biases receptor signaling and confers ligand independence. Mol Cell 70:473-487.e6. https://doi.org/10.1016/j.molcel.2018.03.034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rizza S, Cardaci S, Montagna C et al (2018) S-nitrosylation drives cell senescence and aging in mammals by controlling mitochondrial dynamics and mitophagy. Proc Natl Acad Sci 115:E3388–E3397. https://doi.org/10.1073/pnas.1722452115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Li F, Sonveaux P, Rabbani ZN et al (2007) Regulation of HIF-1α stability through S-nitrosylation. Mol Cell 26:63–74. https://doi.org/10.1016/j.molcel.2007.02.024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang Z (2012) Protein S-nitrosylation and cancer. Cancer Lett 2:123–129. https://doi.org/10.1016/j.canlet.2012.03.009

    Article  CAS  Google Scholar 

  7. Wijasa TS, Sylvester M, Brocke-Ahmadinejad N et al (2020) Quantitative proteomics of synaptosome S-nitrosylation in Alzheimer’s disease. J Neurochem 152:710–726. https://doi.org/10.1111/jnc.14870

    Article  CAS  PubMed  Google Scholar 

  8. Piroddi M, Palmese A, Pilolli F et al (2011) Plasma nitroproteome of kidney disease patients. Amino Acids 40:653–667. https://doi.org/10.1007/s00726-010-0693-1

    Article  CAS  PubMed  Google Scholar 

  9. Hao G, Derakhshan B, Shi L et al (2006) SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures. Proc Natl Acad Sci 103:1012–1017. https://doi.org/10.1073/pnas.0508412103

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  10. Xue Y, Liu Z, Gao X et al (2010) GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm. PLoS ONE 5:e11290. https://doi.org/10.1371/journal.pone.0011290

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  11. Lee T-Y, Chen Y-J, Lu T-C et al (2011) SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity. PLoS ONE 6:e21849. https://doi.org/10.1371/journal.pone.0021849

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  12. Li B-Q, Hu L-L, Niu S et al (2012) Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches. J Proteom 75:1654–1665. https://doi.org/10.1016/j.jprot.2011.12.003

    Article  CAS  Google Scholar 

  13. Xu Y, Ding J, Wu L-Y (2013) iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS ONE 8:e55844. https://doi.org/10.1371/journal.pone.0055844

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  14. Xu Y, Shao X-J, Wu L-Y et al (2013) iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 1:e171. https://doi.org/10.7717/peerj.171

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang J, Zhao X, Sun P (2014) PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou’s PseAAC. Int J Mol Sci 15:11204–11219. https://doi.org/10.3390/ijms150711204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hasan MdM, Manavalan B, MstS K (2019) Prediction of S-nitrosylation sites by integrating support vector machines and random forest. Mol Omics 15:451–458. https://doi.org/10.1039/C9MO00098D

    Article  CAS  PubMed  Google Scholar 

  17. Qiu W-R, Wang Q-K, Guan M-Y et al (2021) Predicting S-nitrosylation proteins and sites by fusing multiple features. Math Biosci Eng 18:9132–9147. https://doi.org/10.3934/mbe.2021450

    Article  PubMed  Google Scholar 

  18. Xie Y, Luo X, Li Y et al (2018) DeepNitro: prediction of protein nitration and nitrosylation sites by deep learning. Genom Proteom Bioinform 16:294–306. https://doi.org/10.1016/j.gpb.2018.04.007

    Article  Google Scholar 

  19. Siraj A, Chantsalnyam T, Tayara H (2021) RecSNO: prediction of protein S-nitrosylation sites using a recurrent neural network. IEEE Access 9:6674–6682. https://doi.org/10.1109/ACCESS.2021.3049142

    Article  Google Scholar 

  20. Fu H, Yang Y, Wang X et al (2019) DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins. BMC Bioinformatics 20:1–10. https://doi.org/10.1186/s12859-019-2677-9

    Article  Google Scholar 

  21. Zhao Q, Ma J, Wang Y et al (2022) Mul-SNO: a novel prediction tool for S-nitrosylation sites based on deep learning methods. IEEE J Biomed Health Inform 26:2379–2387. https://doi.org/10.1109/JBHI.2021.3123503

    Article  PubMed  Google Scholar 

  22. Li S, Yu K, Wu G et al (2021) pCysMod: prediction of multiple cysteine modifications based on deep learning framework. Front Cell Dev Biol 9:617366. https://doi.org/10.3389/fcell.2021.617366

    Article  PubMed  PubMed Central  Google Scholar 

  23. Fu L, Niu B, Zhu Z et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. https://doi.org/10.1093/bioinformatics/bts565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chen Y-J, Ku W-C, Lin P-Y et al (2010) S-alkylating labeling strategy for site-specific identification of the s-nitrosoproteome. J Proteome Res 9:6417–6439. https://doi.org/10.1021/pr100680a

    Article  CAS  PubMed  Google Scholar 

  25. Stephen FA (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402. https://doi.org/10.1093/nar/25.17.3389

    Article  Google Scholar 

  26. Gao W, Xu D, Li H et al (2023) Identification of adaptor proteins by incorporating deep learning and PSSM profiles. Methods 209:10–17. https://doi.org/10.1016/j.ymeth.2022.11.001

    Article  CAS  PubMed  Google Scholar 

  27. Lee T-Y, Chen S-A, Hung H-Y (2011) Incorporating distant sequence features and radial basis function networks to identify ubiquitin conjugation sites. PLoS ONE 6:e17331. https://doi.org/10.1371/journal.pone.0017331

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  28. Chen Z, Zhao P, Li F et al (2018) iFeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics 34:2499–2502. https://doi.org/10.1093/bioinformatics/bty140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li Z-R, Lin HH, Han LY et al (2006) PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 34:W32–W37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ju Z, Wang S-Y (2020) Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou’s 5-steps rule and general pseudo components. Genomics 112:859–866. https://doi.org/10.1016/j.ygeno.2019.05.027

    Article  CAS  PubMed  Google Scholar 

  31. Zhao Y, He N, Chen Z (2020) Identification of protein lysine crotonylation sites by a deep learning framework with convolutional neural networks. IEEE Access 8:14244–14252. https://doi.org/10.1109/ACCESS.2020.2966592

    Article  Google Scholar 

  32. Chou K-C (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19. https://doi.org/10.1093/bioinformatics/bth466

    Article  CAS  PubMed  Google Scholar 

  33. Zhang J, Liu B (2019) A review on the recent developments of sequence-based protein feature extraction methods. Curr Bioinforma 14:190–199. https://doi.org/10.2174/1574893614666181212102749

    Article  CAS  Google Scholar 

  34. Saravanan V, Gautham N (2015) Harnessing computational biology for exact linear B-cell epitope prediction: a novel amino acid composition-based feature descriptor. Omics J Integr Biol 19:648–658. https://doi.org/10.1089/omi.2015.0095

    Article  CAS  Google Scholar 

  35. Dubchak I, Muchnik I, Holbrook SR (1995) Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci 92:8700–8704. https://doi.org/10.1073/pnas.92.19.8700

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  36. Uzma MU, Halim Z (2023) Protein encoder: an autoencoder-based ensemble feature selection scheme to predict protein secondary structure. Expert Syst Appl 213:119081. https://doi.org/10.1016/j.eswa.2022.119081

    Article  Google Scholar 

  37. Kalyan KS, Rajasekharan A, Sangeetha S (2022) AMMU: a survey of transformer-based biomedical pretrained language models. J Biomed Inform 126:103982. https://doi.org/10.1016/j.jbi.2021.103982

    Article  PubMed  Google Scholar 

  38. Deng L, Pan J, Xu X et al (2018) PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine. BMC Bioinformatics 19:522. https://doi.org/10.1186/s12859-018-2527-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Song X, Zhu J, Tan X et al (2022) XGBoost-based feature learning method for mining COVID-19 novel diagnostic markers. Front Public Health 10:926069. https://doi.org/10.3389/fpubh.2022.926069

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hancock JT, Khoshgoftaar TM (2020) CatBoost for big data: an interdisciplinary review. J Big Data 7:94. https://doi.org/10.1186/s40537-020-00369-8

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rigatti SJ (2017) Random forest. J Insur Med 47:31–39. https://doi.org/10.17849/insm-47-01-31-39.1

    Article  PubMed  Google Scholar 

  42. Talebi H, Peeters LJM, Otto A (2022) A truly spatial random forests algorithm for geoscience data analysis and modelling. Math Geosci 54:1–22. https://doi.org/10.1007/s11004-021-09946-w

    Article  MathSciNet  Google Scholar 

  43. Qiu Z, Liu Q (2021) Protein–protein interaction site prediction using random forest proximity distance. J Bioinform Comput Biol 19:2050042. https://doi.org/10.1142/S0219720020500420

    Article  CAS  PubMed  Google Scholar 

  44. Cabras S, Castellanos ME, Staffetti E (2016) A random forest application to contact-state classification for robot programming by human demonstration. Appl Stoch Models Bus Ind 32:209–227. https://doi.org/10.1002/asmb.2145

    Article  MathSciNet  Google Scholar 

  45. Hua S, Sun Z (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 308:397–407. https://doi.org/10.1006/jmbi.2001.4580

    Article  CAS  PubMed  Google Scholar 

  46. Pan X, Shen H-B (2018) Predicting RNA–protein binding sites and motifs through combining local and global deep convolutional neural networks. Bioinformatics 34:3427–3436. https://doi.org/10.1093/bioinformatics/bty364

    Article  CAS  PubMed  Google Scholar 

  47. Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36:193–202. https://doi.org/10.1007/BF00344251

    Article  CAS  PubMed  Google Scholar 

  48. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  CAS  PubMed  Google Scholar 

  49. Bühlmann P, Yu B (2002) Analyzing bagging. Ann Stat 30:927–961. https://doi.org/10.1214/aos/1031689014

    Article  MathSciNet  Google Scholar 

  50. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139. https://doi.org/10.1006/jcss.1997.1504

    Article  MathSciNet  Google Scholar 

  51. Le NQK, Huynh T-T (2019) Identifying SNAREs by incorporating deep learning architecture and amino acid embedding representation. Front Physiol 10:1501. https://doi.org/10.3389/fphys.2019.01501

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This research was funded by the Jiangsu Students' platform for innovation and entrepreneurship training program (Grant No. 202310292023Z) and the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20230626).

Author information

Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China

    Lun Zhu, Liuyang Wang, Zexi Yang & Sen Yang

  2. The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, 213164, China

    Sen Yang

  3. College of Economics and Management, Nanjing Forestry University, Nanjing, 210037, China

    Piao Xu

Authors
  1. Lun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liuyang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zexi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Piao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sen Yang.

Ethics declarations

Conflicts of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical Approval

This article does not contain any studies with animals performed by any of the authors.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, L., Wang, L., Yang, Z. et al. PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information. Interdiscip Sci Comput Life Sci 16, 192–217 (2024). https://doi.org/10.1007/s12539-023-00595-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-023-00595-7

Keywords

Search

Navigation