Skip to main content
SpringerLink
Log in

Machine and Deep Learning for Prediction of Subcellular Localization

  • Protocol
  • First Online:
Proteomics Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2361))

Abstract

Protein subcellular localization prediction (PSLP), which plays an important role in the field of computational biology, identifies the position and function of proteins in cells without expensive cost and laborious effort. In the past few decades, various methods with different algorithms have been proposed in solving the problem of subcellular localization prediction; machine learning and deep learning constitute a large portion among those proposed methods. In order to provide an overview about those methods, the first part of this article will be a brief review of several state-of-the-art machine learning methods on subcellular localization prediction; then the materials used by subcellular localization prediction is described and a simple prediction method, that takes protein sequences as input and utilizes a convolutional neural network as the classifier, is introduced. At last, a list of notes is provided to indicate the major problems that may occur with this method.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Gardy JL, Brinkman FS (2006) Methods for predicting bacterial protein subcellular localization. Nat Rev Microbiol 4(10):741–751

    Article  CAS  PubMed  Google Scholar 

  2. Karp G (2009) Cell and molecular biology: concepts and experiments. Wiley, Hoboken, NJ

    Google Scholar 

  3. Tsien RY (1998) The green fluorescent protein. Annu Rev Biochem 67(1):509–544

    Article  CAS  PubMed  Google Scholar 

  4. Rey S, Gardy JL, Brinkman FS (2005) Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria. BMC Genomics 6(1):162

    Article  PubMed  PubMed Central  Google Scholar 

  5. Shen Y, Ding Y, Tang J, Zou Q, Guo F (2020) Critical evaluation of web-based prediction tools for human protein subcellular localization. Brief Bioinform 21(5):1628–1640

    Article  PubMed  Google Scholar 

  6. Gudenas BL, Wang L (2018) Prediction of LncRNA subcellular localization with deep learning from sequence features. Sci Rep 8(1):1–10

    Article  CAS  Google Scholar 

  7. Javed F, Hayat M (2019) Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou’s PseAAC. Genomics 111(6):1325–1332

    Article  CAS  PubMed  Google Scholar 

  8. Kumar KR, Cowley MJ, Davis RL (2019) Next-generation sequencing and emerging technologies. Semin Thromb Hemost 45(7):661–673

    Article  CAS  Google Scholar 

  9. Zhang S, Duan X (2018) Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J Theor Biol 437:239–250

    Article  CAS  PubMed  Google Scholar 

  10. Chou KC, Cai YD (2004) Prediction of protein subcellular locations by GO–FunD–PseAA predictor. Biochem Biophys Res Commun 320(4):1236–1239

    Article  CAS  PubMed  Google Scholar 

  11. Guo X, Liu F, Ju Y, Wang Z, Wang C (2016) Human protein subcellular localization with integrated source and multi-label ensemble classifier. Sci Rep 6:28087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hasan MAM, Ahmad S, Molla MKI (2017) Protein subcellular localization prediction using multiple kernel learning based support vector machine. Mol BioSyst 13(4):785–795

    Article  CAS  PubMed  Google Scholar 

  13. Almagro Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O (2017) DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics 33(21):3387–3395

    Article  PubMed  Google Scholar 

  14. Wei L, Ding Y, Su R, Tang J, Zou Q (2018) Prediction of human protein subcellular localization using deep learning. J Parallel Distr Com 117:212–217

    Article  Google Scholar 

  15. Cooper GM, Hausman RE (2004) The cell: molecular approach. Medicinska naklada

    Google Scholar 

  16. UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47(D1):D506–D515

    Article  Google Scholar 

  17. Sastry A, Monk J, Tegel H, Uhlen M, Palsson BO, Rockberg J, Brunk E (2017) Machine learning in computational biology to accelerate high-throughput protein expression. Bioinformatics 33(16):2487–2495

    Article  PubMed  PubMed Central  Google Scholar 

  18. Li H, Tian S, Li Y, Fang Q, Tan R, Pan Y, Huang C, Xu Y, Gao X (2020) Modern deep learning in bioinformatics. J Mol Cell Biol 12(11):823–827

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen HU, Huang NI, Sun Z (2006) SubLoc: a server/client suite for protein subcellular location based on SOAP. Bioinformatics 22(3):376–377

    Article  PubMed  Google Scholar 

  20. Shen Y, Tang J, Guo F (2019) Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC. J Theor Biol 462:230–239

    Article  CAS  PubMed  Google Scholar 

  21. Ding Y, Tang J, Guo F (2020) Human protein subcellular localization identification via fuzzy model on kernelized neighborhood representation. Appl Soft Comput 96:106596

    Article  Google Scholar 

  22. He J, Gu H, Liu W (2012) Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites. PLoS One 7(6):e37155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wei L, Liao M, Gao X, Wang J, Lin W (2016) mGOF-loc: a novel ensemble learning method for human protein subcellular localization prediction. Neurocomputing 217:73–82

    Article  Google Scholar 

  24. Wu CH, Huang H, Arminski L, Castro-Alvear J, Chen Y, Hu ZZ, Ledley RS, Lewis KC, Mewes H-W, Orcutt BC, Suzek BE (2002) The protein information resource: an integrated public resource of functional annotation of proteins. Nucleic Acids Res 30(1):35–37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gene Ontology Consortium (2019) The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res 47(D1):D330–D338

    Article  Google Scholar 

  26. Wan S, Mak MW, Kung SY (2012) mGOASVM: multi-label protein subcellular localization based on gene ontology and support vector machines. BMC Bioinformatics 13(1):290

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wan S, Mak MW, Kung SY (2015) mLASSO-Hum: a LASSO-based interpretable human-protein subcellular localization predictor. J Theor Biol 382:223–234

    Article  CAS  PubMed  Google Scholar 

  28. Shen HB, Chou KC (2009) A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal Biochem 394(2):269–274

    Article  CAS  PubMed  Google Scholar 

  29. Zhang ML, Zhou ZH (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

    Article  Google Scholar 

  30. Stormo GD, Schneider TD, Gold L, Ehrenfeucht A (1982) Use of the “Perceptron” algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res 10(9):2997–3011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Stormo GD (2000) DNA binding sites: representation and discovery. Bioinformatics 16(1):16–23

    Article  CAS  PubMed  Google Scholar 

  32. Bhagwat M, Aravind L (2007) Comparative genomics. In: Psi-blast tutorial. Humana Press, Totowa, NJ, pp 177–186

    Google Scholar 

  33. Chou KC (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteomics 6(4):262–274

    Article  CAS  Google Scholar 

  34. Jeong JC, Lin X, Chen XW (2010) On position-specific scoring matrix for protein function prediction. IEEE/ACM Trans Comput Biol Bioinform 8(2):308–315

    Article  Google Scholar 

  35. Nanni L, Brahnam S, Lumini A (2012) Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids 43(2):657–665

    Article  CAS  PubMed  Google Scholar 

  36. Nanni L, Lumini A, Brahnam S (2014) An empirical study of different approaches for protein classification. Sci World J 2014:236717

    Article  Google Scholar 

  37. Pan G, Wang J, Zhao L, Hoskins W, Tang J (2020) Computational methods for predicting DNA binding proteins. Curr Proteomics 17(4):258–270

    Article  CAS  Google Scholar 

  38. Pan G, Jiang L, Tang J, Guo F (2018) A novel computational method for detecting DNA methylation sites with DNA sequence information and physicochemical properties. Int J Mol Sci 19(2):511

    Article  PubMed Central  Google Scholar 

  39. Guo F, Zou Q, Yang G, Wang D, Tang J, Xu J (2019) Identifying protein-protein interface via a novel multi-scale local sequence and structural representation. BMC Bioinformatics 20(15):1–11

    Google Scholar 

  40. Ding Y, Tang J, Guo F (2019) Protein crystallization identification via fuzzy model on linear neighborhood representation. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2019.2954826

  41. Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, pp 160–167

    Google Scholar 

  42. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

    Google Scholar 

  43. Xu L, Ren JS, Liu C, Jia J (2014) Deep convolutional neural network for image deconvolution. In: Advances in neural information processing systems, pp 1790–1798

    Google Scholar 

  44. Lin X, Zhao C, Pan W (2017) Towards accurate binary convolutional neural network. In: Advances in neural information processing systems, pp 345–353

    Google Scholar 

  45. Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods 12(10):931–934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Angermueller C, Lee HJ, Reik W, Stegle O (2017) DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol 18(1):1–13

    Google Scholar 

  47. Zhang Y, An L, Xu J, Zhang B, Zheng WJ, Hu M, Tang J, Yue F (2018) Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus. Nat Commun 9(1):1–9

    Google Scholar 

  48. Zhang H, Weng TW, Chen PY, Hsieh CJ, Daniel L (2018) Efficient neural network robustness certification with general activation functions. In: Advances in neural information processing systems, pp 4939–4948

    Google Scholar 

  49. Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International workshop on artificial neural networks. Springer, Berlin, pp 195–201

    Google Scholar 

  50. De Boer PT, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19–67

    Article  Google Scholar 

  51. Okada S, Ohzeki M, Taguchi S (2019) Efficient partition of integer optimization problems with one-hot encoding. Sci Rep 9(1):1–12

    Article  Google Scholar 

  52. Li J, Si Y, Xu T, Jiang S (2018, 2018) Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques. Math Probl Eng:7354081

    Google Scholar 

  53. Pan G, Tang J, Guo F (2017) Analysis of co-associated transcription factors via ordered adjacency differences on motif distribution. Sci Rep 7(1):1–9

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA

    Gaofeng Pan, Chao Sun, Zijun Liao & Jijun Tang

  2. Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fujian, China

    Zijun Liao

  3. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China

    Jijun Tang

Authors
  1. Gaofeng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zijun Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jijun Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jijun Tang .

Editor information

Editors and Affiliations

  1. Department of Biotechnology, University of Verona, VERONA, Verona, Italy

    Daniela Cecconi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Pan, G., Sun, C., Liao, Z., Tang, J. (2021). Machine and Deep Learning for Prediction of Subcellular Localization. In: Cecconi, D. (eds) Proteomics Data Analysis. Methods in Molecular Biology, vol 2361. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1641-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1641-3_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1640-6

  • Online ISBN: 978-1-0716-1641-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation