Skip to main content

Advertisement

Log in

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Pearce R, Zhang Y (2021) Toward the solution of the protein structure prediction problem. J Biol Chem. https://doi.org/10.1016/j.jbc.2021.100870

    Article  Google Scholar 

  2. Strokach A, Becerra D, Corbi-Verge C, Perez-Riba A, Kim PM (2020) Fast and flexible protein design using deep graph neural networks. Cell Syst 11(4):402–411. https://doi.org/10.1016/j.cels.2020.08.016

    Article  Google Scholar 

  3. Lia S, Yub K, Wang D, Zhang Q, Liu ZX, Zhao L, Cheng H (2020) (2020) Deep learning based prediction of species-specific protein Sglutathionylation sites. Biochim Biophys Acta (BBA) Proteins Proteomics 1868(7):1–6. https://doi.org/10.1016/j.bbapap.2020.140422

    Article  Google Scholar 

  4. Kandathil SM, Greener JG, Jones DT (2019) Recent developments in deep learning applied to protein structure prediction. Proteins Struct Funct Bioinform. https://doi.org/10.1002/prot.25824

    Article  Google Scholar 

  5. Xu J, Wang S (2019) Analysis of distance-based protein structure prediction by deep learning in CASP13. Proteins Struct Funct Bioinform. https://doi.org/10.1002/prot.25810

    Article  Google Scholar 

  6. Lai JK, Ambia J, Wang Y, Barth P (2017) Enhancing structure prediction and design of soluble and membrane proteins with explicit solvent-protein interactions. Structure 25(7):1758–1770. https://doi.org/10.1016/j.str.2017.09.002

    Article  Google Scholar 

  7. Igashov I, Pavlichenko N, Grudinin S (2021) Spherical convolutions on molecular graphs for protein model quality assessment. Mach Learn Sci Technol. https://doi.org/10.1088/2632-2153/abf856

    Article  Google Scholar 

  8. Nguyen SP, Li Z, Xu D, Shang Y (2017) New Deep Learning Methods for Protein Loop Modeling. IEEE Transactions on Computational Biology and Bioinformatics 16(2):596–606. https://doi.org/10.1109/TCBB.2017.2784434

    Article  Google Scholar 

  9. Pearce R, Zhang Y (2021) Deep learning techniques have significantly impacted protein structure prediction and protein design. Struct Biol 68(68):104–207. https://doi.org/10.1016/j.sbi.2021.01.007

    Article  Google Scholar 

  10. Wang S, Li Z, Yu Y, Xu J (2017) Folding membrane proteins by deep transfer learning. Cell Syst 5(3):202–211. https://doi.org/10.1016/j.cels.2017.09.001

    Article  Google Scholar 

  11. Tsuchiya Y, Tomii K (2020) Neural networks for protein structure and function prediction and dynamic analysis. Biophys Rev 12(2):569–573. https://doi.org/10.1007/s12551-020-00685-6

    Article  Google Scholar 

  12. AlQuraishi M (2021) Machine learning in protein structure prediction. Curr Opin Chem Biol Egypt J Med Hum Genet 65(65):1–8. https://doi.org/10.1016/j.cbpa.2021.04.005

    Article  Google Scholar 

  13. Torrisi M, Pollastri G, Le Q (2020) Deep learning methods in protein structure prediction. Comput Struct Biotechnol J 18(18):1301–1310. https://doi.org/10.1016/j.csbj.2019.12.011

    Article  Google Scholar 

  14. Afify HM, Abdelhalim MB, Mabrouk MS, Sayed AY (2021) Protein secondary structure prediction (PSSP) using different machine algorithms. Egypt J Med Hum Genet 22(1):1–10. https://doi.org/10.1186/s43042-021-00173-w

    Article  Google Scholar 

  15. Adhikari B (2020) A fully open-source framework for deep learning protein real-valued distances. Sci Rep. https://doi.org/10.1038/s41598-020-70181-0]

    Article  Google Scholar 

  16. Gao M, Zhou H, Skolnick J (2020) DESTINI: a deep-learning approachto contact-driven protein structureprediction. Sci Rep. https://doi.org/10.1038/s41598-019-40314-1

    Article  Google Scholar 

  17. Zhong W, Gu F (2020) Predicting local protein 3D structures using clustering deep recurrent neural network. ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2020.3005972

    Article  Google Scholar 

  18. Liu Z, Gong Y, Bao Y, Guo Y, Wang H, Lin GN (2021) TMPSS: a deep learning-based predictor for secondary structureand topology structure prediction of alpha-helical transmembrane proteins. Front Bioeng Biotechnol. https://doi.org/10.3389/fbioe.2020.629937

    Article  Google Scholar 

  19. Yufang Q, Xiaoqi Z, Jun W, Ming C, Changjie Z (2015) Prediction of protein structural class based onLinear predictive coding of PSI-BLAST profiles. Open Life Sci 10:529–536. https://doi.org/10.1515/biol-2015-0055

    Article  Google Scholar 

  20. Chen TR, Juan SH, Huang YW, Lin YC, Lo WC (2021) A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction. PLoS ONE. https://doi.org/10.1371/journal.pone.0255076

    Article  Google Scholar 

  21. Bao W, Yuan CA, Zhang Y, Han K, Nandi AK, Honig B, Huang DS (2017) Mutli-features prediction of protein translational modification sites. IEEE/ACM Trans Comput Biol Bioinform 15(5):1453–1460. https://doi.org/10.1109/TCBB.2017.2752703

    Article  Google Scholar 

  22. Spencer M, Eickholt J, Cheng J (2014) A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinform 12(1):103–112. https://doi.org/10.1109/TCBB.2014.2343960

    Article  Google Scholar 

  23. Gao W, Mahajan SP, Sulam J, Gray JJ (2020) Deep learning in protein structural modeling and design. Patterns. https://doi.org/10.1016/j.patter.2020.100142

    Article  Google Scholar 

  24. Tunyasuvunakoo K, Adler J, Wu Z, Green T, Zielinski M (2021) Highly accurate protein structure predictionfor the human proteome. Nature. https://doi.org/10.1038/s41586-021-03828-1

    Article  Google Scholar 

  25. Bouatta N, Sorger P, AlQuraishi M (2021) Protein structure prediction by AlphaFold2: Are attention and symmetries all you need? Acta Crystallogr Sect D Struct Biol 77(8):982–991. https://doi.org/10.1107/S2059798321007531

    Article  Google Scholar 

  26. Igashov I, Pavlichenko N, Grudinin S (2021) Spherical convolutions on molecular graphs for protein model quality assessment. Mach Learn Sci Technol 2(4):045005. https://doi.org/10.1088/2632-2153/abf856

    Article  Google Scholar 

  27. Xu Y, Verma D, Sheridan RP, Liaw A, Ma J, Marshall NM, McIntosh J, Sherer EC, Svetnik V, Johnston JM (2020) Deep dive into machine learning models for protein engineering. J Chem Inf Model 3(60):2773–2790. https://doi.org/10.1021/acs.jcim.0c00073

    Article  Google Scholar 

  28. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L (2020) Improved protein structure prediction usingpotentials from deep learning. Nature 8(428):706–710. https://doi.org/10.1038/s41586-019-1923-7

    Article  Google Scholar 

  29. Evangelia IZ (2017) Prediction of protein function usinga deep convolutional neural networkensemble. Peer J Comput Sci. https://doi.org/10.7717/peerj-cs.124

    Article  Google Scholar 

  30. Yang J, Zhang Y (2019) Protein structure and function prediction using I-TASSER. Curr Protocols Bioinform. https://doi.org/10.1002/0471250953.bi0508s52

    Article  Google Scholar 

  31. Mehmood S, Imran M, Ali A, Munawar A, Khaliq B, Anwar F, Saeed Q, Buck F, Hussain S, Saeed A, Ashraf MY, Akrem A (2020) Model prediction of a Kunitz-type trypsin inhibitor protein from seeds of Acacia nilotica L. with strong antimicrobial and insecticidal activity. Turk J Biol. https://doi.org/10.3906/biy-2002-20

    Article  Google Scholar 

  32. Alakuş TB, Türkoğlu İ (2021) A novel Fibonacci hash method for protein family identification by usingrecurrent neural networks. Turk J Electr Eng Comput Sci 29(1):370–386. https://doi.org/10.3906/elk-2003-116

    Article  Google Scholar 

  33. Istifli ES, Tepe AŞ, Netz PA, Sarikürkcü C, Kilic IH, Tepe B (2021) Determination of the interaction between the receptor binding domain of 2019-nCoV spike protein, TMPRSS2, cathepsin B and cathepsin L and glycosidic and aglycon forms of some flavonols. Turk J Biol. https://doi.org/10.3906/biy-2104-51

    Article  Google Scholar 

  34. Yilmaz C, Gok M (2021) System designs to perform bioinformatics sequence alignment. Turk J Electr Eng Comput Sci. https://doi.org/10.3906/elk-1105-22

    Article  Google Scholar 

  35. Sureyya Rifaioglu A, Doğan T, Jesus Martin M, Cetin-Atalay R, Atalay V (2019) DEEPred: automated protein function prediction with multi-task feed-forward deep neural networks. Sci Rep. https://doi.org/10.1038/s41598-019-43708-3

    Article  Google Scholar 

Download references

Funding

No funds, grants, or other support was received.

Author information

Authors and Affiliations

Authors

Contributions

VN contributed to the conceptualization; methodology; formal analysis and investigation; writing—original draft preparation; writing—review and editing; resources; MS was involved in the supervision.

Corresponding author

Correspondence to Varanavasi Nallasamy.

Ethics declarations

Conflict of interest

The authors have no financial or proprietary interests in any material discussed in this article.

Data availability

Yes.

Code availability

Yes.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor 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

Nallasamy, V., Seshiah, M. Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction. Neural Comput & Applic 35, 1983–2006 (2023). https://doi.org/10.1007/s00521-022-07868-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07868-0

Keywords