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An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction

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Abstract

Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein which is dependent on its secondary structure. In this paper, we focus on improving the accuracy of protein secondary structure prediction. To do so, a Multi-scale convolutional neural network with a Gated recurrent neural network (MCNN-GRNN) is proposed. The novel amino acid encoding method along with layered convolutional neural network and Gated recurrent neural network blocks helps to retrieve local and global relationships between features, which in turn effectively classify the input protein sequence into 3 and 8 states. We have evaluated our algorithm on CullPDB, CB513, PDB25, CASP10, CASP11, CASP12, CASP13, and CASP14 datasets. We have compared our algorithm with different state-of-the-art algorithms like DCNN-SS, DCRNN, MUFOLD-SS, DLBLS_SS, and CGAN-PSSP. The Q3 accuracy of the proposed algorithm is 82–87% and Q8 accuracy is 69–77% on different datasets.

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Data availability

Data used in this study is publicly available.

References

  1. VrushaliBongirwar ASM (2022) Different methods, techniques and their limitations in protein structure prediction: a review. Progr Biophys Mol Biol 173:72–82. https://doi.org/10.1016/j.pbiomolbio.2022.05.002

    Article  Google Scholar 

  2. Chou K-C (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43(3):246–255. https://doi.org/10.1002/prot.10355

    Article  Google Scholar 

  3. Gao CF, Wu XY (2018) Feature extraction method for proteins based on markov tripeptide by compressive sensing. BMC Bioinform 19(1):239–232. https://doi.org/10.1186/s12859-018-2235-x

    Article  Google Scholar 

  4. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202. https://doi.org/10.1006/jmbi.1999.3091

    Article  Google Scholar 

  5. Smolarczyk T, Roterman-Konieczna I, Stapor K (2020) Protein secondary structure prediction: a review of progress and directions. Curr Bioinform 15:90–107. https://doi.org/10.2174/1574893614666191017104639

    Article  Google Scholar 

  6. Chou PY, Fasman GD (1974) Conformational parameters for amino acids in helical, B-sheet, and random coil regions calculated from proteins. Biochemistry 13(2):211–222

    Article  Google Scholar 

  7. Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232(2):584–599. https://doi.org/10.1006/jmbi.1993.1413

    Article  Google Scholar 

  8. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405. https://doi.org/10.1093/bioinformatics/16.4.404

    Article  Google Scholar 

  9. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  Google Scholar 

  10. Ward JJ, McGuffin LJ, Buxton BF, Jones DT (2003) Secondary structure prediction with support vector machines. Bioinformatics 19(3):1950–1955. https://doi.org/10.1093/bioinformatics/btg223

    Article  Google Scholar 

  11. Dor O, Zhou Y (2007) Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training. Proteins 66(4):838–845. https://doi.org/10.1002/prot.21298

    Article  Google Scholar 

  12. Faraggi E, Zhang T, Yang Y, Lukasz Kurgan YZ (2011) SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 33(3):259–267. https://doi.org/10.1002/jcc.21968

    Article  Google Scholar 

  13. Bettella F, Rasinski D, Knapp EW (2012) Protein secondary structure prediction with SPARROW. J Chem Inf Model 52(2):545–556. https://doi.org/10.1021/ci200321u

    Article  Google Scholar 

  14. Ashraf Yaseen YL (2014) Context-based features enhance protein secondary structure prediction accuracy. J Chem Inf Model 54(3):992–1002. https://doi.org/10.1021/ci400647u

    Article  Google Scholar 

  15. Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43(April):389–394. https://doi.org/10.1093/nar/gkv332

    Article  Google Scholar 

  16. Heffernan R, Paliwal K, Lyons J, Dehzangi A, Sharma A (2015) Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Nature Publishing Group. https://doi.org/10.1038/srep11476

    Book  Google Scholar 

  17. Wang S, Peng J, Ma J, Xu J (2016) Protein secondary structure prediction using deep convolutional neural fields. Sci Rep. https://doi.org/10.1038/srep18962

    Article  Google Scholar 

  18. Heffernan R, Yang Y, KuldipPaliwal YZ (2017) Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. Bioinformatics 33(18):2842–2849. https://doi.org/10.1093/bioinformatics/btx218

    Article  Google Scholar 

  19. Zhen Li YY (2016). Protein secondary structure prediction using cascaded convolutional and recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), 2560–2567.

  20. Fang C, Shang Y, Xu D (2018) MUFOLD-SS: new deep inceptioninside- inception networks for protein secondary structure prediction. Proteins 86(5):592–598. https://doi.org/10.1002/prot.25487

    Article  Google Scholar 

  21. Guo L, Jiang Q, Jin X, Liu L, Zhou W, Yao S (2020) A deep convolutional neural network to improve the prediction of protein secondary structure. Curr Bioinform 15:767–777. https://doi.org/10.2174/1574893615666200120103050

    Article  Google Scholar 

  22. Yuan L, Hu X, Ma Y, Liu Y (2022) DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system. RSC Adv 12:33479–33487. https://doi.org/10.1039/d2ra06433b

    Article  Google Scholar 

  23. Jin X, Guo L, Jiang Q, Wu N (2022) Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module. Front Bioeng Biotechnol 10:901018. https://doi.org/10.3389/fbioe.2022.901018

    Article  Google Scholar 

  24. Hasic H, Buza E, Akagic A (2017) A hybrid method for prediction of protein secondary structure based on multiple artificial neural networks. In: 2017 40th international convention on information and communication technology, electronics and microelectronics (MIPRO) pp. 1195-1200. IEEE. https://doi.org/10.23919/MIPRO.2017.7973605

  25. Yavuz BÇ, Yurtay N, Ozkan O (2018) Prediction of protein secondary structure with clonal selection algorithm and multilayer perceptron. IEEE Access 6:45256–45261. https://doi.org/10.1109/ACCESS.2018.2864665

    Article  Google Scholar 

  26. Pollastri G, Przybylski D, Rost B (2003) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47(2):228–235. https://doi.org/10.1002/prot.10082

    Article  Google Scholar 

  27. Wang Z, Zhao F, Peng J, Xu J (2011) Protein 8-class secondary structure prediction using conditional neural fields. Proteomics 11(19):3786–3792. https://doi.org/10.1002/pmic.201100196

    Article  Google Scholar 

  28. Busia A, Jaitly N (2017). Next-step conditioned deep convolutional neural networks to improve protein secondary structure prediction. In: Conference on intelligent systems for molecular biology & European conference on computational biology, 1–11. https://doi.org/10.48550/arXiv.1702.03865

  29. Zhou JTO (2014). Deep supervised and convolutional generative stochastic network for protein secondary structure prediction. In: Proceedings of the 31st International Conference on International Conference on Machine Learning Beijing, China, 1–9. https://doi.org/10.48550/arXiv.1403.1347

  30. Fang C, Shang Y, Xu D (2017). A new deep neighbor residual network for protein secondary structure prediction. In: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), Boston, MA, USA, 66–71.

  31. Guo Y, Li W, Wang B, Liu H, Zhou D (2019) DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction. BMC Bioinform 20(341):1–12. https://doi.org/10.1186/s12859-019-2940-0

    Article  Google Scholar 

  32. Drori I, Dwivedi I, Shrestha P, Wan J, Wang Y, He Y, Mazza A, Krogh-Freeman H, Leggas D, Sandridge K, Nan L, Thakoor K, Joshi C, Goenka S, Keasar C, (2018). High quality prediction of protein q8 secondary structure by diverse neural network architectures. NIPS 2018 Workshop on Machine Learning for Molecules and Materials, 1–10. https://doi.org/10.48550/arXiv.1811.07143

  33. Yang W, Hu Z, Zhou L, Jin Y (2022) Knowledge-based systems protein secondary structure prediction using a lightweight convolutional network and label distribution aware margin loss. Knowl-Based Syst 237:1–12. https://doi.org/10.1016/j.knosys.2021.107771

    Article  Google Scholar 

  34. Hinton GE, Krizhevsky A, Srivastava N, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  Google Scholar 

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Acknowledgements

We thank the Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur for providing NVIDIA GPU A100 for experimentation.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India

    Vrushali Bongirwar

  2. Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India

    A. S. Mokhade

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  1. Vrushali Bongirwar
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Correspondence to Vrushali Bongirwar.

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Bongirwar, V., Mokhade, A.S. An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09822-8

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