Abstract
Protein Structure Prediction (PSP) is one of the most challenging problems in bioinformatics and biomedicine. PSP has obtained significant improvement lately. This is from the growth of the protein data bank (PDB) and the use of Deep Neural Network (DNN) models since DNNs could learn more accurate patterns from more known protein structures in the PDB. Hidden Markov Models (HMM) are a widely used method to extract underlying patterns from given data. HMM profiles of proteins have been used in existing DNN models for protein backbone angle prediction (BAP), but their full potential is yet to be exploited amid the complexities involed with those DNN models. In this paper, for BAP, we propose a simple DNN model that more effectively exploits HMM profiles as features beside other features. Our proposed method significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D, and obtains mean absolute error (MAE) values of 15.45, 18.33, 6.00, and 20.68 respectively for four types of backbone angles \(\phi \), \(\psi \), \(\theta \), and \(\tau \). The differences in MAE values for all four types of angles are between 1.15% to 1.66% compared to the best known results.
F. Mataeimoghadam and M. A. H. Newton—Contributed equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altschul, S.F., et al.: Gapped BLAST AND PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)
Cutello, V., Narzisi, G., Nicosia, G.: A multi-objective evolutionary approach to the protein structure prediction problem. J. Roy. Soc. Interface 3(6), 139–151 (2005)
Faraggi, E., Zhang, T., Yang, Y., Kurgan, L., Zhou, Y.: 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 (2012)
Hanson, J., Paliwal, K., Litfin, T., Yang, Y., Zhou, Y.: Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks. Bioinformatics 34(23), 4039–4045 (2018)
Hanson, J., Paliwal, K., Litfin, T., Yang, Y., Zhou, Y.: Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks. Bioinformatics 35(14), 2403–2410 (2018)
Heffernan, R., et al.: Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci. Rep. 5, 11476 (2015)
Heffernan, R., Yang, Y., Paliwal, K., Zhou, Y.: 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 (2017)
Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolym. Orig. Res. Biomol. 22(12), 2577–2637 (1983)
Lyons, J., et al.: Predicting backbone c\(\alpha \) angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J. Comput. Chem. 35(28), 2040–2046 (2014)
Magnan, C.N., Baldi, P.: SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30(18), 2592–2597 (2014)
Mataeimoghadam, F., et al.: Enhancing protein backbone angle prediction by using simpler models of deep neural networks. Sci. Rep. 10(1), 1–12 (2020)
Mirdita, M., von den Driesch, L., Galiez, C., Martin, M.J., Söding, J., Steinegger, M.: Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45(D1), D170–D176 (2017)
Perez-Rathke, A., Mali, S., Du, L., Liang, J.: Alterations in chromatin folding patterns in cancer variant-enriched loci. In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 1–4. IEEE (2019)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Remmert, M., Biegert, A., Hauser, A., Söding, J.: HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9(2), 173 (2012)
Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S.J., Söding, J.: HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinform. 20(1), 1–15 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Xu, G., Wang, Q., Ma, J.: OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks. Bioinformatics (Oxf. Engl.) 36, 5021–5026 (2020)
Xu, G., Ma, T., Zang, T., Sun, W., Wang, Q., Ma, J.: OPUS-DOSP: a distance-and orientation-dependent all-atom potential derived from side-chain packing. J. Mol. Biol. 429(20), 3113–3120 (2017)
Zhou, Y., Duan, Y., Yang, Y., Faraggi, E., Lei, H.: Trends in template/fragment-free protein structure prediction. Theor. Chem. Acc. 128(1), 3–16 (2011)
Acknowledgements
This research is partially supported by Australian Research Council Discovery Grant DP180102727. We gratefully acknowledge the support of the Griffith University eResearch Service & Specialised Platforms team and the use of the High Performance Computing Cluster “Gowonda” to complete this research.
Author information
Authors and Affiliations
Contributions
F.M. and M.A.H.N. contributed equally and in all parts of the work. R.Z. helped run experiments. A.S. took part in discussions and reviewed the manuscript.
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mataeimoghadam, F., Newton, M.A.H., Zaman, R., Sattar, A. (2021). Improving Protein Backbone Angle Prediction Using Hidden Markov Models in Deep Learning. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-89188-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89187-9
Online ISBN: 978-3-030-89188-6
eBook Packages: Computer ScienceComputer Science (R0)