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A CNN-CBAM-BIGRU model for protein function prediction

  • Lavkush Sharma EMAIL logo , Akshay Deepak , Ashish Ranjan und Gopalakrishnan Krishnasamy
Veröffentlicht/Copyright: 1. Juli 2024
Statistical Applications in Genetics and Molecular Biology
Aus der Zeitschrift Statistical Applications in Genetics and Molecular Biology Band 23 Heft 1

Abstract

Understanding a protein’s function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powerful tool, achieving significant success in protein function prediction. Their strength lies in their ability to automatically learn informative features from protein sequences, which can then be used to predict the protein’s function. This study builds upon these advancements by proposing a novel model: CNN-CBAM+BiGRU. It incorporates a Convolutional Block Attention Module (CBAM) alongside BiGRUs. CBAM acts as a spotlight, guiding the CNN to focus on the most informative parts of the protein data, leading to more accurate feature extraction. BiGRUs, a type of Recurrent Neural Network (RNN), excel at capturing long-range dependencies within the protein sequence, which are essential for accurate function prediction. The proposed model integrates the strengths of both CNN-CBAM and BiGRU. This study’s findings, validated through experimentation, showcase the effectiveness of this combined approach. For the human dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +1.0 % for cellular components, +1.1 % for molecular functions, and +0.5 % for biological processes. For the yeast dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +2.4 % for the cellular component, +1.2 % for molecular functions, and +0.6 % for biological processes.

Keywords: convolutional neural network; convolutional block attention module; gated recurrent unit; protein language models
Corresponding author: Lavkush Sharma, Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, India, E-mail: 

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

References

Bepler, T. and Berger, B. (2021). Learning the protein language: evolution, structure, and function. Cell Syst. 12: 654–669. https://doi.org/10.1016/j.cels.2021.05.017.Suche in Google Scholar PubMed PubMed Central

Bonetta, R. and Valentino, G. (2020). Machine learning techniques for protein function prediction. Proteins 88: 397–413. https://doi.org/10.1002/prot.25832.Suche in Google Scholar PubMed

Branden, C.I. and Tooze, J. (2012). Introduction to protein structure. Garland Sci. 1–414.10.1201/9781136969898Suche in Google Scholar

Cai, Y., Wang, J., and Deng, L. (2020). SDN2GO: an integrated deep learning model for protein function prediction. Front. Bioeng. Biotechnol. 8: 391, https://doi.org/10.3389/fbioe.2020.00391.Suche in Google Scholar PubMed PubMed Central

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing. Association for Computational Linguistics.10.3115/v1/D14-1179Suche in Google Scholar

Dallago, C., Mou, J., Johnston, K.E., Wittmann, B., Bhattacharya, N., Goldman, S., Madani, A., and Yang, K.K. (2021). FLIP: benchmark tasks in fitness landscape inference for proteins. Adv. Neural Inf. Process. Syst. 1.10.1101/2021.11.09.467890Suche in Google Scholar

Dinler, O.B. and Aydin, N. (2020). An optimal feature parameter set based on gated recurrent unit recurrent neural networks for speech segment detection. Appl. Sci. 10: 1273. https://doi.org/10.3390/app10041273.Suche in Google Scholar

Elnaggar, A., Heinzinger, M., Dallago, C., Rihawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al.. (2021). ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing. IEEE Trans. Patern Anal. Mach. Intell. 44: 7112–7127. https://doi.org/10.1109/tpami.2021.3095381.Suche in Google Scholar

Fan, K., Guan, Y., and Zhang, Y. (2020). Graph2GO: a multi-modal attributed network embedding method for inferring protein functions. GigaScience 9: 1–11, https://doi.org/10.1093/gigascience/giaa081.Suche in Google Scholar PubMed PubMed Central

Fang, W., Love, P.E., Luo, H., and Ding, L. (2020). Computer vision for behaviour-based safety in construction: a review and future directions. Adv. Eng. Inf. 43: 100980. https://doi.org/10.1016/j.aei.2019.100980.Suche in Google Scholar

Gers, F.A., Schmidhuber, J., and Cummins, F. (2000). Learning to forget: continual prediction with LSTM. Neural Comput. 2: 2451–2471. https://doi.org/10.1162/089976600300015015.Suche in Google Scholar PubMed

Giri, S.J., Dutta, P., Member, S., Halan, P., and Saha, S. (2020). MultiPredGO: deep multi-modal protein function prediction by amalgamating protein structure, sequence, and interaction information. IEEE J. Biomed. Health Inform. 25: 1832–1838.10.1109/JBHI.2020.3022806Suche in Google Scholar PubMed

Gligorijevic, V., Renfrew, P.D., Kosciolek, T., Leman, J.K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B.C., Fisk, I.M., Vlamakis, H., et al.. (2021). Structure-based protein function prediction using graph convolutional networks. Nat. Commun. 12: 3168. https://doi.org/10.1038/s41467-021-23303-9.Suche in Google Scholar PubMed PubMed Central

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., and Rost, B. (2019). Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinform. 20: 723. https://doi.org/10.1186/s12859-019-3220-8.Suche in Google Scholar PubMed PubMed Central

Hewamalage, H., Bergmeir, C., and Bandara, K. (2020). Recurrent neural networks for time series forecasting: current status and future directions. Int. J. Forecast. 37: 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008.Suche in Google Scholar

Huang, K., Fu, T., Glass, L.M., Zitnik, M., Xiao, C., and Sun, J. (2020). DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics 36: 5545–5547. https://doi.org/10.1093/bioinformatics/btaa1005.Suche in Google Scholar PubMed PubMed Central

Hunter, S., Apweiler, R., Attwood, T.K., Bairoch, A., Bateman, A., Binns, D., Bork, P., Das, U., Daugherty, L., Duquenne, L., et al.. (2009). Interpro: the integrative protein signature database. Nucleic Acids Res. 37: D211–D215. https://doi.org/10.1093/nar/gkn785.Suche in Google Scholar PubMed PubMed Central

Jagannatha, A.N. and Yu, H. (2016). Structured prediction models for RNN based sequence labeling in clinical text. In: Proceedings of the conference on empirical methods in natural language processing. Conference on empirical methods in natural language processing, Vol. 2016. NIH Public Access, p. 856.10.18653/v1/D16-1082Suche in Google Scholar

Jiang, Y., Oron, T.R., Clark, W.T., Bankapur, A.R., D’Andrea, D., Lepore, R., Funk, C.S., Kahanda, I., Verspoor, K.M., Ben-Hur, A., et al.. (2016). An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biol. 17: 184. https://doi.org/10.1186/s13059-016-1037-6.Suche in Google Scholar PubMed PubMed Central

Jones, S. and Thornton, J.M. (1996). Principles of protein-protein interactions. Proc. Natl. Acad. Sci. U. S. A. 93: 13–20. https://doi.org/10.1073/pnas.93.1.13.Suche in Google Scholar PubMed PubMed Central

Kabir, A. and Shehu, A. (2022). GOProFormer: a multi-modal transformer method for geneOntology protein function prediction. Biomolecules 12: 1709.10.3390/biom12111709Suche in Google Scholar PubMed PubMed Central

Kaleel, M., Zheng, Y., Chen, J., Feng, X., Simpson, J.C., Pollastri, G., and Mooney, C. (2020). SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by deep N-to-1 convolutional neural networks. Bioinformatics 36: 3343–3349. https://doi.org/10.1093/bioinformatics/btaa156.Suche in Google Scholar PubMed

Kingma, D.P. and Ba, J. (2014). Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980.Suche in Google Scholar

Kulmanov, M., Khan, M.A., and Hoehndorf, R. (2018). DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics 34: 660–668. https://doi.org/10.1093/bioinformatics/btx624.Suche in Google Scholar PubMed PubMed Central

Kulmanov, M., Zhapa-Camacho, F., and Hoehndorf, R. (2021). DeepGOWeb: fast and accurate protein function prediction on the (semantic) web. Nucleic Acids Res. 49: W140–W146. https://doi.org/10.1093/nar/gkab373.Suche in Google Scholar PubMed PubMed Central

Le Cun, B.B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., and Jackel, L.D. (1989). Handwritten digit recognition with a back-propagation network. In: Proceedings of the advances in neural information processing systems (NIPS), pp. 396–404.Suche in Google Scholar

Li, Y., Wang, S., Tian, Q., and Ding, X. (2015). Feature representation for statistical-learning-based object detection: a review. Pattern Recognit. 48: 3542–3559. https://doi.org/10.1016/j.patcog.2015.04.018.Suche in Google Scholar

Lopes, A.T., de Aguiar, E., De Souza, A.F., and Oliveira-Santos, T. (2017). Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recognit. 61: 610–628. https://doi.org/10.1016/j.patcog.2016.07.026.Suche in Google Scholar

Ma, B., Li, X., Xia, Y., and Zhang, Y. (2020). Autonomous deep learning: a genetic DCNN designer for image classification. Neurocomputing 379: 152–161. https://doi.org/10.1016/j.neucom.2019.10.007.Suche in Google Scholar

Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., and Rives, A. (2021). Language models enable zero-shot prediction of the effects of mutations on protein function. Adv. Neural Inf. Process. Syst. 34: 29287–29303.10.1101/2021.07.09.450648Suche in Google Scholar

Nogueira, K., Penatti, O.A., and dos Santos, J.A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognit. 61: 539–556. https://doi.org/10.1016/j.patcog.2016.07.001.Suche in Google Scholar

Piovesan, D. and Tosatto, S.C.E. (2019). INGA 2.0: improving protein function prediction for the dark proteome. Nucleic Acids Res. 47: W373–W378. https://doi.org/10.1093/nar/gkz375.Suche in Google Scholar PubMed PubMed Central

Qiu, X.-Y., Wu, H., and Shao, J. (2022). TALE-cmap: protein function prediction based on a TALE-based architecture and the structure information from contact map. Comput. Biol. Med. 149: 105938, https://doi.org/10.1016/j.compbiomed.2022.105938.Suche in Google Scholar PubMed

Ranjan, A., Fahad, M.S., Fernandez-Baca, D., Deepak, A., and Tripathi, S. (2019). Deep robust framework for protein function prediction using variable-length protein sequences. IEEE/ACM Trans. Comput. Biol. Bioinf. 17: 1648–1659, https://doi.org/10.1109/tcbb.2019.2911609.Suche in Google Scholar

Ranjan, A., Tiwari, A., and Deepak, A. (2023), A sub-sequence based approach to protein function prediction via multi-attention based multi-aspect network, Vol: 20, Issue: 1, pp. 94–105.10.1109/TCBB.2021.3130923Suche in Google Scholar PubMed

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., et al.. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. U. S. A. 118: 1–12, https://doi.org/10.1073/pnas.2016239118.Suche in Google Scholar PubMed PubMed Central

Sharma, L., Deepak, A., Ranjan, A., and Krishnasamy, G. (2023). A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction. Stat. Appl. Genet. Mol. Biol. 22: 20220057. https://doi.org/10.1515/sagmb-2022-0057.Suche in Google Scholar PubMed

Smaili, F.Z., Tian, S., Roy, A., Alazmi, M., Arold, S.T., Mukherjee, S., Hefty, P.S., Chen, W., and Gao, X. (2021). QAUST: protein function prediction using structure similarity, protein interaction, and functional motifs. Dev. Reprod. Biol. 19: 998–1011. https://doi.org/10.1016/j.gpb.2021.02.001.Suche in Google Scholar PubMed PubMed Central

Sønderby, S.K. and Winther, O. (2014). Protein secondary structure prediction with long short term memory networks, arXiv preprint arXiv:1412.7828.Suche in Google Scholar

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., HuertaCepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K.P., et al.. (2015). String v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43: D447–D452. https://doi.org/10.1093/nar/gku1003.Suche in Google Scholar PubMed PubMed Central

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst. 30: 6000–6010.Suche in Google Scholar

Visin, F., Kastner, K., Courville, A., Bengio, Y., Matteucci, M., and Cho, K. (2015). Reseg: a recurrent neural network for object segmentation. In: Proceedings of the IEEE conference on computer Vision and pattern recognition (CVPR) workshops.10.1109/CVPRW.2016.60Suche in Google Scholar

Widiastuti, N.I. (2019). Convolution neural network for text mining and natural language processing. IOP Conf. Ser. Mater. Sci. Eng. 662: 052010. https://doi.org/10.1088/1757-899X/662/5/052010.Suche in Google Scholar

Woo, S., Park, J., Lee, J.Y., and So Kweon, I. (2018). CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision. ECCV, pp. 3–19.10.1007/978-3-030-01234-2_1Suche in Google Scholar

You, R., Yao, S., Xiong, Y., Huang, X., Sun, F., Mamitsuka, H., and Zhu, S. (2019). Netgo: improving large-scale protein function prediction with massive network information. Nucleic Acids Res. 47: W379–W387. https://doi.org/10.1093/nar/gkz388.Suche in Google Scholar PubMed PubMed Central

Zhang, H., Fusong, J., Zhu, J., He, L., Shao, B., Zheng, N., and Liu, T.-Y. (2021). Co-evolution transformer for protein contact prediction. Adv. Neural Inf. Process. Syst. 34: 14252–14263.Suche in Google Scholar

Zhou, Y., Zhang, Y., Lian, X., Li, F., Wang, C., Zhu, F., Qiu, Y., and Chen, Y. (2022). Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res. 50: D1398–D1407. https://doi.org/10.1093/nar/gkab953.Suche in Google Scholar PubMed PubMed Central

Received: 2024-01-31
Accepted: 2024-06-07
Published Online: 2024-07-01

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Empirically adjusted fixed-effects meta-analysis methods in genomic studies
  4. A CNN-CBAM-BIGRU model for protein function prediction
  5. A heavy-tailed model for analyzing miRNA-seq raw read counts
  6. Flexible model-based non-negative matrix factorization with application to mutational signatures
  7. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data
  8. Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets
  9. A global test of hybrid ancestry from genome-scale data
  10. Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes
  11. Bayesian LASSO for population stratification correction in rare haplotype association studies
https://doi.org/10.1515/sagmb-2024-0004
Schlagwörter für diesen Artikel
convolutional neural network; convolutional block attention module; gated recurrent unit; protein language models

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Empirically adjusted fixed-effects meta-analysis methods in genomic studies
  4. A CNN-CBAM-BIGRU model for protein function prediction
  5. A heavy-tailed model for analyzing miRNA-seq raw read counts
  6. Flexible model-based non-negative matrix factorization with application to mutational signatures
  7. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data
  8. Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets
  9. A global test of hybrid ancestry from genome-scale data
  10. Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes
  11. Bayesian LASSO for population stratification correction in rare haplotype association studies
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