Gaussian-Based Dilated 1-D CNN for Classifying B-Cell Epitopes in Zika and Dengue Protein Sequences
Main Article Content
Abstract
The main goal of designing peptide vaccines, conducting immunodiagnosis, and producing antibodies is to accurately identify linear B-cell epitopes. However, experimental analysis to determine these epitopes is costly. This study focuses on developing a Gaussian-based dilated 1-D CNN model for classifying epitopes and non-epitopes in protein sequences related to Zika and Dengue viruses. The Immune Epitope Database (IEDB) was used, containing a total of 1741 and 7020 linear B-cell epitopes for Zika and Dengue viruses, respectively. Physicochemical features of the protein sequences dataset were extracted using the Gaussian distribution to extract optimal features based on feature probability distribution. The proposed model achieved an accuracy score of 83.00% and 85.00%, precision of 87.00%, recall of 83.00% and 85.00%, and an F1-score of 84.00% and 86.00% over the Zika and Dengue datasets. The suggested model outperforms existing methods, demonstrating the potential of deep learning approaches in bioinformatics for enhancing epitope prediction in viruses, with implications for drug discovery and vaccine development.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Murphy, K., and Weaver, C. (2012). “The induced responses of innate immunity” in Janeway's Immunobiology. 8th ed eds. J. Scobie, E. Lawrence, J. Moldovan, G. Lucas, B. Goatly, and M. Toledo (New York, NY: Garland Science), 75-125.
Sable, N.P., Rathod, V.U. (2023). Rethinking Blockchain and Machine Learning for Resource-Constrained WSN. In: Neustein, A., Mahalle, P.N., Joshi, P., Shinde, G.R. (eds) AI, IoT, Big Data and Cloud Computing for Industry 4.0. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-29713-7_17.
Shirai, H., Prades, C., Vita, R., Marcatili, P., Popovic, B., Xu, J., et al. (2014). Antibody informatics for drug discovery. Biochim Biophys Acta 1844, 2002–2015. doi: 10.1016/j.bbapap.2014.07.006.
Nilesh P. Sable, Vijay U. Rathod, Parikshit N. Mahalle, Jayashri Bagade, Rajesh Phursule ; Internet of Things-based Smart Sensing Mechanism for Mining Applications, Industry 4.0 Convergence with AI, IoT, Big Data and Cloud Computing: Fundamentals, Challenges and Applications IoT and Big Data Analytics (2023) 4: 132. https://doi.org/10.2174/9789815179187123040012.
Reta, D. H., Tessema, T. S., Ashenef, A. S., Desta, A. F., Labisso, W. L., Gizaw, S. T., Abay, S. M., Melka, D. S., & Reta, F. A. (2020). Molecular and Immunological Diagnostic Techniques of Medical Viruses. International journal of microbiology, 2020, 8832728. https://doi.org/10.1155/2020/8832728.
V. U. Rathod and S. V. Gumaste, “Role of Deep Learning in Mobile Ad-hoc Networks”, IJRITCC, vol. 10, no. 2s, pp. 237–246, Dec. 2022.
Stech, Marlitt, and Stefan Kubick. 2015. "Cell-Free Synthesis Meets Antibody Production: A Review" Antibodies 4, no. 1: 12-33. https://doi.org/10.3390/antib4010012.
N. P. Sable, V. U. Rathod, P. N. Mahalle, and P. N. Railkar, “An Efficient and Reliable Data Transmission Service using Network Coding Algorithms in Peer-to-Peer Network”, IJRITCC, vol. 10, no. 1s, pp. 144–154, Dec. 2022.
Parker JM, Guo D, Hodges RS. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry. 1986; 25:5425–32.
N. P. Sable, R. Sonkamble, V. U. Rathod, S. Shirke, J. Y. Deshmukh, and G. T. Chavan, “Web3 Chain Authentication and Authorization Security Standard (CAA)”, IJRITCC, vol. 11, no. 5, pp. 70–76, May 2023.
Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis a virus-neutralizing antibody by a virus-specific synthetic peptide. J. Virol. 1985;55:836–9.
Vijay U. Rathod* & Shyamrao V. Gumaste, “Effect Of Deep Channel To Improve Performance On Mobile Ad-Hoc Networks”, J. Optoelectron. Laser, vol. 41, no. 7, pp. 754–756, Jul. 2022.
Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276:172–4.
Rathod, V.U. and Gumaste, S.V., 2022. Role of Neural Network in Mobile Ad Hoc Networks for Mobility Prediction. International Journal of Communication Networks and Information Security, 14(1s), pp.153-166.
Singh H, Ansari HR, Raghava GPS. Improved method for linear B-cell epitope prediction using Antigen’s primary sequence. PLoS One. 2013;8:e62216.
Y. Mali, Vijay U. Rathod “A Comparative Analysis of Machine Learning Models for Soil Health Prediction and Crop Selection”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 11, no. 10s, pp. 811–828, Aug. 2023.
Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45:W24–9.
N. P. Sable, V. U. Rathod, M. D. Salunke, H. B. Jadhav, R. S. Tambe, and S. R. Kothavle, “Enhancing Routing Performance in Software-Defined Wireless Sensor Networks through Reinforcement Learning”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 11, no. 10s, pp. 73–83, Aug. 2023.
Potocnakova L, Bhide M, Pulzova LB. An introduction to B-cell epitope mapping and in Silico epitope prediction. J Immunol Res. 2016; 2016:6760830.
Zeng, Xincheng, Ganggang Bai, Chuance Sun, and Buyong Ma. 2023. "Recent Progress in Antibody Epitope Prediction" Antibodies 12, no. 3: 52. https://doi.org/10.3390/antib12030052.
Vijay U. Rathod, Yogesh Mali, Nilesh Sable, Deepika Ajalkar, M. Bharathi, and N. Padmaja,” A Network-Centred Optimization Technique for Operative Target Selection”, Journal of Electrical Systems (JEs), vol. 19, no. 2s, pp. 87–96, 2023.
Sato, K., Oide, M. & Nakasako, M. Prediction of hydrophilic and hydrophobic hydration structure of protein by neural network optimized using experimental data. Sci Rep 13, 2183 (2023). https://doi.org/10.1038/s41598-023-29442-x.
Syrlybaeva, R., & Strauch, E. M. (2023). Deep learning of protein sequence design of protein-protein interactions. Bioinformatics (Oxford, England), 39(1), btac733. https://doi.org/10.1093/bioinformatics/btac733.
Wang, Jingjing, Chang Chen, Ge Yao, Junjie Ding, Liangliang Wang, and Hui Jiang. 2023. "Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review" Molecules 28, no. 23: 7865. https://doi.org/10.3390/molecules28237865.
Xia, Y. L., Li, W., Li, Y., Ji, X. L., Fu, Y. X., & Liu, S. Q. (2021). A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2. Computational and mathematical methods in medicine, 2021, 9997669. https://doi.org/10.1155/2021/9997669.
Odorico, M. & Pellequer, J. L. (2003) BEPITOPE: Predicting the location of continuous epitopes and patterns in proteins. J. Mol. Recognit. 16(1), 20-22.
N. P. Sable, V. U. Rathod, R. Sable and G. R. Shinde, "The Secure E-Wallet Powered by Blockchain and Distributed Ledger Technology," 2022 IEEE Pune Section International Conference (PuneCon), Pune, India, 2022, pp. 1-5, doi: 10.1109/PuneCon55413.2022.10014893.
V. U. Rathod and S. V. Gumaste, "Role of Routing Protocol in Mobile Ad-Hoc Network for Performance of Mobility Models," 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, 2023, pp. 1-6, doi: 10.1109/I2CT57861.2023.10126390.
Alix, A. J. P. (1999) Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine 18(3-4), 311–314.
Bahai, A., Asgari, E., Mofrad, M. R. K., Kloetgen, A., & McHardy, A. C. (2021). EpitopeVec: linear epitope prediction using deep protein sequence embeddings. Bioinformatics (Oxford, England), 37(23), 4517–4525. https://doi.org/10.1093/bioinformatics/btab467.
Kozlova EEG, Cerf L, Schneider FS, et al. Computational b‐cell epitope identification and production of neutralizing murine antibodies against atroxlysin‐i. Sci Rep. 2018;8(1):14904.
N. P. Sable, V. U. Rathod, P. N. Mahalle and D. R. Birari, "A Multiple Stage Deep Learning Model for NID in MANETs," 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2022, pp. 1-6, doi: 10.1109/ESCI53509.2022.9758191.
N. P. Sable, M. D. Salunke, V. U. Rathod and P. Dhotre, "Network for Cross-Disease Attention to the Severity of Diabetic Macular Edema and Joint Retinopathy," 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 2022, pp. 1-7, doi: 10.1109/SMARTGENCON56628.2022.10083936.
Han, W., Chen, N., Xu, X. et al. Predicting the antigenic evolution of SARS-COV-2 with deep learning. Nat Commun 14, 3478 (2023). https://doi.org/10.1038/s41467-023-39199-6.
Clifford, J. N., Høie, M. H., Deleuran, S., Peters, B., Nielsen, M., & Marcatili, P. (2022). BepiPred-3.0: Improved B-cell epitope prediction using protein language models. Protein science : a publication of the Protein Society, 31(12), e4497. https://doi.org/10.1002/pro.4497.
Shen, W., Cao, Y., Cha, L., Zhang, X., Ying, X., Zhang, W., Ge, K., Li, W., & Zhong, L. (2015). Predicting linear B-cell epitopes using amino acid anchoring pair composition. BioData mining, 8, 14. https://doi.org/10.1186/s13040-015-0047-3.
Sweredoski, M. J., & Baldi, P. (2009). COBEpro: a novel system for predicting continuous B-cell epitopes. Protein engineering, design & selection : PEDS, 22(3), 113–120. https://doi.org/10.1093/protein/gzn075.
V. U. Rathod, N. P. Sable, N. N. Thorat and S. N. Ajani, "Deep Learning Techniques Using Lightweight Cryptography for IoT Based E-Healthcare System," 2023 3rd International Conference on Intelligent Technologies (CONIT), Hubli, India, 2023, pp. 1-5, doi: 10.1109/CONIT59222.2023.10205808.
V. U. Rathod, Y. Mali, R. Sable, M. D. Salunke, S. Kolpe and D. S. Khemnar, "The Application of CNN Algorithm in COVID-19 Disease Prediction Utilising X-Ray Images," 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, 2023, pp. 1-6, doi: 10.1109/ASIANCON58793.2023.10270221.
Yao B, Zhang L, Liang S, Zhang C (2012) SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity. PLoS ONE 7(9): e45152. https://doi.org/10.1371/journal.pone.0045152.
Maximilian Collatz, Florian Mock, Martin Hölzer, Emanuel Barth, Konrad Sachse, Manja Marz, (2020). EpiDope: A Deep neural network for linear B-cell epitope prediction, bioRxiv 2020.05.12.090019; doi: https://doi.org/10.1101/2020.05.12.090019.
Akash Bahai, Ehsaneddin Asgari, Mohammad R K Mofrad, Andreas Kloetgen, Alice C McHardy, EpitopeVec: linear epitope prediction using deep protein sequence embeddings, Bioinformatics, Volume 37, Issue 23, December 2021, Pages 4517–4525, https://doi.org/10.1093/bioinformatics/btab467.
Janghel RR, Raja R, Cengiz K, Raja H (2022) Next generation healthcare systems using soft computing techniques. CRC Press, New York.
R. A. Mulla, Y. Mali, V. U. Rathod, R. S. Tambe, R. Shirbhate and R. Agnihotri, "Enhancing Query Performance Using Simultaneous Execution and Vertical Query Splitting," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-4, doi: 10.1109/ICCCNT56998.2023.10307920.
Y. Mali, V. U. Rathod, R. S. Tambe, R. Shirbhate, D. Ajalkar and P. Sathawane, "Group-Based Framework for Large Files Downloading," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-4, doi: 10.1109/ICCCNT56998.2023.10308339.
Y. Mali, V. U. Rathod, D. Ajalkar, D. S. Khemnar, S. Kolpe and S. Patil, "Role of Blockchain in Health Application using Blockchain Sharding," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-6, doi: 10.1109/ICCCNT56998.2023.10306760.
Angaitkar, P., Janghel, R.R. & Sahu, T.P. DL-TCNN: Deep Learning-based Temporal Convolutional Neural Network for prediction of conformational B-cell epitopes. 3 Biotech 13, 297 (2023). https://doi.org/10.1007/s13205-023-03716-7.
Angaitkar, P., Janghel, R.R. & Sahu, T.P. gHPCSO: Gaussian Distribution Based Hybrid Particle Cat Swarm Optimization for Linear B-cell Epitope Prediction. Int. j. inf. tecnol. 15, 2805–2818 (2023). https://doi.org/10.1007/s41870-023-01294-8.
Cong Sun, Zhihao Yang, Leilei Su, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang, Chemical–protein interaction extraction via Gaussian probability distribution and external biomedical knowledge, Bioinformatics, Volume 36, Issue 15, August 2020, Pages 4323-4330, https://doi.org/10.1093.
Zhen, X., Chakraborty, R., Vogt, N., Bendlin, B. B., & Singh, V. (2019). Dilated Convolutional Neural Networks for Sequential Manifold-valued Data. Proceedings. IEEE International Conference on Computer Vision, 2019, 10620–10630. https://doi.org/10.1109/iccv.2019.01072.
V. U. Rathod, Y. K. Mali, N. P. Sable, R. R. Rathod, M. N. Rathod and N. A. Rathod, "The Use of Blockchain Technology to Verify KYC Documents," 2023 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS), New Raipur, India, 2023, pp. 1-6, doi: 10.1109/ICBDS58040.2023.10346414.
Y. K. Mali, V. U. Rathod, M. D. Salunke, S. B. Satish, P. Dhamdhere and R. R. Rathod, "Role of IoT in Coal Miner Safety Helmets," 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany, 2023, pp. 221-225, doi: 10.1109/ICCCMLA58983.2023.10346793.
Liu, F., Yuan, C., Chen, H. et al. Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings. Sci Rep 14, 2464 (2024). https://doi.org/10.1038/s41598-024-53028-w.
Ras-Carmona, A., Lehmann, A.A., Lehmann, P.V. et al. Prediction of B cell epitopes in proteins using a novel sequence similarity-based method. Sci Rep 12, 13739 (2022). https://doi.org/10.1038/s41598-022-18021-1.
M. D. Salunke, V. U. Rathod, Y. K. Mali, R. S. Tambe, A. A. Dange and S. R. Kothavle, "A Prediction and Classification Process for DDoS Attacks Using Machine Learning," 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2023, pp. 1-6, doi: 10.1109/ICCUBEA58933.2023.10392278.
Khanna, D. and Rana, P.S. (2020), Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach. IET Syst. Biol., 14: 1-7. https://doi.org/10.1049/iet-syb.2018.5083.
Qi Y, Zheng P and Huang G (2023) DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes. Front. Microbiol. 14:1117027. doi: 10.3389/fmicb.2023.1117027.
V. U. Rathod and S. V. Gumaste, "An Effect on Mobile Ad-Hoc Networks for Load Balancing Through Adaptive Congestion Routing," 2023 International Conference on Integration of Computational Intelligent System (ICICIS), Pune, India, 2023, pp. 1-5, doi: 10.1109/ICICIS56802.2023.10430257.