Harnessing the Potential of Deep Learning to Improve Protein Structure Prediction: Challenges and Strategies
DOI:
https://doi.org/10.54097/8n774f38Keywords:
Protein structure prediction; Deep learning; Challenges; StrategiesAbstract
Proteins serve as the essential functional units of life, and understanding their three-dimensional structures is crucial for uncovering the biological mechanisms at play. Recent advancements in deep learning technologies, particularly those exemplified by AlphaFold, have transformed the conventional approaches to structure prediction. By utilizing a combination of residual neural network architectures and co-evolutionary features, these methods have achieved prediction accuracies that approach experimental results, ushering in a new era of intelligent modeling within computational biology. However, several core challenges persist in the realm of deep learning-driven structure prediction. These include the scarcity and quality issues surrounding training data, which can hinder model performance; the reliance on substantial computational resources, which limits the universality of algorithms; the difficulty in interpreting biophysical mechanisms due to the black-box nature of these models; and the challenges associated with sequence homology, particularly in accurately predicting the structures of orphan proteins. To address these issues, this paper proposes a range of multi-dimensional optimization strategies aimed at enhancing the efficacy of deep learning in protein structure prediction. In conclusion, the paper offers a forward-looking perspective on potential future research directions in this rapidly evolving field.
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[1] Mihăşan, M. and A.I. Cuza, Basic protein structure prediction for the biologist: A review. Archives of Biological Sciences, 2010. 62: p. 857-871.
[2] Chang, Y., et al., A Guide to In Silico Drug Design. Pharmaceutics, 2022. 15(1).
[3] Meier, A. and J. Söding, Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling. PLoS Comput Biol, 2015. 11(10): p. e1004343.
[4] Kairys, V., M.K. Gilson, and M.X. Fernandes, Using protein homology models for structure-based studies: approaches to model refinement. ScientificWorldJournal, 2006. 6: p. 1542-54.
[5] Vallat, B., C. Madrid-Aliste, and A. Fiser, Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures. PLoS Comput Biol, 2015. 11(8): p. e1004419.
[6] Wuyun, Q., et al., Recent Progress of Protein Tertiary Structure Prediction. Molecules, 2024. 29.
[7] Ouyang-Zhang, J., et al. Predicting a Protein's Stability under a Million Mutations. in Neural Information Processing Systems. 2023.
[8] Yousef, M. and J. Allmer, Deep learning in bioinformatics. Turk J Biol, 2023. 47(6): p. 366-382.
[9] Auslander, N., A.B. Gussow, and E.V. Koonin, Incorporating Machine Learning into Established Bioinformatics Frameworks. International Journal of Molecular Sciences, 2021. 22.
[10] Pearce, R., et al., Fast and accurate Ab Initio Protein structure prediction using deep learning potentials. PLoS Computational Biology, 2022. 18.
[11] Zhang, Z., et al. Geometric Deep Learning for Structure-Based Drug Design: A Survey. 2023.
[12] Berman H M, Burley S K. Protein Data Bank (PDB): Fifty-three years young and having a transformative impact on science and society. Quarterly reviews of biophysics, 2025, 58: e9.
[13] Min, S., et al., Pre-Training of Deep Bidirectional Protein Sequence Representations With Structural Information. IEEE Access, 2019. 9: p. 123912-123926.
[14] Liu, L., et al., Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models. ArXiv, 2023. abs/2310.13913.
[15] Golyadkin, M., et al., Refining the ONCE Benchmark with Hyperparameter Tuning. IEEE Access, 2023. 12: p. 3805-3814.
[16] Cai, D., Z. Cai, and M. Li, Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning. ArXiv, 2024. abs/2401.01473.
[17] Burley, S.K., et al., RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Research, 2022. 51: p. D488 - D508.
[18] King, J.E. and D.R. Koes, SidechainNet: An all‐atom protein structure dataset for machine learning. Proteins: Structure, 2020. 89: p. 1489 - 1496.
[19] Khan, A., et al., A Survey of the Self Supervised Learning Mechanisms for Vision Transformers. ArXiv, 2024. abs/2408.17059.
[20] Wang, G., et al., HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle. ArXiv, 2022. abs/2207.05477.
[21] Hu, B., et al. Advances of Deep Learning in Protein Science: A Comprehensive Survey. 2024.
[22] Li, Z., J. Yu, and Q. Ye, SGNet: Folding Symmetrical Protein Complex with Deep Learning. ArXiv, 2024. abs/2403.04395.
[23] Ma, X. and D. Si, Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps. ArXiv, 2024. abs/2410.23321.
[24] Park, H.S., et al., APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics. Proceedings of the National Academy of Sciences of the United States of America, 2023. 121.
[25] Zhong, B., et al., ParaFold: Paralleling AlphaFold for Large-Scale Predictions. International Conference on High Performance Computing in Asia-Pacific Region Workshops, 2021.
[26] Vig, J., et al., BERTology Meets Biology: Interpreting Attention in Protein Language Models. bioRxiv, 2020.
[27] Xu, S. and L. Xie, Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure. ArXiv, 2024. abs/2410.05278.
[28] Simon, E. and J.Y. Zou, InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders. bioRxiv, 2024.
[29] Zhang, M. and Y. Cui, Self-supervised learning-based emotion recognition using physiological signals. Frontiers in Human Neuroscience, 2024. 18.
[30] Wadhawan, K., et al., Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences with Attention. ArXiv, 2021. abs/2108.08077.
[31] Chen, D., et al., Endowing Protein Language Models with Structural Knowledge. ArXiv, 2024. abs/2401.14819.
[32] Kyro, G.W., T. Qiu, and V.S. Batista. A Model-Centric Review of Deep Learning for Protein Design. 2025.
[33] Zheng, Y., et al., Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials. ArXiv, 2024. abs/2409.04481.
[34] Zhang, L., et al., Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation. ArXiv, 2023. abs/2306.01824.
[35] Mirabello, C. and B. Wallner, rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments. PLoS ONE, 2019. 14.
[36] Chen, B., et al., MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training. bioRxiv, 2024.
[37] Rozewicki, J., et al., MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Research, 2019. 47: p. W5 - W10.
[38] Son, A., et al., Integrating Computational Design and Experimental Approaches for Next-Generation Biologics. Biomolecules, 2024. 14.
[39] Desai, D., et al., Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics. Cureus, 2024. 16.
[40] Wei, J., et al., Protein–RNA interaction prediction with deep learning: structure matters. Briefings in Bioinformatics, 2021. 23.
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