Deep Learning for Protein Structure Prediction: From AlphaFold to the Next Frontier
DOI:
https://doi.org/10.31033/ABJAR/5.3.2026.122Keywords:
AlphaFold, deep learning, protein structure prediction, AlphaFold3, ESMFold, ESM3, RoseTTAFold, RFdiffusion, protein designAbstract
Protein structure is closely linked with protein function. For many decades, scientists tried to predict the three-dimensional structure of a protein from its amino acid sequence. Experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy provide reliable structures. However, these methods can be expensive and time-consuming. Computational methods therefore became important alternatives. Early prediction methods were based mainly on sequence similarity, physical energy functions, and structural templates. Their accuracy was limited for many proteins.
Deep learning changed this field. AlphaFold2 showed that artificial intelligence can predict the structures of many proteins with near-experimental accuracy (Jumper et al., 2021). RoseTTAFold provided another powerful deep-learning approach (Baek et al., 2021). Protein language models such as ESMFold later showed that structural information can also be learned directly from very large collections of protein sequences (Lin et al., 2023). AlphaFold3 further expanded the field by predicting interactions among proteins, DNA, RNA, ions, small molecules, and modified residues (Abramson et al., 2024). Generative models such as RFdiffusion and ESM3 are now moving the field from structure prediction toward protein design (Watson et al., 2023; Hayes et al., 2025). Recent open models are also increasing access to advanced biomolecular modelling.
Despite this progress, major challenges remain. Proteins are dynamic molecules. They may adopt several conformations. Their structures are influenced by ligands, membranes, modifications, and the cellular environment. Future models therefore need to predict not only one structure, but also molecular dynamics, interactions, binding strength, and biological function. This review describes the development of deep learning for protein structure prediction and discusses the major directions that may define the next frontier.
Downloads
References
Abramson, J., Adler, J., Dunger, J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630, 493–500. doi:10.1038/s41586-024-07487-w.
Baek, M., DiMaio, F., Anishchenko, I., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373, 871–876. doi:10.1126/science.abj8754.
Callaway, E. (2026). AlphaFold database hits ‘next level’: The AI system now includes protein pairing. Nature. doi:10.1038/d41586-026-00787-3.
Dauparas, J., Anishchenko, I., Bennett, N., et al. (2022). Robust deep learning-based protein sequence design using Protein MPNN. Science, 378, 49–56. doi:10.1126/science.add2187.
Hayes, T., Rao, R., Akin, H., et al. (2025). Simulating 500 million years of evolution with a language model. Science, 387, 850–858. doi:10.1126/science.ads0018.
Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. doi:10.1038/s41586-021-03819-2.
Lin, Z., Akin, H., Rao, R., et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379, 1123–1130. doi:10.1126/science.ade2574.
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: Making protein folding accessible to all. Nature Methods, 19, 679–682. doi:10.1038/s41592-022-01488-1.
Senior, A. W., Evans, R., Jumper, J., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577, 706–710. doi:10.1038/s41586-019-1923-7.
Tunyasuvunakool, K., Adler, J., Wu, Z., et al. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596, 590–596. doi:10.1038/s41586-021-03828-1.
Varadi, M., Bertoni, D., Magana, P., et al. (2024). AlphaFold Protein Structure Database in 2024: Providing structure coverage for over 214 million protein sequences. Nucleic Acids Research, 52, D368–D375. doi:10.1093/nar/gkad1011.
Watson, J. L., Juergens, D., Bennett, N. R., et al. (2023). De novo design of protein structure and function with RFdiffusion. Nature, 620, 1089–1100. doi:10.1038/s41586-023-06415-8.
Wohlwend, J., Corso, G., Passaro, S., et al. (2025). Boltz-1: Democratizing biomolecular interaction modeling. bioRxiv. doi:10.1101/2024.11.19.624167.
Zhang, Y., Gong, C., Zhang, H., Ma, W., Liu, Z., Chen, X., Guan, J., Wang, L., Yang, Y., Xia, Y., & Xiao, W. (2026). Protenix-v1: Toward high-accuracy open-source biomolecular structure prediction. bioRxiv. doi:10.64898/2026.02.05.703733.
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Everest Shiwach, Sandeep Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Research Articles in 'Applied Science and Biotechnology Journal for Advanced Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
