Machine learning (ML) is transforming protein structure prediction. Algorithms can predict 3D structures from amino acid sequences, surpassing slower, more expensive traditional methods.
ML-based tools like AlphaFold offer faster, more accessible, and increasingly accurate predictions, accelerating drug discovery and disease research.
Image Credit: S. Singha/Shutterstock.com
Introduction
Protein structure prediction has long been a central challenge in molecular biology and biochemistry. Understanding the three-dimensional structure of proteins is crucial for various biological processes, drug design, and disease research.¹
Traditionally, experimental methods like X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy have been used to determine protein structures. Still, these methods can be expensive, time-consuming, and technically challenging.¹
The ability to predict protein structures with high accuracy from their amino acid sequences has become a key focus of research in computational biology.² Over the past decade, machine learning (ML) has revolutionized this field, offering more efficient and precise tools for protein structure prediction.²
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How Machine Learning Enhances Protein Structure Prediction
Proteins play a fundamental role in many biological processes.¹ Their function is linked to their three-dimensional structure, a complex architecture determined by the sequence of amino acids and their interactions.³
Deciphering these structures is key for understanding disease mechanisms, developing new drugs, and advancing biotechnology.³ Traditionally, determining protein structure has been a laborious and expensive task, relying heavily on experimental techniques.⁴
However, machine learning (ML) has redefined protein structure prediction, offering a faster, more accessible, and increasingly accurate alternative.⁵ While these techniques are powerful, they often present significant challenges.
X-ray crystallography involves coaxing proteins into a crystalline form and analyzing how X-rays diffract through the crystal lattice.⁶ Although it can produce high-resolution structures, the crystallization of certain proteins, especially membrane proteins, can be challenging.⁶
Nuclear Magnetic Resonance (NMR) spectroscopy utilizes magnetic fields to study the behavior of atomic nuclei in a protein.⁷ Cryo-electron microscopy (Cryo-EM) involves rapidly freezing a protein solution and imaging it with an electron microscope.⁸
The introduction of machine learning has provided a data-driven approach to protein structure prediction, enabling algorithms to analyze complex patterns and relationships within large datasets.⁴
Developed by DeepMind, AlphaFold is a deep learning model that has demonstrated accuracy in predicting protein structures.⁹ It utilizes multiple sequence alignments (MSAs) to capture evolutionary information, predicts inter-residue distances to define the protein's topology, and generates highly accurate 3D models.⁹
Following AlphaFold's success, numerous other ML-based methods have emerged, further pushing the boundaries of structure prediction.⁹⁻¹⁰ These methods explore different architectures, training datasets, and optimization techniques to improve accuracy and efficiency, such as RoseTTAFold, ProteinBERT, DeepFold, and ESMFold.⁹⁻¹⁰
Applications in Drug Discovery and Biotechnology
A better understanding of protein structures helps researchers identify druggable targets, design more effective drugs, and study the molecular basis of diseases.¹¹ In drug design, ML can assist in predicting how small molecules interact with proteins, helping to identify potential therapeutic agents.¹¹
Additionally, ML models can be used to predict how mutations in proteins lead to diseases, possibly enabling the development of personalized treatments based on a patient’s genetic makeup.¹²⁻¹³
For example, ProtGPS is an AI tool that predicts protein localization in cells and how mutations affect disease by identifying related functional disruptions.¹² In the same context, ProMEP (Protein Mutational Effect Predictor) enables zero-shot prediction of mutation effects without requiring multiple sequence alignments.¹³
Machine learning has also been used to predict the structures of proteins involved in the SARS-CoV-2 virus, aiding in the development of vaccines and antiviral drugs.¹⁴ Similarly, ML can help identify new drug targets for diseases like Alzheimer’s or related neurodegenerative disorders and even accelerate treatment through drug repurposing.¹⁵
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Commercialization and Industry Growth
The revolution in protein structure prediction fueled by machine learning is not just a scientific breakthrough; it's creating a burgeoning industry.2,9-10 Companies like DeepMind (AlphaFold) and others, such as RoseTTAFold, are commercializing their platforms, offering access to powerful prediction algorithms and analysis tools. 2,9-10
This is driving growth in sectors like drug discovery, where faster and more accurate structure predictions accelerate the development of new therapeutics.²
The market for ML-based protein structure prediction is expected to expand significantly in the coming years, fueled by increasing demand and ongoing advancements in the technology.9
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Future Directions and Challenges
Protein structure prediction faces several key challenges. Accurately modeling multidomain interactions, protein complex formation, and multiple conformational states remains difficult.¹⁶ Predicting folding pathways and the structures of intrinsically disordered proteins also poses significant hurdles.¹⁶
Future directions include improving accuracy for challenging cases, such as proteins with limited evolutionary information or unusual folds.¹⁶ Integrating diverse experimental data and predicting protein function from the structure are crucial next steps.¹⁶
Scaling models to handle large complexes, improving computational efficiency, and addressing biases in training data are also essential for advancing the field.¹⁶
Conclusion
Machine learning has transformed the field of protein structure prediction by providing powerful tools that can accurately predict the three-dimensional structure of proteins from their amino acid sequences.⁹
Deep learning techniques, evolutionary data, generative models, and reinforcement learning are just some of the methods that have been employed to enhance prediction accuracy.⁹⁻¹³
AlphaFold's success has demonstrated the tremendous potential of machine learning in advancing the understanding of biology and medicine.⁹
The applications of ML in drug discovery, disease research, and personalized medicine are vast. As these technologies continue to evolve, they are expected to play an increasingly important role in biological research and therapeutic development.¹²⁻¹³
With the continued integration of machine learning with experimental data, the future of protein structure prediction looks more promising than ever before.¹⁶
References
- Al-Lazikani, B., Jung, J., Xiang, Z., & Honig, B. (2001). Protein structure prediction. Current Opinion in Chemical Biology, 5(1), 51–56. https://doi.org/10.1016/S1367-5931(00)00164-2
- Jumper, J., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
- Klepeis, J. L., et al. (2009). Long-timescale molecular dynamics simulations of protein structure and function. Current Opinion in Structural Biology, 19(2), 120–127.
- Hou, J., et al. (2019). Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins, 87(12), 1165–1178. https://doi.org/10.1002/prot.25697
- Krepel, D., et al. (2018). Deciphering the structure of the condensin protein complex. Proceedings of the National Academy of Sciences of the United States of America, 115(47), 11911–11916. https://doi.org/10.1073/pnas.1812770115
- Kermani, A. A. (2020). A guide to membrane protein x‐ray crystallography. The FEBS Journal, 288(20), 5788–5804. https://doi.org/10.1111/febs.15676
- Hu, Y., et al. (2021). NMR-based methods for protein analysis. Analytical Chemistry, 93(4), 1866–1879. https://doi.org/10.1021/acs.analchem.0c03830
- Doerr, A. (2015). Single-particle cryo-electron microscopy. Nature Methods, 13(1), 23–23. https://doi.org/10.1038/nmeth.3700
- Baek, M., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science (New York, N.Y.), 373(6557), 871–876. https://doi.org/10.1126/science.abj8754
- Chen, L., et al. (2024). AI-driven deep learning techniques in protein structure prediction. International Journal of Molecular Sciences, 25(15), 8426. https://doi.org/10.3390/ijms25158426
- Trepte, P., et al. (2024). AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-COV-2 inhibitor. Molecular Systems Biology, 20(4), 428–457. https://doi.org/10.1038/s44320-024-00019-8
- Cheng, P., et al. (2024). Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering. Cell Research, 34(9), 630–647. https://doi.org/10.1038/s41422-024-00989-2
- Kilgore, H. R., et al. (2024). Protein codes promote selective subcellular compartmentalization [Preprint]. https://doi.org/10.1101/2024.04.15.589616
- Melero, R., et at. (2020). Continuous flexibility analysis of SARS-CoV-2 Spike prefusion structures. bioRxiv: the preprint server for biology, 2020.07.08.191072. https://doi.org/10.1101/2020.07.08.191072
- Cheng, F., et al. (2024). Artificial Intelligence and open science in the discovery of disease-modifying medicines for Alzheimer’s disease. Cell Reports Medicine, 5(2), 101379. https://doi.org/10.1016/j.xcrm.2023.101379
- Sapoval, N., et al. (2022). Current progress and open challenges for applying deep learning across the biosciences. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-29268-7
Further Reading