Deep learning improves the accuracy and efficiency of protein structure prediction

A review exploring the transformative role of deep learning techniques in revolutionizing protein structure prediction in the rapidly evolving field of computational biology. The review, published in MedComm—Future Medicine, was led by Dr. Xi Yu and Dr. Tian Zhong from the School of Medicine, Macau University of Science and Technology.

This article broadly covers the integration of deep learning techniques in the field of protein structure prediction, highlighting notable advances, comparing them with traditional computational methods, and comparing traditional computational methods to modern deep learning models, e.g. (e.g. AlphaFold 3). Accuracy and coverage of protein prediction.

Proteins are the basis of life activities, and their functional roles are determined by their three-dimensional structure. Accurate protein structure prediction is important for deciphering the functional mechanisms of biomolecules, which exemplifies the central “structure-function” paradigm of molecular biology and improves our understanding of life processes.

Researchers have long relied on experimental techniques such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy to elucidate protein structures. However, these methods are time-consuming and costly, and require specialized knowledge to analyze the data. In recent years, the rapid rise of deep learning technologies, particularly models such as AlphaFold 2, has dramatically improved the accuracy and efficiency of “end-to-end” predictions ranging from amino acid sequences to the three-dimensional structure of proteins.

“Deep learning techniques are changing the research landscape for protein structure prediction,” explains lead author Dr. Xi Yu. “This not only overcomes the limitations of traditional experimental methods, but also provides unprecedented predictive accuracy, offering great potential for drug development and disease research.”

This review article focuses on the following key developments and challenges:

Evolution of protein structure prediction techniques: from traditional template-based and template-free modeling techniques to the application of modern deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and network architectures such as Transformer. . This has dramatically improved the accuracy and efficiency of protein structure prediction. AlphaFold Breakthrough: AlphaFold 2 achieves 98.5% accuracy for human protein structure predictions through its innovative Transformer network. The Evoformer module is utilized to process multiple sequence comparison data and combined with the 3D equivalent structure module to achieve atomic structures. Level protein 3D structure prediction marks a new era in protein structure prediction. Multimodal Prediction: The latest AlphaFold 3 model, combined with diffusion optimization techniques, further facilitates the prediction of complex biomolecular structures such as proteins, nucleic acids, and small molecule complexes. Technology applications and future directions: Deep learning improves protein structure prediction and opens new possibilities for drug discovery, antibody development, and synthetic biology.

“As deep learning technology continues to advance, we expect the application of protein structure prediction to expand dramatically, opening new opportunities across all areas of life sciences,” added co-author Dr. Zhong. Ta.

This review paper is published at a critical moment in protein structure prediction research. With the rapid development of deep learning technology, researchers are gradually solving problems that have long plagued the field, advancing protein structure prediction from basic research to practical application, and providing new solutions for disease treatment and drug discovery. is provided.

“The potential of deep learning lies not only in improving prediction accuracy, but also in bringing new perspectives to biological research and providing a deeper understanding of complex biomolecular networks and their functions,” said Dr. Yu. concluded.

This review also discusses the potential of deep learning techniques in other areas of computational biology, particularly in multimodal prediction of complex biomolecular structures, providing essential guidelines for future scientific research.