Chongfan Technology
News
07
2024
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08
Nature is heavy! Break the questioning! AIphaFold3 will solve the century-old historical problem!
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On May 8, 2024, Google DeepMind and Isomorphic Labs jointly released AlphaFold 3, the latest artificial intelligence model in the protein field, in the journal Nature! This model can accurately predict the structure and interaction of proteins, DNA, RNA and ligands. This is another major breakthrough after AlphaFold 2.
AlphaFold 3 achieves unprecedented accuracy in predicting drug-like interactions, including binding of proteins to ligands and binding of antibodies to their target proteins. In the PoseBusters benchmark,AlphaFold 3 is 50% more accurate than the best existing traditional methods, and does not require any input of structural information, making it the first artificial intelligence system to surpass traditional physical prediction tools.This ability to predict the binding of antibodies to proteins is essential for understanding various aspects of the human immune response and for the design of new antibodies.
The cutting-edge research of deep learning in the field of protein design mainly focuses on protein structure prediction, protein sequence design, protein-protein interaction prediction, protein function annotation and protein optimization and screening. These research directions provide new ideas for the development of new functional proteins and drug targets, and the application of deep learning in the field of protein design in the fields of biomedicine, drug research and development and biomaterials is considered to be one of the current cutting-edge research directions. The continuous development of deep learning in the field of protein design has brought many innovations and opportunities for new drug research and development, biopharmaceuticals, life science research and other fields.
The cutting-edge research of deep learning in the field of protein design mainly focuses on protein structure prediction, protein sequence design, protein-protein interaction prediction, protein function annotation and protein optimization and screening. These research directions provide new ideas for the development of new functional proteins and drug targets, and the application of deep learning in the field of biomedicine, drug development and biomaterials in the field of protein design is considered to be one of the current cutting-edge research directions. The future of protein structure prediction and design will be full of innovation and interdisciplinary development, to provide more possibilities for solving major problems in biomedicine, bioengineering and bioenergy.
Protein folding and structure prediction: Deep learning models can predict the three-dimensional structure of proteins, which is essential for understanding protein function and protein design. Through the application of deep neural network models and attention mechanisms, protein folding and structure can be predicted more accurately. Protein sequence design: Deep learning can be used to predict the function and stability of protein sequences. Through the combination of neural network model and natural language processing technology, protein sequences with specific functions and stability can be designed, such as enzyme activity, drug targets, etc. Protein-protein interaction prediction: Deep learning models can predict the interactions between proteins and proteins, thereby helping to design new protein complexes and antibodies. Protein function annotation and prediction: Deep learning can perform protein function annotation and prediction, and identify protein functional domains, structural domains, and functional sites. This helps to predict the function of the protein and the corresponding biological effects. Protein optimization and screening: Deep learning can be used to optimize and screen designed proteins. Through the application of neural networks and generative adversarial networks, the stability, solubility and activity of proteins can be improved to meet specific design requirements. Protein-protein interaction prediction: Deep learning can be used to predict protein-protein interactions, thereby achieving efficient design and screening of proteins and ligands.What top issues and directions have been issued in recent years:
Nature Communications | Protein Design Using Structure-Based Residue Preference
Nature Biotechnology | Machine Learning for Functional Protein Design
Scientific Report | Deep-WET: A Deep Learning-Based Method for Predicting DNA-Binding Proteins Using Word Embedding Techniques with Weighted Features
Cell systems | Deep learning opens a new era for protein design
National calculations. Science | rotamer-free protein design based on deep learning
Comput Struct Biotechnology | Deep Learning for Protein Design: From Structure to Sequence and Function
This course focuses on the basic and cutting-edge work of protein design, from the prediction and optimization of protein structure to the in-depth teaching of protein design from scratch. This course is mainly aimed at students with programming foundation, explaining the basic knowledge in detail, and explaining the application of relevant technologies in combination with cutting-edge literature. To help students, through this training, students will understand the underlying logic and basic rules of protein design, and master the actual operation of common protein design algorithms in protein design, and have the basic ability and cutting-edge vision of basic protein design algorithm development.
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