Protein Engineering in the Deep Learning Era
Author Information
Author(s): Zhou Bingxin, Tan Yang, Hu Yutong, Zheng Lirong, Zhong Bozitao, Hong Liang
Primary Institution: Shanghai Jiao Tong University
Conclusion
Deep learning significantly enhances protein engineering by improving the understanding of protein sequences and structures, leading to better applications in healthcare and sustainability.
Supporting Evidence
- Deep learning methods provide comprehensive guidance for protein engineering by learning the relationship between protein function and sequence/structure.
- State-of-the-art protein language models and geometric deep learning techniques are summarized.
- The review outlines common downstream tasks and relevant benchmark datasets for training and evaluating deep learning models.
Takeaway
This study shows how computers can help scientists design better proteins by using smart algorithms that learn from lots of data, making it easier to create proteins for medicine and the environment.
Methodology
The review discusses various deep learning techniques applied to protein engineering, including protein language models and geometric deep learning methods.
Potential Biases
There is a risk of overfitting models to noisy data, which could lead to inaccurate predictions.
Limitations
The review highlights the challenges of data scarcity and the need for better experimental validation of predictions.
Digital Object Identifier (DOI)
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