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Computational Protein Design with Deep Learning Neural Networks
Jingxue Wang1, Huali Cao1, John Z H Zhang1,2,3,4
1Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
Scientific Reports
|April 22, 2018
Summary
This study uses deep learning to predict amino acid probabilities for protein design. The new method improves sequence identity in designing natural proteins, advancing computational protein design.
Area of Science:
- Computational biology
- Bioinformatics
- Machine learning
Background:
- Protein design is crucial but challenging.
- Protein structure databases are growing, revealing patterns in protein folds.
- Deep learning excels at analyzing large datasets.
Purpose of the Study:
- To apply deep learning to computational protein design.
- To predict the probability of each of the 20 natural amino acids at specific protein residue positions.
Main Methods:
- Collected a large dataset of protein structures.
- Developed a multi-layer neural network.
- Extracted structural properties as input features for the network.
Main Results:
- The best deep learning network achieved 38.3% accuracy.
- Incorporating network predictions as residue restraints improved sequence identity in protein design using Rosetta.
- The new method yielded ~3% higher sequence identity compared to previous approaches.
Conclusions:
- Deep learning can enhance computational protein design.
- This approach offers a promising direction for future protein design methodologies.
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