SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network
Open accessPublished version of a preprint
Abstract
Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformers, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures, “hallucinated” structures and diffusion models. Results suggest that low complexity regions in the sequences designed by deep learning, for generated structures in particular, remain to be improved, when compared to the native sequences.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
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Robust deep learning based protein sequence design using ProteinMPNN
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Robust deep learning–based protein sequence design using ProteinMPNN
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- Authors
- Zhang, X., Yin, H., Ling, F., Zhan, J., & Zhou, Y.
- Affiliations
- Shenzhen Bay Laboratory (CN)
- South China University of Technology (CN)
- Corresponding author
- Ling, Fei
- Zhan, Jian
- Zhou, Yaoqi
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2023-12-07
- Volume
- 19
- Issue
- 12
- Pages
- e1011330
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC10729952
- OpenAlex
- W4389429454
- Cited by
- 15
- References
- 9
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 9
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Update notices
- new_version (2023-12-19)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Griffith Research Online (submittedVersion, cc-by)
- Funders
- National Key Research and Development Program of China (NO.2021YFF1200400)
- Major Program of Shenzhen Bay Laboratory (S201101001)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein design, contact maps, graph neural networks, contact graphs, k-nearest neighbor graph, low complexity regions, diffusion models
- MeSH terms
- Amino Acid Sequence, Amino Acids, Diffusion, Cluster Analysis, Neural Networks, Computer
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