ArticleScientific reports2018
Computational Protein Design with Deep Learning Neural Networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- ProtSeqGen: a novel deep learning model for protein sequence design.BMC bioinformatics · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- A Practical Guide to Computational Tools for Engineering Biocatalytic Properties.International journal of molecular sciences · 2025Review
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
- Review on Advancement of AI in Cell Engineering and Molecular Biology.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model.Science advances · 2024Article
- Tpgen: a language model for stable protein design with a specific topology structure.BMC bioinformatics · 2024Article
- SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.PLoS computational biology · 2023Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention.Nature communications · 2023Article
- Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe.Scientific data · 2023Article
- Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks.Journal of chemical theory and computation · 2023Article
- Generative design ofChem · 2023Article
- Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy of 38.3%. Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta. Moreover, the predictions from our network show ~3% higher sequence identity than a previous method. Results from this study may benefit further development of computational protein design methods.
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