ArticleProceedings of the National Academy of Sciences of the United States of America2020
A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- SplitSeek-Pro: accurate prediction of splittable sites on protein structures.Nature communications · 2026Article
- A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins.bioRxiv : the preprint server for biology · 2026Article
- Synthetic nucleic acids in a post-agent biosecurity Era.Frontiers in bioengineering and biotechnology · 2026Article
- Designability of α-Helical Protein Filaments.Sub-cellular biochemistry · 2026Review
- Design principles of the common Gly-X6-Gly membrane protein building block.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Combining computational modeling and experimental library screening to affinity-mature VEEV-neutralizing antibody F5.Protein science : a publication of the Protein Society · 2025Article
- A novel decoding strategy for ProteinMPNN to design with less visibility to cytotoxic T-lymphocytes.Computational and structural biotechnology journal · 2025Article
- A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.Protein science : a publication of the Protein Society · 2024Article
- The Damietta Server: a comprehensive protein design toolkit.Nucleic acids research · 2024Article
- A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases.bioRxiv : the preprint server for biology · 2024Article
- A suite of designed protein cages using machine learning and protein fragment-based protocols.Structure (London, England : 1993) · 2024Article
- Vaccination induces broadly neutralizing antibody precursors to HIV gp41.Nature immunology · 2024Article
- Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families.Protein science : a publication of the Protein Society · 2024Article
- Extracellular vesicles for developing targeted hearing loss therapy.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors.Protein engineering, design & selection : PEDS · 2024Article
- TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.Protein engineering, design & selection : PEDS · 2024Article
- Article
- Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production.International journal of molecular sciences · 2023Review
- A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.
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