ArticleScience (New York, N.Y.)2025
Deep learning-guided design of dynamic proteins.
Article in Science (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- Multistate Enzyme Design Enables Efficient and Stereoselective Multistep Catalysis.bioRxiv : the preprint server for biology · 2026Article
- A sequence motif enables widespread use of noncanonical redox cofactors in natural enzymes.Nature chemical biology · 2026Article
- Article
- Automated transition state generation for mechanistic exploration in organic synthesis.Nature communications · 2026Article
- Dynamic view of an allosteric intermediate in a positively cooperative dimer.bioRxiv : the preprint server for biology · 2026Article
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
- Structure and evolution-guided design of minimal RNA-guided nucleases.Science (New York, N.Y.) · 2026Article
- Surface electrostatic networks control hydrophobic core remodeling in a pH-dependent switching protein.bioRxiv : the preprint server for biology · 2026Article
- Physics-Enhanced Deep Learning Optimized Semitransparent Organic Photovoltaics for Building-Integrated Sustainable Energy.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Tools For Building Artificial Biological Nanostructures.ACS nano · 2026Review
- Protein design and RNA design: Perspectives.Quantitative biology (Beijing, China) · 2026Article
- Fold-Switching Proteins.Annual review of biophysics · 2026Review
- Seven complete comparative maps of allosteric mutations in a protein family.Nature communications · 2026Article
- Dynamic-Structure Redesign of Calmodulin Reveals Mechanistic Constraints on Ryr2 Regulation.bioRxiv : the preprint server for biology · 2026Article
- Phage-assisted evolution of allosteric protein switches.Nature communications · 2026Article
- Review
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Steric confinement-induced emission probe for monitoring protein conformations in live cells.Communications chemistry · 2026Article
- Computational design of dynamic biosensors for emerging synthetic opioids.Nature communications · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Deep learning has advanced the design of static protein structures, but the controlled conformational changes that are hallmarks of natural signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep learning-guided approach for de novo design of dynamic changes between intradomain geometries of proteins, similar to switch mechanisms prevalent in nature, with atomic-level precision. We solve four structures that validate the designed conformations, demonstrate modulation of the conformational landscape by orthosteric ligands and allosteric mutations, and show that physics-based simulations are in agreement with deep-learning predictions and experimental data. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable, and controllable protein signaling behavior de novo.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.