ArticleProceedings of the National Academy of Sciences of the United States of America2025
Modeling protein-small molecule conformational ensembles with PLACER.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- AbioRxiv : the preprint server for biology · 2026Article
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- ProteinMCP: An agentic AI framework for autonomous protein engineering.Protein science : a publication of the Protein Society · 2026Article
- Review
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Atom-level enzyme active site scaffolding using RFdiffusion2.Nature methods · 2026Article
- Flexible protein-ligand docking with diffusion-based side-chain packing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Accelerating Biomolecular Modeling with AtomWorks and RF3.bioRxiv : the preprint server for biology · 2025Article
- Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.Proceedings of machine learning research · 2025Article
- Computational design of serine hydrolases.Science (New York, N.Y.) · 2025Article
- De novo design and evolution of an artificial metathase for cytoplasmic olefin metathesis.Nature catalysis · 2025Article
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12 authors.
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Abstract
Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state, an entirely atomic-level description could have advantages in speed and generality. We developed a graph neural network called PLACER (protein-ligand atomistic conformational ensemble resolver) trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. PLACER accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding. When given a description of the larger protein context, it builds up structures of small molecules and protein side chains for protein-small molecule docking. Because PLACER is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using PLACER to assess the accuracy and preorganization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with a
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