ArticleProceedings of the National Academy of Sciences of the United States of America2024
An all-atom protein generative model.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Programming biological behavior with AI-guided de novo protein design.Molecular cell · 2026Review
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Accelerating protein design by scaling experimental characterization.Nature communications · 2026Article
- Diffusion-Based Protein Structure Design: Geometric Modelling, Validation Strategies, and Thermodynamic Challenges.International journal of molecular sciences · 2026Review
- Benchmarking Generative AI Protein Models Reveals Differences Between Structural and Sequence-based Approaches.Genomics, proteomics & bioinformatics · 2026Article
- Zero-shot design of abioRxiv : the preprint server for biology · 2026Article
- A generalizable system for antigenic peptide targeting across HLA-I allotypes.bioRxiv : the preprint server for biology · 2026Article
- Fast and Ultra-Capable Protein Design: Advancing the Frontier Through Atomistic SE(3)-Equivariance with Genie 3.bioRxiv : the preprint server for biology · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Generative design of intrinsically disordered protein regions with IDiom.bioRxiv : the preprint server for biology · 2026Article
- Review
- Constraint Decoupled Latent Diffusion for Protein Backmapping.Journal of chemical theory and computation · 2026Article
- Atom-level enzyme active site scaffolding using RFdiffusion2.Nature methods · 2026Article
- AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026Review
- Artificial intelligence-driven computational methods for antibody design and optimization.mAbs · 2025Review
- BoltzGen: Toward Universal Binder Design.bioRxiv : the preprint server for biology · 2025Article
- 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
- SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025Article
- Efficient protein structure generation with sparse denoising models.Nature machine intelligence · 2025Article
- Assessing generative model coverage of protein structures with SHAPES.Cell systems · 2025Article
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7 authors.
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Abstract
Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.
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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.