ArticleNature communications2022
Protein sequence design with a learned potential.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
What it found
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Who cites it
96 citing papers in PubMed.
- Intracellular protein binders for imaging, control and future therapeutics.Nature biomedical engineering · 2026Review
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Bioinformatics (Oxford, England) · 2026Article
- Customizing the structure of minimal TIM barrels to craft efficient de novo enzymes.Nature chemical biology · 2026Article
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Generative Protein Design: From Deep Learning Algorithms to Translational Applications.International journal of molecular sciences · 2026Review
- Designing de novo TIM barrels: insights into stabilization, diversification, and functionalization strategies.Biochemical Society transactions · 2026Review
- AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026Review
- Ensemble-conditioned protein sequence design with Caliby.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
- Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?Protein science : a publication of the Protein Society · 2025Article
- ProDualNet: dual-target protein sequence design method based on protein language model and structure model.Briefings in bioinformatics · 2025Article
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.Proceedings of machine learning research · 2025Article
- Unsupervised learning reveals landscape of local structural motifs across protein classes.Bioinformatics (Oxford, England) · 2025Article
- DivPro: diverse protein sequence design with direct structure recovery guidance.Bioinformatics (Oxford, England) · 2025Article
- Combining transformer and 3DCNN models to achieve co-design of structures and sequences of antibodies in a diffusional manner.Journal of pharmaceutical analysis · 2025Article
- Local structural flexibility drives oligomorphism in computationally designed protein assemblies.Nature structural & molecular biology · 2025Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.
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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.