ArticleNature communications2023
Improving de novo protein binder design with deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 202 papers.
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Who cites it
202 citing papers in PubMed.
- De novo design of cysteine proteases.Nature · 2026Article
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Antibody Hotspot-CentricACS medicinal chemistry letters · 2026Article
- Multi-dimensional orchestration of binders for improved CAR-T immunotherapy.Cancer letters · 2026Review
- A designed ubiquitin-binding protein selectively enriches and visualizes unanchored ubiquitin.Science advances · 2026Article
- De novo design of protease-activatable cytokine prodrugs.bioRxiv : the preprint server for biology · 2026Article
- Latent generative search unlocks de novo design of untapped biomolecular interactions at scale.bioRxiv : the preprint server for biology · 2026Article
- Sequence and structural determinants of efficacious de novo chimaeric antigen receptors.Nature biomedical engineering · 2026Article
- Article
- Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.Briefings in bioinformatics · 2026Article
- Intracellular protein binders for imaging, control and future therapeutics.Nature biomedical engineering · 2026Review
- AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins.Nature communications · 2026Article
- De Novo-Designed Bifunctional Proteins for Targeted Protein Degradation.Journal of the American Chemical Society · 2026Article
- Computationally Evidence-Grounded Sequence-First Design of Peptide Binders.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- De novo design of miniproteins targeting GPCRs.Nature · 2026Article
- De novo design of potent CRISPR-Cas13 inhibitors.Nature chemical biology · 2026Article
- Article
- Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- Towards Cost-Effective and Sustainable Media Formulations for Terrestrial and Aquatic Cellular Agriculture.Foods (Basel, Switzerland) · 2026Review
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
142 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
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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.