ReviewThe Journal of biological chemistry
Recent advances in de novo protein design: Principles, methods, and applications.
Review in The Journal of biological chemistry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
107 citing papers in PubMed.
- Antibody Hotspot-CentricACS medicinal chemistry letters · 2026Article
- Evolutionary Dynamics-AwareACS omega · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Protein design and RNA design: Perspectives.Quantitative biology (Beijing, China) · 2026Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- Negative design enables cell-free expression and folding of designed transmembrane β-barrels.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Review
- De novo functional protein sequence generation: overcoming data scarcity through regeneration and large language models.Briefings in bioinformatics · 2026Article
- Data storage and retrieval with unnatural proteins expressed via E. coli.Nature communications · 2026Article
- Designing de novo TIM barrels: insights into stabilization, diversification, and functionalization strategies.Biochemical Society transactions · 2026Review
- Self-assembling protein cages: from coiled-coil module to machine learning-drivenMaterials advances · 2026Review
- De novo protein design: a transformative frontier in clinical protein applications.Journal of translational medicine · 2026Review
- Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.Protein science : a publication of the Protein Society · 2026Article
- Functional Peptide-Based Biomaterials for Pharmaceutical Application: Sequences, Mechanisms, and Optimization Strategies.Journal of functional biomaterials · 2026Review
- From latent dynamics to data geometry: Nonlinear diffusion modelling for protein structures.Computational and structural biotechnology journal · 2026Article
- All-atom protein sequence design using discrete diffusion models.Journal of cheminformatics · 2025Article
- Article
- Article
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- A Complementarity-Based Approach to De Novo Binder Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
47 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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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.