ReviewCold Spring Harbor perspectives in biology2024
Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.
Review in Cold Spring Harbor perspectives in biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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
18 citing papers in PubMed.
- Bispecific antibodies for cancer therapy: evolution of structural formats and co-targeting strategies from wet-lab to AI-driven in silico modeling.Cancer letters · 2026Review
- Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance.Journal of computer-aided molecular design · 2026Review
- A brief guideline for the studies of structure-function relationship of ion channels using AlphaFold3.Fundamental research · 2026Review
- Therapeutic peptides and proteins: Status and developments in drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Evaluation of AI-Predicted GH11 Xylanase Models Against a Previously Unreported Experimental Structure: Implications for Conformational Accuracy and Ligand Binding.International journal of molecular sciences · 2026Article
- Review
- A CSGNN model-based method for essential protein identification.Frontiers in bioinformatics · 2026Article
- Mass spectrometry integrates protein design into structural biology method development.QRB discovery · 2026Review
- Integrating multi-omics data to reveal the host-microbiota interactome in inflammatory bowel disease.Gut microbes · 2025Review
- High-efficiency detoxification of fumonisin BToxicon: X · 2025Article
- A novel DCSTAMP antagonist impedes preosteoclast fusion via modulation of RAP1B-RAC1-mediated cytoskeletal remodeling.Experimental & molecular medicine · 2025Article
- Multistate and functional protein design using RoseTTAFold sequence space diffusion.Nature biotechnology · 2025Article
- Tokenized and continuous embedding compressions of protein sequence and structure.Patterns (New York, N.Y.) · 2025Article
- Epitope-imprinted biomaterials with tailor-made molecular targeting for biomedical applications.Bioactive materials · 2025Review
- Seven critical challenges in synthetic one-carbon assimilation and their potential solutions.FEMS microbiology reviews · 2025Review
- RoseTTAFold diffusion-guided short peptide design: a case study of binders against Keap1/Nrf2.Computational and structural biotechnology journal · 2025Article
- The next frontier in antibody-drug conjugates: challenges and opportunities in cancer and autoimmune therapy.Cancer drug resistance (Alhambra, Calif.) · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein 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.