ReviewNature reviews. Molecular cell biology2024
Opportunities and challenges in design and optimization of protein function.
Review in Nature reviews. Molecular cell biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
74 citing papers in PubMed, 139 citations in OpenAlex.
- Latent generative search unlocks de novo design of untapped biomolecular interactions at scale.bioRxiv : the preprint server for biology · 2026Article
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- Soluble protein analog selection engine (SPASE): An automated AI-powered server to improve protein engineering workflows.Protein science : a publication of the Protein Society · 2026Article
- Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors.Nature biotechnology · 2026Article
- AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins.Nature communications · 2026Article
- Automated synthetic cell-based screening for designed proteins with emergent functions.Nature communications · 2026Article
- Recombinant Thermostable DNA Polymerases: Current Approaches to Production, Molecular Engineering, and Applications in Biotechnology and Diagnostics.International journal of molecular sciences · 2026Review
- Evolutionary and physics-guided modulation of CaJournal of molecular modeling · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Rethinking Sustainability in Plant-Based Proteins: A Systems Perspective from Crop to Consumer.Foods (Basel, Switzerland) · 2026Review
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- Synthetic Biology of Sclareol: From the Plant Biosynthetic Pathway to Engineered Microbial and Photosynthetic Chassis.Biotechnology journal · 2026Review
- Synthetically designed anti-defense proteins overcome barriers to bacterial transformation and phage infection.Nature communications · 2026Article
- Tools For Building Artificial Biological Nanostructures.ACS nano · 2026Review
- AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- ProtSeqGen: a novel deep learning model for protein sequence design.BMC bioinformatics · 2026Article
- Peptidic product derived from trypsin autolysis modulates insect digestive proteases and supports plant biochemical defense.Pest management science · 2026Article
- Intrinsically disordered protein droplet-enhanced oligonucleotide assembly enables rapid oligonucleotide-to-protein expression.Nucleic acids research · 2026Article
- Artificial allosteric protein switches with machine-learning-designed receptors.Nature biotechnology · 2026Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
Indexed as
Identifiers
What OpenQuestion holds
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.