ReviewNature chemical biology2024
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins.
Review in Nature chemical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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Who cites it
91 citing papers in PubMed.
- Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis.International microbiology : the official journal of the Spanish Society for Microbiology · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Integrative AI-assisted modeling suggests CPPF binding at a composite α/β-tubulin interface pocket dominated by β-tubulin contacts.PLoS computational biology · 2026Article
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- Conserved protein sequence-structure signatures identify antibiotic resistance genes from the human microbiome.Microbiome · 2026Article
- Computational Characterization of Ligand Recognition and Inhibition in a 1,4-Dioxane-Degrading Propane Monooxygenase.Journal of chemical information and modeling · 2026Article
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- Assessing Fluoroacetate Defluorination Potential across Diverse Enzymes Using Quantum Chemistry.The journal of physical chemistry. B · 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
- Evaluation of AlphaFold3 for Predicting Human Heme-Binding Protein Structures.International journal of molecular sciences · 2026Article
- Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins.bioRxiv : the preprint server for biology · 2026Article
- Constitutive activation of a hybrid two-component regulator reveals cross-regulation of polysaccharide utilization genes in Bacteroides.The Journal of biological chemistry · 2026Article
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The LiPP Benchmark Set for Modeling Lipid-Protein Complexes: Comparison of Co-Folding and Docking Methods.Journal of chemical information and modeling · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- The "Survivor Peptide" Hypothesis: Structural Resilience and Immunological Persistence of Food Allergens in the Gut-Mammary Axis.Nutrients · 2026Review
- AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Membrane protein structure and dynamics probed by MicroED.Biochemical Society transactions · 2026Review
31 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.
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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.