ArticleNature communications2024
Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Evaluation of methods for AlphaFold-based integrative modeling.bioRxiv : the preprint server for biology · 2026Article
- The potential and therapeutic advances of the integrin family in neurological disorders.Neural regeneration research · 2026Article
- Modeling conformational transitions in DNA, RNA, and protein-nucleic acid complexes.Nucleic acids research · 2026Article
- Design optimization of antibody-ligand motifs to enhance CAR-T redirection activity against solid tumors.Cell reports. Medicine · 2026Article
- Smart-driven bioengineering techniques for enhancing microalgal biohydrogen production.Biotechnology for biofuels and bioproducts · 2026Review
- Efficient sampling of large-scale transition pathways and intermediate conformations in sub-mesoscopic protein complexes.Nature communications · 2026Article
- Disentangling coevolutionary constraints for modeling protein conformational heterogeneity.Communications chemistry · 2026Article
- Self-assembling protein cages: from coiled-coil module to machine learning-drivenMaterials advances · 2026Review
- Guiding AlphaFold predictions with experimental knowledge to inform dynamics and interactions with VAIRO.Protein science : a publication of the Protein Society · 2026Article
- Structural insights into the antibacterial function of the Pseudomonas putida effector Tke5.The EMBO journal · 2026Article
- Advantages and Limitations of AlphaFold in Structural Biology: Insights from Recent Studies.The protein journal · 2026Review
- Chimeric Approach to Identify Molecular Determinants of Nicotinic Acetylcholine Receptors.International journal of molecular sciences · 2026Review
- Adapting Co-Folding Models for Structure-Based Protein-Protein Docking Through Flow Matching.bioRxiv : the preprint server for biology · 2025Article
- Protein-Protein Interactions as Promising Molecular Targets for Novel Antimicrobials Aimed at Gram-Negative Bacteria.International journal of molecular sciences · 2025Review
- Mettl15-Mettl17 modulates the transition from early to late pre-mitoribosome.Structure (London, England : 1993) · 2025Article
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- gapTrick-structural characterization of protein-protein interactions using AlphaFold.Bioinformatics (Oxford, England) · 2025Article
- Therapeutic Antibodies for Infectious Diseases: Recent Past, Present, and Future.Biochemistry · 2025Review
- Structure of the ciliary tip central pair reveals the unique role of the microtubule-seam binding protein SPEF1.Current biology : CB · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and optimisations at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFold's capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This approach generates high quality (DockQ score > 0.8) structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked is developed and optimised to fill incomplete experimental structures (structural inpainting), which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently.
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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.