ArticleNature biotechnology2023
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
- Predicting RNA and Protein:RNA Complexes with AlphaFold: Possibilities, Limitations, and Practical Guidelines.Methods in molecular biology (Clifton, N.J.) · 2027Article
- 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
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- Evaluation of methods for AlphaFold-based integrative modeling.bioRxiv : the preprint server for biology · 2026Article
- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- Proteomic investigation of signaling dynamics: from static maps to network rewiring.Bioscience reports · 2026Review
- Scalable discovery of homomeric protein-protein interactions from cross-linking mass spectrometry data with CLAUDIO 2.0.Protein science : a publication of the Protein Society · 2026Article
- Interface swapping orchestrates carbon transfer in the archaeal acetyl-CoA decarbonylase/synthase.bioRxiv : the preprint server for biology · 2026Article
- Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.Mass spectrometry reviews · 2026Review
- Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network improves protein structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles.Nature biotechnology · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets.bioRxiv : the preprint server for biology · 2026Article
- Inherent MS-cleavability of diazirine photo-cross-links enables residue-level structural analysis.Nature communications · 2026Article
- Bayesian-Steered Structure Prediction of Mechanical Biomolecules Using Twisted Diffusion.bioRxiv : the preprint server for biology · 2026Article
- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.Nature methods · 2026Article
29 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the AlphaFold2 algorithm that incorporates experimental distance restraint information into its network architecture. By employing sparse experimental contacts as anchor points, AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets. We confirm this experimentally by using the noncanonical amino acid photo-leucine to obtain information on residue-residue contacts inside cells by crosslinking mass spectrometry. The program can predict distinct conformations of proteins on the basis of the distance restraints provided, demonstrating the value of experimental data in driving protein structure prediction. The noise-tolerant framework for integrating data in protein structure prediction presented here opens a path to accurate characterization of protein structures from in-cell data.
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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.