Interpreting chemical crosslinks: Score-based approaches and deep neural networks
Xingyu Chen1, Kai Steffen Stroh2, Jan Erzberger3
1Konstanz Research School Chemical Biology, University of Konstanz, Universitätsstrasse 10, Konstanz 78457, Germany; Department of Biology, University of Konstanz, Universitätsstraße 10 78457, Konstanz, Germany.
Current Opinion in Structural Biology
|March 3, 2026
Summary
Chemical cross-linking coupled with mass spectrometry (XL-MS) reveals protein complex structures. Recent AI advancements enhance this technique for modeling large biological systems under near-physiological conditions.
Area of Science:
- Structural biology
- Biochemistry
- Computational biology
Background:
- Chemical cross-linking coupled with mass spectrometry (XL-MS) provides residue-level proximity data for macromolecular assemblies.
- XL-MS offers distance restraints that complement techniques like electron microscopy for protein complex modeling.
- XL-MS is advantageous for its applicability under near-physiological conditions and scalability.
Purpose of the Study:
- To review recent advancements in XL-MS, focusing on AI's role in structure prediction.
- To illustrate a hybrid modeling protocol integrating XL-MS with AI for complex structural analysis.
Main Methods:
- Integration of XL-MS data with electron microscopy and domain-level structures.
- Application of the Integrative Modeling Platform (IMP).
- Utilizing the deep neural network Chai-1 for structure prediction and refinement.
Main Results:
- Demonstration of a hybrid protocol combining IMP and Chai-1 for structural modeling.
- Successful docking and refinement of the helicase Dbp10 onto a ribosome biogenesis intermediate using XL-MS restraints.
- Highlighting the impact of AI-driven structure prediction on XL-MS applications.
Conclusions:
- XL-MS is a versatile tool for studying macromolecular assemblies, especially when combined with computational methods.
- AI-driven structure prediction significantly enhances the power and throughput of XL-MS.
- The described hybrid protocol offers a robust approach for modeling complex biological systems.
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