The dynamic conformations of intracellular proteins and their interaction networks constitute the structural foundation of life, precisely regulating various biological processes, from signal transduction to metabolic homeostasis [1]. The deep understanding of the spatiotemporal features of these dynamic biological events is essential for understanding physiology, elucidating the pathogenesis of diseases and providing key targets for innovative drug development [2]. In recent years, structural biology techniques have made remarkable progress, X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy can now resolve high-resolution structures of proteins and their complexes, and cryo-electron microscopy (cryo-EM) has established itself as a mainstream structural determination method. However, these approaches have limitations in studying dynamic protein systems with compositional and conformational heterogeneity, particularly for protein complexes with multiple conformational states and intrinsically disordered regions (IDRs) [3]. These challenges have spurred the development of novel MS-based structural biology methods, such as XL-MS [4], hydrogen-deuterium exchange (HDX) [5], and surface labeling [6]. Along with advanced bioinformatics analysis, these techniques provide new avenues for investigating dynamic protein conformations and interaction networks. Among them, XL-MS serves as an important complement to high-resolution techniques, offering spatial distance constraints that bridge the gap between static structural information and dynamic functional studies [7].

The principal advantage of XL-MS lies in its ability to capture information on the spatial proximity of amino acid residues within and between protein complexes under near-physiological conditions. By employing rationally designed cross-linkers, XL-MS can stabilize residue pairs within the cross-linking distance constraint, thereby enabling the identification of weak or transient interactions that could hardly be captured by other methods. In addition, XL-MS generally has lower requirements for sample purity and can be applied to complex biological samples, including cell lysates [8], tissue extracts [9], intact cells [10], and even tissue masses [11], thus broadening its range of applications in physiologically relevant environments. The data generated by XL-MS provides both interaction and conformation information, allowing the identification of interacting protein pairs, mapping of interaction interfaces, and the acquisition of distance information at the residue level. It should be noted, however, that XL-MS typically provides a “structural snapshot” reflecting the state of proteins at a specific time point, rather than a dynamic trajectory of conformational changes. Integrating XL-MS data with other approaches, such as cryo-EM, molecular dynamics simulations (MD), AI-based modeling, and quantitative proteomics, can offer more comprehensive insights into protein conformational dynamics, interaction networks, and regulatory mechanisms (Figure 1).

Recent advances in cross-linker chemistry, MS-based identification methods, and computational analysis algorithms have further improved the coverage, sensitivity, and reliability of XL-MS, enabling the applications in various protein systems, from binary to large protein assemblies. This article systematically reviews the main advances in XL-MS, mostly in the past two years, focusing on novel cross-linker design, in vivo cross-linking applications, and multi-technique integration strategies.