The potential of cross-linking mass spectrometry in the development of protein-protein interaction modulators

Do Not Waste Time─Ensure Success in Your Cross-Linking Mass Spectrometry Experiments before You Begin

Cross-linking mass spectrometry (XL-MS) has become a very useful tool for studying protein complexes and interactions in living systems. It enables the investigation of many large and dynamic assemblies in their native state, providing an unbiased view of their protein interactions and restraints for integrative modeling. More researchers are turning toward trying XL-MS to probe their complexes of interest, especially in their native environments. However, due to the presence of other potentially higher abundant proteins, sufficient cross-links on a system of interest may not be reached to achieve satisfactory structural and interaction information. There are currently no rules for predicting whether XL-MS experiments are likely to work or not; in other words, if a protein complex of interest will lead to useful XL-MS data. Here, we show that a simple iBAQ (intensity-based absolute quantification) analysis performed from trypsin digest data can provide a good understanding of whether proteins of interest are abundant enough to achieve successful cross-linking data. Comparing our findings to large-scale data on diverse systems from several other groups, we show that proteins of interest should be at least in the top 20% abundance range to expect more than one cross-link found per protein. We foresee that this guideline is a good starting point for researchers who would like to use XL-MS to study their protein of interest and help ensure a successful cross-linking experiment from the beginning. Data are available via ProteomeXchange with identifier PXD045792.

Chemical reagents for the enrichment of modified peptides in MS-based identification

Chemical reagents with special groups as enrichable handles have empowered the ability to label and enrich modified peptides. Here is an overview of different chemical reagents with affinity tags to isolate labeled peptides and the latest developments of enrichment strategies. Biotin is the most used affinity tag due to its high interaction with avidin. To decrease the unfavorable influence of biotin for its poor efficiency in ionization and fragmentation in downstream MS analysis, cleavable moieties were installed between the reactive groups and biotin to release labeled peptides from the biotin. To minimize the steric hindrance of biotin, a two-step method was developed, for which alkyne- or azide-tagged linkers were firstly used to label peptides and then biotin was installed through click chemistry. Recently, new linkers using a small phosphonic acid as the affinity tag for IMAC or TiO2 enrichment have been developed and successfully used to isolate chemically labeled peptides in XL-MS. A stable P-C instead of P-O bond was introduced to linkers to differentiate labeled and endogenous phosphopeptides. Furthermore, a membrane-permeable phosphonate-containing reagent was reported, which facilitated the study of living systems. Taking a cue from classic chemical reactions, stable metal-complex intermediates, including cobalt and palladium complexes, have been developed as peptide purification systems. Advanced enrichment strategies have also been proposed, such as the two-stage IMAC enrichment method and biotin-based two-step reaction strategy, allowing the reduction of unwanted peptides and improvements for the analysis of specific labeled peptides. Finally, future trends in the area are briefly discussed.

Understanding the cell: Future views of structural biology

Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.