The Mass Spec Studio package was designed to support the extraction of hydrogen-deuterium exchange and covalent labeling data for a range of mass spectrometry (MS)-based workflows, to integrate with restraint-driven protein modeling activities. In this report, we present an extension of the underlying Studio framework and provide a plug-in for crosslink (XL) detection. To accommodate flexibility in XL methods and applications, while maintaining efficient data processing, the plug-in employs a peptide library reduction strategy via a presearch of the tandem-MS data. We demonstrate that prescoring linear unmodified peptide tags using a probabilistic approach substantially reduces search space by requiring both crosslinked peptides to generate sparse data attributable to their linear forms. The method demonstrates highly sensitive crosslink peptide identification with a low false positive rate. Integration with a Haddock plug-in provides a resource that can combine multiple sources of data for protein modeling activities. We generated a structural model of porcine transferrin bound to TbpB, a membranebound receptor essential for iron acquisition in Actinobacillus pleuropneumoniae. Using mutational data and crosslinking restraints, we confirm the mechanism by which TbpB recognizes the iron-loaded form of transferrin, and note the requirement for disparate sources of restraint data for accurate model construction. The software plugin is freely available at www.msstudio. Integrative methods in structural biology use data from disparate sources to generate accurate models of large protein structures and assemblies (1). In this way, the reach of classical structure providers such as x-ray crystallography and NMR can be extended. Biophysical data with an underlying spatial component can be combined with "building block" structures in a molecular modeling framework, to generate high-fidelity models of systems of impressive size and complexity (2-5). Mass spectrometry can provide rich sets of data in support these activities, in the form of topographical footprints (covalent labeling, CL) 1 (6 -8), conformational dynamics (hydrogen/deuterium exchange, HX) (9, 10) and distance restraints (crosslinking, XL) (11-13). We have built informatics routines within the Mass Spec Studio framework to mine restraints from both CL and HX data (14), to support such data-driven molecular modeling activities. In this study, we describe a new plug-in built into the Studio for identifying crosslinks from LC-MS/MS data sets.Advances in instrumentation, methods and cross-linking protocols have generated renewed interest in what is an older technique. However, useful informatics routines are essential for gaining access to quality crosslinking information as site identification is not a trivial problem (15). Some noteworthy tools that have emerged in the last few years include xQuest (16), Merox (17), Stavrox (18), pLink (20), XlinkX (21), and XiQ (22). The proliferation of such tools is a strong indication that new XL reagents and methods re...

Lactoferrin binding protein B (LbpB) is a bi-lobed outer membrane-bound lipoprotein that comprises part of the lactoferrin (Lf) receptor complex in Neisseria meningitidis and other Gram-negative pathogens. Recent studies have demonstrated that LbpB plays a role in protecting the bacteria from cationic antimicrobial peptides due to large regions rich in anionic residues in the C-terminal lobe. Relative to its homolog, transferrin-binding protein B (TbpB), there currently is little evidence for its role in iron acquisition and relatively little structural and biophysical information on its interaction with Lf. In this study, a combination of crosslinking and deuterium exchange coupled to mass spectrometry, information-driven computational docking, bio-layer interferometry, and site-directed mutagenesis was used to probe LbpB:hLf complexes. The formation of a 1:1 complex of iron-loaded Lf and LbpB involves an interaction between the Lf C-lobe and LbpB N-lobe, comparable to TbpB, consistent with a potential role in iron acquisition. The Lf N-lobe is also capable of binding to negatively charged regions of the LbpB C-lobe and possibly other sites such that a variety of higher order complexes are formed. Our results are consistent with LbpB serving dual roles focused primarily on iron acquisition when exposed to limited levels of iron-loaded Lf on the mucosal surface and effectively binding apo Lf when exposed to high levels at sites of inflammation.

Hydrogen-deuterium exchange mass spectrometry (HX-MS) has made important contributions to the study of protein structure and function. Unfortunately, it is not known for low limits of detection, when compared with other forms of peptide-based or bottom-up protein MS methods. Systems perform poorly on sub-pmol quantities of protein states with greater than 300 kDa of unique sequences. The HX-MS analysis of complex protein states would be possible if proteomics-grade configurations could be used reliably, but temperature and temporal constraints have proven to be significant design challenges. Here, we describe an integrated HX-MS ion source operating on a vented-column geometry, which brings regulated column cooling right to the spray tip. The design offers chromatographic peak widths of 2-6 s (FWHM). It provides stable operation at 500 nL min, while retaining deuteration levels comparable to conventional geometries. We demonstrate at least a 50-fold improvement in protein consumption levels, and illustrate robustness by measuring peptide-averaged protection factors for 90% of DNA-PKcs, a 469 kDa protein, from 0.5 pmol injections.

The data analysis practices associated with hydrogen− deuterium exchange mass spectrometry (HX-MS) lag far behind that of most other MS-based protein analysis tools. A reliance on external tools from other fields and a persistent need for manual data validation restrict this powerful technology to the expert user. Here, we provide an extensive upgrade to the HX data analysis suite available in the Mass Spec Studio in the form of two new apps (HX-PIPE and HX-DEAL), completing a workflow that provides an HXtailored peptide identification capability, accelerated validation routines, automated spectral deconvolution strategies, and a rich set of exportable graphics and statistical reports. With these new tools, we demonstrate that the peptide identifications obtained from undeuterated samples generated at the start of a project contain information that helps predict and control the extent of manual validation required. We also uncover a large fraction of HX-usable peptides that remains unidentified in most experiments. We show that automated spectral deconvolution routines can identify exchange regimes in a project-wide manner, although they remain difficult to accurately assign in all scenarios. Taken together, these new tools provide a robust and complete solution suitable for the analysis of high-complexity HX-MS data.