SUMMARY The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and such insertion data provide a rich, but disparate set of modeling restraints. We have developed a software platform that integrates the analysis of label-based MS data with protein modeling activities (Mass Spec Studio). Analysis packages can mine any labeling data from any mass spectrometer in a proteomics-grade manner, and link labeling methods with data-directed protein interaction modeling using HADDOCK. Support is provided for hydrogen/ deuterium exchange (HX) and covalent labeling chemistries, including novel acquisition strategies such as targeted HX-tandem MS (MS2) and data-independent HX-MS2. The latter permits the modeling of highly complex systems, which we demonstrate by the analysis of microtubule interactions.

Trypsin dominates bottom-up proteomics, but there are reasons to consider alternative enzymes. Improving sequence coverage, exposing proteomic "dark matter," and clustering post-translational modifications in different ways and with higher-order drive the pursuit of reagents complementary to trypsin. Additionally, enzymes that are easy to use and generate larger peptides that capitalize upon newer fragmentation technologies should have a place in proteomics. We expressed and characterized recombinant neprosin, a novel prolyl endoprotease of the DUF239 family, which preferentially cleaves C-terminal to proline residues under highly acidic conditions. Cleavage also occurs C-terminal to alanine with some frequency, but with an intriguingly high "skipping rate." Digestion proceeds to a stable end point, resulting in an average peptide mass of 2521 units and a higher dependence upon electron-transfer dissociation for peptide-spectrum matches. In contrast to most proline-cleaving enzymes, neprosin effectively degrades proteins of any size. For 1251 HeLa cell proteins identified in common using trypsin, Lys-C, and neprosin, almost 50% of the neprosin sequence contribution is unique. The high average peptide mass coupled with cleavage at residues not usually modified provide new opportunities for profiling clusters of post-translational modifications. We show that neprosin is a useful reagent for reading epigenetic marks on histones. It generates peptide 1-38 of histone H3 and peptide 1-32 of histone H4 in a single digest, permitting the analysis of co-occurring post-translational modifications in these important N-terminal tails.

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.

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.

Covalent labeling with mass spectrometry (CL-MS) provides a direct measure of the chemical and structural features of proteins with the potential for resolution at the amino-acid level. Unfortunately, most applications of CL-MS are limited to narrowly defined differential analyses, where small numbers of residues are compared between two or more protein states. Extending the utility of high-resolution CL-MS for structure-based applications requires more robust computational routines and the development of methodology capable of reporting of labeling yield accurately. Here, we provide a substantial improvement in the analysis of CL-MS data with the development of an extended plug-in built within the Mass Spec Studio development framework (MSS-CLEAN). All elements of data analysisfrom database search to site-resolved and normalized labeling outputare accommodated, as illustrated through the nonselective labeling of the human kinesin Eg5 with photoconverted 3,3′-azibutan-1-ol. In developing the new features within the CL-MS plug-in, we identified additional complexities associated with the application of CL reagents, arising primarily from digestion-induced bias in yield measurements and ambiguities in site localization. A strategy is presented involving the use of redundant site labeling data from overlapping peptides, the imputation of missing data, and a normalization routine to determine relative protection factors. These elements together provide for a robust structural interpretation of CL-MS/MS data while minimizing the over-reporting of labeling site resolution. Finally, to minimize bias, we recommend that digestion strategies for the generation of useful overlapping peptides involve the application of complementary enzymes that drive digestion to completion.

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