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.

Doublecortin (DCX) is a microtubule (MT) associated protein that regulates MT structure and function during neuronal development and mutations in DCX lead to a spectrum of neurological disorders. The structural properties of MT-bound DCX that explain these disorders are incompletely determined. Here, we describe the molecular architecture of the DCX-MT complex through an integrative modeling approach that combines data from X-ray crystallography, cryo-EM and a high-fidelity chemical crosslinking method. We demonstrate that DCX interacts with MTs through its N-terminal domain and induces a lattice-dependent self-association involving the C-terminal structured domain and its disordered tail, in a conformation that favors an open, domain-swapped state. The networked state can accommodate multiple different attachment points on the MT lattice, all of which orient the C-terminal tails away from the lattice. As numerous disease mutations cluster in the C-terminus, and regulatory phosphorylations cluster in its-tail, our study shows that lattice-driven self-assembly is an important property of DCX.

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Crosslinking mass spectrometry (XL-MS) is a valuable technique for the generation of point-to-point distance measurements in protein space. Applications involving in situ chemical crosslinking have created the possibility of mapping whole protein interactomes with high spatial resolution. However, an XL-MS experiment carried out directly on cells requires highly efficient software that can detect crosslinked peptides with sensitivity and controlled error rates. Many algorithmic approaches invoke a filtering strategy designed to reduce the size of the database prior to mounting a search for crosslinks, but concern has been expressed over the possibility of reduced sensitivity with such strategies. Here we present a full upgrade to CRIMP, the crosslinking app in the Mass Spec Studio, which implements a new strategy for the detection of both component peptides in the MS2 spectrum. Using several published datasets, we demonstrate that this pre-searching method is sensitive and fast, permitting whole proteome searches on a conventional desktop computer for both cleavable and noncleavable crosslinkers. We introduce a new strategy for scoring crosslinks, adapted from computer vision algorithms, that properly resolves conflicting XL hits from other crosslinking reaction products, and we present a method for enhancing the detection of protein-protein interactions that relies upon compositional data.

Proteomics methodology has expanded to include protein structural analysis, primarily through cross-linking mass spectrometry (XL-MS) and hydrogen–deuterium exchange mass spectrometry (HX-MS). However, while the structural proteomics community has effective tools for primary data analysis, there is a need for structure modeling pipelines that are accessible to the proteomics specialist. Integrative structural biology requires the aggregation of multiple distinct types of data to generate models that satisfy all inputs. Here, we describe IMProv, an app in the Mass Spec Studio that combines XL-MS data with other structural data, such as cryo-EM densities and crystallographic structures, for integrative structure modeling on high-performance computing platforms. The resource provides an easily deployed bundle that includes the open-source Integrative Modeling Platform program (IMP) and its dependencies. IMProv also provides functionality to adjust cross-link distance restraints according to the underlying dynamics of cross-linked sites, as characterized by HX-MS. A dynamics-driven conditioning of restraint values can improve structure modeling precision, as illustrated by an integrative structure of the five-membered Polycomb Repressive Complex 2. IMProv is extensible to additional types of data.