Ion mobility−mass spectrometry (IM−MS) has become an important addition to the structural biology toolbox, but separating closely related protein conformations remain challenging. Collision-induced unfolding (CIU) has emerged as a valuable technique for distinguishing iso-crosssectional protein and protein complex ions through their distinct unfolding pathways in the gas phase. The speed and sensitivity of CIU analyses, coupled with their information-rich data sets, have resulted in the rapid growth of CIU for applications, ranging from the structural assessment of protein complexes to the characterization of biotherapeutics. This growth has occurred despite a lag in the capabilities of informatics tools available to process the complex data sets generated by CIU experiments, resulting in laborious manual analysis remaining commonplace. Here, we present CIUSuite 2, a software suite designed to enable robust, automated analysis of CIU data across the complete range of current CIU applications and to support the implementation of CIU as a true high-throughput technique. CIUSuite 2 uses statistical fitting and modeling methods to reliably quantify features of interest within CIU data sets, particularly in data with poor signal quality that cannot be interpreted with existing analysis tools. By reducing the signal-to-noise requirements for handling CIU data, we are able to demonstrate reductions in acquisition time of up to 2 orders of magnitude over current workflows. CIUSuite 2 also provides the first automated system for classifying CIU fingerprints, enabling the next generation of ligand screening and structural analysis experiments to be accomplished in a high-throughput fashion.

Membrane proteins represent most current therapeutic targets, yet remain understudied due to their insolubility in aqueous solvents and generally low yields during purification and expression. Ion mobility-mass spectrometry and collision induced unfolding experiments have recently garnered attention as methods capable of directly detecting and quantifying ligand binding within a wide range of membrane protein systems. Despite prior success, ionized surfactant often creates chemical noise patterns resulting in significant challenges surrounding the study of small membrane protein–ligand complexes. Here, we present a new data analysis workflow that overcomes such chemical noise and then utilize this approach to quantify and classify ligand binding associated with the 36 kDa dimer of translocator protein (TSPO). Following our denoising protocol, we detect separate gas-phase unfolding signatures for lipid and protoporphyrin TSPO binders, molecular classes that likely interact with separate regions of the protein surface. Further, a detailed classification analysis reveals that lipid alkyl chain saturation levels can be detected within our gas-phase protein unfolding data. We combine these data and classification schemes with mass spectra acquired directly from liquid–liquid extracts to propose an identity for a previously unknown endogenous TSPO ligand.

How the distinctive lipid composition of mammalian plasma membranes impacts membrane protein structure is largely unexplored, partly because of the dearth of isotropic model membrane systems that contain abundant sphingolipids and cholesterol. This gap is addressed by showing that sphingomyelin and cholesterol-rich (SCOR) lipid mixtures with phosphatidylcholine can be cosolubilized by n-dodecyl-β-melibioside to form bicelles. Small-angle X-ray and neutron scattering, as well as cryo-electron microscopy, demonstrate that these assemblies are stable over a wide range of conditions and exhibit the bilayered-disc morphology of ideal bicelles even at low lipid-to-detergent mole ratios. SCOR bicelles are shown to be compatible with a wide array of experimental techniques, as applied to the transmembrane human amyloid precursor C99 protein in this medium. These studies reveal an equilibrium between low-order oligomer structures that differ significantly from previous experimental structures of C99, providing an example of how ordered membranes alter membrane protein structure.

Peripheral myelin protein (PMP22) is an integral membrane protein that traffics inefficiently even in wild-type (WT) form, with only 20% of the WT protein reaching its final plasma membrane destination in myelinating Schwann cells. Misfolding of PMP22 has been identified as a key factor in multiple peripheral neuropathies, including Charcot-Marie-Tooth disease and Dejerine–Sottas syndrome. While biophysical analyses of disease-associated PMP22 mutants show altered protein stabilities, leading to reduced surface trafficking and loss of PMP22 function, it remains unclear how destabilization of PMP22 mutations causes mistrafficking. Here, native ion mobility–mass spectrometry (IM-MS) is used to compare the gas phase stabilities and abundances for an array of mutant PM22 complexes. We find key differences in the PMP22 mutant stabilities and propensities to form homodimeric complexes. Of particular note, we observe that severely destabilized forms of PMP22 exhibit a higher propensity to dimerize than WT PMP22. Furthermore, we employ lipid raft–mimicking SCOR bicelles to study PMP22 mutants, and find that the differences in dimer abundances are amplified in this medium when compared to micelle-based data, with disease mutants exhibiting up to 4 times more dimer than WT when liberated from SCOR bicelles. We combine our findings with previous cellular data to propose that the formation of PMP22 dimers from destabilized monomers is a key element of PMP22 mistrafficking.