Evaluating protein cross-linking as a therapeutic strategy to stabilize SOD1 variants in a mouse model of familial ALS.

Mutations in the gene encoding Cu-Zn superoxide dismutase 1 (SOD1) cause a subset of familial amyotrophic lateral sclerosis (fALS) cases. A shared effect of these mutations is that SOD1, which is normally a stable dimer, dissociates into toxic monomers that seed toxic aggregates. Considerable research effort has been devoted to developing compounds that stabilize the dimer of fALS SOD1 variants, but unfortunately, this has not yet resulted in a treatment. We hypothesized that cyclic thiosulfinate cross-linkers, which selectively target a rare, 2 cysteine-containing motif, can stabilize fALS-causing SOD1 variants in vivo. We created a library of chemically diverse cyclic thiosulfinates and determined structure-cross-linking-activity relationships. A pre-lead compound, "S-XL6," was selected based upon its cross-linking rate and drug-like properties. Co-crystallographic structure clearly establishes the binding of S-XL6 at Cys 111 bridging the monomers and stabilizing the SOD1 dimer. Biophysical studies reveal that the degree of stabilization afforded by S-XL6 (up to 24°C) is unprecedented for fALS, and to our knowledge, for any protein target of any kinetic stabilizer. Gene silencing and protein degrading therapeutic approaches require careful dose titration to balance the benefit of diminished fALS SOD1 expression with the toxic loss-of-enzymatic function. We show that S-XL6 does not share this liability because it rescues the activity of fALS SOD1 variants. No pharmacological agent has been proven to bind to SOD1 in vivo. Here, using a fALS mouse model, we demonstrate oral bioavailability; rapid engagement of SOD1G93A by S-XL6 that increases SOD1G93A's in vivo half-life; and that S-XL6 crosses the blood-brain barrier. S-XL6 demonstrated a degree of selectivity by avoiding off-target binding to plasma proteins. Taken together, our results indicate that cyclic thiosulfinate-mediated SOD1 stabilization should receive further attention as a potential therapeutic approach for fALS.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes.

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with ß-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. RESULTS: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multimodal platform for assessing drug distribution and response in clinical trials.

BACKGROUND: Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS: Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS: PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS: The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.

Peak learning of mass spectrometry imaging data using artificial neural networks.

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.

Raf promotes dimerization of the Ras G-domain with increased allosteric connections.

Ras dimerization is critical for Raf activation. Here we show that the Ras binding domain of Raf (Raf-RBD) induces robust Ras dimerization at low surface densities on supported lipid bilayers and, to a lesser extent, in solution as observed by size exclusion chromatography and confirmed by SAXS. Community network analysis based on molecular dynamics simulations shows robust allosteric connections linking the two Raf-RBD D113 residues located in the Galectin scaffold protein binding site of each Raf-RBD molecule and 85 Å apart on opposite ends of the dimer complex. Our results suggest that Raf-RBD binding and Ras dimerization are concerted events that lead to a high-affinity signaling complex at the membrane that we propose is an essential unit in the macromolecular assembly of higher order Ras/Raf/Galectin complexes important for signaling through the Ras/Raf/MEK/ERK pathway.

Cyclic Thiosulfinates as a Novel Class of Disulfide Cleavable Cross-Linkers for Rapid Hydrogel Synthesis.

We recently reported that cyclic thiosulfinates are cysteine selective cross-linkers that avoid the "dead-end" modifications that contribute to other cross-linkers' toxicity. In this study, we generalize the chemistry of cyclic thiosulfinates to that of thiol selective cross-linking and apply them to the synthesis of hydrogels. Thiol-functionalized four-arm poly(ethylene glycol) and hyaluronic acid monomers were cross-linked with 1,2-dithiane-1-oxide to form disulfide cross-linked hydrogels within seconds. The synthesized hydrogel could be reduced with physiological concentrations of glutathione, which modulated hydrogel mechanical properties and degradation kinetics. Bovine serum albumin protein was successfully encapsulated in hydrogel, and diffusion-mediated release was demonstrated in vitro. Hep G2 cells grew in the presence of preformed hydrogel and during hydrogel synthesis, demonstrating acceptable cytotoxicity. We encapsulated cells within a hydrogel and demonstrated cell growth and recovery up to 10 days, with and without cell adhesion peptides. In summary, we report cyclic thiosulfinates as a novel class of cross-linkers for the facile synthesis of biodegradable hydrogels.

Localized Metabolomic Gradients in Patient-Derived Xenograft Models of Glioblastoma.

Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood-brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. SIGNIFICANCE: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment.See related commentary by Tan and Weljie, p. 1231.

Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data.

Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).

Molecular Characterization of Prostate Cancer with Associated Gleason Score Using Mass Spectrometry Imaging.

Diagnosis of prostate cancer is based on histologic evaluation of tumor architecture using a system known as the "Gleason score." This diagnostic paradigm, while the standard of care, is time-consuming, shows intraobserver variability, and provides no information about the altered metabolic pathways, which result in altered tissue architecture. Characterization of the molecular composition of prostate cancer and how it changes with respect to the Gleason score (GS) could enable a more objective and faster diagnosis. It may also aid in our understanding of disease onset and progression. In this work, we present mass spectrometry imaging for identification and mapping of lipids and metabolites in prostate tissue from patients with known prostate cancer with GS from 6 to 9. A gradient of changes in the intensity of various lipids was observed, which correlated with increasing GS. Interestingly, these changes were identified in both regions of high tumor cell density, and in regions of tissue that appeared histologically benign, possibly suggestive of precancerous metabolomic changes. A total of 31 lipids, including several phosphatidylcholines, phosphatidic acids, phosphatidylserines, phosphatidylinositols, and cardiolipins were detected with higher intensity in GS (4+3) compared with GS (3+4), suggesting they may be markers of prostate cancer aggression. Results obtained through mass spectrometry imaging studies were subsequently correlated with a fast, ambient mass spectrometry method for potential use as a clinical tool to support image-guided prostate biopsy. IMPLICATIONS: In this study, we suggest that metabolomic differences between prostate cancers with different Gleason scores can be detected by mass spectrometry imaging.

Integrated mapping of pharmacokinetics and pharmacodynamics in a patient-derived xenograft model of glioblastoma.

Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.

Rapid discrimination of pediatric brain tumors by mass spectrometry imaging.

PURPOSE: Medulloblastoma, the most common primary pediatric malignant brain tumor, originates in the posterior fossa of the brain. Pineoblastoma, which originates within the pineal gland, is a rarer malignancy that also presents in the pediatric population. Medulloblastoma and pineoblastoma exhibit overlapping clinical features and have similar histopathological characteristics. Histopathological similarities confound rapid diagnoses of these two tumor types. We have conducted a pilot feasibility study analyzing the molecular profile of archived frozen human tumor specimens using mass spectrometry imaging (MSI) to identify potential biomarkers capable of classifying and distinguishing between medulloblastoma and pineoblastoma. METHODS: We performed matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry imaging on eight medulloblastoma biopsy specimens and three pineoblastoma biopsy specimens. Multivariate statistical analyses were performed on the MSI dataset to generate classifiers that distinguish the two tumor types. Lastly, the molecules that were discriminative of tumor type were queried against the Lipid Maps database and identified. RESULTS: In this pilot study we show that medulloblastoma and pineoblastoma can be discriminated using molecular profiles determined by MSI. The highest-ranking discriminating classifiers of medulloblastoma and pineoblastoma were glycerophosphoglycerols and sphingolipids, respectively. CONCLUSION: We demonstrate proof-of-concept that medulloblastoma and pineoblastoma can be rapidly distinguished by using MSI lipid profiles. We identified biomarker candidates capable of distinguishing these two histopathologically similar tumor types. This work expands the current molecular knowledge of medulloblastoma and pineoblastoma by characterizing their lipidomic profiles, which may be useful for developing novel diagnostic, prognostic and therapeutic strategies.

Cyclic Thiosulfinates and Cyclic Disulfides Selectively Cross-Link Thiols While Avoiding Modification of Lone Thiols.

This work addresses the need for chemical tools that can selectively form cross-links. Contemporary thiol-selective cross-linkers, for example, modify all accessible thiols, but only form cross-links between a subset. The resulting terminal "dead-end" modifications of lone thiols are toxic, confound cross-linking-based studies of macromolecular structure, and are an undesired, and currently unavoidable, byproduct in polymer synthesis. Using the thiol pair of Cu/Zn-superoxide dismutase (SOD1), we demonstrated that cyclic disulfides, including the drug/nutritional supplement lipoic acid, efficiently cross-linked thiol pairs but avoided dead-end modifications. Thiolate-directed nucleophilic attack upon the cyclic disulfide resulted in thiol-disulfide exchange and ring cleavage. The resulting disulfide-tethered terminal thiolate moiety either directed the reverse reaction, releasing the cyclic disulfide, or participated in oxidative disulfide (cross-link) formation. We hypothesized, and confirmed with density functional theory (DFT) calculations, that mono- S-oxo derivatives of cyclic disulfides formed a terminal sulfenic acid upon ring cleavage that obviated the previously rate-limiting step, thiol oxidation, and accelerated the new rate-determining step, ring cleavage. Our calculations suggest that the origin of accelerated ring cleavage is improved frontier molecular orbital overlap in the thiolate-disulfide interchange transition. Five- to seven-membered cyclic thiosulfinates were synthesized and efficiently cross-linked up to 104-fold faster than their cyclic disulfide precursors; functioned in the presence of biological concentrations of glutathione; and acted as cell-permeable, potent, tolerable, intracellular cross-linkers. This new class of thiol cross-linkers exhibited click-like attributes including, high yields driven by the enthalpies of disulfide and water formation, orthogonality with common functional groups, water-compatibility, and ring strain-dependence.

In Vitro Liquid Extraction Surface Analysis Mass Spectrometry (ivLESA-MS) for Direct Metabolic Analysis of Adherent Cells in Culture.

Conventional metabolomic methods include extensive sample preparation steps and long analytical run times, increasing the likelihood of processing artifacts and limiting high throughput applications. We present here in vitro liquid extraction surface analysis mass spectrometry (ivLESA-MS), a variation on LESA-MS, performed directly on adherent cells grown in 96-well cell culture plates. To accomplish this, culture medium was aspirated immediately prior to analysis, and metabolites were extracted using LESA from the cell monolayer surface, followed by nano-electrospray ionization and MS analysis in negative ion mode. We applied this platform to characterize and compare lipidomic profiles of multiple breast cancer cell lines growing in culture (MCF-7, ZR-75-1, MDA-MB-453, and MDA-MB-231) and revealed distinct and reproducible lipidomic signatures between the cell lines. Additionally, we demonstrated time-dependent processing artifacts, underscoring the importance of immediate analysis. ivLESA-MS represents a rapid in vitro metabolomic method, which precludes the need for quenching, cell harvesting, sample preparation, and chromatography, significantly shortening preparation and analysis time while minimizing processing artifacts. This method could be further adapted to test drugs in vitro in a high throughput manner.

Secretion, isotopic labeling and deglycosylation of N-acylethanolamine acid amidase for biophysical studies.

N-acylethanolamine acid amidase (NAAA) is an N-terminal nucleophile (Ntn) enzyme with a catalytic cysteine residue that has highest activity at acidic pH. The most prominent substrate hydrolyzed is palmitoylethanolamine (PEA), which regulates inflammation. Inhibitors of NAAA have been shown to increase endogenous levels of PEA, and are of interest as potential treatments for inflammatory disorders and other maladies. Currently, there are no X-ray or NMR structures of NAAA available to inform medicinal chemistry. Additionally, there are a limited number of enzyme structures available that are within the Ntn-hydrolase family, have a catalytic cysteine residue, and have a high sequence homology. For these reasons, we developed expression and purification methods for the production of enzyme samples amenable to structural characterization. Mammalian cells are necessary for post-translational processing, including signal sequence cleavage and glycosylation, that are required for a correctly folded zymogen before conversion to active, and mature enzyme. We have identified an expression construct, mammalian cell line, specific media and additives to express and secrete hNAAA zymogen and we further optimized propagation conditions and show this secretion method is suitable for isotopic labeling of the protein. We refined purification methods to achieve a high degree of protein purity potentially suited to crystallography. Glycosylated proteins can present challenges to biophysical methods. Therefore we deglycosylate the enzyme and show that the activity of the mature enzyme is not affected by deglycosylation.

Artifacts to avoid while taking advantage of top-down mass spectrometry based detection of protein S-thiolation.

Bottom-up MS studies typically employ a reduction and alkylation step that eliminates a class of PTM, S-thiolation. Given that molecular oxygen can mediate S-thiolation from reduced thiols, which are abundant in the reducing intracellular milieu, we investigated the possibility that some S-thiolation modifications are artifacts of protein preparation. Cu/Zn-superoxide dismutase (SOD1) was chosen for this case study as it has a reactive surface cysteine residue, which is readily cysteinylated in vitro. The ability of oxygen to generate S-thiolation artifacts was tested by comparing purification of SOD1 from postmortem human cerebral cortex under aerobic and anaerobic conditions. S-thiolation was ∼50% higher in aerobically processed preparations, consistent with oxygen-dependent artifactual S-thiolation. The ability of endogenous small molecule disulfides (e.g. cystine) to participate in artifactual S-thiolation was tested by blocking reactive protein cysteine residues during anaerobic homogenization. A 50-fold reduction in S-thiolation occurred indicating that the majority of S-thiolation observed aerobically was artifact. Tissue-specific artifacts were explored by comparing brain- and blood-derived protein, with remarkably more artifacts observed in brain-derived SOD1. Given the potential for such artifacts, rules of thumb for sample preparation are provided. This study demonstrates that without taking extraordinary precaution, artifactual S-thiolation of highly reactive, surface-exposed, cysteine residues can result.

Molecular imaging of drug transit through the blood-brain barrier with MALDI mass spectrometry imaging.

Drug transit through the blood-brain barrier (BBB) is essential for therapeutic responses in malignant glioma. Conventional methods for assessment of BBB penetrance require synthesis of isotopically labeled drug derivatives. Here, we report a new methodology using matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) to visualize drug penetration in brain tissue without molecular labeling. In studies summarized here, we first validate heme as a simple and robust MALDI MSI marker for the lumen of blood vessels in the brain. We go on to provide three examples of how MALDI MSI can provide chemical and biological insights into BBB penetrance and metabolism of small molecule signal transduction inhibitors in the brain - insights that would be difficult or impossible to extract by use of radiolabeled compounds.

Structural consequences of cysteinylation of Cu/Zn-superoxide dismutase.

The metalloenzyme Cu/Zn-superoxide dismutase (SOD1) catalyzes the reduction of superoxide anions into molecular oxygen and hydrogen peroxide. Hydrogen peroxide can oxidize SOD1, resulting in aberrant protein conformational changes, disruption of SOD1 function, and DNA damage. Cells may have evolved mechanisms of regulation that prevent such oxidation. We observed that cysteinylation of cysteine 111 (Cys111) of SOD1 prevents oxidation by peroxide (DOI 10.1021/bi4006122 ). In this article, we characterize cysteinylated SOD1 using differential scanning fluorometry and X-ray crystallography. The stoichiometry of binding was one cysteine per SOD1 dimer, and there does not appear to be free volume for a second cysteine without disrupting the dimer interface. Much of the three-dimensional structure of SOD1 is unaffected by cysteinylation. However, local conformational changes are observed in the cysteinylated monomer that include changes in conformation of the electrostatic loop (loop VII; residues 133-144) and the dimer interface (loop VI; residues 102-115). In addition, our data shows how cysteinylation precludes oxidation of cysteine 111 and suggests possible cross-talk between the dimer interface and the electrostatic loop.

Post-translational modification by cysteine protects Cu/Zn-superoxide dismutase from oxidative damage.

Reactive oxygen species (ROS) are cytotoxic. To remove ROS, cells have developed ROS-specific defense mechanisms, including the enzyme Cu/Zn superoxide dismutase (SOD1), which catalyzes the disproportionation of superoxide anions into molecular oxygen and hydrogen peroxide. Although hydrogen peroxide is less reactive than superoxide, it is still capable of oxidizing, unfolding, and inactivating SOD1, at least in vitro. To explore the relevance of post-translational modification (PTM) of SOD1, including peroxide-related modifications, SOD1 was purified from postmortem human nervous tissue. As much as half of all purified SOD1 protein contained non-native post-translational modifications (PTMs), the most prevalent modifications being cysteinylation and peroxide-related oxidations. Many PTMs targeted a single reactive SOD1 cysteine, Cys111. An intriguing observation was that unlike native SOD1, cysteinylated SOD1 was not oxidized. To further characterize how cysteinylation may protect SOD1 from oxidation, cysteine-modified SOD1 was prepared in vitro and exposed to peroxide. Cysteinylation conferred nearly complete protection from peroxide-induced oxidation of SOD1. Moreover, SOD1 that has been cysteinylated and peroxide oxidized in vitro comprised a set of PTMs that bear a striking resemblance to the myriad of PTMs observed in SOD1 purified from human tissue.

Mass spectrometry tools for analysis of intermolecular interactions.

The small quantities of protein required for mass spectrometry (MS) make it a powerful tool to detect binding (protein-protein, protein-small molecule, etc.) of proteins that are difficult to express in large quantities, as is the case for many intrinsically disordered proteins. Chemical cross-linking, proteolysis, and MS analysis, combined, are a powerful tool for the identification of binding domains. Here, we present a traditional approach to determine protein-protein interaction binding sites using heavy water ((18)O) as a label. This technique is relatively inexpensive and can be performed on any mass spectrometer without specialized software.