Blockade of Tim-3 binding to phosphatidylserine and CEACAM1 is a shared feature of anti-Tim-3 antibodies that have functional efficacy.

Both in vivo data in preclinical cancer models and in vitro data with T cells from patients with advanced cancer support a role for Tim-3 blockade in promoting effective anti-tumor immunity. Consequently, there is considerable interest in the clinical development of antibody-based therapeutics that target Tim-3 for cancer immunotherapy. A challenge to this clinical development is the fact that several ligands for Tim-3 have been identified: galectin-9, phosphatidylserine, HMGB1, and most recently, CEACAM1. These observations raise the important question of which of these multiple receptor:ligand relationships must be blocked by an anti-Tim-3 antibody in order to achieve therapeutic efficacy. Here, we have examined the properties of anti-murine and anti-human Tim-3 antibodies that have shown functional efficacy and find that all antibodies bind to Tim-3 in a manner that interferes with Tim-3 binding to both phosphatidylserine and CEACAM1. Our data have implications for the understanding of Tim-3 biology and for the screening of anti-Tim-3 antibody candidates that will have functional properties in vivo.

Proteasome inhibition for treatment of leishmaniasis, Chagas disease and sleeping sickness.

Chagas disease, leishmaniasis and sleeping sickness affect 20 million people worldwide and lead to more than 50,000 deaths annually. The diseases are caused by infection with the kinetoplastid parasites Trypanosoma cruzi, Leishmania spp. and Trypanosoma brucei spp., respectively. These parasites have similar biology and genomic sequence, suggesting that all three diseases could be cured with drugs that modulate the activity of a conserved parasite target. However, no such molecular targets or broad spectrum drugs have been identified to date. Here we describe a selective inhibitor of the kinetoplastid proteasome (GNF6702) with unprecedented in vivo efficacy, which cleared parasites from mice in all three models of infection. GNF6702 inhibits the kinetoplastid proteasome through a non-competitive mechanism, does not inhibit the mammalian proteasome or growth of mammalian cells, and is well-tolerated in mice. Our data provide genetic and chemical validation of the parasite proteasome as a promising therapeutic target for treatment of kinetoplastid infections, and underscore the possibility of developing a single class of drugs for these neglected diseases.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis.

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

Isotope-Coded Labeling for Accelerated Protein Interaction Profiling Using MS.

Protein interaction surface mapping using MS is widely applied but comparatively resource-intensive. Here, a workflow adaptation for use of isotope-coded tandem mass tags for the purpose is reported. The key benefit of improved throughput derived from sample acquisition multiplexing and automated analysis is shown to be maintained in the new application. Mapping of the epitopes of two monoclonal antibodies on their respective targets serves to illustrate the novel approach. We conclude that the approach enables mapping of interactions by MS at significantly larger scales than hereto possible.

Feature based retention time alignment for improved HDX MS analysis.

An algorithm for retention time alignment of mass shifted hydrogen-deuterium exchange (HDX) data based on an iterative distance minimization procedure is described. The algorithm performs pairwise comparisons in an iterative fashion between a list of features from a reference file and a file to be time aligned to calculate a retention time mapping function. Features are characterized by their charge, retention time and mass of the monoisotopic peak. The algorithm is able to align datasets with mass shifted features, which is a prerequisite for aligning hydrogen-deuterium exchange mass spectrometry datasets. Confidence assignments from the fully automated processing of a commercial HDX software package are shown to benefit significantly from retention time alignment prior to extraction of deuterium incorporation values.

Subzero temperature chromatography for reduced back-exchange and improved dynamic range in amide hydrogen/deuterium exchange mass spectrometry.

Amide hydrogen/deuterium exchange is a commonly used technique for studying the dynamics of proteins and their interactions with other proteins or ligands. When coupled with liquid chromatography and mass spectrometry, hydrogen/deuterium exchange provides several unique advantages over other structural characterization techniques including very high sensitivity, the ability to analyze proteins in complex environments, and a large mass range. A fundamental limitation of the technique arises from the loss of the deuterium label (back-exchange) during the course of the analysis. A method to limit loss of the label during the separation stage of the analysis using subzero temperature reversed-phase chromatography is presented. The approach is facilitated by the use of buffer modifiers that prevent freezing. We evaluated ethylene glycol, dimethyl formamide, formamide, and methanol for their freezing point suppression capabilities, effects on peptide retention, and their compatibilities with electrospray ionization. Ethylene glycol was used extensively because of its good electrospray ionization compatibility; however, formamide has potential to be a superior modifier if detrimental effects on ionization can be overcome. It is demonstrated using suitable buffer modifiers that separations can be performed at temperatures as low as -30 °C with negligible loss of the deuterium label, even during long chromatographic separations. The reduction in back-exchange is shown to increase the dynamic range of hydrogen/deuterium exchange mass spectrometry in terms of mixture complexity and the magnitude with which changes in deuteration level can be quantified.

Comparison of different signal thresholds on data dependent sampling in Orbitrap and LTQ mass spectrometry for the identification of peptides and proteins in complex mixtures.

We evaluate the effect of ion-abundance threshold settings for data-dependent acquisition on a hybrid LTQ-Orbitrap mass spectrometer, analyzing features such as the total number of spectra collected, the signal to noise ratio of the full MS scans, the spectral quality of the tandem mass spectra acquired, and the number of peptides and proteins identified from a complex mixture. We find that increasing the threshold for data-dependent acquisition generally decreases the quantity but increases the quality of the spectra acquired. This is especially true when the threshold setting is set above the noise level of the full MS scan. We compare two distinct experimental configurations: one where full MS scans are acquired in the Orbitrap analyzer while tandem MS scans are acquired in the LTQ analyzer, and one where both full MS and tandem MS scans are acquired in the LTQ analyzer. We examine the number of spectra, peptides, and proteins identified under various threshold conditions, and we find that the optimal threshold setting is at or below the respective noise level of the instrument regardless of whether the full MS scan is performed in the Orbitrap or in the LTQ analyzer. When comparing the high-throughput identification performance of the two analyzers, we conclude that, used at optimal threshold levels, the LTQ and the Orbitrap identify similar numbers of peptides and proteins. The higher scan speed of the LTQ, which results in more spectra being collected, is roughly compensated by the higher mass accuracy of the Orbitrap, which results in improved database searching and peptide validation software performance.

A quantitative analysis software tool for mass spectrometry-based proteomics.

We describe Census, a quantitative software tool compatible with many labeling strategies as well as with label-free analyses, single-stage mass spectrometry (MS1) and tandem mass spectrometry (MS/MS) scans, and high- and low-resolution mass spectrometry data. Census uses robust algorithms to address poor-quality measurements and improve quantitative efficiency, and it can support several input file formats. We tested Census with stable-isotope labeling analyses as well as label-free analyses.

Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression saccharomyces cerevisiae strains: the comparison of two quantitative methods.

The quantitative proteomic analysis of complex protein mixtures is emerging as a technically challenging but viable systems-level approach for studying cellular function. This study presents a large-scale comparative analysis of protein abundances from yeast protein lysates derived from both wild-type yeast and yeast strains lacking key components of the Snf1 kinase complex. Four different strains were grown under well-controlled chemostat conditions. Multidimensional protein identification technology followed by quantitation using either spectral counting or stable isotope labeling approaches was used to identify relative changes in the protein expression levels between the strains. A total of 2388 proteins were relatively quantified, and more than 350 proteins were found to have significantly different expression levels between the two strains of comparison when using the stable isotope labeling strategy. The stable isotope labeling based quantitative approach was found to be highly reproducible among biological replicates when complex protein mixtures containing small expression changes were analyzed. Where poor correlation between stable isotope labeling and spectral counting was found, the major reason behind the discrepancy was the lack of reproducible sampling for proteins with low spectral counts. The functional categorization of the relative protein expression differences that occur in Snf1-deficient strains uncovers a wide range of biological processes regulated by this important cellular kinase.

Quantitative mass spectrometry identifies insulin signaling targets in C. elegans.

DAF-2, an insulin receptor-like protein, regulates metabolism, development, and aging in Caenorhabditis elegans. In a quantitative proteomic study, we identified 86 proteins that were more or less abundant in long-lived daf-2 mutant worms than in wild-type worms. Genetic studies on a subset of these proteins indicated that they act in one or more processes regulated by DAF-2, including entry into the dauer developmental stage and aging. In particular, we discovered a compensatory mechanism activated in response to reduced DAF-2 signaling, which involves the protein phosphatase calcineurin.

Quantification of the synaptosomal proteome of the rat cerebellum during post-natal development.

Large-scale proteomic analysis of the mammalian brain has been successfully performed with mass spectrometry techniques, such as Multidimensional Protein Identification Technology (MudPIT), to identify hundreds to thousands of proteins. Strategies to efficiently quantify protein expression levels in the brain in a large-scale fashion, however, are lacking. Here, we demonstrate a novel quantification strategy for brain proteomics called SILAM (Stable Isotope Labeling in Mammals). We utilized a (15)N metabolically labeled rat brain as an internal standard to perform quantitative MudPIT analysis on the synaptosomal fraction of the cerebellum during post-natal development. We quantified the protein expression level of 1138 proteins in four developmental time points, and 196 protein alterations were determined to be statistically significant. Over 50% of the developmental changes observed have been previously reported using other protein quantification techniques, and we also identified proteins as potential novel regulators of neurodevelopment. We report the first large-scale proteomic analysis of synaptic development in the cerebellum, and we demonstrate a useful quantitative strategy for studying animal models of neurological disease.

Relative quantification of stable isotope labeled peptides using a linear ion trap-Orbitrap hybrid mass spectrometer.

The quantitative analysis of complex biological samples has emerged as a key research area in the field of proteomics. Although quantitative proteomic experiments remain challenging, these strategies have been greatly facilitated by the development of newer high-performance mass spectrometers. In this work, we have evaluated the use of the LTQ-Orbitrap, a hybrid mass spectrometer in which a linear ion trap is coupled to an Orbitrap mass analyzer, for quantitative analyses. By analyzing a range of yeast protein standards, we found that the high mass accuracy, high resolution, large ion capacity, and large dynamic range of the LTQ-Orbitrap led to as much as a 4-5-fold improvement in the number and quality of the peptide ratio measurements compared to similar analyses done on the LTQ. We also successfully quantified protein expression differences that occur in metabolically labeled rat synapses during brain development to further demonstrate the suitability of the LTQ-Orbitrap for the comparative analysis of complex tissue samples.

15N metabolic labeling of mammalian tissue with slow protein turnover.

We previously reported the metabolic 15N labeling of a rat where enrichment ranged from 94% to 74%. We report here an improved labeling strategy which generates 94% 15N enrichment throughout all tissues of the rat. A high 15N enrichment of the internal standard is necessary for accurate quantitation, and thus, this approach will allow quantitative mass spectrometry analysis of animal models of disease targeting any tissue.

Automated ultra-high-pressure multidimensional protein identification technology (UHP-MudPIT) for improved peptide identification of proteomic samples.

Multidimensional separation is one of the most successful approaches for proteomics studies that deal with complex samples. We have developed an automated ultra-high-pressure multidimensional liquid chromatography system that operates up to approximately 20 kpsi to improve separations and increase protein coverage from limited amount of samples. The reversed-phase gradient is operated in the constant-flow mode opposed to the constant-pressure mode, which is typical of previous ultra-high-pressure systems. In contrast to constant-pressure systems, the gradient shape is fully controllable and can be optimized for the type of samples to be run. The system also features fast sample loading/desalting using a vented column approach to improve sample throughput. This approach was validated on a soluble fraction from yeast lysate where we achieved approximately 30% more protein identifications using a 60-cm-long triphasic capillary column than with our traditional approach. Advantages of the use of a relatively long reversed-phase column (approximately 50 cm) for MudPIT-type experiments are also discussed.

Cross-correlation algorithm for calculation of peptide molecular weight from tandem mass spectra.

We have developed an approach to identify the molecular weight of a peptide ion directly from its corresponding tandem mass spectrum using a cross-correlation function. We have shown that the monoisotopic molecular weight can be calculated for approximately 90% of tandem mass spectra identified from tryptic digests of complex protein mixtures. The accuracy of the calculated monoisotopic masses was dependent on the resolution and mass accuracy of the spectra analyzed, but was typically <0.25 amu for linear ion trap mass spectra. The ability to calculate accurate monoisotopic molecular weights for low-resolution ion trap data should significantly improve both the speed and performance of database searches for which typical mass accuracies of approximately 3 amu are employed. In addition, this strategy can also be used to identify the precursor ion for tandem mass spectra acquired using large ion selection windows in data-independent collision-activated dissociation and has the potential to identify multiplexed tandem mass spectra.

Quantitative phosphoproteomic analysis of the tumor necrosis factor pathway.

Protein phosphorylation has become a focus of many proteomic studies due to the central role that it plays in biology. We combine peptide-based gel-free isoelectric focusing and immobilized metal affinity chromatography to enhance the detection of phosphorylation events within complex protein samples using LC-MS. This method is then used to carry out a quantitative phosphoproteomic analysis of the tumor necrosis factor (TNF) pathway using HeLa cells metabolically labeled with 15N-containing amino acids, where 145 phosphorylation sites were found to be up-regulated upon the activation of the TNF pathway.

Identification of the anti-inflammatory protein tristetraprolin as a hyperphosphorylated protein by mass spectrometry and site-directed mutagenesis.

Tristetraprolin (TTP) is a zinc-finger protein that binds to AREs (AU-rich elements) within certain mRNAs and causes destabilization of those mRNAs. Mice deficient in TTP develop a profound inflammatory syndrome with erosive arthritis, autoimmunity and myeloid hyperplasia. Previous studies showed that TTP is phosphorylated extensively in intact cells. However, limited information is available about the identities of these phosphorylation sites. We investigated the phosphorylation sites in human TTP from transfected HEK-293 cells by MS and site-directed mutagenesis. A number of phosphorylation sites including Ser66, Ser88, Thr92, Ser169, Ser186, Ser197, Ser218, Ser228, Ser276 and Ser296 were identified by MS analyses using MALDI (matrix-assisted laser-desorption-ionization)-MS, MALDI-tandem MS, LC (liquid chromatography)-tandem MS and multidimensional protein identification technology. Mutations of Ser197, Ser218 and Ser228 to alanine in the human protein significantly increased TTP's gel mobility (likely to be stoichiometric), whereas mutations at the other sites had little effect on its gel mobility. Dephosphorylation and in vivo labelling studies showed that mutant proteins containing multiple mutations were still phosphorylated, and all were able to bind to RNA probes containing AREs. Confocal microscopy showed a similar cytosolic localization of TTP among the various proteins. Ser197, Ser218 and Ser228 are predicted by motif scanning to be potential sites for protein kinase A, glycogen synthase kinase-3 and extracellular-signal-regulated kinase 1 (both Ser218 and Ser228) respectively. The present study has identified multiple phosphorylation sites in the anti-inflammatory protein TTP in mammalian cells and should provide the molecular basis for further studies on the function and regulation of TTP in controlling pro-inflammatory cytokines.

Impact of ion trap tandem mass spectra variability on the identification of peptides.

Peptide identification based on tandem mass spectrometry and database searching algorithms has become one of the central technologies in proteomics. At the heart of this technology is the ability to reproducibly acquire high-quality tandem mass spectra for database interrogation. The variability in tandem mass spectra generation is often assumed to be minimal, and peptide identifications are typically based on a single tandem mass spectrum. In this paper, we characterize the variance of scores derived from replicate tandem mass spectra using several database search algorithms and demonstrate the effects of spectral variability on the correct identification of peptides. We show that the variance associated with the collection of tandem mass spectra can be substantial leading to sizable errors in search algorithm scores ( approximately 5-25% RSD) and ultimately incorrect assignments. Processing strategies are discussed to minimize the impact of tandem mass spectra variability on peptide identification.

Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra.

To take advantage of the potential quantitative benefits offered by tandem mass spectrometry, we have modified the method in which tandem mass spectrum data are acquired in 'shotgun' proteomic analyses. The proposed method is not data dependent and is based on the sequential isolation and fragmentation of precursor windows (of 10 m/z) within the ion trap until a desired mass range has been covered. We compared the quantitative figures of merit for this method to those for existing strategies by performing an analysis of the soluble fraction of whole-cell lysates from yeast metabolically labeled in vivo with (15)N. To automate this analysis, we modified software (RelEx) previously written in the Yates lab to generate chromatograms directly from tandem mass spectra. These chromatograms showed improvements in signal-to-noise ratio of approximately three- to fivefold over corresponding chromatograms generated from mass spectrometry scans. In addition, to demonstrate the utility of the data-independent acquisition strategy coupled with chromatogram reconstruction from tandem mass spectra, we measured protein expression levels in two developmental stages of Caenorhabditis elegans.

Optimizing the multielement analysis capabilities of an ICP quadrupole mass spectrometer using electrothermal vaporization sample introduction.

A mathematical solution and an experimental procedure are described for calculating the maximum number of transient signals, such as those obtained using ETV sample introduction into an ICPMS, that can be monitored by a scanning spectrometer, such as a quadrupole mass analyzer. The total number of masses that can be monitored per ETV firing is shown to be dependent on the values chosen for data collection (i.e., scan time and dwell time) and the necessary limits of detection required by the method. The theory shows the effect that statistical noise, peak shapes, and inconsistent peak appearance times have on the overall variance calculated for the sample concentration based on total counts or "peak area". Also included in the variance calculation is the contribution made by the autosampler as an example of one type of error that is not associated with the data collection parameters. The theory is validated by two experiments where 21 and 68 transient signals are monitored per ETV firing; and as predicted, a 10 ppb sample is accurately quantified with precision better than 9% in both cases.