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The Janus family of tyrosine kinases (JAK1, JAK2, JAK3, and TYK2) play an essential role in the receptor signaling of cytokines that have been implicated in the pathogenesis of severe asthma, and there is emerging interest in the development of small-molecule-inhaled JAK inhibitors as treatments. Here, we describe the optimization of a quinazoline series of JAK inhibitors and the results of mouse lung pharmacokinetic (PK) studies where only low concentrations of parent compound were observed. Subsequent investigations revealed that the low exposure was due to metabolism by aldehyde oxidase (AO), so we sought to identify quinazolines that were not metabolized by AO. We found that specific substituents at the quinazoline 2-position prevented AO metabolism and this was rationalized through computational docking studies in the AO binding site, but they compromised kinome selectivity. Results presented here highlight that AO metabolism is a potential issue in the lung.

Optimization of a lead series of PI3Kδ inhibitors based on a dihydroisobenzofuran core led to the identification of potent, orally bioavailable compound 19. Selectivity profiling of compound 19 showed similar potency for class III PI3K, Vps34, and PI3Kδ, and compound 19 was not well-tolerated in a 7-day rat toxicity study. Structure-based design led to an improvement in selectivity for PI3Kδ over Vps34 and, a focus on oral phramacokinetics properties resulted in the discovery of compound 41, which showed improved toxicological outcomes at similar exposure levels to compound 19.

Chemical proteomics offers a unique approach for target identification of small molecule inhibitors directly from cell extracts, thus enabling characterization of target proteins under close to physiological conditions. Here, we describe a competition binding procedure that is based on affinity enrichment of potential target proteins on a probe matrix in the presence of increasing amounts of free test compound in solution. Reduced binding of target proteins to the probe matrix as a function of test compound concentration can be measured and thus, enables calculation of IC(50) values. The method employs quantitative mass spectrometry using isobaric mass tags which enables determination of potency for a large number of target proteins in a single analysis.

Chemical proteomics offers a unique approach for target identification of small molecule inhibitors directly from cell extracts, thus enabling characterization of target proteins under close to physiological conditions. Here, we describe a competition binding procedure that is based on affinity enrichment of potential target proteins on a probe matrix in the presence of increasing amounts of free test compound in solution. Reduced binding of target proteins to the probe matrix as a function of test compound concentration can be measured and thus, enables calculation of IC(50) values. The method employs quantitative mass spectrometry using isobaric mass tags which enables determination of potency for a large number of target proteins in a single analysis.

The development of selective histone deacetylase (HDAC) inhibitors with anti-cancer and anti-inflammatory properties remains challenging in large part owing to the difficulty of probing the interaction of small molecules with megadalton protein complexes. A combination of affinity capture and quantitative mass spectrometry revealed the selectivity with which 16 HDAC inhibitors target multiple HDAC complexes scaffolded by ELM-SANT domain subunits, including a novel mitotic deacetylase complex (MiDAC). Inhibitors clustered according to their target profiles with stronger binding of aminobenzamides to the HDAC NCoR complex than to the HDAC Sin3 complex. We identified several non-HDAC targets for hydroxamate inhibitors. HDAC inhibitors with distinct profiles have correspondingly different effects on downstream targets. We also identified the anti-inflammatory drug bufexamac as a class IIb (HDAC6, HDAC10) HDAC inhibitor. Our approach enables the discovery of novel targets and inhibitors and suggests that the selectivity of HDAC inhibitors should be evaluated in the context of HDAC complexes and not purified catalytic subunits.

Gamma-secretase is a unique intramembrane-cleaving protease complex, which cleaves the Alzheimer's disease-associated beta-amyloid precursor protein (APP) and a number of other type I membrane proteins. Human gamma-secretase consists of the catalytic subunit presenilin (PS) (PS1 or PS2), the substrate receptor nicastrin, APH-1 (APH-1a or APH-1b), and PEN-2. To facilitate in-depth biochemical analysis of gamma-secretase, we developed a fast and convenient multistep purification procedure for the endogenous enzyme. The enzyme was purified from HEK293 cells in an active form and had a molecular mass of approximately 500 kDa. Purified gamma-secretase was capable of producing the major amyloid-beta peptide (Abeta) species, such as Abeta40 and Abeta42, from a recombinant APP substrate in physiological ratios. Abeta generation could be modulated by pharmacological gamma-secretase modulators. Moreover, the Abeta42/Abeta40 ratio was strongly increased by purified PS1 L166P, an aggressive familial Alzheimer's disease mutant. Tandem mass spectrometry analysis revealed the consistent coisolation of several proteins with the known gamma-secretase core subunits. Among these were the previously described gamma-secretase interactors CD147 and TMP21 as well as other known interactors of these. Interestingly, the Niemann-Pick type C1 protein, a cholesterol transporter previously implicated in gamma-secretase-mediated processing of APP, was identified as a major copurifying protein. Affinity capture experiments using a biotinylated transition-state analogue inhibitor of gamma-secretase showed that these proteins are absent from active gamma-secretase complexes. Taken together, we provide an effective procedure for isolating endogenous gamma-secretase in considerably high grade, thus aiding further characterization of this pivotal enzyme. In addition, we provide evidence that the copurifying proteins identified are unlikely to be part of the active gamma-secretase enzyme.

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.

Stable isotope labeling (SIL) has emerged as a powerful tool to measure the relative quantitative differences between samples in many differential display-type proteomic applications. However, current SIL procedures tend to suffer from the fact that one needs to decide very early in a biochemical strategy whether or not a sample will be subjected to relative quantification. Typically, the entire strategy has to be adapted to the needs of the particular quantification method chosen which might limit the range of biochemical experiments amenable to quantification. Metabolic labeling approaches, albeit very sensitive, can only be applied to studies using appropriate cell culture systems which might not necessarily be compatible with the biological system under investigation. Chemical labeling of complex protein mixtures by, e.g., isotope-coded affinity tags (ICAT), can offer great simplification of protein mixtures but is restricted by the accessibility of the often few suitable peptides (i.e. cysteine containing peptides) for both protein identification and quantification. Here, we describe a post-digest 18O-labeling method that can circumvent some of the above limitations by separating protein identification from quantification. An aliquot of all samples in a set can be used for rapid protein ID using, e.g., matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). In a second step, relative quantification is performed using trypsin-catalyzed 18O incorporation into all tryptic peptides. This two-stage procedure introduces significant experimental flexibility because it enables postponement of the decision about which pairs of samples from a given set of experiments are to be compared until after the protein ID stage. In-gel digested protein quantities between 50 fmol and 15 pmol are amenable to this new method, with a dynamic range of 1:10 within one sample. Accuracy for measured relative abundances is similar to those reported for other SIL strategies (errors typically