Development of a high-throughput dual-stream liquid chromatography-tandem mass spectrometry method to screen for inhibitors of glutamate carboxypeptidase II.

RATIONALE: Glutamate carboxypeptidase II (GCPII) catalyzes the hydrolysis of N-acetylaspartylglutamate (NAAG) to yield glutamate (Glu) and N-acetylaspartate (NAA). Inhibition of GCPII has been shown to remediate the neurotoxicity of excess Glu in a variety of cell and animal disease models. A robust high-throughput liquid chromatography-tandem mass spectrometry (LC/MS/MS) method was needed to quantify GCPII enzymatic activity in a biochemical high-throughput screening assay. METHODS: A dual-stream LC/MS/MS method was developed. Two parallel eluent streams ran identical HILIC gradient methods on BEH-Amide (2 × 30 mm) columns. Each LC channel was run independently, and the cycle time was 2 min per channel. Overall throughput was 1 min per sample for the dual-channel integrated system. Multiply injected acquisition files were split during data review, and batch metadata were automatically paired with raw data during the review process. RESULTS: Two LC sorbents, BEH-Amide and Penta-HILIC, were tested to separate the NAAG cleavage product Glu from isobaric interference and ion suppressants in the bioassay matrix. Early elution of NAAG and NAA on BEH-Amide allowed interfering species to be diverted to waste. The limit of quantification was 0.1 pmol for Glu. The Z-factor of this assay averaged 0.85. Over 36 000 compounds were screened using this method. CONCLUSIONS: A fast gradient dual-stream LC/MS/MS method for Glu quantification in GCPII biochemical screening assay samples was developed and validated. HILIC separation chemistry offers robust performance and unique selectivity for targeted positive mode quantification of Glu, NAA, and NAAG.

Target Class Profiling of Small-Molecule Methyltransferases.

Target class profiling (TCP) is a chemical biology approach to investigate understudied biological target classes. TCP is achieved by developing a generalizable assay platform and screening curated compound libraries to interrogate the chemical biological space of members of an enzyme family. In this work, we took a TCP approach to investigate inhibitory activity across a set of small-molecule methyltransferases (SMMTases), a subclass of methyltransferase enzymes, with the goal of creating a launchpad to explore this largely understudied target class. Using the representative enzymes nicotinamide N-methyltransferase (NNMT), phenylethanolamine N-methyltransferase (PNMT), histamine N-methyltransferase (HNMT), glycine N-methyltransferase (GNMT), catechol O-methyltransferase (COMT), and guanidinoacetate N-methyltransferase (GAMT), we optimized high-throughput screening (HTS)-amenable assays to screen 27,574 unique small molecules against all targets. From this data set, we identified a novel inhibitor which selectively inhibits the SMMTase HNMT and demonstrated how this platform approach can be leveraged for a targeted drug discovery campaign using the example of HNMT.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.