The Indian cobra reference genome and transcriptome enables comprehensive identification of venom toxins.

Snakebite envenoming is a serious and neglected tropical disease that kills ~100,000 people annually. High-quality, genome-enabled comprehensive characterization of toxin genes will facilitate development of effective humanized recombinant antivenom. We report a de novo near-chromosomal genome assembly of Naja naja, the Indian cobra, a highly venomous, medically important snake. Our assembly has a scaffold N50 of 223.35 Mb, with 19 scaffolds containing 95% of the genome. Of the 23,248 predicted protein-coding genes, 12,346 venom-gland-expressed genes constitute the 'venom-ome' and this included 139 genes from 33 toxin families. Among the 139 toxin genes were 19 'venom-ome-specific toxins' (VSTs) that showed venom-gland-specific expression, and these probably encode the minimal core venom effector proteins. Synthetic venom reconstituted through recombinant VST expression will aid in the rapid development of safe and effective synthetic antivenom. Additionally, our genome could serve as a reference for snake genomes, support evolutionary studies and enable venom-driven drug discovery.

Rapid determination of multiple linear kinase substrate motifs by mass spectrometry.

Kinase-substrate recognition depends on the chemical properties of the phosphorylatable residue as well as the surrounding linear sequence motif. Detailed knowledge of these characteristics increases the confidence of linking identified phosphorylation sites to kinases, predicting phosphorylation sites, and designing optimal peptide substrates. Here, we present a mass spectrometry-based approach for determining linear kinase substrate motifs by elaborating the positional and chemical preference of the kinase for a phosphorylatable residue using libraries of naturally-occurring peptides that are amenable to peptide identification by commonly used proteomics platforms. We applied this approach to a structurally and functionally diverse set of purified kinases, which recapitulated their previously described substrate motifs and discovered additional ones, including preferences of certain kinases for phosphorylatable residues adjacent to peptide termini. Furthermore, we identify specific and distinguishable motif elements for the four members of the polo-like kinase (Plk) family and verify members of these motif elements for Plk1 in vivo.

Tempest: GPU-CPU computing for high-throughput database spectral matching.

Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

Quantitative phosphoproteomics identifies substrates and functional modules of Aurora and Polo-like kinase activities in mitotic cells.

Mitosis is a process involving a complex series of events that require careful coordination. Protein phosphorylation by a small number of kinases, in particular Aurora A, Aurora B, the cyclin-dependent kinase-cyclin complex Cdk1/cyclinB, and Polo-like kinase 1 (Plk1), orchestrates almost every step of cell division, from entry into mitosis to cytokinesis. To discover more about the functions of Aurora A, Aurora B, and kinases of the Plk family, we mapped mitotic phosphorylation sites to these kinases through the combined use of quantitative phosphoproteomics and selective targeting of kinase activities by small-molecule inhibitors. Using this integrated approach, we connected 778 phosphorylation sites on 562 proteins with these enzymes in cells arrested in mitosis. By connecting the kinases to protein complexes, we associated these kinases with functional modules. In addition to predicting previously unknown functions, this work establishes additional substrate-recognition motifs for these kinases and provides an analytical template for further use in dissecting kinase signaling events in other areas of cellular signaling and systems biology.

MacroSEQUEST: efficient candidate-centric searching and high-resolution correlation analysis for large-scale proteomics data sets.

Modern mass spectrometers are now capable of producing tens of thousands of tandem mass (MS/MS) spectra per hour of operation, resulting in an ever-increasing burden on the computational tools required to translate these raw MS/MS spectra into peptide sequences. In the present work, we describe our efforts to improve the performance of one of the earliest and most commonly used algorithms, SEQUEST, through a wholesale redesign of its processing architecture. We call this new program MacroSEQUEST, which exhibits a dramatic improvement in processing speed by transiently indexing the array of MS/MS spectra prior to searching FASTA databases. We demonstrate the performance of MacroSEQUEST relative to a suite of other programs commonly encountered in proteomics research. We also extend the capability of SEQUEST by implementing a parameter in MacroSEQUEST that allows for scalable sparse arrays of experimental and theoretical spectra to be implemented for high-resolution correlation analysis and demonstrate the advantages of high-resolution MS/MS searching to the sensitivity of large-scale proteomics data sets.

Enhanced analysis of metastatic prostate cancer using stable isotopes and high mass accuracy instrumentation.

The primary goal of proteomics is to gain a better understanding of biological function at the protein expression level. As the field matures, numerous technologies are being developed to aid in the identification, quantification and characterization of protein expression and post-translational modifications on a near-global scale. Stable isotope labeling by amino acids in cell culture is one such technique that has shown broad biological applications. While we have recently shown the application of this technology to a model of metastatic prostate cancer, we now report a substantial improvement in quantitative analysis using a linear ion-trap Fourier transform ion cyclotron resonance mass spectrometer (LTQ FT) and novel quantification software. This resulted in the quantification of nearly 1400 proteins, a greater than 3-fold increase in comparison to our earlier study. This dramatic increase in proteome coverage can be attributed to (1) use of a double-labeling strategy, (2) greater sensitivity, speed and mass accuracy provided by the LTQ FT mass spectrometer, and (3) more robust quantification software. Finally, by using a concatenated target/decoy protein database for our peptide searches, we now report these data in the context of an estimated false-positive rate of one percent.

Optimization and use of peptide mass measurement accuracy in shotgun proteomics.

Mass spectrometers that provide high mass accuracy such as FT-ICR instruments are increasingly used in proteomic studies. Although the importance of accurately determined molecular masses for the identification of biomolecules is generally accepted, its role in the analysis of shotgun proteomic data has not been thoroughly studied. To gain insight into this role, we used a hybrid linear quadrupole ion trap/FT-ICR (LTQ FT) mass spectrometer for LC-MS/MS analysis of a highly complex peptide mixture derived from a fraction of the yeast proteome. We applied three data-dependent MS/MS acquisition methods. The FT-ICR part of the hybrid mass spectrometer was either not exploited, used only for survey MS scans, or also used for acquiring selected ion monitoring scans to optimize mass accuracy. MS/MS data were assigned with the SEQUEST algorithm, and peptide identifications were validated by estimating the number of incorrect assignments using the composite target/decoy database search strategy. We developed a simple mass calibration strategy exploiting polydimethylcyclosiloxane background ions as calibrant ions. This strategy allowed us to substantially improve mass accuracy without reducing the number of MS/MS spectra acquired in an LC-MS/MS run. The benefits of high mass accuracy were greatest for assigning MS/MS spectra with low signal-to-noise ratios and for assigning phosphopeptides. Confident peptide identification rates from these data sets could be doubled by the use of mass accuracy information. It was also shown that improving mass accuracy at a cost to the MS/MS acquisition rate substantially lowered the sensitivity of LC-MS/MS analyses. The use of FT-ICR selected ion monitoring scans to maximize mass accuracy reduced the number of protein identifications by 40%.

Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations.

Researchers have several options when designing proteomics experiments. Primary among these are choices of experimental method, instrumentation and spectral interpretation software. To evaluate these choices on a proteome scale, we compared triplicate measurements of the yeast proteome by liquid chromatography tandem mass spectrometry (LC-MS/MS) using linear ion trap (LTQ) and hybrid quadrupole time-of-flight (QqTOF; QSTAR) mass spectrometers. Acquired MS/MS spectra were interpreted with Mascot and SEQUEST algorithms with and without the requirement that all returned peptides be tryptic. Using a composite target decoy database strategy, we selected scoring criteria yielding 1% estimated false positive identifications at maximum sensitivity for all data sets, allowing reasonable comparisons between them. These comparisons indicate that Mascot and SEQUEST yield similar results for LTQ-acquired spectra but less so for QSTAR spectra. Furthermore, low reproducibility between replicate data acquisitions made on one or both instrument platforms can be exploited to increase sensitivity and confidence in large-scale protein identifications.