moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets

Increasingly, multiple omics approaches are being applied to understand the complexity of biological systems. Yet, computational approaches that enable the efficient integration of such data are not well developed. Here, we describe a novel algorithm, termed moCluster, which discovers joint patterns among multiple omics data. The method first employs a multiblock multivariate analysis to define a set of latent variables representing joint patterns across input data sets, which is further passed to an ordinary clustering algorithm in order to discover joint clusters. Using simulated data, we show that moCluster’s performance is not compromised by issues present in iCluster/iCluster+ (notably, the nondeterministic solution) and that it operates 100× to 1000× faster than iCluster/iCluster+. We used moCluster to cluster proteomic and transcriptomic data from the NCI-60 cell line panel. The resulting cluster model revealed different phenotypes across cellular subtypes, such as doubling time and drug response. Applying moCluster to methylation, mRNA, and protein data from a large study on colorectal cancer patients identified four molecular subtypes, including one characterized by microsatellite instability and high expression of genes/proteins involved in immunity, such as PDL1, a target of multiple drugs currently in development. The other three subtypes have not been discovered before using single data sets, which clearly illustrates the molecular complexity of oncogenesis and the need for holistic, multidata analysis strategies

Effect of Astringent Stimuli on Salivary Protein Interactions Elucidated by Complementary Proteomics Approaches

The interaction of astringent substances with salivary proteins, which results in protein precipitation, is considered a key event in the molecular mechanism underlying the oral sensation of puckering astringency. As the chemical nature of orally active astringents is diverse and the knowledge of their interactions with salivary proteins rather fragmentary, human whole saliva samples were incubated with suprathreshold and isointensity solutions of the astringent polyphenol (−)-epigallocatechin gallate, the multivalent metal salt iron­(III) sulfate, the amino-functionalized polysaccharide chitosan, and the basic protein lysozyme. After separation of the precipitated proteins, the proteins affected by the astringents were identified and relatively quantified for the first time by complementary bottom-up and top-down mass spectrometry-based proteomics approaches. Major salivary target proteins, which may be involved in astringency perception, are reported here for each astringent stimulus

Comparing Immobilized Kinase Inhibitors and Covalent ATP Probes for Proteomic Profiling of Kinase Expression and Drug Selectivity

Kinases are involved in the regulation of many cellular processes and aberrant kinase signaling has been implicated in human disease. As a consequence, kinases are attractive drug targets. Assessing kinase function and drug selectivity in a more physiological context is challenging and often hampered by the generally low expression level of kinases and the extensive post-translation modification <i>in vivo</i>. Kinase drug selectivity screens by chemical proteomics have gained attention because they allow the profiling of hundreds of kinases against one drug at the same time. Here, we directly compared two such methods, notably, immobilized broad spectrum kinase inhibitors (kinobeads) and active site labeling using desthiobiotin-ATP and -ADP probes. Affinity purification of ∼100 kinases by either kinobeads or ATP/ADP probes was readily achieved using 1 mg of cellular protein. Bioinformatic analysis revealed a high degree of complementarity of the two techniques. Kinobeads covered the Tyrosine Kinase family particularly well and ATP probes enriched higher numbers of STE family kinases. A consecutive combination of both enrichment strategies therefore allowed for the coverage of a larger part of the kinome than any one technique alone. While kinobeads are very selective for kinases, the ATP/ADP probes also enriched a large number of other nucleotide binding proteins. Both methods were applied to the selectivity profiling of the small molecular Aurora kinase inhibitor tozasertib in K562 cells. Our data confirmed Aurora A, B, and BCR-ABL as the main targets of tozasertib and identified TNK1, STK2, RPS6KA1, and RPS6KA3 as submicromolar off targets

Comparing Immobilized Kinase Inhibitors and Covalent ATP Probes for Proteomic Profiling of Kinase Expression and Drug Selectivity

Kinases are involved in the regulation of many cellular processes and aberrant kinase signaling has been implicated in human disease. As a consequence, kinases are attractive drug targets. Assessing kinase function and drug selectivity in a more physiological context is challenging and often hampered by the generally low expression level of kinases and the extensive post-translation modification <i>in vivo</i>. Kinase drug selectivity screens by chemical proteomics have gained attention because they allow the profiling of hundreds of kinases against one drug at the same time. Here, we directly compared two such methods, notably, immobilized broad spectrum kinase inhibitors (kinobeads) and active site labeling using desthiobiotin-ATP and -ADP probes. Affinity purification of ∼100 kinases by either kinobeads or ATP/ADP probes was readily achieved using 1 mg of cellular protein. Bioinformatic analysis revealed a high degree of complementarity of the two techniques. Kinobeads covered the Tyrosine Kinase family particularly well and ATP probes enriched higher numbers of STE family kinases. A consecutive combination of both enrichment strategies therefore allowed for the coverage of a larger part of the kinome than any one technique alone. While kinobeads are very selective for kinases, the ATP/ADP probes also enriched a large number of other nucleotide binding proteins. Both methods were applied to the selectivity profiling of the small molecular Aurora kinase inhibitor tozasertib in K562 cells. Our data confirmed Aurora A, B, and BCR-ABL as the main targets of tozasertib and identified TNK1, STK2, RPS6KA1, and RPS6KA3 as submicromolar off targets

Comparing Immobilized Kinase Inhibitors and Covalent ATP Probes for Proteomic Profiling of Kinase Expression and Drug Selectivity

Kinases are involved in the regulation of many cellular processes and aberrant kinase signaling has been implicated in human disease. As a consequence, kinases are attractive drug targets. Assessing kinase function and drug selectivity in a more physiological context is challenging and often hampered by the generally low expression level of kinases and the extensive post-translation modification <i>in vivo</i>. Kinase drug selectivity screens by chemical proteomics have gained attention because they allow the profiling of hundreds of kinases against one drug at the same time. Here, we directly compared two such methods, notably, immobilized broad spectrum kinase inhibitors (kinobeads) and active site labeling using desthiobiotin-ATP and -ADP probes. Affinity purification of ∼100 kinases by either kinobeads or ATP/ADP probes was readily achieved using 1 mg of cellular protein. Bioinformatic analysis revealed a high degree of complementarity of the two techniques. Kinobeads covered the Tyrosine Kinase family particularly well and ATP probes enriched higher numbers of STE family kinases. A consecutive combination of both enrichment strategies therefore allowed for the coverage of a larger part of the kinome than any one technique alone. While kinobeads are very selective for kinases, the ATP/ADP probes also enriched a large number of other nucleotide binding proteins. Both methods were applied to the selectivity profiling of the small molecular Aurora kinase inhibitor tozasertib in K562 cells. Our data confirmed Aurora A, B, and BCR-ABL as the main targets of tozasertib and identified TNK1, STK2, RPS6KA1, and RPS6KA3 as submicromolar off targets

MScDB: A Mass Spectrometry-centric Protein Sequence Database for Proteomics

Protein sequence databases are indispensable tools for life science research including mass spectrometry (MS)-based proteomics. In current database construction processes, sequence similarity clustering is used to reduce redundancies in the source data. Albeit powerful, it ignores the peptide-centric nature of proteomic data and the fact that MS is able to distinguish similar sequences. Therefore, we introduce an approach that structures the protein sequence space at the peptide level using theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry-centric protein sequence database (MScDB). The core modules of MScDB are an <i>in-silico</i> proteolytic digest and a peptide-centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. Analysis of various MScDB uses cases against five complex human proteomes, resulting in 69 peptide identifications not present in UniProtKB as well as 79 putative single amino acid polymorphisms. MScDB retains ∼99% of the identifications in comparison to common databases despite a 3–48% increase in the theoretical peptide search space (but comparable protein sequence space). In addition, MScDB enables cross-species applications such as human/mouse graft models, and our results suggest that the uncertainty in protein assignments to one species can be smaller than 20%

Comparing Immobilized Kinase Inhibitors and Covalent ATP Probes for Proteomic Profiling of Kinase Expression and Drug Selectivity

Kinases are involved in the regulation of many cellular processes and aberrant kinase signaling has been implicated in human disease. As a consequence, kinases are attractive drug targets. Assessing kinase function and drug selectivity in a more physiological context is challenging and often hampered by the generally low expression level of kinases and the extensive post-translation modification <i>in vivo</i>. Kinase drug selectivity screens by chemical proteomics have gained attention because they allow the profiling of hundreds of kinases against one drug at the same time. Here, we directly compared two such methods, notably, immobilized broad spectrum kinase inhibitors (kinobeads) and active site labeling using desthiobiotin-ATP and -ADP probes. Affinity purification of ∼100 kinases by either kinobeads or ATP/ADP probes was readily achieved using 1 mg of cellular protein. Bioinformatic analysis revealed a high degree of complementarity of the two techniques. Kinobeads covered the Tyrosine Kinase family particularly well and ATP probes enriched higher numbers of STE family kinases. A consecutive combination of both enrichment strategies therefore allowed for the coverage of a larger part of the kinome than any one technique alone. While kinobeads are very selective for kinases, the ATP/ADP probes also enriched a large number of other nucleotide binding proteins. Both methods were applied to the selectivity profiling of the small molecular Aurora kinase inhibitor tozasertib in K562 cells. Our data confirmed Aurora A, B, and BCR-ABL as the main targets of tozasertib and identified TNK1, STK2, RPS6KA1, and RPS6KA3 as submicromolar off targets

MScDB: A Mass Spectrometry-centric Protein Sequence Database for Proteomics

Protein sequence databases are indispensable tools for life science research including mass spectrometry (MS)-based proteomics. In current database construction processes, sequence similarity clustering is used to reduce redundancies in the source data. Albeit powerful, it ignores the peptide-centric nature of proteomic data and the fact that MS is able to distinguish similar sequences. Therefore, we introduce an approach that structures the protein sequence space at the peptide level using theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry-centric protein sequence database (MScDB). The core modules of MScDB are an <i>in-silico</i> proteolytic digest and a peptide-centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. Analysis of various MScDB uses cases against five complex human proteomes, resulting in 69 peptide identifications not present in UniProtKB as well as 79 putative single amino acid polymorphisms. MScDB retains ∼99% of the identifications in comparison to common databases despite a 3–48% increase in the theoretical peptide search space (but comparable protein sequence space). In addition, MScDB enables cross-species applications such as human/mouse graft models, and our results suggest that the uncertainty in protein assignments to one species can be smaller than 20%

New Affinity Probe Targeting VEGF Receptors for Kinase Inhibitor Selectivity Profiling by Chemical Proteomics

Solid tumors are dependent for growth on nutrients and the supply of oxygen, which they often acquire via neoangiogenesis. Vascular endothelial growth factors and the corresponding receptors (VEGFRs) play central roles in this process, and consequently, the blockade of this pathway is one therapeutic strategy for cancer treatment. A number of small molecules inhibiting VEGFR inhibitors have been developed for clinical use, and a comprehensive view of target selectivity is important to assess the therapeutic as well as risk potential of a drug molecule. Recent advances in mass spectrometry-based chemical proteomics allow analyses of drug–target interactions under close-to-physiological conditions, and in this study, we report on the design, synthesis, and application of a small molecule affinity probe as a tool for the selectivity profiling of VEGFR and other kinase inhibitors. The probe is capable of binding >132 protein kinases, including angiokinases such as VEGFRs, PDGFRs, and c-KIT from lysates of cancer cell lines or human placenta tissue. Combining the new probe with Kinobeads in competitive binding assays, we were able to identify nanomolar off-targets of the VEGFR/PDGFR inhibitors pazopanib and axitinib. Because of its broad binding spectrum, the developed chemical tool can be generically used for the discovery of kinase inhibitor targets, which may contribute to a more comprehensive understanding of the mechanisms of action of such drugs

MScDB: A Mass Spectrometry-centric Protein Sequence Database for Proteomics

Protein sequence databases are indispensable tools for life science research including mass spectrometry (MS)-based proteomics. In current database construction processes, sequence similarity clustering is used to reduce redundancies in the source data. Albeit powerful, it ignores the peptide-centric nature of proteomic data and the fact that MS is able to distinguish similar sequences. Therefore, we introduce an approach that structures the protein sequence space at the peptide level using theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry-centric protein sequence database (MScDB). The core modules of MScDB are an <i>in-silico</i> proteolytic digest and a peptide-centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. Analysis of various MScDB uses cases against five complex human proteomes, resulting in 69 peptide identifications not present in UniProtKB as well as 79 putative single amino acid polymorphisms. MScDB retains ∼99% of the identifications in comparison to common databases despite a 3–48% increase in the theoretical peptide search space (but comparable protein sequence space). In addition, MScDB enables cross-species applications such as human/mouse graft models, and our results suggest that the uncertainty in protein assignments to one species can be smaller than 20%