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

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%

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%

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%