52 research outputs found
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 moClusterās performance
is not compromised by issues present in iCluster/iCluster+ (notably,
the nondeterministic solution) and that it operates 100Ć to 1000Ć
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
(ā)-epigallocatechin gallate, the multivalent metal salt ironĀ(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 ā¼100 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 ā¼100 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 ā¼100 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
ā¼99% of the identifications in comparison to common databases
despite a 3ā48% 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 ā¼100 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
ā¼99% of the identifications in comparison to common databases
despite a 3ā48% 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 drugātarget 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
ā¼99% of the identifications in comparison to common databases
despite a 3ā48% 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%
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