An Automated Proteoform Characterization Control System
An Automated Proteoform Characterization Control System
批准号:
10010454
负责人:
Kenneth Durbin
金额:
$20.2万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-04-01 至 2021-09-30
关键词:
Amino Acid SequenceBiologicalBiotechnologyChargeCollectionComputer softwareConsumptionCustomDataData AnalysesData SetDatabasesDecision MakingDiseaseEscherichia coliFamilyFeedbackGenerationsGenesGrantImmunoprecipitationIndustryIntelligenceKnowledgeLaboratoriesManualsMass Spectrum AnalysisModelingMolecular WeightNamesOutputPeptidesPhasePost-Translational Protein ProcessingProbabilityProceduresPropertyProteinsProteomicsReportingResearchResearch PersonnelResolutionRunningSamplingScanningScientistSmall Business Innovation Research GrantSpeedSumSurveysSystemTechnologyTestingTimeUniversitiesUpdateValidationVariantVisualization softwarebasecomputerized data processingdata acquisitiondata visualizationdrug developmentexperimental studyflexibilityfollow-upinsightinstrumentinterestmass spectrometernovelsoftware systemsstemsuccesstherapeutic proteintool
中文摘要
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英文摘要
ABSTRACT
Current mass spectrometers have benefited from a technological revolution over recent years, with great
increases to speed and resolution as well as significant improvements in fragmentation capabilities for
biomolecule analyses. Proteomics results that were previously unthinkable only a few years ago are now routine
and being performed by hundreds of laboratories across the world. However, despite these advancements and
successes, the software systems guiding mass spectrometry data acquisition have not seen technology
improvements on the same scale. As such, these new mass spectrometers have not yet reached their full potential,
particularly for intact protein characterization where analytes are more varied than peptides and acquisition
parameters are tougher to generalize. A software named AutoProt is being developed to automate the targeted
characterization of proteoforms from a proteoform family. AutoProt controls the acquisition of mass
spectrometry data, initiating a data-driven fragmentation routine when proteoforms-of-interest from a
proteoform family are detected in survey scans. As each fragmentation data scan is collected, AutoProt matches
experimental data to theoretical proteoform fragments and uses the corresponding results to make an informed
decision on how to further characterize the proteoform. By using instant feedback, fragmentation settings are
customized specifically and in real-time for each proteoform being analyzed, leading to better characterization
results in less overall time. In Phase I, AutoProt will be further developed to handle the characterization of
multiple proteoforms such as modified and truncated forms from the same proteoform family in the same
acquisition run, enabling a wider assessment of the proteoforms present in a sample (e.g., all the proteoforms
from the same UniProt gene accession after immunoprecipitation). A database will be integrated into AutoProt
to store run data and allow sample characterization to be resumed for additional acquisition runs. Furthermore,
the characterization routine will be made more robust in this grant through the collection of a large amount of
fragmentation data on many proteoforms and then the distillation of that data into a general fragmentation
model. AutoProt will then be able to broadly characterize proteoforms with different properties (e.g., molecular
weight and fragmentation propensities). Lastly, AutoProt will be outfitted with report generation to
automatically output experimental report files compatible with the proteoform data visualization tool,
TDCollider. With these features in place, AutoProt will be a first-of-its-kind automated characterization software
with first-class proteoform characterization abilities and acquisition-to-report capabilities, eliminating time-
consuming set up of acquisition and processing for proteoform characterization data.
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A proteoform-centric informatics platform for targeted top-down characterization and quantitation
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批准号:10325492
-
项目类别:
-
资助金额:$25.2万
-
财政年份:2021
-
负责人:Kenneth Durbin
-
依托单位:
Data-Driven Software to Automate Top-Down Mass Spectrometry of Large Molecules
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批准号:10761429
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项目类别:
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资助金额:$95.56万
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财政年份:2020
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负责人:Kenneth Durbin
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依托单位:
A comprehensive solution for native top down mass spectrometry data analyses across structural biology and biopharma
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批准号:10621751
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项目类别:
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资助金额:$79.67万
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财政年份:2019
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负责人:Kenneth Durbin
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依托单位:
A comprehensive solution for native top down mass spectrometry data analyses across structural biology and biopharma
-
批准号:10384677
-
项目类别:
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资助金额:$76.42万
-
财政年份:2019
-
负责人:Kenneth Durbin
-
依托单位:
海外基金