Large-scale mapping of the protein function landscape
Large-scale mapping of the protein function landscape
批准号:
8686612
负责人:
Philip Anthony Romero
金额:
$5.33万
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-07-01 至 2015-06-30
关键词:
AbateAffectAmino Acid SequenceBiological AssayDNADNA SequenceDataDiseaseEngineeringEnzymesEvolutionGene MutationGenetic EpistasisGenomicsGoalsHereditary DiseaseHigh-Throughput DNA SequencingHourHumanHuman GeneticsHyperargininemiaIn VitroIndividualKnowledgeLaboratoriesLeadMalignant NeoplasmsMapsMedicineMethodsMicrofluidic MicrochipsMicrofluidicsModelingMolecularMutationNaturePathway interactionsPeptide Sequence DeterminationPropertyProtein EngineeringProteinsResearchRoleSchoolsStatistical ModelsTechnologyTestingTherapeuticVariantarginasebasebiophysical propertieschemotherapeutic agentdesignhigh throughput screeninginsightinterestpredictive modelingprotein functionpublic health relevancesequence learningurea cycle
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): Knowledge of how mutations affect protein function is important for understanding how natural proteins evolved, engineering new proteins with useful properties, and predicting disease-associated mutations. Epistasis is the phenomenon where the effect of one mutation depends on the presence of another mutation. These epistatic interactions stymie our ability to predict the phenotypic consequences of mutations, and can restrict the mutational pathways that are available to evolution. Laboratory evolution studies have highlighted the multifaceted nature of protein function and how competing biophysical properties, such as enzymatic activity and protein stability, can generate epistatic interactions between residues. I hypothesize that most epistasis arises as a result of these pleiotropic mechanisms, rather than direct physical interactions between residues. The human urea cycle enzyme Arginase I (hArgI) will be used to study how sequence changes affect expression, stability, and enzymatic activity. The experimental approach will leverage recent advances in parallel DNA sequencing and ultra-high- throughput screening to map the protein function landscape on an unprecedented scale. These data will be used to explore how combinations of biophysical properties generate mutational epistasis and define the space of functional protein sequences.
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项目类别:
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资助金额:$34.92万
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财政年份:2023
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Large-scale mapping of the protein function landscape
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项目类别:
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资助金额:$4.92万
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负责人:Philip Anthony Romero
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依托单位:
海外基金