De novo development of small CRISPR-Cas proteins using artificial intelligence algorithms
De novo development of small CRISPR-Cas proteins using artificial intelligence algorithms
批准号:
10544772
负责人:
Jin Liu
金额:
$18.14万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-01-01 至 2024-12-31
关键词:
3-DimensionalAddressAmino AcidsArtificial IntelligenceAttentionBCAR1 geneBacteriophagesBase SequenceBiochemicalBiologicalBiological AssayCRISPR/Cas technologyCellsClassificationClinicalClustered Regularly Interspaced Short Palindromic RepeatsComputer softwareDNADevelopmentEvaluationGenesGraphHumanLearningLocationMachine LearningMediatingMethodsModelingMolecular ConformationNaturePerformancePlayPositioning AttributeProtein EngineeringProteinsPsychological reinforcementReportingResearchSequence AlignmentSiteStructureTechnologyTestingTherapeuticTrainingValidationartificial intelligence algorithmdesignds-DNAfeasibility testinggene therapygenerative adversarial networkhigh dimensionalityimprovedlearning algorithmmolecular dynamicsnanoparticleneural networknovelprogramsprotein data bankprotein structureprotein structure predictionsuccesstechnology developmentthree dimensional structuretoolunsupervised learning
中文摘要
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英文摘要
Abstract
The large-sized CRISPR Cas proteins have hindered the effective use of CRISPR-mediated gene
editing in human therapeutics due to the lack of delivery strategies for these large-sized proteins to
reach their target locations. To seek solutions to this challenge, multiple efforts have been reported to
search for the smaller-sized Cas proteins in nature. Recent successes in artificial intelligence (AI)
application in the biological field demonstrated the power of AI in solving biological problems and
brought a new perspective to address this challenge. In this application, we propose to develop a proof-
of-concept technology to adapt the AI algorithms used in protein structure prediction and strategic board
games to develop novel protein design tools to minimize the Cas protein size while maintaining its
function. Our objectives are to develop a novel protein design technology to optimize the size of protein
and develop experimentally-validated artificial mini-Cas proteins. We hypothesize that the artificial mini-
Cas proteins with guided double-strand DNA cleavage function can be designed using AI technologies.
To test this hypothesis, we propose to test the feasibility of developing this mini-Cas-design technology
with two approaches. In Aim 1, we will use the sequence-based methods with the generative and
attention-based neural networks to design mini-Cas proteins. In Aim 2, the structure-based semi-
unsupervised reinforcement learning will be used for the technology development. The top candidates
of designed mini-Cas protein will be evaluated by molecular dynamics simulations followed by the
biochemical and cell-based assays. Our proposed technology will have the great potential to advance
the technical field of protein design by providing tools to optimize the size of proteins, shifting the
paradigm of the CRISPR research field from “searching from nature” to “designing in the lab”, and
delivering the first artificially designed Cas proteins validated in biochemical and cell-based assays to
address the CRISPR delivery challenge.
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DMS/NIGMS 2: Integrated Analysis of Fusion Protein Conformational Changes for Virus Entry
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批准号:10794657
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项目类别:
-
资助金额:$27.87万
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财政年份:2023
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负责人:Jin Liu
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依托单位:
De novo development of small CRISPR-Cas proteins using artificial intelligence algorithms
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批准号:10358980
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项目类别:
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资助金额:$23.31万
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财政年份:2022
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负责人:Jin Liu
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依托单位:
海外基金