Predicting Protein-DNA Interactions with Structural Models
Predicting Protein-DNA Interactions with Structural Models
批准号:
8516529
负责人:
Philip Bradley
金额:
$31.46万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-08-10 至 2015-07-31
关键词:
AffinityAmino Acid SequenceBHLH ProteinBindingBinding SitesBiologicalBiological ModelsBiological ProcessCell physiologyCellsCharacteristicsComplexComplicationComputer softwareDNADNA BindingDNA SequenceDNA StructureDNA-Protein InteractionDataData SetDatabasesDevelopmentDisease susceptibilityGenesGeneticGenetic ProgrammingGenomeGoalsHomology ModelingHuman Genome ProjectIn VitroLeadMapsMethodsModelingMolecular ModelsMuscle CellsMuscle DevelopmentMyoD ProteinPeptide Sequence DeterminationProcessProtein DatabasesProteinsQualifyingRegulationRelative (related person)ResearchResearch Project GrantsResolutionSamplingSet proteinSiteSkeletal MuscleSoftware ToolsSpecificityStructural ModelsStructureTechniquesabstractingbasecofactorgenetic regulatory proteingenome sequencingimprovedinterestmodels and simulationmolecular modelingmolecular recognitionmulti-scale modelingmyogenesisnovelprotein structureprotein structure predictionsimulationstructural genomicssuccessthree dimensional structuretooltranscription factor
中文摘要
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英文摘要
Project Summary/Abstract
Interactions between proteins and DNA molecules are central to a wide range of genetic and regulatory
processes. This research project will lead to improved tools for computationally predicting protein-DNA
structures and interactions using only protein sequence data (such as that generated by the human genome
project). We will develop methods for predicting the three-dimensional structures of complexes between
proteins and DNA molecules using the experimentally determined structures of related proteins. Our novel
contribution will be the development of simulation techniques that can move the three-dimensional structure of
the related protein in complex with DNA closer to the structure of the protein of interest bound to DNA. The
lack of existing techniques that can achieve this task is widely recognized as a major impediment to the
widespread transfer of structural information between proteins. Successful completion of this component of the
project would greatly increase the impact of the ongoing structural genomics projects - which aim to determine
by experimental means the structures of a representative set of proteins - by allowing high-resolution structural
data to be used to understand the structures and functions of uncharacterized proteins.
The goal of the second component of this project is to use the structural models we generate to make
concrete predictions about the biological functions of the proteins in question. More precisely, we propose to
develop methods that will predict which specific sequences of DNA a given protein will bind to. We will go
about this by building structural models of the protein in complex with a variety of DNA sequences and
evaluating its affinity for these different sequences using advanced force fields. Success will hinge on the
accuracy of the force fields we use to calculate the energies of interaction between the protein and its potential
partners. As an additional complication, the structures of these DNA molecules may all be slightly different, and
the protein itself may adapt structurally to different sequences in different ways to achieve an optimal fit.
In the final component of this project, we will apply these methods to study a specific protein of great
biological importance. This protein, MyoD, has been called a 'master-regulator' of skeletal muscle development
for its remarkable ability to turn cells of a variety of types into muscle cells. We will investigate the mechanisms
by which MyoD performs its critical functions during development by building structural models of the
interactions between MyoD and specific sites in the genome. These models will include partner molecules that
help to target MyoD to biologically relevant sites. The eventual goal of these studies will be to predict the sites
in the genome at which MyoD and other key regulatory proteins exert their effect.
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DOI:
10.1093/bfgp/elu044
发表时间:
2015
期刊:
Briefings in functional genomics
影响因子:
4
作者:
[Adam P. Joyce;Chi Zhang;P. Bradley;J. Havranek]
通讯作者:
Adam P. Joyce;Chi Zhang;P. Bradley;J. Havranek
DOI:
10.1126/science.1216211
发表时间:
2012-02-10
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
[Mak AN, Bradley P, Cernadas RA, Bogdanove AJ, Stoddard BL]
通讯作者:
Stoddard BL
Targeting G with TAL effectors: a comparison of activities of TALENs constructed with NN and NK repeat variable di-residues.
用 TAL 效应器靶向 G:用 NN 和 NK 重复可变二残基构建的 TALEN 的活性比较
DOI:
10.1371/journal.pone.0045383
发表时间:
2012
期刊:
PloS one
影响因子:
3.7
作者:
[Christian ML, Demorest ZL, Starker CG, Osborn MJ, Nyquist MD, Zhang Y, Carlson DF, Bradley P, Bogdanove AJ, Voytas DF]
通讯作者:
Voytas DF
DOI:
10.1016/j.sbi.2012.06.002
发表时间:
2012-08
期刊:
Current opinion in structural biology
影响因子:
6.8
作者:
[Liu LA, Bradley P]
通讯作者:
Bradley P
DOI:
10.1371/journal.pone.0082120
发表时间:
2013
期刊:
PloS one
影响因子:
3.7
作者:
[Doyle EL, Hummel AW, Demorest ZL, Starker CG, Voytas DF, Bradley P, Bogdanove AJ]
通讯作者:
Bogdanove AJ
共 6 条
Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation
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资助金额:$0.0万
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财政年份:2022
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负责人:Philip Bradley
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Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation
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Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation
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资助金额:$28.75万
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财政年份:2022
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依托单位:
Molecular modeling and machine learning for protein structures and interactions
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批准号:10191763
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项目类别:
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资助金额:$13.13万
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财政年份:2021
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负责人:Philip Bradley
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依托单位:
Molecular modeling and machine learning for protein structures and interactions
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批准号:10707065
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项目类别:
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资助金额:$44.0万
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财政年份:2021
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依托单位:
Molecular modeling and machine learning for protein structures and interactions
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批准号:10631595
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资助金额:$16.06万
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财政年份:2021
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依托单位:
Molecular modeling and machine learning for protein structures and interactions
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批准号:10406274
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项目类别:
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资助金额:$44.0万
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财政年份:2021
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负责人:Philip Bradley
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依托单位:
High-resolution modeling of protein-RNA interfaces
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批准号:10641354
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项目类别:
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资助金额:$11.4万
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财政年份:2017
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负责人:Philip Bradley
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依托单位:
Rational design and functionalization of circular tandem repeat proteins
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批准号:9301141
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项目类别:
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资助金额:$34.54万
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财政年份:2017
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负责人:Philip Bradley
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依托单位:
High-resolution modeling of protein-RNA interfaces
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批准号:10013238
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项目类别:
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资助金额:$30.02万
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财政年份:2017
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负责人:Philip Bradley
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依托单位:
Rational design and functionalization of circular tandem repeat proteins
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批准号:9897572
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项目类别:
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资助金额:$34.54万
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财政年份:2017
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负责人:Philip Bradley
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依托单位:
High-resolution modeling of protein-RNA interfaces
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批准号:9388893
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项目类别:
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资助金额:$45.24万
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财政年份:2017
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负责人:Philip Bradley
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依托单位:
Prediction and Design of Nucleic Acid Recognition by Repeat Proteins
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批准号:8733185
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项目类别:
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资助金额:$22.0万
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财政年份:2013
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负责人:Philip Bradley
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依托单位:
Prediction and Design of Nucleic Acid Recognition by Repeat Proteins
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批准号:8492692
-
项目类别:
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资助金额:$26.4万
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财政年份:2013
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负责人:Philip Bradley
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依托单位:
Predicting Protein-DNA Interactions with Structural Models
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批准号:7910393
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项目类别:
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资助金额:$32.93万
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财政年份:2009
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负责人:Philip Bradley
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依托单位:
Predicting Protein-DNA Interactions with Structural Models
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批准号:8118972
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项目类别:
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资助金额:$32.6万
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财政年份:2009
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负责人:Philip Bradley
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依托单位:
Predicting Protein-DNA Interactions with Structural Models
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批准号:8310185
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项目类别:
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资助金额:$32.6万
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财政年份:2009
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负责人:Philip Bradley
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依托单位:
海外基金