Project 1/Computational Core
项目1/计算核心
基本信息
- 批准号:7449170
- 负责人:
- 金额:$ 10.65万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2008
- 资助国家:美国
- 起止时间:2008-05-01 至 2013-04-30
- 项目状态:已结题
- 来源:
- 关键词:AccountingAddressAgreementAlgorithmsAmyloid beta-ProteinAprotininBackBehaviorBeliefCell NucleusCerealsClassificationCollectionCommitCommunitiesComplexComputational TechniqueComputer softwareCoupledCrystallographyDataData AnalysesData QualityData SetDeerDependencyDepthDescriptorDevelopmentElectron Spin Resonance SpectroscopyElectrostaticsEnvironmentEquilibriumEvaluationExhibitsFacility Construction Funding CategoryFluorescence Resonance Energy TransferGenerationsGoalsHomology ModelingIn SituIndividualLabelLaboratoriesLigand Binding DomainMagnetismMeasurementMeasuresMethodologyMethodsMetricModelingModificationMuramidaseNMR SpectroscopyNatureNumbersOccupationsPositioning AttributeProceduresProcessProtein DynamicsProteinsProtocols documentationProtonsPublishingRangeRecommendationRelaxationReportingResearchResearch DesignResearch Project GrantsResolutionRunningSample SizeSamplingScoreSeriesSeveritiesShapesSideSignal TransductionSiteSolutionsSolventsSpectrum AnalysisSpin LabelsStagingStructural ModelsStructural ProteinStructureSuggestionSystemTechniquesTestingTextTimeTorsionValidationVertebral columnWorkbasecaN protocolcomparison groupcomputerized toolsconceptdesignextracellularglobular proteinimprovedmodels and simulationmutantnitroxylprogramsprotein structureprotein structure predictionresearch studysecretin receptorsimulationthree dimensional structurethree-dimensional modelingtooltool developmenttrenduser-friendly
项目摘要
The Computational Core research plan has been revised to address specific questions and concerns
raised by the reviewers, and to emphasize the principle focus of this core. The primary goals of the
Computational Core are 1) to develop a set of computational tools and protocols to facilitate the analysis and
interpretation of EPR spectral data, including distance measurements obtained from DEER experiments for
doubly spin-labeled proteins, and 2) provide basic computational support for the individual research projects.
For Project 1, basic computational support entails a series of equilibrium MD simulations to support EPR
spectral calculations. In Project 2, this computational support includes detailed equilibrium MD simulations for
CDB3 to explore possible conformational changes triggered by the P327R point mutant, and preliminary
results are described above in the Project 2 Research Plan. Basic computational support for Project 3 includes
routine structure refinement calculations for conventional 2D-NMR experiments and paramagnetic resonance
enhancement NMR experiments, as well as MD simulations to explore conformational trends for spin labels
introduced in the amyloid-beta peptides. This conformational analysis will be important to address distance
dependencies on spin label side chain conformational behavior in both EPR experiments and paramagnetic
resonance enhancement NMR studies.
The development of practical computational tools and protocols to facilitate EPR data analysis depends
crucially on data obtained in Project 1, and requires several discreet steps. First, it is important to establish that
we can use conventional equilibrium MD simulations that describe spin label side chain dynamics and protein
backbone dynamics, coupled with Brownian dynamics calculations that model global protein tumbling, to
compute EPR spectra directly for singly labeled proteins. As the reviewers noted, previous published attempts
to exploit this type of strategy have not been completely satisfactory or convincing. However, these previous
studies were based on rather limited MD simulations, and possibly suffered from some other issues that we
address in more detail in the Research Plan below. It is essential to establish that a simulation strategy can be
used to compute EPR spectra, in order to establish that we can capture the important features and behavior of
spin-labeled proteins that give rise to unique EPR spectra for different samples (e.g., the sharp, distinct
spectral signal typical of a completely mobile spin label versus the broader, more complex signals
representative of partially immobilized spin labels). As discussed in the Project 1 Research Plan, we now have
preliminary results that indicate we can compute EPR spectra more accurately and reliably than has been
reported previously. There is still need for improvement, and we present detailed analysis of current MD-based
EPR spectral simulations below that highlight possible inadequacies in the current methodology, and discuss
specific strategies and tests to address these problems. Only after we have established convincingly that we
can calculate EPR spectra directly with the combined MD/Brownian dynamics simulation protocol can we
address seriously the calculation of spin label pair distances obtained in EPR DEER experiments, or pursue
development of simpler computational strategies that do not require multiple, lengthy MD simulations with
explicit solvent to estimate these distances. A number of issues impact the reliable MD simulation of spin label
pair distances, including several raised by the reviewers for Project 1 (E.g., potential function parameters,
electrostatics treatment, periodic boundary effects, etc.) We present preliminary data in the revised Research
Plan below that addresses these issues and other important factors, and the strategies to achieve improved
EPR spectral calculations and DEER distance estimates are presented in the context of a new Specific Aim 1.
Aim 1 in the original proposal (now renumbered Specific Aim 2) contained a detailed discussion of previous
studies designed to explore the impact of (limited) long-range distance constraints on 3D structural model
generation. Reviewer #1 noted that the general strategy outlined in this Aim was reasonable, but rather timeconsuming.
We note below some specific efficiency improvements for certain steps that reduce the overall
computational expense for this protocol (although this is still a non-trivial computational task). Reviewer #1 also
noted several specific concerns or suggestions related to this aim. Alternate metrics, such as backbone torsion
angles rather than protein backbone RMSD values, were suggested for structural comparisons and clustering.
This is certainly a reasonable recommendation, and we have explored some simple alternative comparison
metrics. Backbone torsion angle comparisons, or other simple quantitative assessments such as volumetric or
shape descriptors are intrinsically appealing, although those metrics are somewhat less "intuitive" for structural
comparison (at least for us at this stage). We discuss below the use of backbone torsion angles as a potentially
quite useful and efficient comparison metric in new work proposed. We have also discussed this issue with
several colleagues who focus on protein structure prediction and thus perform these types of calculations
routinely. Interestingly, we were referred back to the SUPPOSE algorithm for backbone RMSD comparisons by
these groups (this program has clearly become more popular than we realized). Reviewer #1 also
recommended that we consider alternate programs for the actual clustering process, and this is most
reasonable. Nothing in our protocol commits us to use Jeff Barton's "OC" program, and it is straightforward to
integrate alternate clustering algorithms in our job control scripts, so we will explore other algorithms after we
have established the applicability and scope of our protocol. Reviewer #1 also suggested that we consider
strategies to enhance the structural "diversity" in our relatively small 3D model datasets; this suggestion is
closely coupled to the concern raised by reviewer #2 that 10,000-20,000 trial structures per run will be
inadequate to sample 3D structural space adequately. It is our belief that an appropriate set of long-range
distance constraints will limit the feasible 3D structural solution space sufficiently to reduce the severity of this
problem. Our previous results, as well as those of several other research groups, have shown clearly that a
small number of long-range distance constraints can dramatically reduce the 3D conformational search space
for protein model construction, although there is no guarantee that any arbitrary set of long-range distance
constraints will achieve this goal, and we must perform additional tests outlined in Specific Aim 2 to better
understand how effective a relatively small collection of long-range distance constraints might be in reducing
the search space. We also describe a new strategy to improve the structural "diversity" of the trial structures,
which utilizes 3D model generation techniques incorporated in Rosetta (Wollacott, et al., 2007; Rohl, et al.,
2004). Both reviewers expressed concerns regarding the scoring functions used to "rank" structural solutions.
There is no easy or obvious answer here, and we can only pursue the strategies outlined in the Research Plan
below. Our real solution to this problem is to use an iterative process of model generation and additional DEER
distance measurements to systematically reduce the number of acceptable structural models. We now provide
a more detailed discussion of the strategy we use for selection of additional labeling sites to illustrate more
clearly how we expect this process will work, as requested by Reviewer #1. We also provide a more detailed
explanation for how we have coupled the 3D model generation protocol with motif identification and homology
modeling techniques for the test systems we have studied to date. Finally, we discuss in the revised Aims 2
and 3 ways to include additional EPR experimental data beyond inter-residue distances in the model
generation and refinement procedures. We should reemphasize that the goal for calculations outlined in
Specific Aim 2 is generation of low- to intermediate-resolution 3D models. It is inappropriate at this stage to talk
about true 3D structure refinement from EPR DEER distance measurements in the same context as, for
example, conventional NMR or x-ray structure refinement procedures. A more realistic goal at this point is
structural motif identification for previously uncharacterized proteins, and our previous studies for test systems
presented below suggest that this is feasible.
Aim 2 in the original proposal (now Specific Aim 3) focused primarily on development of tools for analysis
of inter-residue distances obtained from DEER measurements. This section has been modified significantly to
better describe the tight integration of this work with Project 1, as well as to address various concerns raised by
the reviewers. More methodological detail is provided for various strategies, and the planned implementation of
coarse-grained models is discussed in greater detail. We discuss issues related to the adequacy of
conformational sampling in depth, and criteria for "validation" of computed results such as distance
distributions. Reviewer #1 also raised a question regarding constraint quality, and this is a rather tricky issue
with EPR distance measurements. In some contexts, a measured distance that exhibits a large distance
distribution might be classified as a lesser-quality data point (at least in the context of 3D model construction or
refinement). However, many in the EPR field would take exception to such a characterization, arguing correctly
that a large distance distribution is itself an important and informative piece of data. We discuss this issue in
more detail in the new Specific Aim 3.
Original Aim 3 (now Aim 4) entails primarily "toolkit" design and application to specific tasks in Projects 1-3,
followed by packaging for wider dissemination to the general user community. These goals are unmodified
from the original proposal.
Major revisions in the Research Plan are demarcated by bold square brackets [] around the relevant text.
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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TERRY P LYBRAND其他文献
TERRY P LYBRAND的其他文献
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{{ truncateString('TERRY P LYBRAND', 18)}}的其他基金
Protein Structure and Dynamics from EPR Spectroscopy and MD Simulations
EPR 光谱和 MD 模拟的蛋白质结构和动力学
- 批准号:
7440013 - 财政年份:2008
- 资助金额:
$ 10.65万 - 项目类别:
Protein Structure and Dynamics from EPR Spectroscopy and MD Simulations
EPR 光谱和 MD 模拟的蛋白质结构和动力学
- 批准号:
7616796 - 财政年份:2008
- 资助金额:
$ 10.65万 - 项目类别:
Protein Structure and Dynamics from EPR Spectroscopy and MD Simulations
EPR 光谱和 MD 模拟的蛋白质结构和动力学
- 批准号:
8064814 - 财政年份:2008
- 资助金额:
$ 10.65万 - 项目类别:
Protein Structure and Dynamics from EPR Spectroscopy and MD Simulations
EPR 光谱和 MD 模拟的蛋白质结构和动力学
- 批准号:
7843617 - 财政年份:2008
- 资助金额:
$ 10.65万 - 项目类别:
Protein Structure and Dynamics from EPR Spectroscopy and MD Simulations
EPR 光谱和 MD 模拟的蛋白质结构和动力学
- 批准号:
8277917 - 财政年份:2008
- 资助金额:
$ 10.65万 - 项目类别:
THREE DIMENSIONAL MODELS FOR MEMBRANE RECEPTOR PROTEINS
膜受体蛋白的三维模型
- 批准号:
2272004 - 财政年份:1995
- 资助金额:
$ 10.65万 - 项目类别:
Molecular recognition in the streptavidin-biotin system
链霉亲和素-生物素系统中的分子识别
- 批准号:
7336305 - 财政年份:1995
- 资助金额:
$ 10.65万 - 项目类别:
THREE DIMENSIONAL MODELS FOR MEMBRANE RECEPTOR PROTEINS
膜受体蛋白的三维模型
- 批准号:
2745735 - 财政年份:1995
- 资助金额:
$ 10.65万 - 项目类别:
Molecular recognition in the streptavidin-biotin system
链霉亲和素-生物素系统中的分子识别
- 批准号:
7209341 - 财政年份:1995
- 资助金额:
$ 10.65万 - 项目类别:
THREE DIMENSIONAL MODELS FOR MEMBRANE RECEPTOR PROTEINS
膜受体蛋白的三维模型
- 批准号:
6126263 - 财政年份:1995
- 资助金额:
$ 10.65万 - 项目类别:
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