Directed Protein Evolution for Design of LAGLIDADGs with Novel Recognition Speci
Directed Protein Evolution for Design of LAGLIDADGs with Novel Recognition Speci
批准号:
7500081
负责人:
ANDREW M. SCHARENBERG
金额:
$44.62万
依托单位国家:
美国
项目类别:
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-09-25 至 2012-06-30
关键词:
AlgorithmsB-LymphocytesBackBase PairingBindingBiochemicalBiological AssayCell LineCell surfaceCellsChimeric ProteinsCleaved cellCodeDNA BindingDataDiscriminationDoxycyclineEvolutionFeedsFlow CytometryGenerationsGoalsHomingImmune systemImmunoglobulin Somatic HypermutationImmunoglobulinsLabelLightMethodsMonitorMutateMutationMutation SpectraNBS1 geneNijmegen Breakage SyndromeOligonucleotidesOutputPopulationProcessPropertyProtein OverexpressionProteinsPseudogenesQuantum DotsRateRoleSiteSorting - Cell MovementSpecificitySurfaceTechnologyTransgenesVariantWorkactivation-induced cytidine deaminasebasedesigndesireendonucleaseexperiencefollow-upimmunoglobulin light chain locusimprovedinsertion/deletion mutationmigrationnanosensorsnovelscaffold
中文摘要
脊椎动物免疫系统利用的突变和选择的迭代循环是一种强大的
产生具有不同性质的蛋白质的方法。为了将此方法应用于生成
具有新的DNA结合和切割特性的LAGLIDG归巢内切酶(LHEs),我们有
建立了在培养的B细胞表面以融合蛋白的形式表达LHEs的方法。表面
表达的融合蛋白允许快速评估特定的结合和切割特性
LHE可用于流式细胞术,也可用于分离表达LHE的细胞群
有不同的特点。基于这些数据,我们提出了以下具体目标:在具体目标1中,
我们将基于生成的新的LHE生成并表征新的表面表达的LHE支架
由组件2(Monnat)和组件3(Baker)组成。在具体目标2中,我们将对曲面进行积分
将LHE表达到DT40细胞系的免疫球蛋白基因座上,使它们对
内源性体细胞高突变机制(S)在DT40细胞中的作用。然后我们将使用这些LHE超突变
执行两种迭代突变/选择策略的品系以识别新的LHE
所需的结合和切割特性。在第一个示例中,我们将使用组件3(Baker)中的LHE
预先优化的DNA/蛋白质与靶点的接口,并将尝试直接选择LHE变体
能够绑定和切割预测的目标。在第2部分中,我们将尝试逐个碱基对迁移
策略,从目前可用的表面表达的LHE支架开始。在具体目标3中,我们将
进一步完善和加强我们的迭代突变/选择的方法和技术
他的变种。在此目标的一个部分中,我们将使用组件2(Monnat)和组件5
(Stoddard)关于开发一种基于量子点纳米传感器的提高灵敏度的切割试验,
用于流式细胞术和可溶性LHE分析。在第二个例子中,我们将利用过度表达
参与体细胞超突变的蛋白质以提高超突变率和增强光谱
包括更高的插入和缺失比率的突变。
这一目标的输出也直接反馈到NGEC LHE设计周期,因为确定了变体
这里将传递给组件2(Monnat)和组件5(Stoddard),用于生化和
生物物理分析,将由此得出的信息并入PSSM矩阵,以确定最佳
可由组件2设计的站点(Monnat),以及由开发的计算设计算法
组件3(面包师)。
英文摘要
The iterative cycling of mutation and selection utilized by vertebrate immune systems is a powerful
means for generating proteins with diverse properties. In order to apply this approach to the generation of
LAGLIDADG homing endonucleases (LHEs) with novel DNA binding and cleavage specificities, we have
developed methods to express LHEs as fusion proteins on the surface of cultured B-cells. The surface
expressed fusion proteins allow the rapid assessment of the specific binding and cleavage properties of
the LHE using flow cytometry, and also can be used to separate populations of cells expressing LHE's
with different specificities. Based on these data, we propose the following Specific Aims: In Specific Aim 1,
we will generate and characterize new surface expressed LHE scaffolds based on novel LHE's generated
by Component 2 (Monnat) and Component 3 (Baker). In Specific Aim 2, we will integrate surface
expressed LHE's into immunoglobulin loci of the DT40 cell line such that they become susceptible to the
endogenous somatic hypermutation mechanism(s) operating in DT40 cells. We will then use these LHEhypermutating
lines to execute two strategies of iterative mutation/selection to identify novel LHE's with
desired binding and cleavage properties. In the first, we will use LHE's from Component 3 (Baker) with
pre-optimized DNA/protein interfaces towards target sites, and will attempt to directly select LHE variants
able to bind and cleave the predicted target. In the 2nd, we will attempt a base pair-by-base pair migration
strategy, beginning with presently available surface expressed LHE scaffolds. In Specific Aim 3, we will
work on further refining and enhancing our methods and technologies for iterative mutation/selection of
LHE variants. In one part of this aim, we will work with Component 2 (Monnat) and Component 5
(Stoddard) on developing an increased sensitivity cleavage assay based on quantum dot nanosensors, for
use in both flow cytometry and soluble LHE assays. In the second, we will utilize overexpression of
proteins involved in somatic hypermutation to incrase the rate of hypermutation and enhance the spectrum
of mutations to include a higher rate of insertions and deletions.
The output of this aim directly feeds back to the NGEC LHE design cycle as well, as variants identified
here will be passed on to Component 2 (Monnat) and Component 5 (Stoddard) for biochemical and
biophysical analysis, with information thus derived incorporated into PSSM matrices for identifying the best
engineerable sites by Component 2 (Monnat), and into computational design algorithms developed by
Component 3 (Baker).
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