Protein Structure, Stability, and Amyloid Formation
Protein Structure, Stability, and Amyloid Formation
批准号:
7338385
负责人:
Ruth Nussinov
金额:
$0.0万
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:
至
中文摘要
我们提出了积木折叠模型。该模型假设蛋白质折叠是一个自上而下的分层过程。构成折叠的基本单元,称为疏水折叠单元,是一组“结构单元”组合组装的结果。“通过计算切割程序获得的结果产生的片段与通过有限蛋白水解实验获得的片段一致。我们提出了一个三阶段的计划,从其序列的蛋白质结构的预测。首先,序列被切割成片段,每个片段被分配一个结构。其次,将指定的结构组合起来,形成整体的3D组织。第三,完成并细化高排名的预测安排。正如预期的那样,来自同一家族的蛋白质给出了非常相似的构建块。然而,不同的蛋白质也可以提供结构相似的构建块。在这种情况下,构建块在序列、稳定性、与其他构建块的接触以及它们在蛋白质结构中的3D位置方面不同。我们在许多情况下反复观察到的这一结果使我们得出结论,虽然构建块受到其环境的影响,但它可以被视为一个独立的单元。有了这个结论,就有可能开发出一种算法来预测结构未知的蛋白质序列的构建块分配。为了实现这一目标,我们已经创建了一个连续的非冗余数据库的积木序列。蛋白质序列可以与这些序列进行比对,以便与一组潜在的构建模块相匹配。为了解决第一步,我们开发了一种分配算法,该算法选择最佳组合来“覆盖”蛋白质序列。我们的研究结果包括来自不同类别的蛋白质,其构建模块不一定来自相同的蛋白质类别。这些结果是令人鼓舞的,表明折叠的部分和部分组装可能有助于蛋白质折叠问题的进一步进展。在这个方案的第二步,我们开发了CombDock,一种组合对接算法。CombDock获得一组有序的蛋白质子结构,并预测它们的整体组织。我们减少了组合装配的图论问题,并给出了一个启发式多项式解决这个计算困难的问题。我们使用越来越扭曲的输入来测试CombDock,其中天然结构单元被从同源蛋白质中提取的类似折叠单元所取代,并且在更困难的情况下,从全局不相关的蛋白质中提取。该算法具有鲁棒性,对输入失真不敏感。利用蛋白质构建块的概念,我们提出了一个从头计算算法,类似于组合洗牌实验。我们的目标是设计与现有蛋白质具有低同源性的天然折叠。首先将选定的蛋白质划分为其构建块。结构单元被从具有整体低序列同一性但具有相似的疏水/亲水模式和高结构相似性的其他蛋白质中获得的片段取代。通过显式水分子动力学模拟来测试工程化蛋白质的稳定性。设计的关键是使用相对稳定的片段,具有较高的群体时间。我们在纳米设计中采用了一种相关的(修改的)策略。我们的目标是使用天然存在的蛋白质构建块进行纳米管设计。我们选择纳米管的几何形状。给定这个目标几何形状,我们的目标是扫描候选构建块部件库,将它们组合成形状并测试稳定性。
英文摘要
We have presented the building block folding model. The model postulates that protein folding is a hierarchical top-down process. The basic unit from which a fold is constructed, referred to as a hydrophobic folding unit, is the outcome of combinatorial assembly of a set of "building blocks." Results obtained by the computational cutting procedure yield fragments that are in agreement with those obtained experimentally by limited proteolysis. We proposed a three-stage scheme for the prediction of a protein structure from its sequence. First, the sequence is cut to fragments that are each assigned a structure. Second, the assigned structures are combinatorially assembled to form the overall 3D organization. Third, highly ranked predicted arrangements are completed and refined. As expected, proteins from the same family give very similar building blocks. However, different proteins can also give building blocks that are similar in structure. In such cases the building blocks differ in sequence, stability, contacts with other building blocks, and in their 3D locations in the protein structure. This result, which we have repeatedly observed in many cases, led us to conclude that while a building block is influenced by its environment, nevertheless, it can be viewed as a stand-alone unit. With this conclusion in hand, it is possible to develop an algorithm that predicts the building block assignment of a protein sequence whose structure is unknown. Toward this goal, we have created a sequentially nonredundant database of building block sequences. A protein sequence can be aligned against these, in order to be matched to a set of potential building blocks. To address the first step, we have developed an assignment algorithm that selects optimal combinations to "cover" the protein sequence. Our results include proteins from different classes, with building blocks that are not necessarily assigned from the same protein class. These results are encouraging, indicating that folding by parts and part assembly may contribute to further progress in the protein-folding problem. Toward the second step of this scheme, we developed CombDock, a combinatorial docking algorithm. CombDock gets an ordered set of protein sub-structures and predicts their overall organization. We reduce the combinatorial assembly to a graph-theory problem, and give a heuristic polynomial solution to this computationally hard problem. We tested CombDock using increasingly distorted input, where the native structural units were replaced by similarly folded units extracted from homologous proteins and, in the more difficult cases, from globally unrelated proteins. The algorithm is robust, showing low sensitivity to input distortion. Utilizing concepts of protein building blocks, we proposed a de novo computational algorithm that is similar to combinatorial shuffling experiments. Our goal is to engineer naturally occurring folds with low homology to existing proteins. A selected protein is first partitioned into its building blocks. The building blocks are substituted by fragments taken from other proteins with overall low sequence identity, but with a similar hydrophobic/hydrophilic pattern and a high structural similarity. The stabilities of the engineered proteins are tested by explicit water molecular dynamics simulations. The key in the design is using relatively stable fragments, with a high population time. We adopt a related (modified) strategy in nanodesign.our goal is to carry out nanotube design using naturally occurring protein building blocks. We pick the nanotube geometry. Given this target geometry, our goal is to scan a library of candidate building block parts, combinatorially assembling them into the shape and testing the stability.
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Method Development: Efficient Computer Vision Based Algo
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批准号:7291814
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项目类别:
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资助金额:$0.0万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algorithms
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批准号:7965320
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项目类别:
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资助金额:$13.03万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algorithms
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批准号:8937737
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项目类别:
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资助金额:$10.87万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:9153571
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项目类别:
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资助金额:$43.97万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algorithms
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批准号:8349006
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项目类别:
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资助金额:$12.85万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Protein Structure, Stability, and Amyloid Formation
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批准号:8349004
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项目类别:
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资助金额:$64.26万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:8349005
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项目类别:
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资助金额:$51.4万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Protein Structure, Stability, and Amyloid Formation
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批准号:8552693
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项目类别:
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资助金额:$53.14万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:10014370
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项目类别:
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资助金额:$68.71万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algorithms
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批准号:10262089
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项目类别:
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资助金额:$11.83万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:10262088
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项目类别:
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资助金额:$47.34万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:7291812
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项目类别:
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资助金额:$0.0万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:8552694
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项目类别:
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资助金额:$42.51万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algorithms
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批准号:8552695
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项目类别:
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资助金额:$10.63万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Protein Structure, Stability, and Amyloid Formation
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批准号:10702352
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项目类别:
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资助金额:$69.45万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algo
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批准号:7338445
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项目类别:
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资助金额:$0.0万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Method Development: Efficient Computer Vision Based Algorithms
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批准号:8763103
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项目类别:
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资助金额:$9.89万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Biomolecular Recognition and Binding Mechanisms
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批准号:7733032
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项目类别:
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资助金额:$67.32万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Protein Structure, Stability, and Amyloid Formation
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批准号:7592701
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项目类别:
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资助金额:$57.07万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
Protein Structure, Stability, and Amyloid Formation
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批准号:10262087
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项目类别:
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资助金额:$59.17万
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财政年份:--
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负责人:Ruth Nussinov
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依托单位:
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