Protein Structure, Stability, and Amyloid Formation

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的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，我们刚刚更新了我们的连续非冗余构建块序列数据库。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。现在，分配已经自动化，并用于同源模型蛋白质结构。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测试了工程蛋白的稳定性。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相关的（修改后的）策略。目前，我们正在开发一个基于CHARMM的计划，用于自动化和有效地优化纳米管。我们的目标是利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。由于自组装发生在计算负担不起的时间尺度上，我们提出了一种策略，用于蛋白质纳米管设计的第一步：我们将候选构建块映射到平面片材上，并将其缠绕在具有目标尺寸的圆柱体上。考虑到当前计算资源的限制和分子力学力场的准确性，进行从头计算以尝试自动自组装纳米结构是不可行的。然而，通过结合任何可用的实验数据（如电子显微镜（EM）图像）来构建而不是预测原子模型是可以实现的。在技术方面，构建的纳米管的壁厚和积木尺寸越大，管径越小，设计难度越大：相互作用的蛋白质积木的扭曲会增加。我们研究的例子蛋白质纳米管的原子模型的细节，有实验数据：到目前为止，两个肽和一个蛋白质。构建的原子纳米结构具有两个主要目的。首先，我们可能会问，构建的纳米结构是否真的支持所有可用的实验数据。如果是的话，所建立的原子模型将是有价值的进一步设计向纳米器件。如果没有，所构建的模型与实验数据之间的差异将有助于下一轮的构建。第二，为了设计具有特定几何形状的纳米结构，构建这种设计的纳米结构的能力是第一步。Peter Grodzinski对我们的管形成很感兴趣，他也提出了用药物进行实验的建议。目前，实验正在检查我们预测的纳米结构稳定方案中的一些合成残基。我们还在脂双层中设计了与实验一致的阿尔茨海默A-β蛋白的毒性淀粉样蛋白通道

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 just updated our 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. Now the assignment has been automated and used to homology model protein structures. 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. Currently we are developing a CHARMM based scheme for an automated and efficient optimization of the nanotube. 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. Since self-assembly takes place on time scales not affordable for computations, we propose a strategy for the very first step in protein nanotube design: we map the candidate building blocks onto a planar sheet and wrap it around a cylinder with the target dimensions. Given the current limitations of the computational resources and the accuracy of the molecular mechanics force field, it is infeasible to carry out ab initio calculations in an attempt to self-assemble a nanostructure automatically. However, it is achievable to construct, rather than predict, an atomic model by incorporating any available experimental data such as images from electron microscopy (EM). On the technical side, the larger the wall thickness of the constructed nanotube and the building block size, and the smaller the tube diameter, the larger the design difficulty: the distortion of the interacting protein building blocks will increase. We study examples of protein nanotubes in atomistic model detail for which there are experimental data: so far two peptides and one protein. A constructed atomic nanostructure serves two main purposes. First, we may ask if a constructed nanostructure truly supports all available experimental data. If yes, the established atomic model will be valuable for further design toward a nanodevice. If no, the discrepancy between the constructed model and experimental data will be useful for the next round of construction. Second, in order to design a nanostructure with a specified geometry, the capability of constructing such a designed nanostructure is the very first step. Peter Grodzinski who was interested in our tube formation, also made the suggestion of experimenting with drugs. Currently experiment is checking some of our predicted synthetic residues in the nano structure stabilization scheme. We have also designed toxic amyloid channels in the lipid bilayer for the Alzheimer A-beta protein consistent with experiment