Molecular Design and Structural Basis of Peptide Inhibitors against Amyloid-beta Aggregation

淀粉样蛋白是一种高度有序的蛋白质聚集体，与许多神经退行性疾病有关，包括阿尔茨海默病和帕金森病。越来越多的证据表明，可溶性淀粉样蛋白低聚物是导致神经元功能障碍和细胞死亡的主要毒性物质。因此，在早期阶段抑制初始淀粉样蛋白寡聚化和聚集可能是预防或延迟神经退行性疾病发病的有效（预）临床治疗方法。但是，由于缺乏淀粉样蛋白低聚物的高分辨率结构、淀粉样蛋白与抑制剂相互作用的原子细节以及高成本效益的高通量筛选方法，导致基于结构的抑制剂的合理设计和对淀粉样蛋白抑制机制的基本理解存在困难。智力优势：本研究将生物信息学模型、分子模拟和生物物理实验结合起来，筛选/设计、表征和鉴定一系列小六肽，以破坏或预防淀粉样蛋白- <e:2> (a<e:2>)寡聚、纤维形成和与阿尔茨海默病相关的毒性。借助PI实验室确定的a<s:2>低聚物的原子结构，并通过原子力显微镜（AFM），电子显微镜（EM）和核磁共振（NMR）数据验证，开发了3D-QSAR （3d -定量构效关系），分子对接和分子动力学模拟的协同三步计算方法，从第一原理系统有效地筛选和设计六肽抑制剂。然后通过生物物理实验验证计算设计的抑制剂的抑制活性。为了实现这一目标，有三个具体目标：(1)开发一种高效、新颖的3D-QSAR模型，以虚拟筛选和合理设计<s:2>抑制剂；(2)计算检验破坏或结合<s:2>低聚物的肽的抑制活性；(3)实验测试计算设计的多肽在防止细胞聚集和毒性方面的抑制能力。通过这些目标，我们努力合理设计有效的肽抑制剂，建立抑制剂序列、结构和抑制活性之间的预测关系，并在原子水平上更好地了解抑制剂与<s:2>寡聚物之间的抑制机制，最终提供具有良好特征的结构和生物学数据的六肽抑制剂数据库。所提出的工作可以在涉及蛋白质折叠，结合和蛋白质功能的其他生物系统中具有变革性。广泛影响：该抗淀粉样蛋白项目的成功将弥合对分子水平上的淀粉样蛋白结构、聚集、毒性和抑制机制的基本理解与淀粉样蛋白抑制剂的实际设计原则之间的差距，这对开发针对阿尔茨海默病的医疗诊断和自动化高通量设备至关重要。该方法也可以普遍适用于设计针对其他淀粉样变性疾病（如帕金森病和II型糖尿病）的肽抑制剂，使科学界和整个社会受益。该项目的跨学科性质为所有水平的学生，特别是那些来自代表性不足的群体的学生，提供了一个独特的机会，学习一般生物学，结构生物学，生物信息学和药物设计的概念和工具，并开展对公共卫生重要的基础研究项目。这一提案的成果将继续为“生物系统的分子建模与模拟”和“生物材料与生物纳米技术”两门新课程提供帮助。知识也将通过高影响力的论文、会议发言、课程课程、暑期实习和其他外展活动传播。此外，淀粉样蛋白也代表了一类具有良好定义的纳米结构的纳米材料，可以作为模板来生产具有理想功能的新型基于蛋白质的自组装纳米/生物材料。对淀粉样肽异常自组装形成如此明确的纳米结构及其与抑制剂相互作用的分子理解，对于合理设计用于预防或治疗神经退行性疾病的新药至关重要。

1158447 ZhengAmyloids are highly ordered protein aggregates associated with many neurodegenerative diseases including Alzheimer and Parkinson diseases. Accumulating evidences suggest that soluble amyloid oligomers are major toxic specie responsible for neuronal dysfunction and cell death. Thus, inhibiting initial amyloid oligomerization and aggregation at the very early stage could be an effective (pre)clinical treatment for preventing or delaying the onset of neurodegenerative diseases. But, the lacks of high-resolution structures of amyloid oligomers, atomic details of amyloid-inhibitor interactions, and cost-effective high-throughput screening methods lead to the difficulty in the rational design of structural-based inhibitors and in the fundamental understanding of amyloid inhibition mechanism.Intellectual Merits: The proposed work combines bioinformatics models, molecular simulations, and biophysical experiments to screen/design, characterize, and identify a series of small hexapeptides to disrupt or prevent amyloid-â (Aâ) oligomerization, fibrillogenesis, and toxicity associated with Alzheimer disease. With assistance of atomic structures of Aâ oligomers determined by the PI's lab and validated by atomic force microscopy (AFM), electron microscopy (EM), and nuclear magnetic resonance (NMR) data, the synergistic three-step computational approaches of 3D-QSAR (3D-Quantitative Structure-Activity Relationship), molecular docking, and molecular dynamics simulation is developed to systematically and efficiently screen and design hexapeptide inhibitors from the first principle. Computationally designed inhibitors are then validated for their inhibition activity by biophysical experiments. To reach its goal, three specific aims are: (1) to develop an efficient and novel 3D-QSAR model to virtually screen and rationally design Aâ inhibitors; (2) to computationally examine inhibitory activity of peptides that disrupt or bind to Aâ oligomers; and (3) to experimentally test inhibitory ability of computationally designed peptides to prevent Aâ aggregation and toxicity. Through these aims, we strive to rationally design of effective peptide inhibitors, to establish the predictive relationship among sequence, structure, and inhibitory activity of inhibitors, and to better understand the inhibition mechanisms between inhibitors and Aâ oligomers at the atomic level, and eventually to provide a hexapeptide inhibitor database with well-characterized structural and biological data. The proposed work can be transformative in other biological systems involving protein folding, binding, and protein function.Broad Impacts: The success of this anti-amyloid project will bridge the gap between a fundamental understanding of Aâ structure, aggregation, toxicity, and inhibition mechanism at molecular level and a practical design principle of Aâ inhibitors, both critical for the development of medical diagnostics and automated high-throughput devices against Alzheimer's disease. The proposed approach can also be generally applicable to designing peptide inhibitors against other amyloidogenic diseases such as Parkinson's and diabetes type II, benefiting both scientific community and entire society. The interdisciplinary nature of the project provides a unique opportunity for all-level students, particularly those from underrepresented groups, to learn the concepts and tools in general biology, structural biology, bioinformatics, and drug design and to carry out a fundamental research project important for public health. The results from this proposal will continuously contribute to two new courses of "Molecular Modeling and Simulation of Biological Systems" and "Biomaterials and Bionanotechnology". The knowledge will also be disseminated through high-impact papers, conference presentations, curriculum courses, summer internships, and other outreach activities. In addition, amyloids also represent a general class of nanomaterials with well-defined nanostructures, which can be used as templates to produce novel protein-based self-assembled nano-/bio-materials with desirable functionalities. Molecular understanding of the abnormal self-assembly of amyloid peptides into such well-defined nanoarchitectures and the role of their interactions with inhibitors is essential for the rational design of novel drugs for prevention or treatment of neurodegenerative disorders.