Computational and Experimental Studies of Protein Structure and Design

蛋白质结构和设计的计算和实验研究

• 批准号: 10793426
• 负责人: Bruce R. Donald
• 金额: $ 17.99万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10793426
• 关键词: 3-Dimensional; Administrative Supplement; Algorithm Design; Algorithms; Antibodies; Antigens; Area; Binding; Biochemical; Biological; Cells; Combinatorial Optimization; Computational Geometry; Computer Models; Computer software; Computing Methodologies; Disease; Disease Resistance; Drug Design; Drug Targeting; Drug resistance; Equipment; Future; Generations; Goals; Human; In Vitro; Investigation; Machine Learning; Measurement; Measures; Methodology; Methods; Modeling; Molecular; Molecular Biology; Morbidity - disease rate; Mutation; Probability; Process; Program Sustainability; Protein Dynamics; Protein Engineering; Proteins; Research; Research Project Grants; Resistance; Structure; System; Techniques; Technology; Testing; Therapeutic; Therapeutic Intervention; Viral Antibodies; biophysical properties; computer cluster; computer studies; data modeling; design; drug candidate; experimental study; improved; in vivo; inhibitor; mortality; neutralizing antibody; new therapeutic target; novel; novel therapeutics; open source; parent grant; pharmacologic; protein protein interaction; protein structure; resilience; resistance mutation; response

Project summary: This is an administrative supplement application for equipment, to purchase a GPU-enabled computer cluster. For the parent grant, the determination of 3D protein structures is essential not only for revealing molecular mechanism of disease processes, but also for structure-based drug design. Concomitantly, computational advances in protein design could revolutionize therapeutic treatment. With these advances, proteins and other molecules can be designed to act on today’s undruggable proteins or tomorrow’s drug-resistant diseases. My MIRA research project focuses on computational and experimental studies of protein structure and design (PS&D). The interlocking goals are to (A) determine protein structure and dynamics in systems of biomedical importance; and (B) design proteins, inhibitors, and their molecular interactions, especially to predict and overcome resistance. We develop novel algorithms in structural molecular biology. To surmount the challenges therein, our algorithms exploit combinatorial optimization, computational geometry and topology, and integrate advanced machine learning techniques. We believe software for PS&D must be I) Open-Source and II) Free software. This is the goal of OSPREY. Thus, we will (C) continue to develop free, open-source algorithms and software not only for challenging problems in the design of proteins and their interactions, but also to determine difficult protein structures and characterize their dynamics. We will use structural data and computational models to understand molecular mechanism and the basis of therapeutic interventions, and perform detailed experimental measurements in vitro and in vivo to confirm, iterate, and improve both our understanding of protein structure and molecular designs. The resulting models of protein structures and dynamics, together with our novel design methodology, will illuminate targets of biochemical and pharmacological significance. We will also advance PS&D by making algorithmic and modeling advances. We will test our methods and predictions by creating designed protein and inhibitor constructs, solving empirical structures, and performing in vitro experiments to measure enhanced biophysical properties on purified components, and in-cell experiments to measure biological efficacy. We will apply our PS&D algorithms to several areas of biomedical importance. We will solve structures of systems under our investigation and further develop the paradigm of protein structure as a continuous probability distribution. A set of synergistic research thrusts is proposed, in which, for example, we will (1) predict future resistance mutations in protein targets of novel drugs, (2) design protein-protein interaction (PPI) inhibitors that target “undruggable” proteins, and (3) use our PS&D methodology to characterize and design antibody:antigen constructs, with the ultimate goal of creating pan-neutralizing antibodies for viral targets. Our sustained program in developing novel computational methods to accurately predict potential drug target mutations in response to early-stage leads should drive the design of more resilient and durable first-generation drug candidates.

项目概要：这是一个设备的行政补充申请，用于购买支持GPU的设备 对于家长资助来说，3D 蛋白质结构的确定不仅对于揭示至关重要。 疾病过程的分子机制，同时也适用于基于结构的药物设计。 蛋白质设计的进步可能会彻底改变蛋白质和其他分子的治疗方法。 可以设计用于作用于今天的不可成药的蛋白质或明天的耐药疾病。 专注于蛋白质结构和设计（PS&D）的计算和实验研究，其相互关联的目标是： (A) 确定具有生物医学重要性的系统中的蛋白质结构和动力学；以及 (B) 设计蛋白质、抑制剂、 以及它们的分子相互作用，特别是预测和克服阻力，我们开发了结构方面的新颖算法。 为了克服其中的挑战，我们的算法利用组合优化、计算。 几何和拓扑，并集成先进的机器学习技术，我们相信 PS&D 的软件一定是 I) 开源和 II) 免费软件，这是 OSPREY 的目标，因此，我们将 (C) 继续开发免费、开源。 算法和软件不仅可以解决蛋白质设计及其相互作用中的挑战性问题，还可以 难以确定蛋白质结构并表征其动态。 我们将使用结构数据和计算模型来理解分子机制和治疗基础 干预措施，并在体外和体内进行详细的实验测量，以确认、迭代和改进两者 我们对蛋白质结构和分子设计的理解由此产生的蛋白质结构和动力学模型， 与我们新颖的设计方法一起，将阐明具有生化和药理学意义的目标。 我们还将通过算法和建模方面的进步来推进 PS&D，我们将通过以下方式测试我们的方法和预测。 创建设计的蛋白质和抑制剂结构，解决经验结构，并进行体外实验 测量纯化成分的增强生物物理特性，以及细胞内实验来测量生物功效。 我们将把我们的 PS&D 算法应用到几个具有生物医学重要性的领域，我们将解决以下系统的结构。 我们的研究并进一步发展了蛋白质结构作为一组连续概率分布的范式。 提出了协同研究重点，例如，我们将（1）预测蛋白质中未来的抗性突变 新药的靶标，(2) 设计针对“不可成药”蛋白质的蛋白质-蛋白质相互作用 (PPI) 抑制剂，以及 (3) 使用我们的 PS&D 方法来表征和设计抗体：抗原构建体，最终目标是创建 针对病毒靶点的泛中和抗体。我们持续计划开发新颖的计算方法来 准确预测响应早期先导的潜在药物靶点突变应该推动更多药物的设计 具有弹性和持久性的第一代候选药物。