Development of a pipeline for parallel elucidation of protein structures

开发并行阐明蛋白质结构的管道

• 批准号: 10231713
• 负责人: Gabriel C Lander
• 金额: $ 22.19万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10231713
• 关键词: Active Sites; Amino Acid Sequence; Biochemical; Biochemical Process; Biological; Biological Assay; Biophysics; Biotechnology; Classification; Clinic; Complex; Computer Models; Cryoelectron Microscopy; Crystallography; Data; Data Analyses; Data Set; Development; Diagnostic; Disease; Drug Design; Engineering; Enzymes; Feedback; Foundations; Genetic Variation; Heart; Human Genome; Image; Knowledge; Lead; Libraries; Location; Medical; Methodology; Methods; Modeling; Modification; Molecular Conformation; Molecular Weight; Mutation; Names; Nature; Performance; Preparation; Production; Property; Protein Biochemistry; Protein Engineering; Proteins; Proteomics; Research Design; Research Personnel; Resolution; Sampling; Shapes; Statistical Methods; Structural Models; Structural Protein; Structure; Structure-Activity Relationship; System; Techniques; Technology; Testing; Therapeutic; Training; Variant; Work; X-Ray Crystallography; base; classification algorithm; de novo mutation; deep learning; design; detection limit; experimental study; genome sequencing; human disease; image processing; improved; in silico; insight; knowledge base; machine learning algorithm; particle; prevent; programs; protein complex; protein folding; protein function; protein structure; protein structure prediction; rapid technique; screening; success; therapeutic development; tool; variant of unknown significance

Advances in biophysical technologies have accelerated our ability to probe the mechanisms of even the most complex cellular systems, and such studies have enabled researchers to design modifications to known protein structures and design completely new proteins. This “protein design” technology has given rise to an ability to manipulate protein structures as a means of improving on or introducing new medical diagnostics and therapeutics. The bases of these studies rely on computational modeling of protein candidates, although the accuracy of protein structure prediction, protein de novo design, and single-mutation effects prediction remain below the threshold for many use cases, such as structure-guided drug design and rational enzyme engineering. Thus, success of a protein engineering effort relies on high-resolution structure determination, which involves laborious screening and optimization in order to obtain stable proteins or active enzyme variants. However, our ability to observe protein structure using common structure determination strategies (X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM)) lags far behind our ability to design and produce new sequences, creating a knowledge gap that prevents biochemists from accessing the range of protein functions seen in nature. While current technologies enable rapid synthesis of hundreds of proteins with varied sequences, there do not exist technologies for rapid structural characterization of these generated proteins. The ability to obtain high- resolution structural information for hundreds of sequences in parallel would provide invaluable insights in protein engineering methods. Importantly, rapid structure determination would enable structural characterization of genetic variation in the human genome underlying disease by enabling the structural and mechanistic interpretation of rare and de novo disease-related variants. Cryo-EM enables numerous high-resolution structures to be determined from a small amount of sample without requiring homogeneity, an aspect of this method that we plan to exploit for parallel elucidation of protein structures. We will establish the feasibility of this technique for rapidly investigate the structures of engineered protein libraries, where the molecular weight range is near or below the lower detection limit of cryo-EM. We will also probe the limits of our ability to identify the location and structural impact of tested mutations at limited structural locations, such as active sites. We will explore the feasibility of our parallel structure determination approach in two aims: Aim 1 will identify the limit of current single-particle analysis methods to discriminate between structurally similar protein complexes. Aim 2 will implement machine learning algorithms to push the current limits of classification using a combination of synthetic and real data. These exploratory studies will pave the way to rapid structure determination of multiple protein complexes from a single cryo-EM experiment, providing the ability to rapidly obtain high-resolution structures for many engineered proteins, thereby enabling unprecedented design and testing feedback cycles to help treat human disease.

生物物理技术的进步加快了我们探索最复杂机制的能力。 复杂的细胞系统，此类研究使研究人员能够设计对已知蛋白质的修饰 结构和设计全新的蛋白质。这种“蛋白质设计”技术已经产生了一种能力 操纵蛋白质结构作为改进或引入新的医学诊断的手段 疗法。这些研究的基础依赖于候选蛋白质的计算模型，尽管 蛋白质结构预测、蛋白质从头设计和单突变效应预测的准确性仍然存在 低于许多用例的阈值，例如结构引导的药物设计和合理的酶工程。 因此，蛋白质工程工作的成功依赖于高分辨率结构测定，其中涉及 为了获得稳定的蛋白质或活性酶变体，需要进行艰苦的筛选和优化。然而，我们的 能够使用常见的结构测定策略（X 射线晶体学、 核磁共振和冷冻电子显微镜（cryo-EM））远远落后于我们设计和生产新序列的能力， 造成了知识差距，阻止生物化学家了解自然界中看到的蛋白质功能范围。 虽然当前的技术能够快速合成数百种具有不同序列的蛋白质，但还没有 现有技术可快速表征这些生成的蛋白质的结构。获得高的能力 并行解析数百个序列的结构信息将为蛋白质提供宝贵的见解 工程方法。重要的是，快速结构测定将使结构表征成为可能 人类基因组中的遗传变异通过使结构和机制成为可能，从而导致疾病 罕见和新发疾病相关变异的解释。冷冻电镜可实现多种高分辨率 从少量样品中确定结构而不需要同质性，这是这一点的一个方面 我们计划利用该方法来并行阐明蛋白质结构。我们将确定此方案的可行性 快速研究工程蛋白质文库结构的技术，其中分子量范围 接近或低于冷冻电镜的检测下限。我们还将探讨我们识别能力的极限 测试突变在有限结构位置（例如活性位点）的位置和结构影响。我们将 探索我们的并行结构确定方法在两个目标中的可行性：目标 1 将确定 目前的单粒子分析方法可以区分结构相似的蛋白质复合物。目标2 将使用机器学习算法的组合来突破当前分类的极限 合成数据和真实数据。这些探索性研究将为快速确定多种化合物的结构铺平道路 来自单个冷冻电镜实验的蛋白质复合物，提供快速获得高分辨率的能力 许多工程蛋白质的结构，从而实现前所未有的设计和测试反馈周期 帮助治疗人类疾病。