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: 10434001
• 负责人: Gabriel C Lander
• 金额: $ 26.63万
• 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/10434001
• 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; 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; Structure; Structure-Activity Relationship; System; Techniques; Technology; Testing; Therapeutic; Training; Variant; Work; X-Ray Crystallography; base; classification algorithm; clinical diagnostics; de novo mutation; deep learning; design; detection limit; experimental study; genome sequencing; human disease; image processing; improved; in silico; insight; knowledgebase; 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速了我们探究最甚至最多机制的能力 复杂的细胞系统以及此类研究使研究人员能够对已知蛋白质进行修改 结构和设计全新的蛋白质。这种“蛋白质设计”技术已引起了能力 操纵蛋白质结构，以改善或引入新的医学诊断和 疗法。这些研究的基础依赖于蛋白质候选者的计算建模，尽管 蛋白质结构预测的准确性，从头设计和单突击效应预测仍然存在 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。 蛋白质工程工作的成功取决于高分辨率结构的确定，这涉及 为了获得稳定的蛋白质或活性酶变体，费力的筛选和优化。但是，我们的 能够使用常见结构确定策略观察蛋白质结构（X射线晶体学， NMR和冷冻电子显微镜（Cryo-EM））远远落后于我们设计和产生新序列的能力， 建立知识差距，以防止生物化学家访问自然界中看到的蛋白质功能的范围。 虽然当前的技术可以快速合成具有多种序列的数百种蛋白质，但没有 存在这些产生的蛋白质快速结构表征的技术。获得高级的能力 平行数百个序列的分辨率结构信息将为蛋白质提供宝贵的见解 工程方法。重要的是，快速结构确定将使结构表征 通过实现结构和机械性的人类基因组的遗传变异 解释罕见和从头疾病相关的变体。冷冻EM实现了许多高分辨率 从少量样本中确定的结构而无需同质性，这是一个方面 我们计划利用并行阐明蛋白质结构的方法。我们将确定这一点的可行性 快速研究工程蛋白质库的结构，其中分子量范围 靠近或低于低温EM的较低检测极限。我们还将调查我们识别能力的限制 测试突变在有限的结构位置（例如活性位点）的位置和结构影响。我们将 探索两个目的的平行结构确定方法的可行性：AIM 1将确定的极限 当前的单粒子分析方法以区分结构相似的蛋白质复合物。目标2 将实现机器学习算法以使用组合的组合来推动当前的分类限制 合成和真实数据。这些探索性研究将为快速结构确定多个的方法铺平了道路 来自单个冷冻EM实验的蛋白质复合物，提供了快速获得高分辨率的能力 许多工程蛋白的结构，从而使前所未有的设计和测试反馈周期 帮助治疗人类疾病。