High-throughput discovery of protein energy landscapes in natural and designed proteomes

天然和设计蛋白质组中蛋白质能量景观的高通量发现

• 批准号: 10002881
• 负责人: Gabriel Jacob Rocklin
• 金额: $ 234.79万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10002881
• 关键词: Affect; Age; Antibodies; Biological; Biology; Biomedical Engineering; Cessation of life; Communities; Computer Models; Custom; Dangerousness; Development; Drug Interactions; Drug Targeting; Equilibrium; Generations; Hand; Hydrogen; Lead; Libraries; Light; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Methods; Modeling; Molecular Conformation; Organ failure; Pharmaceutical Preparations; Physiological; Property; Protein Engineering; Proteins; Proteome; Research; Signal Transduction; Site; Structure; Sum; Time; Training; blind; computerized tools; design; experimental study; high throughput screening; immunogenicity; predictive modeling; protein aggregation; protein folding; screening; therapeutic protein; tool

Project Summary All folded proteins continuously fluctuate between their low-energy native structures and higher-energy “hidden” conformations that can be partially or fully unfolded. Although each protein molecule passes through its high- energy conformations only a small fraction of the time, these states have major physiological consequences. Partially-folded states of natural proteins can lead to protein aggregation, organ failure, and death. Partially- folded states of therapeutic proteins can induce dangerous anti-drug antibodies. The energetic balance between the native, folded state and these diverse higher-energy states – in other words, the overall “energy landscape” – is thus critically important in protein aggregation and immunogenicity, as well as in allostery, signaling, off- target drug interactions and numerous other phenomena. Despite decades of research into energy landscapes our overall understanding is very limited: few proteins have been characterized in depth, accurate computational predictions are very challenging, and experimental measurements are expensive, slow, and labor-intensive. We propose a transformational approach to understand protein energy landscapes by integrating a new massively parallel experimental method, machine learning, and protein design. First, we are developing a new high-throughput assay using hydrogen exchange mass spectrometry to measure energy landscapes for thousands of proteins in parallel. This method finally brings the study of protein energy landscapes into the “omics” age. Critically, these experiments reveal both the overall folding stability and the energies of conformational fluctuations in each protein. For a subset of proteins (tens to hundreds), these parallel experiments reveal the specific sites of conformational fluctuations as well. Using this approach, we will measure the energy landscapes of thousands of natural proteins and tens of thousands of computationally designed proteins custom-built to systematically probe how specific properties affect energy landscapes. We will then train machine learning models to predict energy landscapes from sequence and structure, as well as optimize physical force fields to accurately model high-energy protein states. We will also catalyze advances in modeling throughout the community by organizing large-scale competitions at blind prediction of energy landscapes. Finally, with these new predictive models in hand, we will pursue a unique application: the development of energetically-optimized screening libraries for therapeutic protein and biological probe discovery. This overcomes a major challenge in drug and probe development. In sum, this study provides the experimental and computational tools to bring hidden protein states to light quantitatively on a massive scale. This fundamentally shifts our perspective: instead of examining energy landscapes only when they cause problems, we can make energy landscape analysis a central tool in biology and bioengineering.

项目摘要 所有折叠的蛋白质继续在其低能原生结构和更高能源的“隐藏”之间波动 可以部分或完全展开的构象。尽管每个蛋白质分子都通过其高 能量构象仅在一小部分时间内，这些状态产生了重大的物理后果。 天然蛋白质的部分折叠状态可导致蛋白质聚集，器官衰竭和死亡。部分 - 治疗蛋白的折叠状态可以诱导危险的抗药物抗体。之间的充满活力的平衡 本地，折叠状态和这些不同的高能状态 - 换句话说，总体“能量景观” - 因此，在蛋白质聚集和免疫原性以及变构，信号，离子方面至关重要 靶向药物相互作用和许多其他现象。尽管对能源景观进行了数十年的研究 我们的整体理解非常有限：很少有蛋白质在深度，准确的计算中表征 预测非常挑战，实验测量昂贵，缓慢且劳动力密集。 我们提出了一种转化方法，通过整合新的 大量平行的实验方法，机器学习和蛋白质设计。首先，我们正在开发一个新的 使用氢交换质谱法测量能量景观的高通量测定 并联成千上万的蛋白质。这种方法最终将蛋白质能景观的研究带入 “ OMICS”时代。至关重要的是，这些实验既揭示了总体折叠稳定性和能量 每种蛋白质中的构象波动。对于蛋白质的子集（数十万），这些平行 实验也揭示了构象波动的特定位点。 使用这种方法，我们将测量数千种天然蛋白质和数十万种的能量景观 数千种计算设计的蛋白质定制为系统地探测特定属性的方式 影响能源景观。然后，我们将训练机器学习模型以预测能源景观 序列和结构，以及优化物理力场，以准确模拟高能蛋白态。 我们还将通过组织大规模比赛的大规模比赛来促进整个社区建模的进步 能源景观的盲目预测。最后，借助这些新的预测模型，我们将追求独特的 应用：用于热蛋白和生物学的基本优化筛选文库的开发 探针发现。这克服了药物和探测开发方面的重大挑战。总之，这项研究提供了 实验和计算工具将隐藏的蛋白质态在大规模上进行定量启动。 这从根本上改变了我们的观点：仅当能源景观引起时才检查 问题，我们可以使能源景观分析成为生物学和生物工程领域的核心工具。