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.

项目概要 所有折叠的蛋白质在其低能量天然结构和高能量“隐藏”结构之间不断波动 尽管每个蛋白质分子都通过其高位点，但构象可以部分或完全展开。 能量构象只占一小部分时间，但这些状态具有重大的生理后果。 天然蛋白质的部分折叠状态可能导致蛋白质聚集、器官衰竭和死亡。 治疗性蛋白质的折叠状态可以诱导危险的抗药物抗体之间的能量平衡。 原生的折叠态和这些不同的高能态——换句话说，就是整体的“能量景观” – 因此对于蛋白质聚集和免疫原性以及变构、信号传导、关闭至关重要 尽管对能源景观进行了数十年的研究，但仍以药物相互作用和许多其他现象为目标。 我们的整体理解非常有限：很少有蛋白质被深入、准确的计算表征 预测非常具有挑战性，实验测量昂贵、缓慢且劳动密集型。 我们提出了一种通过整合新的方法来理解蛋白质能量景观的变革性方法 大规模并行实验方法、机器学习和蛋白质设计首先，我们正在开发一种新的方法。 使用氢交换质谱法测量能量景观的高通量测定 这种方法最终将蛋白质能量景观的研究带入了现实。 至关重要的是，这些实验揭示了整体折叠稳定性和能量。 对于蛋白质的子集（数十到数百），这些平行。 实验还揭示了构象波动的具体位点。 使用这种方法，我们将测量数千种天然蛋白质和数十种的能量景观 定制数千个计算设计的蛋白质，用于系统地探究特定性质 然后，我们将训练机器学习模型来预测能源景观。 序列和结构，以及优化物理力场以准确模拟高能蛋白质状态。 我们还将通过组织大型比赛来促进整个社区建模的进步 最后，利用这些新的预测模型，我们将追求独特的能源景观。 应用：开发用于蛋白质治疗和生物的大力优化的筛选文库 总之，这项研究克服了药物和探针开发中的重大挑战。 大规模定量揭示隐藏蛋白质状态的实验和计算工具。 这从根本上改变了我们的观点：而不是仅在能源景观引起问题时才对其进行检查 问题，我们可以使能源景观分析成为生物学和生物工程的核心工具。