High-throughput thermodynamic and kinetic measurements for variant effects prediction in a major protein superfamily

用于预测主要蛋白质超家族变异效应的高通量热力学和动力学测量

• 批准号: 10752370
• 负责人: Micah Olivas
• 金额: $ 4.77万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752370
• 关键词: Address; Adoption; Affect; Affinity; Agreement; Alanine; Algorithms; Amino Acid Sequence; Amino Acid Substitution; Base Sequence; Binding; Biochemical; Biological Assay; Biophysics; Catalysis; Cells; ClinVar; Clinical; Cloning; Code; Collaborations; Complex; Computer Models; Coupled; DNA sequencing; Data; Data Set; Databases; Development; Disease; Engineering; Enzymes; Explosion; Family; Fellowship; Fluorescence; Foundations; Free Energy; Funding; Gene Expression Regulation; Genome; Genomic medicine; Glycine; Goals; Human; Human Genome; In Vitro; Kinetics; Laboratories; Learning; Libraries; MEKs; Machine Learning; Maps; Measurement; Measures; Metabolism; Methods; Microfluidics; Missense Mutation; Modeling; Molecular Conformation; Mutagenesis; Mutation; Nature; Orthologous Gene; Output; Pathogenicity; Performance; Phenotype; Positioning Attribute; Postdoctoral Fellow; Property; Protein Biochemistry; Proteins; Publishing; Reaction; Recombinants; Research; Research Design; Resources; Role; Scanning; Sequence Analysis; Series; Structure; Techniques; Testing; Thermodynamics; Training; Translating; United States National Institutes of Health; Universities; Valine; Variant; Vertebral column; Work; biophysical properties; blind; cofactor; computer framework; computerized tools; cost; data integration; deep learning; deep learning model; design; disease phenotype; experimental study; functional outcomes; genomic data; human disease; improved; in vitro testing; in vivo; insight; machine learning model; microfluidic technology; molecular sequence database; mutant; novel; precision medicine; predictive modeling; protein expression; protein folding; protein function; protein structure prediction; skills; success; supervised learning

PROJECT SUMMARY Many disease-associated variants in coding regions of the genome affect translated protein and enzyme products by perturbing their folded conformation or their function, such as interactions with substrates or macromolecular partners. However, we lack a unified predictive framework to predict functional effects of coding variants, limiting how genomic data can be used in precision medicine. Machine learning models trained on large sequence databases have claimed to predict deleterious effects from coding variants in several model proteins, but to date their practical usage has been limited because of two major challenges. The first is the lack of descriptive, “ground truth” biophysical datasets relating sequence variation to native protein properties, due to the low throughput of traditional biochemical and biophysical experiments. The second is that there is not a well- established method for integrating these data in state-of-the-art predictive models. To address these critical limitations, I propose to apply cutting-edge microfluidic techniques to generate large quantitative biophysical datasets connecting sequence variation to function in human acylphosphatase (ACYP), a model protein of the alpha/beta fold family (found in ~10% of human proteins), and leverage these data to enhance predictive models. This microfluidic platform (HT-MEK) contains an array of chambers that allow for parallel expression and purification of >1,700 proteins, and provides measurements of in vitro kinetic and thermodynamic constants for each. In Aim 1, I will engineer a series of ACYP functional assays using HT-MEK and derivative microfluidic technologies, first testing in vitro expression, on-chip stability, and catalytic turnover of a small library of ACYP variants and finally comparing to traditional biochemical measurements. In Aim 2, I will rapidly generate scanning mutagenesis libraries in ACYP and make measurements across hundreds of ACYP variants on HT-MEK. In Aim 3, in collaboration with ML experts, I will use this unprecedented quantitative biochemical dataset to fine-tune a cutting-edge deep learning to provide the first variant effects predictor enhanced by in vitro data at scale. My preliminary data has shown that this model can generate de novo ACYP sequences that fold and are catalytically proficient, suggesting that it will provide a strong foundation for functional prediction. Together, my results will provide insight into the utility of in vitro, biochemical datasets from human proteins in training better predictors of disease phenotypes. The training that I will obtain in carrying out these Aims will allow me to (1) develop skills in research design, analysis, and interpretation of protein biophysics data; (2) learn advanced techniques in protein biochemistry and statistical sequence analysis; and (3) obtain a competitive post-doctoral fellowship with the long-term goal of establishing an independently-funded laboratory at a research-intensive university.

项目摘要 基因组的编码区域中许多与疾病相关的变体影响翻译的蛋白质和酶 通过扰动其折叠会议或功能，例如与基板的互动或 大分子伴侣。但是，我们缺乏一个统一的预测框架来预测编码的功能效应 变体，限制了如何将基因组数据用于精确医学。大型培训的机器学习模型 序列数据库声称可以预测几种模型蛋白中编码变体的有害效应， 但是迄今为止，由于两个主要挑战，他们的实际用途受到限制。首先是缺乏 描述性的“地面真相”生物物理数据集将序列变化与天然蛋白质特性有关 传统生化和生物物理实验的低通量。第二个是没有一个很好的 建立的方法将这些数据集成到最新的预测模型中。解决这些关键 局限性，我建议应用尖端的微流体技术来生成大量的定量生物物理 将序列变化连接到人酰基磷酸酶（ACYP）的功能的数据集，这是一种模型蛋白 alpha/beta Fold家族（在约10％的人类蛋白质中发现），并利用这些数据来增强预测模型。 该微流体平台（HT-MEK）包含一个允许并行表达式和 纯化> 1,700蛋白，并为体外动力学和热力学常数提供测量 每个。在AIM 1中，我将使用HT-MEK和衍生微流体进行一系列ACYP功能测定 技术，首次测试体外表达，片上稳定性和催化性周转率 变体，最终与传统的生化测量值进行比较。在AIM 2中，我将迅速产生扫描 ACYP中的诱变文库，并在HT-MEK上进行数百个ACYP变体进行测量。目标 3，与ML专家合作，我将使用此前所未有的定量生化数据集进行微调 尖端深度学习以通过大规模的体外数据增强了第一个变体效应预测变量。我的 初步数据表明，该模型可以生成从头序列的折叠并催化的序列 熟练程度，表明它将为功能预测提供坚实的基础。在一起，我的结果将会 提供有关人类蛋白质中体外生化数据集的启用训练中的效用 疾病表型。我将在执行这些目标时获得的培训将使我能够（1）发展技能 在蛋白质生物物理学数据的研究设计，分析和解释中； （2）学习高级技术 蛋白质生物化学和统计序列分析； （3）与 在研究密集型大学建立独立资助的实验室的长期目标。