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 基因组编码区的许多疾病相关变异影响翻译的蛋白质和酶 通过扰乱其折叠构象或其功能，如与底物的相互作用， 大分子伴侣然而，我们缺乏一个统一的预测框架来预测编码的功能效应 变异，限制了基因组数据如何用于精准医学。机器学习模型在大型 序列数据库已经声称预测了几种模型蛋白中编码变体的有害作用， 但迄今为止，由于两个主要挑战，它们的实际使用受到限制。首先是缺乏 描述性的，“地面实况”生物物理数据集，将序列变异与天然蛋白质性质相关， 传统生物化学和生物物理实验的低通量。第二个是没有一个好的- 建立了将这些数据整合到最先进的预测模型中的方法。以应对这些严峻的 限制，我建议应用尖端的微流体技术，以产生大量的定量生物物理 将人类酰基磷酸酶（ACYP）的序列变异与功能联系起来的数据集，ACYP是一种模型蛋白质 α/β折叠家族（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）包含允许平行表达的腔室阵列， 纯化> 1，700蛋白质，并提供体外动力学和热力学常数的测量， 每个.在目标1中，我将设计一系列使用HT-MEK和衍生物微流体的ACYP功能测定 技术，首先测试ACYP的小文库的体外表达、芯片上稳定性和催化周转率。 最后与传统的生化测量进行比较。在目标2中，我将快速生成扫描 在ACYP中的突变文库中，并在HT-MEK上对数百个ACYP变体进行测量。在Aim中 3、与机器学习专家合作，我将使用这个前所未有的定量生化数据集来微调一个 尖端的深度学习提供通过大规模体外数据增强的第一个变体效应预测因子。我 初步数据表明，该模型可以产生从头ACYP序列， 这表明它将为功能预测提供坚实的基础。综合起来，我的结果 提供了对体外效用的洞察，来自人类蛋白质的生化数据集在训练更好的预测因子中的作用。 疾病表型在实现这些目标的过程中，我将获得的培训将使我能够（1）发展技能 在研究设计，分析和蛋白质生物物理学数据的解释;（2）学习先进的技术， 蛋白质生物化学和统计序列分析;（3）获得具有竞争力的博士后奖学金， 在一所研究密集型大学建立一个独立资助的实验室的长期目标。