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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要基因组编码区中许多与疾病相关的变异会影响翻译的蛋白质和酶通过扰乱产品的折叠构象或功能，例如与底物的相互作用或然而，我们缺乏统一的预测框架来预测编码的功能效果。变体，限制了基因组数据在精准医学中的使用方式。在大型机器学习模型上进行训练。序列数据库声称可以预测几种模型蛋白质中编码变体的有害影响，但迄今为止，由于两个主要挑战，它们的实际使用受到限制。描述性的、“真实的”生物物理数据集将序列变异与天然蛋白质特性相关联，由于传统生物化学和生物物理实验的通量低，第二是没有良好的方法。建立了将这些数据集成到最先进的预测模型中的方法，以解决这些关键问题。由于局限性，我建议应用尖端的微流体技术来产生大量的定量生物物理连接序列变异与人类酰基磷酸酶 (ACYP) 功能的数据集，ACYP 是一种模型蛋白 α/β 折叠家族（存在于约 10% 的人类蛋白质中），并利用这些数据来增强预测模型。该微流体平台 (HT-MEK) 包含一系列腔室，允许并行表达和纯化 > 1,700 种蛋白质，并提供体外动力学和热力学常数的测量在目标 1 中，我将使用 HT-MEK 和衍生微流体设计一系列 ACYP 功能测定。技术，首先测试小型 ACYP 文库的体外表达、芯片稳定性和催化周转在目标2中，我将快速生成扫描 ACYP 中的诱变文库，并在 HT-MEK 上对数百个 ACYP 变体进行测量。 3、与ML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尖端深度学习提供第一个通过大规模体外数据增强的变异效应预测器。初步数据表明，该模型可以从头生成可折叠并催化的 ACYP 序列熟练，这表明它将为功能预测提供坚实的基础。深入了解人类蛋白质的体外生化数据集在训练更好的预测因子方面的效用我在实现这些目标时获得的培训将使我能够 (1) 培养技能。 (2) 学习蛋白质生物物理数据的研究设计、分析和解释的先进技术；蛋白质生物化学和统计序列分析；以及（3）获得具有竞争力的博士后奖学金在研究密集型大学建立独立资助的实验室的长期目标。