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

项目总结 基因组编码区的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，如与底物或 大分子伙伴。然而，我们缺乏一个统一的预测框架来预测编码的功能效果 变异，限制了基因组数据在精确医学中的使用。机器学习模型在大型计算机上的训练 序列数据库声称可以预测几种模型蛋白质中编码变异的有害影响， 但到目前为止，由于两个主要挑战，它们的实际使用一直受到限制。第一个是缺乏 将序列变化与天然蛋白质属性联系起来的描述性、“基本事实”生物物理数据集，原因是 传统生化和生物物理实验的低通量。二是没有一口井-- 建立了将这些数据整合到最先进的预测模型中的方法。要解决这些关键问题 限制，我建议应用尖端微流控技术来产生大量定量的生物物理 人类酰基磷酸酶(ACYP)的序列变异与功能相关的数据集 Alpha/Beta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。 该微流控平台(HT-MEK)包含允许并行表达和 纯化&gt；1,700蛋白质，并提供了体外动力学和热力学常数的测量 每个人。在目标1中，我将使用HT-MEK和衍生微流控技术设计一系列ACYP功能分析 技术，首先测试ACYP的体外表达、芯片上的稳定性和催化周转率 最后将其与传统的生化测量方法进行比较。在目标2中，我将快速生成扫描 ACYP中的突变文库，并在HT-MEK上对数百个ACYP变体进行测量。在AIM 3，与ML专家合作，我将使用这个史无前例的定量生化数据集来微调 尖端深度学习提供了第一个通过体外数据在规模上增强的变异效应预测因子。我的 初步数据表明，这个模型可以产生从头开始的ACYP序列，这些序列可以折叠并被催化 精通，表明它将为功能预测提供坚实的基础。总而言之，我的结果将 洞察来自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 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 研究设计、分析和解释蛋白质生物物理数据；(2)学习先进技术 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研究员职位 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。

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.