Machine Learning-Guided Engineering of Protease Modulators

机器学习引导的蛋白酶调节剂工程

• 批准号: 10353932
• 负责人: Carl Denard
• 金额: $ 21.94万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10353932
• 关键词: Active Sites; Address; Affinity; Algorithms; Antibodies; Autoimmune Diseases; Autoimmunity; Bacteriophages; Binding; Biochemical; Biomedical Research; Bypass; COVID-19; Clinical; Color; Communicable Diseases; Consumption; Cysteine; DNA sequencing; Data; Data Set; Development; Disease; Engineering; Enzymes; Epitope Mapping; Epitopes; Fluorescence Polarization; Fluorescence-Activated Cell Sorting; Foundations; Future; Genetic Transcription; Goals; Guidelines; Hepatitis C virus; Homeostasis; Infection; Insulinase; Lead; Length; Libraries; Ligand Binding; Ligands; MMP2 gene; Machine Learning; Malignant Neoplasms; Maps; Matrix Metalloproteinases; Methods; Modeling; Nerve Degeneration; Peptide Hydrolases; Pharmaceutical Preparations; Phenotype; Physiological; Process; Property; Protease Inhibitor; Proteins; Public Health; Reporter; Research; Serine Protease; Site; Structure; Technology; Thermodynamics; Time; Toxic effect; Training; Work; Yeasts; base; beta secretase; beta-site APP cleaving enzyme 1; convolutional neural network; design; experimental study; functional outcomes; genetic selection; high dimensionality; high throughput screening; in silico; inhibitor; interest; machine learning algorithm; mathematical model; microbial; nanobodies; new technology; next generation; novel; side effect; small molecule; small molecule inhibitor; therapeutic target; tool

Project Summary Modulating the activity of proteases is a central strategy for treating cancer, autoimmunity, and infection. However, the discovery and design of selective and potent therapeutics targeting proteases (small-molecules and antibodies) largely rely on inefficient, iterative processes. As a result, it takes several years to develop a protease drug, and even then, most protease drugs are active site inhibitors that often suffer from low selectivity in distinguishing related proteases. Due to the complexity of proteolytic dysregulation, restoring homeostasis requires not only selective inhibitors but also ligands that can reprogram protease selectivity. Unfortunately, no platform exists to engineer protease modulators based on systematic, and quantitative design principles. To address these challenges, this proposal seeks to combine for the first time Machine Learning tools, Next- Generation DNA sequencing, and a yeast-based high-throughput functional screen to accelerate the isolation and design of nanobody-based protease modulators. The functional selection will perform two tasks: (i) select nanobodies from synthetic libraries based on a desired function and (ii) correlate ligand: epitope interactions to a functional outcome. These experiments will generate high-quality datasets that will train machine learning algorithms (ML) to predict the potency, selectivity, and mechanisms of nanobody-based modulators based on their sequence features alone. This machine learning-aided strategy will accelerate the discovery of rare and potent protease modulators and bypass the limitations of structure-based methods. Moreover, curated datasets of protease modulatory nanobody sequences will provide reference and design guidelines for future experimental and in silico campaigns. This work is of significant interest to biomedical research and public health and includes select proteases such as Hepatitis C virus protease, MMPs, transmembrane serine protease 2 (COVID-19), β-secretase, and insulin-degrading enzyme. Moreover, the proposed studies provide a foundation to answering fundamental biochemical questions on how synthetic ligands can map and modulate the functional landscape of proteases and other protein-modifying enzymes.

项目摘要 调节蛋白酶的活性是治疗癌症、自身免疫和感染的核心策略。但 靶向蛋白酶（小分子和抗体）的选择性和有效治疗剂的发现和设计主要依赖于 低效率的、重复的过程。因此，开发一种蛋白酶药物需要几年时间，即使这样，大多数蛋白酶 药物是活性位点抑制剂，在区分相关蛋白酶时通常具有低选择性。由于 由于蛋白水解失调的复杂性，恢复体内平衡不仅需要选择性抑制剂， 可以重新编程蛋白酶的选择性。不幸的是，不存在基于系统的， 定量设计原则。 为了应对这些挑战，该提案寻求首次将机器学习工具联合收割机，Next- 代DNA测序，以及基于酵母的高通量功能筛选，以加速分离和 基于纳米抗体的蛋白酶调节剂的设计。功能选择将执行两个任务： 基于所需的功能从合成文库中分离，和（ii）将配体：表位相互作用与功能结果相关联。 这些实验将生成高质量的数据集，这些数据集将训练机器学习算法（ML）来预测 基于纳米抗体的调节剂的效力、选择性和机制仅基于它们的序列特征。 这种机器学习辅助策略将加速发现罕见而有效的蛋白酶调节剂， 绕过基于结构的方法的限制。此外，蛋白酶调节纳米抗体序列的策划数据集 将为未来的实验和计算机模拟活动提供参考和设计指南。这项工作具有重要意义 对生物医学研究和公共健康有意义，包括选择蛋白酶，如丙型肝炎病毒蛋白酶，MMP， 跨膜丝氨酸蛋白酶2（COVID-19）、β-分泌酶和胰岛素降解酶。此外，拟议的研究 提供了一个基础，以回答基本的生物化学问题，如何合成配体可以映射和调节 蛋白酶和其他蛋白质修饰酶的功能景观。