Machine Learning-Guided Engineering of Protease Modulators

机器学习引导的蛋白酶调节剂工程

基本信息

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
项目类别：
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

项目摘要

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.

项目概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。针对蛋白酶（小分子和抗体）的选择性和有效疗法的发现和设计很大程度上依赖于因此，开发一种蛋白酶药物需要数年时间，即使如此，大多数蛋白酶也需要数年时间。是活性位点抑制剂，药物在区分相关蛋白酶时往往选择性较低。由于蛋白水解失调的复杂性，恢复稳态不仅需要选择性抑制剂，还需要配体不幸的是，不存在基于系统的、工程化蛋白酶调节剂的平台。和定量设计原则。为了应对这些挑战，该提案首次寻求结合机器学习工具，下一步- 一代 DNA 测序和基于酵母的高通量功能筛选可加速分离和基于纳米抗体的蛋白酶调节剂的设计。功能选择将执行两项任务：（i）选择纳米抗体。来自基于所需功能的合成文库和（ii）将配体相关联：表位相互作用与功能结果。这些实验将生成高质量的数据集，用于训练机器学习算法 (ML) 来预测仅基于其序列特征的纳米抗体调节剂的效力、选择性和机制。这种机器学习辅助策略将加速稀有且有效的蛋白酶调节剂的发现和此外，还绕过了基于结构的方法的局限性。将为未来的实验和计算机模拟活动提供参考和设计指南。对生物医学研究和公共卫生的兴趣，包括精选的蛋白酶，如丙型肝炎病毒蛋白酶、MMP、此外，拟议的研究还涉及跨膜丝氨酸蛋白酶 2 (COVID-19)、β-分泌酶和胰岛素降解酶。为回答有关合成配体如何映射和调节的基本生化问题提供基础蛋白酶和其他蛋白质修饰酶的功能景观。