Molecular mechanisms of proton-coupled dynamic processes in biology

生物学中质子耦合动态过程的分子机制

• 批准号: 10552201
• 负责人: Jana Shen
• 金额: $ 38.63万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 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 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552201
• 关键词: 3-Dimensional; ABCG2 gene; Acceleration; Aspartic Endopeptidases; Biology; Caspase; Chemicals; Coupled; Cryoelectron Microscopy; Cysteine; Data; Development; Excretory function; Grant; Growth; Human; Human Cell Line; Kidney; Knowledge; Lysine; Machine Learning; Membrane Transport Proteins; Methods; Molecular; Motion; Opioid Receptor; Phosphotransferases; Physics; Positioning Attribute; Process; Proteins; Proteome; Proteomics; Protons; Resolution; Site; Sodium-Hydrogen Antiporter; Structure; Urate; cancer drug resistance; chemoproteomics; drug discovery; efflux pump; human disease; molecular dynamics; multi drug transporter; neural network; particle; protein structure; simulation; structural genomics; tool; tool development; whole genome

Project Summary/Abstract Our understanding of biology and human diseases is taking a significant leap forward due to access to whole genome sequences and detailed protein structural information. The number of high-resolution protein structures determined by single particle cryogenic electron microscopy (cryo-EM) is growing exponentially. The AlphaFold neural network may soon provide high-resolution structures for the entire human proteome. Starting from a three- dimensional protein structure, physics-based molecular dynamics (MD) simulation offers an atomic-level view of protein’s motion. Fueled by the exponential growth of computing power, MD is becoming a powerful tool for structure-function studies and assisting target-based drug discovery. Despite the aforementioned progress, molecular mechanisms of pH-driven and proton-coupled dynamic pro- cesses remain poorly understood. This is because proton positions are not resolved in most experimental struc- tures and conventional MD does not describe proton-coupled dynamics or explicitly account for solution pH. One such example is the human ATP-binding cassette subfamily G member 2 protein (ABCG2), which contributes to cancer drug resistance as well as renal excretion of urate. While high-resolution structures for the entire proteome may soon become available, a large fraction of the pro- teome is currently considered undruggable, i.e., intractable to traditional drug discovery efforts. The development of chemical proteomics platforms for discovery of reactive and ligandable cysteines and lysines in human cell lines holds the promise to significantly expand the druggable space. Nonetheless, the covalent ligandability of a large fraction of the proteome remains unexplored, and a systematic knowledge is lacking. In the previous R01 grant period, the Shen group has made significant progress in the development of GPU- accelerated continuous constant pH MD (CpHMD) methods and application to elucidate proton-coupled structure- dynamics-function relationships of various aspartyl proteases, cysteine proteases, kinases, as well as the mul- tidrug efflux pump AcrB, sodium-proton antiporter NhaA, and µ-opioid receptor. The Shen group has also de- veloped and applied a CpHMD method to predict reactive cysteine and lysine sites in a large number of kinases and other proteins. Building on the progress and taking advantage of the vast data from structural genomics and chemical proteomics, this R35 project seeks to fill the aforementioned gaps in tool development and knowledge. We will tackle the remaining challenges in the development of the all-atom CpHMD method to enable routine studies of proton-coupled dynamic processes. We will apply the all-atom CpHMD and other state-of-the-art MD tools to illuminate the mechanism of the multidrug transporter and urate exporter ABCG2. Finally, we will evalu- ate the entire proteome for covalent inhibition by integrating CpHMD, machine learning, and structure as well as chemoproteomics data.

项目概要/摘要 由于获得了完整的信息，我们对生物学和人类疾病的理解正在取得重大飞跃。 基因组序列和详细的蛋白质结构信息 高分辨率蛋白质结构的数量。 通过单粒子低温电子显微镜 (cryo-EM) 测定的 AlphaFold 正在呈指数级增长。 神经网络可能很快就会为整个人类蛋白质组提供高分辨率的结构。 三维蛋白质结构，基于物理的分子动力学 (MD) 模拟提供了原子级视图 在计算能力指数级增长的推动下，MD 正在成为一种强大的工具。 结构功能研究并协助基于靶标的药物发现。 尽管讨论了进展，但 pH 驱动和质子耦合动态亲和力的分子机制 这是因为大多数实验结构中质子的位置都没有得到解决。 理论和传统 MD 没有描述质子耦合动力学或明确说明溶液 pH 值。 例如人类 ATP 结合盒亚家族 G 成员 2 蛋白 (ABCG2)，它有助于 癌症耐药性以及肾脏对尿酸盐的排泄。 虽然整个蛋白质组的高分辨率结构可能很快就会变得可用，但大部分蛋白质组 目前，Teome 被认为是不可成药的，即传统药物发现工作难以处理。 用于发现人类细胞中反应性和可配体半胱氨酸和赖氨酸的化学蛋白质组学平台 然而，线的共价配位性有望显着扩大药物空间。 蛋白质组的很大一部分仍未被探索，并且缺乏系统的知识。 在之前的R01资助期内，沉课题组在GPU的开发方面取得了重大进展—— 加速连续恒定 pH MD (CpHMD) 方法和应用来阐明质子耦合结构 - 各种天冬氨酰蛋白酶、半胱氨酸蛋白酶、激酶以及多酶的动力学-功能关系 药物外排泵 AcrB、钠质子逆向转运蛋白 NhaA 和 µ-阿片受体。 开发并应用 CpHMD 方法来预测大量激酶中的反应性半胱氨酸和赖氨酸位点 以及其他蛋白质。 化学蛋白质组学，这个 R35 项目旨在填补工具开发和知识方面的空白。 我们将解决全原子 CpHMD 方法开发中剩余的挑战，以实现常规 我们将应用全原子 CpHMD 和其他最先进的 MD 进行研究。 阐明多药物转运蛋白和尿酸盐输出蛋白 ABCG2 机制的工具最后，我们将评估- 通过整合 CpHMD、机器学习和结构以及 化学蛋白质组学数据。

项目成果

Why is the Omicron main protease of SARS-CoV-2 less stable than its wild-type counterpart? A crystallographic, biophysical, and theoretical study of the free enzyme and its complex with inhibitor 13b-K.

为什么 SARS-CoV-2 的 Omicron 主要蛋白酶不如野生型对应物稳定？

• DOI: 10.1101/2024.03.04.583178
• 发表时间: 2024
• 期刊: bioRxiv : the preprint server for biology
• 作者: Ibrahim,Mohamed;Sun,Xinyuanyuan;deOliveira,ViniciusMartins;Liu,Ruibin;Clayton,Joseph;Kilani,HaifaEl;Shen,Jana;Hilgenfeld,Rolf
• 通讯作者: Hilgenfeld,Rolf