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.

项目摘要/摘要 我们对生物学和人类疾病的理解正取得了重大飞跃 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 由单个颗粒低温电子显微镜（Cryo-EM）确定的呈指数增长。 Alphafold 神经网络可能很快为整个人类蛋白质组提供高分辨率结构。从三个开始 尺寸蛋白质结构，基于物理学的分子动力学（MD）模拟提供了原子水平的视图 蛋白质的运动。随着计算能力的指数增长的推动，医学博士正在成为一个强大的工具 结构功能研究并协助基于目标的药物发现。 尽管取得了推理的进展，但pH驱动和质子耦合动力学的分子机制 塞斯斯仍然很了解。这是因为在大多数实验结构中未解决质子位置 - Tures和常规MD不会描述质子耦合的动力学或明确解释溶液pH值。一 这样的例子是人类ATP结合纸盒亚家族G成员2蛋白（ABCG2），这有助于 癌症耐药性和尿酸肾脏排泄。 尽管整个蛋白质组的高分辨率结构可能很快就会可用，但主要的一小部分 目前，Teome被认为是不可能的，即对传统的药物发现工作棘手。发展 用于发现人类细胞中反应性和可韧带半胱氨酸和赖氨酸的化学蛋白质组学平台 线条有望显着扩大可吸毒的空间。尽管如此， 大部分蛋白质组仍然是出乎意料的，并且缺乏系统的知识。 在上一个R01赠款期间，沉集体在GPU的发展方面取得了重大进展 加速连续恒定pH MD（CPHMD）方法和应用以阐明质子耦合结构 - 各种天冬氨酸蛋白酶，半胱氨酸蛋白酶，激酶以及mul-的动态功能功能关系 Tidrug外排泵ACRB，钠 - 普罗替型抗植物NHAA和µ阿片受体。沉集团也有 在大量激酶中预测反应性半胱氨酸和赖氨酸位点的CPHMD方法 和其他蛋白质。基于进度并利用结构性基因组学和大量数据 化学蛋白质组学，这个R35项目旨在填补工具开发和知识的大致空白。 我们将解决全部CPHMD方法开发的剩余挑战，以实现例行程序 质子耦合动态过程的研究。我们将应用全原子CPHMD和其他最先进的MD 阐明多药转运蛋白和Urate Exporter 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