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.

项目总结/摘要 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）确定的纳米粒子的数量呈指数增长。阿尔法折叠 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。从一个三- 三维蛋白质结构，基于物理的分子动力学（MD）模拟提供了一个原子级的观点， 蛋白质的运动在计算能力指数级增长的推动下，MD正在成为一个强大的工具， 结构-功能研究和辅助靶向药物发现。 尽管有上述进展，pH驱动和质子耦合的动态亲核的分子机制， cess仍然知之甚少。这是因为在大多数实验结构中，质子的位置是无法分辨的。 理论和常规MD没有描述质子耦合动力学或明确地解释溶液pH。 这样的例子是人ATP结合盒亚家族G成员2蛋白（ABCG 2），其有助于 癌症耐药性以及尿酸盐的肾排泄。 虽然整个蛋白质组的高分辨率结构可能很快就会出现，但大部分蛋白质组的蛋白质组结构可能会被破坏。 目前认为TEome是不可药用的，即，传统的药物发现努力难以解决。发展 化学蛋白质组学平台，用于发现人类细胞中的反应性和可配体的半胱氨酸和赖氨酸 Lines承诺将大大扩展可药物化的空间。尽管如此， 蛋白质组的大部分仍然未被探索，并且缺乏系统的知识。 在之前的R 01资助期内，申群在GPU的开发上取得了重大进展- 加速连续恒定pH MD（CpHMD）方法和应用，以阐明质子耦合结构- 各种乙酰基蛋白酶、半胱氨酸蛋白酶、激酶以及穆尔- tidrug外排泵AcrB、钠质子反向转运蛋白NhaA和μ-阿片受体。沈氏集团也已- 建立并应用CpHMD方法预测大量激酶中的半胱氨酸和赖氨酸活性位点 和其他蛋白质。在这一进展的基础上，利用结构基因组学的大量数据， 化学蛋白质组学，这个R35项目旨在填补上述工具开发和知识的空白。 我们将解决在全原子CpHMD方法的发展中剩余的挑战，以实现常规的 质子耦合动力学过程的研究。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尿酸盐输出蛋白ABCG 2机制的工具。最后，我们将评估- 通过整合CpHMD、机器学习和结构， 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