Decrypting the functional significance of cryptic surfaces by combining simulations and experiments

通过结合模拟和实验来解密神秘表面的功能意义

• 批准号: 2218156
• 负责人: Gregory Bowman
• 金额: $ 100万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218156&HistoricalAwards=false
• 关键词: Decrypting; functional; significance; cryptic; surfaces

Proteins are molecular machines that are responsible for many of the active processes we associate with life, from muscle contraction to sensing light and sound. Like cars and other familiar machines, proteins have moving parts that are essential to their function. However, the field is typically only able to capture a snapshot of what a protein typically looks like using experimental methods. The goal of this project is to combine computer simulations and experiments to gain a better understanding of proteins’ moving parts, particularly motions that expose parts of the protein thought to be buried based on existing experimental structures. The team will explore the hypothesis that these ‘cryptic’ surfaces can play important roles in function (e.g. by binding to other proteins) and provide a new means to control a protein’s function (e.g. by designing small molecules that bind cryptic surfaces and inhibit or enhance function). As specific examples, the project will focus on proteins from Ebola and coronaviruses. They will also engage the public in this research through the Folding@home distributed computing project, which enables anyone with a computer and an internet connection to become a citizen scientist by contributing their personal computing power to run simulations of proteins. This project will also involve members of blind community.In this project, the research team will explore whether excited states that expose cryptic surfaces play an important role in protein-protein and protein-nucleic acid interactions, either by providing a means to allosterically control function or by directly engaging with natural binding partners Specific aims include: 1) assessing the functional significance of a cryptic pocket that the team recently discovered in the interferon inhibitory domain (IID) of Ebola’s viral protein 35 (VP35) in filoviruses, and 2) assessing the functional significance of dramatic opening of the team predicted in the SARS-CoV-2 spike in coronaviruses. This work will be enabled by the powerful combination of approaches the research team has developed for characterizing the ensemble of structures that a protein adopts that includes physics-based simulations, machine learning, the Folding@home distributed computing platform, and biophysical experiments. For example, the team has developed adaptive sampling algorithms for uncovering cryptic pockets, deep learning methods for inferring how mutations modulate the structural preferences of distant functional sites, and a variety of experimental assays for testing computational predictions. These include thiol labeling assays to test predicted cryptic surfaces and high-throughput screens for testing the impact of mutations and compounds on protein function. This project is funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质是分子机器，负责我们与生命相关的许多活跃过程，从肌肉收缩到感知光和声音。就像汽车和其他熟悉的机器一样，蛋白质也有活动的部分，这些部分对它们的功能至关重要。然而，该领域通常只能通过实验方法捕捉到蛋白质典型外观的快照。该项目的目标是将计算机模拟和实验相结合，以更好地了解蛋白质的运动部分，特别是那些暴露出基于现有实验结构而被认为被掩埋的蛋白质部分的运动。该团队将探索这些“隐体”表面可以在功能中发挥重要作用的假设（例如，通过与其他蛋白质结合），并提供控制蛋白质功能的新方法（例如，通过设计结合隐体表面并抑制或增强功能的小分子）。作为具体的例子，该项目将重点关注埃博拉病毒和冠状病毒的蛋白质。他们还将通过Folding@home分布式计算项目吸引公众参与这项研究，该项目使任何拥有计算机和互联网连接的人都可以通过贡献个人计算能力来运行蛋白质模拟，从而成为一名公民科学家。这个项目也将包括盲人社区的成员。在这个项目中，研究小组将探索暴露隐藏表面的激发态是否在蛋白质-蛋白质和蛋白质-核酸相互作用中发挥重要作用，无论是通过提供一种变构控制功能的手段，还是通过直接与自然结合伙伴接触。1)评估该团队最近在丝状病毒中埃博拉病毒蛋白35 （VP35）的干扰素抑制结构域（IID）中发现的一个隐口袋的功能意义，以及2)评估该团队在冠状病毒SARS-CoV-2峰值中预测的戏剧性开放的功能意义。这项工作将通过研究团队为描述蛋白质所采用的结构集合而开发的强大方法组合来实现，这些方法包括基于物理的模拟、机器学习、Folding@home分布式计算平台和生物物理实验。例如，该团队开发了用于发现隐藏口袋的自适应采样算法，用于推断突变如何调节遥远功能位点的结构偏好的深度学习方法，以及用于测试计算预测的各种实验分析。其中包括用于测试预测的隐表面的硫醇标记分析和用于测试突变和化合物对蛋白质功能的影响的高通量筛选。该项目由分子和细胞生物科学部的分子生物物理集群资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Apolipoprotein E4 has extensive conformational heterogeneity in lipid-free and lipid-bound forms.

• DOI: 10.1073/pnas.2215371120
• 发表时间: 2023-02-14
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Stuchell-Brereton, Melissa D.;Zimmerman, Maxwell I.;Miller, Justin J.;Mallimadugula, Upasana L.;Incicco, J. Jeremias;Roy, Debjit;Smith, Louis G.;Cubuk, Jasmine;Baban, Berevan;DeKoster, Gregory T.;Frieden, Carl;Bowman, Gregory R.;Soranno, Andrea
• 通讯作者: Soranno, Andrea

Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network.

• DOI: 10.1038/s41467-023-36699-3
• 发表时间: 2023-03-01
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Meller, Artur;Ward, Michael;Borowsky, Jonathan;Kshirsagar, Meghana;Lotthammer, Jeffrey M.;Oviedo, Felipe;Ferres, Juan Lavista;Bowman, Gregory R.
• 通讯作者: Bowman, Gregory R.

Accelerating Cryptic Pocket Discovery Using AlphaFold.

• DOI: 10.1021/acs.jctc.2c01189
• 发表时间: 2023-07-25
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Meller, Artur;Bhakat, Soumendranath;Solieva, Shahlo;Bowman, Gregory R.
• 通讯作者: Bowman, Gregory R.