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

蛋白质是一种分子机器，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。就像汽车和其他人们熟悉的机器一样，蛋白质也有对它们的功能至关重要的运动部件。然而，该领域通常只能使用实验方法捕捉蛋白质通常的样子的快照。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，特别是基于现有实验结构暴露出被认为被埋藏的蛋白质部分的运动。该团队将探索这样一种假设，即这些“神秘”表面可以在功能上发挥重要作用(例如，通过与其他蛋白质结合)，并提供一种控制蛋白质功能的新方法(例如，通过设计结合神秘表面并抑制或增强功能的小分子)。作为具体的例子，该项目将专注于埃博拉和冠状病毒的蛋白质。他们还将通过Folding@Home分布式计算项目让公众参与这项研究，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，从而成为公民科学家。在这个项目中，研究小组将探索暴露隐蔽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，无论是通过提供变构控制功能的手段，还是直接与自然结合伙伴接触。具体目标包括：1)评估团队最近在丝状病毒中发现的埃博拉病毒蛋白35(VP35)的干扰素抑制域(IID)中发现的隐蔽口袋的功能意义；以及2)评估在SARS-CoV-2冠状病毒尖峰中预测的团队急剧开放的功能意义。这项工作将通过研究团队开发的描述蛋白质采用的结构集合的强大组合方法来实现，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，该团队开发了用于揭开神秘口袋的自适应采样算法，用于推断突变如何调节远程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测试。其中包括用于测试预测的隐蔽表面的硫醇标记分析，以及用于测试突变和化合物对蛋白质功能影响的高通量筛选。该项目由分子和细胞生物科学部的分子生物物理组资助。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.