The Experimental Energy Landscape and Protein Function

我们的亲本R01致力于研究局部展开在调节生物功能中的作用 在过去的20年里，这项资助的一个重要目标是使用实验性的 数据来发展和完善我们的蛋白质结构波动的演变模型，以便研究 新功能和新疾病的演变。本项目增刊的目标是利用最近的 这一发现直接源于这个项目，借此我们能够确定“抗原模仿”的作用 在SARS COV-2(新冠肺炎的病原体)中，导致对特定蛋白质的自身免疫 在感染患者的子集中。我们独特的方法是(据我们所知)唯一的预测模型 使我们能够识别结构和化学上不同的蛋白质之间的热力学相似性 序列。我们证明，通过为每一种蛋白质产品生成热力学指纹 SARS-COV-2病毒，我们可以将指纹与整个人类蛋白质组的指纹进行比较 并识别统计上有意义的匹配。我们最初的假设是相似的热力学 指纹将通过常见的多克隆抗体反应来识别。为了挑战这一假设， 我们确定了许多SARS-COV-2蛋白的高度同源性匹配，其中一个 示例蛋白质是orf10，预计它与人类蛋白质CD53具有相同的特征。直接 为了验证这一初步预测，我们利用了一种商业上可用的“芯片上的蛋白质组”技术来筛选 Orf10特异性多克隆抗体与每一种表达的人类蛋白结合的能力。 值得注意的是，orf10的多克隆抗体与CD53发生交叉反应，从而验证了 我们的假设。作为“长冠状病毒病”的一种可能的病原体，涉及病毒的高发病率- 对于诱导自身免疫，我们的方法非常适合从机制上解决这个问题。我们的 该方法不仅识别哪些人类蛋白质与每种病毒蛋白质相似，因此哪些是 潜在的自动免疫候选者，它还识别最负责 高度相似。这种能力不仅为医学界提供了一个目标的起点 机械学研究，它允许我们和其他人调查人类基因变异的影响 人口。在这里，我们将；1)使用我们的方法来识别SARS-COV-2之间的所有预测匹配 蛋白质和人类蛋白质组，以及2)用商业方法实验验证这些预测 可用的“芯片上的蛋白质组”技术。所有的计算匹配和实验验证 数据将在我们完善的网络服务器上提供。

Our parent R01 is focused on investigating the role of local unfolding in mediating the biological function of proteins, and a significant goal over the past 20 years of this grant has been the use of experimental data to develop and refine our evolving model of protein structural fluctuations so as to investigate the evolution of new function and disease. The goal of this project supplement is to capitalize on a recent discovery directly stemming from this project, whereby we are able to identify the role of “antigen mimicry” in the SARS COV-2 (the causative agent of COVID-19), which results in autoimmunity to specific proteins in a subset of infected patients. Our unique approach is (to our knowledge) the only predictive model that allows us to identify thermodynamic similarity between structurally and chemically different protein sequences. We show that by generating a thermodynamic fingerprint for each protein product of the SARs-COV-2 virus, we can compare the fingerprint with the fingerprints of the entire human proteome and identify statistically significant matches. Our original hypothesis was that similar thermodynamic fingerprints will be recognized by a common polyclonal antibody response. To challenge this hypothesis, we identified a number of high-identity matches for numerous proteins of the SARS-COV-2, one such example protein is orf10, which is predicted to share a signature with the human protein CD53. To directly test this initial prediction, we utilized a commercially available “proteome on a chip” technology to screen for the ability of orf10-specific polyclonal antibodies to bind with every expressed human protein. Remarkably, polyclonal antibodies to orf10 cross-reacted specifically with CD53, thus validating our hypothesis. As one possible disease etiology of “long-COVID” involves the high instances for viral- induced autoimmunity, our approach is uniquely suited to address this issue mechanistically. Our approach not only identifies which human proteins are similar with each viral protein and thus which are potential candidates for auto-immunity, it also identifies the sequence elements most responsible for the high similarity. This capability not only provides the medical community with a starting point to target mechanistic studies, it allows us and others to investigate the effects of genotypic variation with the human population. Here we will; 1) use our approach to identify all predicted matches between SARS-COV-2 proteins and the human proteome, and 2) experimentally test these predictions using commercially available “proteome on a chip” technology. All of the computed matches and the experimental validation data will be made available on our well-established web-server.