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Manipulating and predicting the unfolded ensembles of disordered proteins

操纵和预测无序蛋白质的未折叠整体

基本信息

批准号：
10224244
负责人：
Patricia Louise Clark
金额：
$ 34.95万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10224244
关键词：
Address Adopted Affect Amino Acid Sequence Amino Acids Behavior Cell physiology Charge Collaborations Complex Computer Models Computing Methodologies Consensus Data Data Analyses Deuterium Development Dimensions Disease Evolution Fluorescence Resonance Energy Transfer Foundations Geometry Goals Gram-Negative Bacteria Hour Huntington Disease Hydrogen Hydrogen Bonding Hydrophobicity In Vitro Knowledge Lead Measurement Measures Mediator of activation protein Membrane Methods Modeling Molecular Molecular Conformation Mutation Pattern Physiological Play Positioning Attribute Procedures Property Protein Secretion Proteins Radial Research Roentgen Rays Role Science Solvents Stretching Structure Sum Surface System Testing Vertebral column Virulence Walking Water amyloid formation biophysical techniques experimental study improved in vivo innovation maltose-binding protein molecular recognition non-Native novel periplasm pertactin physical property polyglutamine polypeptide predictive modeling preference protein folding protein function simulation tool

项目摘要

PROJECT SUMMARY The physical properties of intrinsically disordered proteins (IDPs) affect their positive (functional) and negative (disease-causing) roles in cell function. Yet despite intense effort, we still lack a predictive understanding of the physical properties that govern whether a given polypeptide chain sequence will adopt an expanded or collapsed conformational ensemble under physiological conditions – and by extension, how collapse affects protein function. The Sosnick and Clark labs have formed a collaboration to develop precisely this understanding. This project consists of experimental studies of IDPs tightly integrated with new data analysis procedures and computational modeling tools. This project builds on our recent findings with “PNt”, a 334 residue IDP that under physiological conditions adopts an expanded ensemble of conformations well approximated by a self-avoiding random walk, despite having an amino acid content that, according to the current paradigm, should lead to a collapsed, self-associated state. We hypothesize that current models fail to predict the behavior of PNt due to current knowledge gaps regarding which sequence patterns, beyond global sequence composition, lead to collapse. We propose that deviations from an expanded state, e.g., adopting a collapsed globule, are due to specific sequence patterns including local stretches of hydrophobic residues. We will test this hypothesis by reordering (“shuffling”) the amino acid sequence of PNt to induce collapse, and likewise shuffle the amino acid sequence of maltose binding protein (MBP) to promote expansion of its denatured state, which we recently demonstrated is highly collapsed. We will use a battery of biophysical methods (SAXS, hydrogen-deuterium exchange, NMR) to measure the extent of local versus global collapse, assessing the sensitivity of conformational ensembles to subtle changes in sequence order, and the relationship between collapse and hydrogen bonding. We will determine which hydrophobicity scales yield the best predictions of experimental results for polypeptide chain collapse, and use the effects of shuffling to parameterize simulations. Finally, we will test the impact of altering IDP collapse on protein function in vivo, specifically the efficient secretion of autotransporter virulence proteins to the surface of Gram-negative bacteria. Our overall goal is to accurately predict the conformational ensemble for a user-inputted amino acid sequence and solvent condition.

项目摘要内在无序蛋白质（IDP）的物理性质影响其正性（功能性）和负性（功能性）。（致病）细胞功能的作用。然而，尽管付出了巨大的努力，我们仍然缺乏对未来的预测性理解。控制给定的多肽链序列是否将采用扩展的或在生理条件下的崩溃构象系综-以及通过扩展，崩溃如何影响蛋白质功能索斯尼克和克拉克实验室已经形成了一个合作，以开发正是这一点认识该项目包括对国内流离失所者的实验性研究，与新的数据分析紧密结合程序和计算建模工具。这个项目建立在我们最近对"PNt"的发现之上，残基IDP在生理条件下很好地采用扩展的构象集合通过自避免随机游走来近似，尽管具有氨基酸含量，根据当前的范式，应该导致一个崩溃的，自我关联的国家。我们假设目前的模型无法预测PNt的行为，由于目前的知识差距，关于哪些序列模式，超越全球序列组成，导致崩溃。我们建议，偏离扩展状态，例如，采用塌缩的小球是由于特定的序列模式，包括疏水残基的局部延伸。我们将通过重新排序（"改组"）PNt的氨基酸序列以诱导崩溃来测试这一假设，同样，重组麦芽糖结合蛋白（MBP）的氨基酸序列，以促进其扩增，变性状态，我们最近证明了它是高度坍缩的。我们将使用一系列生物物理学方法（SAXS，氢-氘交换，NMR）来测量局部相对于整体塌陷的程度，评估构象系综对序列顺序的细微变化的敏感性，崩溃和氢键之间的关系。我们将确定哪些疏水性尺度产生最好的预测实验结果的多肽链崩溃，并使用改组的影响，参数化模拟最后，我们将测试改变IDP崩溃对体内蛋白质功能的影响，特别是自转运蛋白毒力蛋白的有效分泌到革兰氏阴性菌的表面，细菌我们的总体目标是准确地预测用户输入的氨基酸的构象系综顺序和溶剂条件。