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Predicting Integral Membrane Protein Expression from Multiscale Simulation Model of Translocon-Mediated Membrane Integration

从易位子介导的膜整合的多尺度模拟模型预测整合膜蛋白表达

基本信息

批准号：
9026487
负责人：
Reid Van Lehn
金额：
$ 2.13万
依托单位：
CALIFORNIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-01-07 至 2016-05-20
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9026487
关键词：
Address Biochemical Biological Biological Process Cells Cellular Membrane Cereals Crystallization Databases Disease Docking Drug Targeting Electrostatics Elements Employee Strikes Engineering Environment Escherichia coli Eukaryota Eukaryotic Cell Goals Health Homologous Gene Homologous Protein Human Hydrophobicity Integral Membrane Protein Kinetics Laboratories Lead Lipids Literature Measurable Measurement Measures Mediating Membrane Membrane Proteins Methods Modeling Modification Mutation Outcome Pathway interactions Peptides Pharmacologic Substance Point Mutation Prokaryotic Cells Property Protein Biosynthesis Protein Engineering Proteins Proteome Protocols documentation Ribosomes Sampling Signal Transduction Signaling Protein Solvents Sorting - Cell Movement Structure System Testing Thermodynamics Time Translations Water Work Yeasts cost improved insight membrane model models and simulation mutant novel overexpression physical process polypeptide protein expression protein function protein structure public health relevance research study simulation structural biology

项目摘要

DESCRIPTION (provided by applicant): Integral membrane proteins embed within the cellular membrane and perform crucial functions to modulate cellular transport, signaling, and anchoring. Although 20-30% of the proteins in the human proteome are membrane proteins, they represent less than 1% of the crystal structures in the Protein Data Bank despite of their biological importance. Increasing the number of solved membrane protein structures is extremely important to understand protein function, understand disease consequences due to protein misassembly, and increase potential drug targets. However, membrane proteins are insoluble in water and as a result their structural characterization is greatly inhibited by their inability to be reliably crystallized outside of the membrane environment. It is thus necessary to find novel methods to overexpress membrane proteins in order to facilitate structure determination. The hypothesis of this project is that protein expression can be increased by enhancing the efficiency with which membrane proteins are integrated into the bilayer during translation. In both prokaryotic and eukaryotic cells, proteins are inserted into the membrane during translation by the Sec translocon protein-conducting channel. In eukaryotes, the ribosome docks to the cytoplasmic opening of the channel and transfers a nascent polypeptide chain into the translocon during translation. Depending on the recognition of particular signals, the protein is directed to either integrate with the membrane or translocate through it. However, correctly directing nascent peptides to one of these outcomes depends on both thermodynamic and kinetic factors and as a result proteins are frequently sorted incorrectly or misassembled. If protein expression requires the correct integration of nascent membrane proteins, then designing protein mutants that enhance integration efficiency should increase expression yields by correcting errors in translocon-mediated insertion. We propose a hierarchical, multiscale simulation strategy to model the full pathway of co-translational membrane protein integration. The goals of this work are to: 1) predict experimentally-measurable protein expression yields using a coarse-grained model of membrane integration and 2) engineer mutants to optimize protein expression. To accomplish these goals, we will optimize a recently developed coarse-grained model that is able to reach the minute-long timescales necessary for quantifying integration efficiency. Results of this model will be compared to protein expression measurements in E. coli using protein homologs of different expression efficiency. Comparisons between simulation and experiments will be used to identify specific protein features that enhance expression. Iteratively refining the model and incorporating additional system properties, such as lipid composition and protein structure, will improve these quantitative predictions. These findings will have a broad impact on structural biology, drug targeting, and membrane protein characterization by significantly reducing the barrier to membrane protein crystallization.

描述（由申请人提供）：整合膜蛋白嵌入细胞膜内，并执行调节细胞转运、信号传导和锚定的关键功能。尽管人类蛋白质组中20-30%的蛋白质是膜蛋白，但它们在蛋白质数据库中的晶体结构中所占的比例不到1%，尽管它们具有生物学重要性。增加溶解的膜蛋白结构的数量对于理解蛋白质功能、理解由于蛋白质错误组装引起的疾病后果以及增加潜在的药物靶点是极其重要的。然而，膜蛋白不溶于水，因此它们的结构表征由于它们不能在膜环境外可靠地结晶而受到极大抑制。因此，有必要找到新的方法来过表达膜蛋白，以促进结构确定。该项目的假设是，可以通过提高膜蛋白在翻译过程中整合到双层中的效率来增加蛋白质表达。在原核和真核细胞中，蛋白质在翻译过程中通过Sec易位蛋白传导通道插入膜中。在真核生物中，核糖体停靠在通道的细胞质开口处，并在翻译过程中将新生多肽链转移到易位子中。根据对特定信号的识别，蛋白质被引导与膜整合或穿过膜移位。然而，正确引导新生肽到这些结果之一取决于热力学和动力学因素，因此蛋白质经常被错误地分选或错误地组装。如果蛋白质表达需要新生膜蛋白的正确整合，那么设计增强整合效率的蛋白质突变体应该通过纠正translocon介导的插入中的错误来增加表达产量。我们提出了一个分层的，多尺度的模拟策略，以模拟完整的共翻译膜蛋白整合的途径。这项工作的目标是：1）使用膜整合的粗粒度模型预测实验可测量的蛋白质表达产量和2）工程突变体以优化蛋白质表达。为了实现这些目标，我们将优化最近开发的粗粒度模型，该模型能够达到量化集成效率所需的分钟级时间尺度。该模型的结果将与E.大肠杆菌中使用不同表达效率的蛋白质同源物。模拟和实验之间的比较将用于识别增强表达的特定蛋白质特征。迭代地改进模型并结合额外的系统属性，如脂质组成和蛋白质结构，将改善这些定量预测。这些发现将通过显着降低膜蛋白结晶的障碍，对结构生物学、药物靶向和膜蛋白表征产生广泛影响。