Elucidating sequence, structural and dynamic basis of the functional regulation of membrane proteins

阐明膜蛋白功能调节的序列、结构和动态基础

• 批准号: 10275155
• 负责人: Diwakar Shukla
• 金额: $ 35.46万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10275155
• 关键词: Algorithms; Carrier Proteins; Cells; Computer Models; Computing Methodologies; Coupled; Coupling; Data; Data Set; Development; Emerging Technologies; Fluorescence; Free Energy; G-Protein-Coupled Receptors; In Vitro; Investigation; Ion Cotransport; Ions; Kinetics; Libraries; Measures; Membrane Proteins; Membrane Transport Proteins; Methods; Modeling; Molecular Conformation; Mutagenesis; Mutation; Neurons; Neurotransmitter Receptor; Neurotransmitters; Pathway interactions; Protein Conformation; Proteins; Psychological Transfer; Regulation; Research; Research Project Grants; Resolution; Role; Site; Sodium; Sorting - Cell Movement; Structure; Techniques; Variant; algorithm development; base; conformational conversion; deep learning; design; experimental study; improved; in silico; machine learning algorithm; molecular dynamics; monoamine; mutant; mutation screening; neurotransmitter transport; programs; protein function; protein structure function; simulation; success; sugar

Project Summary The overall aim of the research is Shukla group is to develop computational methods that facilitate investigation of rare conformational transitions in proteins and help guide the design of experiments to validate the in silico predictions. In par- ticular, we apply these computational methods to investigate functional regulation of membrane proteins such as membrane transporters and G-protein coupled receptors (GPCRs). Here, we propose development of transfer learning based methods to predict the effect of mutations on protein function and apply these methods to investigate monoamine transporters, sugar transporters and Class C GPCRs. Deep mutagenesis, whereby tens of thousands of mutational effects are determined by combining in vitro selections of sequence variants with Illumina sequencing, is an emerging technology for indirectly interrogating and observing protein conformations in living cells; the solving of an integrative structure of a neuronal class C G protein-coupled receptor in an active conformation by deep mutagenesis-guided modeling is one prominent example of this approach's success. Using deep mutagenesis and molecular dynamics simulations to inform each other, we plan to determine the mechanism of ion- coupled neurotransmitter import by monoamine transporters at atomic resolution. Fluorescent substrates have enabled us to use fluorescence-based sorting of libraries of transporter mutants to find mutations along the entire permeation pathway that increase or decrease substrate import. These comprehensive mutational landscapes will be used to interpret and support/reject hypotheses from simulations, including the role of ion-coupling in substrate transport regulation, proposed free energy barriers in the conformational-free energy landscape that limit import kinetics, and how sodium-neurotransmitter symport is coupled by a shared cytosolic exit pathway. Other notable features that arise from the deep mutational scans (e.g. putative regulatory sites) will be further explored, and a machine learning algorithm will be applied to transfer mutagenesis information to related transporters; the predicted mutational landscapes will then be validated by a small number of informative targeted mutants. We will further relate sequence to conformation and activity in metabotropic neurotransmitter receptors and sugar transporters. Finally, we plan to improve the proposed transfer algorithms by using deep learning techniques, which will facilitate integration of features derived from simulation datasets and multiple deep mutational scans to inform the effect of mutations on related proteins or tasks. The success of the proposed research program of results will be measured by development of algorithms that can accurately predict the variant effects on protein structure and function, elucidation of the mechanisms of ion-coupled regulation of neurotransmitter transport, selectivity mechanisms in sugar transporters and activation mechanisms of class C GPCRs.

项目摘要 这项研究的总体目标是Shukla集团开发出便于研究稀有物质的计算方法 蛋白质的构象转变，并有助于指导实验设计，以验证电子预测。在票面上- 特别是，我们应用这些计算方法来研究膜蛋白如膜的功能调节 转运蛋白和G蛋白偶联受体。在这里，我们提出了基于迁移学习的方法的发展 预测突变对蛋白质功能的影响，并应用这些方法研究单胺转运体，糖 运输商和C类GPCR。 深度诱变，通过结合体外选择的几种基因来确定成千上万的突变效应 Illumina测序技术是一种用于间接询问和观察蛋白质的新兴技术 活细胞的构象；解决神经元类C G蛋白偶联受体的整合结构 通过深度突变引导的建模的活跃构象是这种方法成功的一个突出的例子。vbl.使用 深度诱变和分子动力学模拟相互告知，我们计划确定离子- 由单胺转运体以原子分辨率输入的偶联神经递质。荧光衬底使我们能够 使用基于fl荧光的转运蛋白突变体文库的分类来沿着整个渗透途径来fi和突变 增加或减少基材进口。这些综合的变异景观将被用来解释和 支持/拒绝来自模拟的假设，包括离子耦合在底物运输调节中的作用 构象自由能格局中限制进口动力学的自由能障碍，以及钠神经递质如何 Symport通过一条共享的胞质退出途径连接。从深度突变扫描中产生的其他显著特征 (例如，假定的监管地点)将进一步探索，并将应用机器学习算法进行转移 突变信息到相关的转运体；预测的突变景观将通过一个小的 提供信息的目标突变体的数量。我们将进一步将序列与新陈代谢的构象和活动联系起来 神经递质受体和糖转运体。最后，我们计划通过使用以下方法来改进提出的传输算法 深度学习技术，这将促进从模拟数据集和多个深度获取的特征的集成 突变扫描，告知突变对相关蛋白质或任务的影响。 拟议的结果研究计划的成功将通过开发能够准确地 预测对蛋白质结构和功能的不同影响，阐明离子偶联调节的机制 神经递质转运、糖转运体的选择性机制和C类GPCRs的激活机制。