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.

项目概要 Shukla 小组研究的总体目标是开发计算方法，以促进稀有物质的研究 蛋白质的构象转变并有助于指导实验设计以验证计算机预测。 特别是，我们应用这些计算方法来研究膜蛋白的功能调节，例如膜 在这里，我们建议开发基于迁移学习的方法。 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蛋白、糖 转运蛋白和 C 类 GPCR。 深度诱变，通过结合体外选择来确定数以万计的突变效应 Illumina 测序的序列变体是一种间接询问和观察蛋白质的新兴技术 活细胞中的构象；解析神经元 C 类 G 蛋白偶联受体的整合结构 通过深度诱变引导建模形成的活性构象是这种方法成功的一个突出例子。 深层诱变和分子动力学模拟相互告知，我们计划确定离子- 单胺转运蛋白在原子分辨率下耦合神经递质输入使我们能够 使用基于荧光的转运蛋白突变体库分类来查找整个渗透途径中的突变 这些综合突变景观将用于解释和减少底物输入。 支持/拒绝模拟假设，包括离子耦合在底物传输调节中的作用，提出 无构象能量景观中限制输入动力学的自由能壁垒，以及钠神经递质如何 共转运通过共享的胞质退出途径耦合，深层突变扫描产生的其他显着特征。 （例如假定的监管站点）将被进一步探索，并将应用机器学习算法来转移 相关转运蛋白的诱变信息；然后将由小型验证预测的突变景观 我们将进一步将序列与代谢型构象和活性联系起来。 最后，我们计划使用神经递质受体和糖转运蛋白来改进所提出的传输算法。 深度学习技术，这将有助于集成来自模拟数据集和多个深度学习的特征 突变扫描以告知突变对相关蛋白质或任务的影响。 所提出的研究计划的成功与否将通过开发能够准确地预测结果的算法来衡量 预测对蛋白质结构和功能的变异影响，阐明离子耦合调节的机制 神经递质转运、糖转运蛋白的选择性机制和 C 类 GPCR 的激活机制。