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

项目摘要 研究的总体目标是Shukla小组开发计算方法，以促进罕见的 蛋白质中的构象转变，并帮助指导实验设计，以验证计算机预测。在同等条件下- 特别地，我们应用这些计算方法来研究膜蛋白的功能调节，如膜蛋白， 转运蛋白和G蛋白偶联受体（GPCR）。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，并将这些方法应用于研究单胺转运蛋白、糖 转运蛋白和C类GPCR。 深度诱变，其中通过组合以下的体外选择来确定数万个突变效应： 序列变异与Illumina测序，是一种新兴的技术，间接询问和观察蛋白质 在活细胞中的构象;神经元C类G蛋白偶联受体的整合结构的解决， 通过深度诱变引导建模的活性构象是这种方法成功的一个突出例子。使用 深度诱变和分子动力学模拟相互通报，我们计划确定离子- 通过单胺转运蛋白以原子分辨率耦合神经递质输入。荧光底物使我们能够 使用转运蛋白突变体文库的基于荧光的分类来发现沿着整个渗透途径的突变 增加或减少底物输入。这些全面的突变景观将被用来解释和 支持/拒绝从模拟假设，包括离子耦合在基板运输调节的作用，提出 构象自由能景观中限制输入动力学的自由能障碍，以及钠-神经递质 共转运通过共享的胞质出口途径偶联。从深层突变扫描中发现的其他显著特征 (e.g.将进一步探索假定的调控位点），并将应用机器学习算法来转移 诱变信息相关的转运蛋白;预测的突变景观，然后将验证一个小的 提供信息的靶向突变体的数量。我们将进一步将序列与构象和代谢活性联系起来， 神经递质受体和糖转运蛋白。最后，我们计划通过使用 深度学习技术，这将有助于整合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果是否成功，将由算法的发展来衡量， 预测变异对蛋白质结构和功能的影响，阐明离子偶联调控的机制， 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。

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.