De novo structure prediction of helical transmembrane proteins using RosettaTMH and pseudocontact shift NMR data

膜蛋白占所有细胞蛋白的近30%，占所有已知药物靶点的50%以上。它们在许多生理过程中发挥着关键作用，包括信号转导、代谢产物的运输和神经传递，但也参与了癌症、糖尿病和肥胖症等病理情况的发展。尽管膜蛋白具有巨大的医学相关性，但在蛋白质数据库中存在的所有蛋白质结构中，膜蛋白所占比例不到2%。尽管X射线结晶学和核磁共振波谱等结构生物学方法取得了重大进展，但膜蛋白结构的测定仍然是一个艰巨的挑战，通常只能得出有限的实验数据。即使在没有实验数据的情况下，计算方法也可以提供结构模型。通过这种方式，可以生成可溶蛋白质的结构模型；然而，用于膜蛋白质结构预测的计算方法还处于起步阶段。作为比较模型模板的同源蛋白的结构缺失，因此需要从氨基酸序列开始从头折叠。这些方法的成功，例如Rosetta膜，到目前为止仅限于小蛋白质(&lt；150个氨基酸)，但可以通过结合有限数量的实验结构信息来显著改进。拟议的研究项目的目的是开发一种新的创新算法，利用有限的伪接触位移(PCS)核磁共振数据来预测α-螺旋膜蛋白的从头结构。顺磁性稀土离子产生的PCSS产生的长程距离和取向信息可以指导结构预测算法和验证所生成的结构模型。该项目旨在开发一个将多氯联苯纳入RosettaTMH的计算框架。这个算法将把预测的跨膜螺旋(TMH)组装成膜蛋白拓扑结构。随后的肽片段插入完成了环区，并对蛋白质构象空间进行了详尽的采样。开发的名为RosettaTMH+PCS的方法的性能将在包含不同大小(2-15 TMH)和折叠的膜蛋白的基准集上进行评估。此外，RosettaTMH+PCS将通过对整合膜蛋白CNIH1的结构测定来进行实验测试，CNIH1是Chronicon蛋白家族的成员之一。因此，该项目旨在开发一种用于膜蛋白的稀土标记和使用基于TROSY的核磁共振实验来及时高效地收集PCS数据的实验方案，这将加快结构预测过程。该方法被认为是膜蛋白结构预测的通用途径，有望产生比以前的算法更准确的结构模型，特别是对于大的膜蛋白。

Membrane proteins constitute nearly 30% of all cellular proteins and more than 50% of all known drug targets. They play a key role in a plethora of physiological processes including signal transduction, transport of metabolites and neurotransmission, but are also involved in the development of pathological situations as cancer, diabetes and obesity. Despite of their enormous medical relevance, membrane proteins make up less than 2% of all protein structures present in the Protein Data Bank. Despite significant advances in methods of structural biology as X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR), membrane protein structure determination remains a formidable challenge often yielding limited experimental data. Computational methods can provide structural models even in the absence of experimental data. In this way, structural models of soluble proteins can be generated; however, computational methods for the structure prediction of membrane proteins are in their infancy. Structures of homolog proteins as template for a comparative modeling are missing, for which reason a de novo folding, starting from the amino acid sequence, is necessary. The success of these methods, e.g. Rosetta-Membrane, is so far limited to small proteins (<150 amino acids), but can be drastically improved by the incorporation of a limited amount of experimental structural information.The aim of the proposed research project is the development of a novel, innovative algorithm for the de novo structure prediction of alpha-helical membrane proteins using a limited set of pseudocontact shift (PCS) NMR data. PCSs are caused by paramagnetic lanthanide ions and give rise to long range distance and orientation information that can guide the structure prediction algorithm and verify the generated structural models. The project aims to develop a computational framework for the integration of PCSs into RosettaTMH. This algorithm will assemble predicted transmembrane helices (TMHs) into membrane protein topologies. Subsequent peptide fragment insertion completes loop regions and exhaustively samples the protein conformational space. The performance of the developed method, termed RosettaTMH+PCS, will be evaluated on a benchmark set comprising membrane proteins of different sizes (2-15 TMHs) and folds. Furthermore, RosettaTMH+PCS will be tested experimentally through the structure determination of the integral membrane protein CNIH1, a member of the chornicon protein family. Thereby, the project aims to develop an experimental protocol for the lanthanide tagging of membrane proteins and the time efficient collection of PCS data using TROSY-based NMR experiments, which will speed up the structure prediction process. The proposed method is conceived as a general route for membrane protein structure prediction and is expected to produce more accurate structural models, especially for large membrane proteins, than previous algorithms.