对域/肽识别中线性模体的结构生物信息学分析

Linear motifs, also known as short linear motifs (SLiMs), are short stretches of protein sequence that are defined by specific arrangement pattern of amino acids and are necessary for the biological function of their parent proteins.Previously, most studies around SLiMs have been performed at primary sequence level. These works, however, were incapable of revealing molecular mechanism underlying the sequence pattern and biological behavior of SLiMs.In this project, we attempt to address these issues by systematically dissecting and comprehensively investigating the three-dimensional structure data of SLiM-mediated protein complexes deposited in the Protein Data Bank (PDB) database, aiming to elucidate physicochemical nature and structural basis hidden under the apparent sequence pattern of SLiMs, and to develop a series of new and powerful methods/tools to analyze, characterize and predict SLiMs in diverse metastable protein-protein interactions. The content of this project could be divided into three dependent sections, that is, (i) structural analysis: in order to gain structural knowledge and molecular information about SLiMs, we will first mine and analyze all the protein complex structure data in the PDB systematically, (ii) methodological study: the reaped knowledge and information would be utilized to develop new methods and tools for treating SLiMs; on this basis, a integrated platform will be established for automatically designing and optimizing mimic peptide inhibitors to interfere corresponding metastable protein-protein interactions, and (iii) experimental investigation: we will employ this platform to design several high-affinity peptide ligands to the third PDZ domain (PDZ3) of postsynaptic density 95 protein (PSD-95); these designed peptides will be chemically synthesized by using Fmoc-solid phase peptide synthesis (Fmoc-SPPS) technique, and their affinity Kd to PSD-95 PDZ3 will be determined using a fluorescence polarization-based competitive binding assay. Furthermore, findings and conclusions stemming from the experimental investigation would also be used to correct theoretical models and computational strategies.

线性模体是在序列局部范围决定蛋白质特定生物学功能的氨基酸特征排列模式；先前对其研究多在一级序列水平开展，却无法阐释其行使生物职能的深层次分子基础。本项目拟利用目前丰富的蛋白质三维结构数据对线性模体在域/肽识别和相互作用中所扮演的角色进行深度探索，目的是揭示隐藏于线性模体序列表象之下的物理化学本质，进而在结构层次发展用于其生物信息分析处理的新方法和新工具。研究内容主要包括三个依次递进的方面，即①结构分析：系统挖掘PDB数据库中记录的域/肽复合物结构数据，获得其中线性模体的结构知识和分子信息；②方法开拓：利用挖掘所得的知识和信息进行线性模体相关方法学研究，并以此搭建用于域/肽识别抑制剂设计的计算平台；③实验论证：以PSD-95蛋白的PDZ3结构域为具体对象，利用新收获的理论知识和方法策略设计、合成、测试和构效研究一系列对该域具有高亲和力的肽配基；并利用实验结论反回去修正理论模型,实现干湿相济。

近年来，发现、注释和分析各类线性模体以及它们参与的域/肽相互作用引起了相关领域研究者的浓厚兴趣，在本项目的支持下，我们主要研究了引发宫颈癌的高危型人乳头瘤病毒HPV中关键致癌蛋白所涉及的相关作用。结合分子模拟与分子对接技术，研究内容包括但不局限于如下：.对比了HPV16/18 E6蛋白与小分子抑制剂RITA和姜黄素的结合模式的研究，发现在HPV16/18 E6蛋白41~49残基形成的疏水口袋RITA和姜黄素具有一致的构象和相似的结合模式，可能是两者产生抑制作用的主要位点。这一结果表明高危型HPVE6蛋白作为药物靶点的可行性，为治疗宫颈癌特异性小分子药物的研究和设计提供了理论指导。. 在对治疗宫颈癌天然药物的调研中，收集整理了八种黄酮类天然产物并将它们对高危型HPV16/18 E6蛋白的亲和性进行了分子对接研究，对接结果反映了有利于增强抗宫颈癌活性的基团或原子。. 同时放眼于现下最流行的针对癌症的生物疗法，我们综述了PD-1/PD-L1信号通路抑制剂在宫颈癌治疗中的研究进展..此外，为了使分析更全面，数据更翔实，我们还对引起宫颈病变的核心基因和关键miRNA的生物信息学进行了综合分析，确定HPV16持续感染进展为CIN3 +的生物标志物。结果鉴定出了457个差异表达基因（DEGs），包括371个上调基因和86个下调基因。结合miRNA的预测靶标，鉴定出23种DE-miR。靶基因主要富集单核细胞聚集的正调节、炎症反应、VEGF信号通路等密切相关的途径，在PPI网络和miRNA-mRNA相互作用整合的调控网络中，当然所有结果均有待于后续试验的进一步论证。.后续研究中，结合线性模体研究中的生物信息学技术，我们希望将生物信息学的实验结果进一步引申至实验室实验中来，能够更好实现生物信息辅助实验设计的“干湿相济”模式，更好服务于实验。