Genome-wide structure-based analysis of protein-protein interactions and networks

项目摘要 我们的实验室工作在计算结构生物学的一般领域，虽然它也包括一个 实验成分我们进行理论和计算研究，并开发软件工具， 我们的努力是以具有生物医学重要性的各种应用为指导的。在过去，我们已经阐明 蛋白质-蛋白质和蛋白质-核酸相互作用的结构和能量起源， 用于蛋白质结构预测，并检测基于蛋白质之间的新结构和功能关系， 它们的几何相似性。我们目前专注于两个不同的领域：结构信息的开发 在全基因组范围内预测蛋白质功能和细胞-细胞识别的分子基础。前 主题是当前提案的主题，其重点是预测蛋白质-蛋白质相互作用（PPI） 和蛋白质相互作用网络。我们的首要目标是提供一个结构化的视角， 系统生物学领域，从而填补了这一快速增长的生物医学研究领域的主要空白。 我们的研究计划来自于我们对PrePPI算法和相应的 人类PPI数据库。PrePPI提供PPI的蛋白质组范围内基于结构的预测，并发现 其他方法无法获得的关系。P-HIPSTer算法源自PrePPI， 1000种人类感染病毒的病毒-人PPI的类似信息。两种资源的可靠性 已经被实验验证，两者都揭示了新的生物学见解。PrePPI，与 其它PPI数据库是细胞环境独立的，并且例如不区分组织和肿瘤 类型为了应对这一挑战，我们开发了OncoSig算法，该算法使用机器学习方法来 将联合收割机PrePPI与来自患者基因组数据的调控相互作用相结合。产生肿瘤特异性的 PPI，称为SigSets，然后可以映射到上下文相关的PPI网络或SigMap。我们还 开发了将蛋白质结构空间与化合物空间联系起来的新方法。 目前的建议是在这些成就的基础上，制定新的方法， 应用于网络生物学。我们计划将PrePPI与来自遗传学的PPI信息整合在一起， 来自基因谱在许多条件下的相关性的相互作用（例如肿瘤类型、细胞系或 药物治疗）。这将为蛋白质相互作用提供前所未有的结构和背景依赖性观点 网络.其他计划包括将PrePPI扩展到非人类基因组和P-HIPSTer的扩展 细菌病原体。我们的总体愿景包括开发一套集成的软件工具， 这些数据库将推动尖端的生物医学应用。这些工具的范围从蛋白质- 蛋白质相互作用网络，结构衍生蛋白质功能注释以及网络生物学的链接 到化学化合物空间，这将表明网络内的药物目标，并提供小的线索， 可以针对单个蛋白质的分子。

Project Summary Our lab works in the general area of computational structural biology although it also includes an experimental component. We carry out theoretical and computational research and develop software tools, with our efforts being guided by a variety of applications of biomedical importance. In the past we have elucidated the structural and energetic origins of protein-protein and protein-nucleic acid interactions, developed methods for protein structure prediction, and detected novel structural and functional relationships between proteins based on their geometric similarity. We currently focus on two distinct areas: the exploitation of structural information to predict protein function on a genome-wide scale and the molecular basis of cell-cell recognition. The former topic is the subject of the current proposal which focuses on the prediction of protein-protein interactions (PPIs) and protein interaction networks. Our overarching goal isto provide a structure-informed perspective in multiple areas of systems biology, thus filling a major gap in this rapidly growing area of biomedical research. Our research plans are derived from our development of the PrePPI algorithm and corresponding database of human PPIs. PrePPI provides proteome-wide structure-based predictions of PPIs, and discovers relationships not obtainable from other methods. The P-HIPSTer algorithm, which is derived from PrePPI, offers analogous information for virus-human PPIs for 1000 human-infecting viruses. The reliability of both resources has been validated experimentally, and both have revealed novel biological insights. PrePPI, in common with other PPI databases, is cell-context independent and, for example, does not distinguish among tissue and tumor types. To address this challenge, we developed the OncoSig algorithm that uses machine learning methods to combine PrePPI with regulatory interactions from patient genomic data. The generation of tumor-specific lists of PPIs, called SigSets, can then be mapped onto a context-dependent PPI network, or SigMap. We have also developed novel methodologies that link protein structure space with chemical compound space. The current proposal builds on these accomplishments with new methodological developments and new applications to network biology. We plan to integrate PrePPI with PPI information derived from genetic interactions derived from the correlation of gene profiles across many conditions (e.g. tumor types, cell lines or drug treatments). This will provide an unprecedented structure- and context-dependent view of protein interaction networks. Other plans include the extension of PrePPI to non-human genomes and the extension of P-HIPSTer to bacterial pathogens. Our overall vision includes the development of an integrated set of software tools and databases that will advance cutting edge biomedical applications. These tools will range in scope from protein- protein interaction networks, structure-derived protein function annotation and to the linking of network biology to chemical compound space which will suggest druggable targets within networks and provide leads for small molecules that can target individual proteins.