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

基于全基因组结构的蛋白质-蛋白质相互作用和网络分析

• 批准号: 10542796
• 负责人: BARRY H HONIG
• 金额: $ 40.5万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10542796
• 关键词: Address; Algorithms; Area; Bacterial Infections; Biological; Biology; Biomedical Research; Cell Line; Cell physiology; Cells; Chemicals; Databases; Development; Disease; Generations; Genes; Genetic; Genome; Goals; Human; Individual; Link; Malignant Neoplasms; Maps; Methodology; Methods; Modeling; Molecular; Nucleic Acids; Patients; Pharmaceutical Preparations; Pharmacotherapy; Protein Analysis; Proteins; Proteome; Research; Resources; Software Tools; Structure; Systems Biology; Tissues; Virus; Virus Diseases; Vision; Work; druggable target; genome-wide; genomic data; human disease; insight; link protein; machine learning method; novel; pathogenic bacteria; protein function; protein protein interaction; protein structure; protein structure prediction; small molecule; software development; structural biology; three dimensional structure; tool; tumor

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 is to 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.

项目摘要 我们的实验室致力于计算结构生物学的一般领域，尽管它还包括一个 实验组件。我们进行理论和计算研究，并开发软件工具， 我们的努力以各种具有生物医学重要性的应用为指导。在过去，我们已经澄清了 蛋白质-蛋白质和蛋白质-核酸相互作用的结构和能量来源，发展的方法 用于蛋白质结构预测，并检测到基于蛋白质的新的结构和功能关系 关于它们的几何相似性。我们目前专注于两个截然不同的领域：结构信息的利用 在全基因组范围内预测蛋白质的功能和细胞-细胞识别的分子基础。前者 该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。我们的首要目标是在多个方面提供了解结构的观点 因此，填补了这一快速增长的生物医学研究领域的一个重大空白。 我们的研究计划源于我们开发的PrePPI算法和相应的 人类PPI数据库。PrePPI提供基于蛋白质组全结构的PPI预测，并发现 用其他方法得不到的关系。派生自PrePPI的P-Hister算法提供了 病毒的类似信息--1000种人类感染病毒的人类PPI。这两种资源的可靠性 已经在实验上得到了验证，两者都揭示了新的生物学见解。PrePPI，与 其他PPI数据库是独立于细胞上下文的，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，它使用机器学习方法来 将PrePPI与来自患者基因组数据的调控交互作用结合起来。生成特定于肿瘤的列表 然后，可以将称为SigSet的PPI映射到上下文相关的PPI网络或SigMap。我们还有 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。 目前的提案建立在这些成就的基础上，有新的方法发展和新的 在网络生物学中的应用。我们计划将PrePPI与来自基因的PPI信息整合 来自多种条件(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一种前所未有的依赖于结构和上下文的观点 网络。其他计划包括将PrePPI扩展到非人类基因组和扩展P-Hister 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和 将推动尖端生物医学应用的数据库。这些工具的范围将从蛋白质- 蛋白质相互作用网络、结构衍生蛋白质功能注释及其与网络生物学的联系 到化学化合物领域，这将建议网络中的可用药目标，并为小型 可以针对单个蛋白质的分子。