3D Probabilistic Profiles of Protein/Peptide Interactions

蛋白质/肽相互作用的 3D 概率图

• 批准号: 7051878
• 负责人: RICHARD Masten FINE
• 金额: $ 10.69万
• 依托单位: BIOCOMPUTING GROUP, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7051878
• 关键词: active sites; binding sites; combinatorial chemistry; computer program /software; computer system design /evaluation; conformation; mathematical model; model design /development; molecular probes; peptide library; protein localization; protein protein interaction; three dimensional imaging /topography; transcription factor

DESCRIPTION (provided by applicant): Peptides play a decisive role in many physiological processes and as a result are playing an increasing role in the development of vaccines and peptide, peptidomimetic, and small-molecule drugs. Because of an explosion of functional and structural-genomic data, there is an urgent need for new methods to analyze and predict peptide-protein interactions, to allow this data to be effectively distilled into drugs and vaccines. In this proposal, we describe a new solution to this problem, through development of a new approach to describe and predict peptide-protein interactions for structurally solved proteins using Markov Random Fields (MRF). Free energy minimization of the MRF yields a probability distribution called a 3D probabilistic peptide profile or 3D profile. The 3D profile probabilistically specifies types, locations, orientations, and conformations of amino acids within active sites that can be connected to form energetically favorable, preferably long, polypeptide chains. 3D profiles can then be used to (a) recognize peptides that will bind, or to (b) generate optimized combinatorial libraries of peptides for testing. MRF models incorporate detailed energetic information and can incorporate prior knowledge on the target system including (i) sequences of peptides known to bind; (ii) structurally determined peptide/protein complexes; (iii) protein active site mutagenic information; and (iv) NMR-derived distance constraints. Multiple MRF models can be combined to account for protein flexibility. MRF models are created by initially positioning amino-acid probes into a fine grid in the active site. Fast Belief Propagation methods then minimize the internal MRF free energy, by optimizing beliefs for specific amino acids at specific active site positions while adjusting their positions and orientations. Final peptide conformations and libraries are obtained by marginalizing the profile. The MRF approach is novel and has significant principled advantages over existing methods that docking individual peptides to a target. A robust software prototype has been implemented; initial results are given for a PDZ domain. In Phase I, we will complete the prototype and apply it to SH2/SH3 domains, PDZ domains, and MHC l/ll domains. In Phase II, we will optimize and utilize the methods to tackle problems of pharmaceutical and biodefense interest that may include development of substrate-competitive inhibitors to kinases or inhibitors of YopH, a Yersinia Pestis protein tyrosine phosphatase.

描述（由申请人提供）：肽在许多生理过程中发挥决定性作用，因此在疫苗和肽、肽模拟物和小分子药物的开发中发挥越来越大的作用。由于功能和结构基因组数据的爆炸，迫切需要新的方法来分析和预测肽-蛋白质相互作用，以使这些数据能够有效地提取到药物和疫苗中。在这个建议中，我们描述了一个新的解决方案，这个问题，通过开发一种新的方法来描述和预测结构解决蛋白质的肽-蛋白质相互作用，使用马尔可夫随机场（MRF）。MRF的自由能最小化产生称为3D概率肽谱或3D谱的概率分布。3D图谱概率性地指定活性位点内的氨基酸的类型、位置、取向和构象，所述活性位点可以连接以形成能量上有利的优选长的多肽链。3D图谱然后可用于（a）识别将结合的肽，或（B）产生用于测试的肽的优化组合文库。MRF模型包含详细的能量信息，并且可以包含关于靶系统的先验知识，包括（i）已知结合的肽序列;（ii）结构确定的肽/蛋白质复合物;（iii）蛋白质活性位点诱变信息;以及（iv）NMR衍生的距离约束。可以组合多个MRF模型来考虑蛋白质的灵活性。MRF模型是通过最初将氨基酸探针定位到活性位点的细网格中来创建的。快速信念传播方法，然后最大限度地减少内部MRF自由能，通过优化信念的特定氨基酸在特定的活性位点的位置，同时调整其位置和方向。最终的肽构象和文库是通过边缘化的配置文件。MRF方法是新颖的，并且与将单个肽对接到靶标的现有方法相比具有显著的原则性优势。一个强大的软件原型已经实施，初步结果给出了PDZ域。在第一阶段，我们将完成原型并将其应用于SH 2/SH 3结构域、PDZ结构域和MHC I/II结构域。在第二阶段，我们将优化和利用这些方法来解决制药和生物防御方面的问题，其中可能包括开发底物竞争性激酶抑制剂或YopH（鼠疫耶尔森氏菌蛋白酪氨酸磷酸酶）抑制剂。