3D Probabilistic Profiles of Protein/Peptide Interactions

描述（由申请人提供）：肽在许多生理过程中起着决定性作用，因此在疫苗和肽、拟肽和小分子药物的开发中发挥着越来越大的作用。由于功能和结构基因组数据的爆炸式增长，迫切需要新的方法来分析和预测肽-蛋白相互作用，以便将这些数据有效地提取到药物和疫苗中。在本提案中，我们描述了一种新的解决方案，通过开发一种新的方法来描述和预测使用马尔可夫随机场（MRF）结构解决的蛋白质的肽-蛋白质相互作用。MRF的自由能最小化产生称为3D概率肽剖面或3D剖面的概率分布。所述三维轮廓概率地指定活性位点内氨基酸的类型、位置、方向和构象，这些活性位点可以连接以形成能量上有利的、优选的长多肽链。然后，3D轮廓可用于(a)识别将结合的肽，或(b)生成用于测试的优化的肽组合库。MRF模型包含详细的能量信息，并且可以包含目标系统的先验知识，包括(i)已知结合的肽序列；（ii）结构确定的肽/蛋白复合物；（iii）蛋白质活性位点致突变信息；（iv）核磁共振衍生的距离约束。多个MRF模型可以结合起来解释蛋白质的灵活性。MRF模型是通过首先将氨基酸探针定位到活性部位的细网格中来创建的。快速信念传播方法通过优化特定活性位点上特定氨基酸的信念，同时调整它们的位置和方向，从而使内部MRF自由能最小化。最终的肽构象和文库是通过边缘化的轮廓得到的。MRF方法是新颖的，与现有的将单个肽对接到目标的方法相比，具有显著的原则优势。实现了一个健壮的软件原型；给出了PDZ域的初始结果。在第一阶段，我们将完成原型，并将其应用于SH2/SH3域、PDZ域和MHC 1 /ll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，其中可能包括开发对鼠疫杆菌蛋白酪氨酸磷酸酶YopH的激酶或抑制剂的底物竞争性抑制剂。

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.