Combined computational and structural studies to create novel macromolecular recognition properties

项目摘要 大分子结合相互作用和复合物的设计，以及结合的相应改变 特异性，是一个具有挑战性的奋进，仍然是计算方法的障碍。都是如此 用于产生蛋白质-蛋白质复合物（其由主要由以下因素建立的生物学变化驱动： 立体化学互补性，平衡大的竞争熵变化）和重新设计 蛋白质-DNA复合物（其严重依赖于DNA弯曲、氢键、静电 接触，以及整个分子界面中溶剂和抗衡离子的存在）。 在过去的几年里，我们与几个计算小组合作，帮助开发和 验证用于蛋白质-蛋白质识别、蛋白质-DNA 识别和蛋白质-小分子识别。这些研究为一些新的 计算工程方法，包括结合蛋白质联合收割机从头设计的混合策略 折叠和结合位点，“Rotamer Interaction费尔德”（RIF）对接协议，用于蛋白质的有效采样 序列和构象，以及新的参数设计方法，以创建新的串联重复蛋白质。 我们建议通过两个具体目标继续这项工作， 蛋白质设计的计算方法。作为该项目的一部分，我们将解决原子分辨率晶体 许多选择和设计的分子复合物的结构，并提供给我们的直接 合作者以及公共结构预测项目，计算预测的挑战。 目标1.我们将设计和表征新的自缔合环状串联重复蛋白（使用两种设计方法）。 novo计算设计和使用高通量选择），然后进一步设计它们以进行 配体诱导的蛋白质-蛋白质缔合。除了结合蛋白质支架设计和 配体结合设计，这一目标的动机是确定结构和机制的特点， 小分子配体结合和力的平衡，其促进配体诱导的蛋白质-蛋白质缔合。 目标2.我们将提高我们的理解和设计新的蛋白质-DNA识别特异性的能力， 行为。为此，我们将：（1）系统地选择和优化一个模型的一系列变体 DNA结合蛋白，在两个部分重叠的区域显示改变的结合特异性。 碱基对和相邻蛋白质残基的连续簇。(2)确定高分辨率结构 以及每个构建体的结合行为。(3)监督盲态计算工作，使用多种 方法来预测相同的结构。(4)比较和分析计算预测的结果 相对于多个计算预测策略，以定义影响预测准确性的特征。 对于这两个目标，我们将进一步利用我们的晶体结构的计算'逆向工程' 每个构建体使用验证的蛋白质结构，以进一步了解设计方法的性能。

PROJECT SUMMARY The design of macromolecular binding interactions and complexes, and corresponding alteration of binding specificity, is a challenging endeavor that remains recalcitrant to computational approaches. This is true both for the creation of protein-protein complexes (which are driven by a enthalpic changes established primarily by stereochemical complementarity, balanced against large competing entropic changes) and for the redesign of protein-DNA complexes (which are heavily dependent upon DNA bending, hydrogen-bonds, electrostatic contacts, and the presence of solvent and counterions throughout the molecular interface). Over the past several years we have collaborated with several computational groups to help develop and validate computational approaches for the design and optimization of protein-protein recognition, protein-DNA recognition, and protein-small molecule recognition. Those studies have contributed to several new computational engineering approaches, including hybrid strategies that combine ab initio design of protein folds and binding sites, the ‘Rotamer Interaction Feld’ (RIF) docking protocol for efficient sampling of protein sequence and conformation, and novel parametric design approaches to create new tandem repeat proteins. We propose to continue this work through two specific aims to further develop and improve upon computational approaches for protein design. As part of this project, we will solve atomic resolution crystal structures of many selected and designed molecular complexes and provide them to our immediate collaborators as well as to a public structure prediction project, for computational prediction challenges. Aim 1. We will design and characterize novel self-associating circular tandem repeat proteins (using both de novo computational design and using high-throughput selections) and then further design them to undergo ligand-induced protein-protein association. Beyond the challenge of combining protein scaffold design and ligand binding design, the motivation for this aim is to determine the structural and mechanistic features of small molecule ligand-binding, and balance of forces, that facilitate ligand-induced protein-protein association. Aim 2. We will improve our understanding and ability to design novel protein-DNA recognition specificities and behaviors. To accomplish this, we will: (1) Systematically select and optimize a series of variants of a model DNA-binding protein, that display altered binding specificity across two regions of partially overlapping sequential clusters of basepairs and neighboring protein residues. (2) Determine the high-resolution structures and binding behavior of each construct. (3) Supervise blinded computational efforts, using multiple approaches, to predict the same structures. (4) Compare and analyze the results of computational predictions versus multiple computational prediction strategies to define features influencing predictive accuracy. For both aims, we will further exploit our crystallographic structures by computationally ‘reverse engineering’ each construct using validated protein structures, to further understand the performance of design approaches.