Combined computational and structural studies to create novel macromolecular recognition properties

项目总结 大分子结合相互作用和络合物的设计，以及相应的结合改变 专一性，是一项具有挑战性的努力，仍然顽固地反对计算方法。这两件事都是真的 为了产生蛋白质-蛋白质复合体(由主要由 立体化学互补性，与巨大的竞争熵变化相平衡)和重新设计 蛋白质-DNA复合体(严重依赖DNA弯曲、氢键、静电 接触，以及整个分子界面上溶剂和反离子的存在)。 在过去的几年里，我们与几个计算小组合作，帮助开发和 验证蛋白质-蛋白质识别、蛋白质-DNA设计和优化的计算方法 识别和蛋白质-小分子识别。这些研究已经促成了几个新的 计算工程方法，包括结合蛋白质从头计算设计的混合策略 折叠和结合位点，用于有效采样蛋白质的“旋转分子相互作用场”(RIF)对接协议 序列和构象，以及新的参数设计方法来创建新的串联重复蛋白。 我们建议通过两个具体目标继续这项工作，以进一步发展和改进 蛋白质设计的计算方法。作为这个项目的一部分，我们将解决原子分辨率晶体 许多精选和设计的分子络合物的结构，并将它们提供给我们的直接 合作者以及一个公共结构预测项目，以应对计算预测挑战。 目的1.我们将设计和鉴定新的自结合环串联重复蛋白(使用两个De Novo计算设计和使用高通量选择)，然后进一步设计它们以通过 配基诱导的蛋白质-蛋白质结合。超越将蛋白质支架设计和 配体结合设计，其目的是确定配体的结构和机制特征 小分子配体结合和力的平衡，促进配体诱导的蛋白质-蛋白质结合。 目标2.我们将提高我们对设计新的蛋白质-DNA识别特异性和 行为。为此，我们将：(1)系统地选择和优化模型的一系列变体 DNA结合蛋白，在部分重叠的两个区域显示出改变的结合特异性 碱基和邻近蛋白质残基的连续簇。(2)确定高分辨率构造 以及每个构造物的结合行为。(3)监督盲目的计算工作，使用多个 方法，预测相同的结构。(4)对计算预测结果进行比较分析 与定义影响预测准确性的特征的多个计算预测策略相比。 为了达到这两个目标，我们将通过计算“逆向工程”进一步开发我们的晶体结构。 每一种构建都使用了经过验证的蛋白质结构，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.