Combined computational and structural studies to create novel macromolecular recognition properties

结合计算和结构研究来创造新的大分子识别特性

• 批准号: 10372918
• 负责人: BARRY L. STODDARD
• 金额: $ 13.71万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372918
• 关键词: Affinity; Algorithms; Amino Acid Sequence; Behavior; Binding; Binding Proteins; Binding Sites; Biochemical; Biological; Blinded; Complex; Complex Mixtures; Computer Analysis; Computer Models; Crystallization; DNA; DNA Binding; DNA-Binding Proteins; Data; Dissection; Docking; Drug Design; Electrostatics; Engineering; Entropy; Equilibrium; Hybrids; Hydrogen Bonding; Ligand Binding; Ligands; Modeling; Molecular; Motivation; Mutagenesis; Performance; Process; Property; Protein Conformation; Protein Engineering; Proteins; Protocols documentation; Resolution; Reverse engineering; Running; Sampling; Scaffolding Protein; Series; Solvents; Specificity; Structure; Supervision; Surface; System; Tandem Repeat Sequences; Testing; Variant; Work; X-Ray Crystallography; base; design; improved; novel; pressure; protein complex; protein folding; protein structure; small molecule; statistical and machine learning

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.

项目概要 大分子结合相互作用和复合物的设计以及相应的结合改变 特异性，是一项具有挑战性的努力，对计算方法来说仍然是顽固的。这都是真的 用于创建蛋白质-蛋白质复合物（其由主要由建立的焓变驱动） 立体化学互补性，与大的竞争性熵变化相平衡）并重新设计 蛋白质-DNA 复合物（很大程度上依赖于 DNA 弯曲、氢键、静电 接触，以及整个分子界面中溶剂和反离子的存在）。 在过去的几年里，我们与多个计算小组合作，帮助开发和 验证蛋白质-蛋白质识别、蛋白质-DNA 的设计和优化的计算方法 识别和蛋白质-小分子识别。这些研究促成了一些新的 计算工程方法，包括结合蛋白质从头设计的混合策略 折叠和结合位点，“旋转异构体相互作用场”(RIF) 对接方案，用于高效蛋白质采样 序列和构象，以及新颖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。作为该项目的一部分，我们将解决原子分辨率晶体问题 许多选定和设计的分子复合物的结构，并将它们提供给我们直接的 合作者以及公共结构预测项目，以应对计算预测挑战。 目标 1. 我们将设计并表征新型自缔合环状串联重复蛋白（使用 de 新计算设计并使用高通量选择），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。超越将蛋白质支架设计与 配体结合设计，这一目标的动机是确定配体结合的结构和机制特征 小分子配体结合和力平衡，促进配体诱导的蛋白质-蛋白质结合。 目标 2. 我们将提高设计新型蛋白质-DNA 识别特异性和 行为。为了实现这一目标，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，在两个部分重叠的区域中显示出改变的结合特异性 碱基对和邻近蛋白质残基的连续簇。 (2) 确定高分辨率结构 以及每个构造的结合行为。 (3) 使用多个监督盲法计算工作 方法来预测相同的结构。 (4) 计算预测结果的比较分析 与多种计算预测策略来定义影响预测准确性的特征。 为了这两个目标，我们将通过计算“逆向工程”进一步开发我们的晶体结构 每个构建体都使用经过验证的蛋白质结构，以进一步了解设计方法的性能。