Computational Design of Protein-Ligand Interfaces - a Therapeutic Strategy

蛋白质-配体界面的计算设计 - 一种治疗策略

基本信息

批准号：
8664893
负责人：
Jens Meiler
金额：
$ 33.15万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2016-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8664893
关键词：
Accounting Affinity Aging Amino Acids Bacterial Infections Binding Binding Sites Biomolecular Nuclear Magnetic Resonance Cations Chemicals Cloning Computer Simulation Computing Methodologies Covalent Interaction Crystallization Crystallography Detection Development Diagnostic Docking Education Enzymes Feedback Genes Geometry Hydrogen Hydrogen Bonding Individual Investments Laboratories Libraries Ligands Malignant neoplasm of prostate Maps Methodology Methods Modeling Modification Molecular Conformation Molecular Structure Mutation Nuclear Magnetic Resonance Pathway interactions Pharmaceutical Preparations Positioning Attribute Protein Binding Proteins Protocols documentation Research Sampling Secure Signal Transduction Site Sodium Chloride Speed Techniques Therapeutic United States National Institutes of Health base biophysical techniques cocaine overdose density design experience flexibility functional group improved in vivo knowledge base member molecular recognition mutant programs public health relevance research study scaffold screening small molecule success therapeutic protein

项目摘要

DESCRIPTION (provided by applicant): Proteins that bind small molecules can act as therapeutics by sequestering ligands, stimulating signal- ing pathways, delivering other molecules to sites of action, and serving as in vivo diagnostics. Although the computational design of proteins that can bind to any given ligand is not yet possible, recent successes in de novo enzyme design suggests that it is within reach. Current methods still fail to predict optimal amino acids even in the first shell around the ligand. Short-range interactions with partial covalent character (e.g. hydrogen bonds, salt bridges, and cation-¿-interactions) are often critical for achieving precise positioning within the binding site but are difficult to model becaue their strength is determined by the geometry, polarity, and polarizability of orbitals attached to the interacting functional groups. Existing docking techniques have difficulty handling flexibility of the binding partners, so the structural plasticity of the interface is not taken into account. W believe that computational de novo design of protein-ligand interfaces can not only expand our under- standing of the basic forces involved in molecular recognition, but can also contribute to the development of protein therapeutics, if certain technological limitations can be overcome. The objective of this proposal is to develop a computational protocol for the de novo design of protein- ligand interfaces. The computational design program, ROSETTA, will be expanded through a new scoring func- tion that uses Knowledge-Based Potentials that capture Partial Covalent Interactions (PCI-KBP) at protein- ligand interfaces. Additionally, a fragment-based approach for sampling small molecule conformations will be implemented. The new sampling strategy models ligand flexibility at the binding interface and exploits the speed of amino acid rotamer sampling used for protein design. The accuracy of the computational models will be assessed through redesign and experimental characterization of a panel of 16 protein mutants, each optimized to bind one small molecule out of a focused library of 16 related compounds. Target binding will be determined for the entire set of 16x16=256 combinations using nuclear magnetic resonance (NMR)-based screening experiments. NMR allows detection of weak binding, determination of binding affinities, and verification of the binding site at atomic-level detail. This approach creates a detailed map of the designed interfaces and captures effects on binding through chemical modification of the ligand (derivatization) as well as the protein (mutation). The matrix of experimentally-determined binding affinities will be compared to those predicted by ROSETTA, providing feedback on the accuracy of individual components of the energy function and the efficiency of the sampling strategy. Page: 1 PUBLIC HEALTH RELEVANCE: This proposal will create advanced methodology to computationally design and experimentally verify protein-ligand interfaces. It will further explore use of these methods to create protein therapeutics for treatment of prostate cancer, bacterial infection, and cocaine overdosing. Page: 1

描述（由适用提供）：结合小分子的蛋白质可以通过隔离配体，刺激信号途径，将其他分子传递到作用位点并用作体内诊断。尽管尚不可能与任何给定配体结合的蛋白质的计算设计是不可能的，但最新的从头酶设计中取得了成功表明它可以触及。即使在配体周围的第一个壳中，当前方法仍然无法预测最佳氨基酸。与部分共价特征（例如氢键，盐桥和阳离子 - 交互）的短距离相互作用通常对于在结合位点实现精确定位至关重要，但是很难建模，因为它们的强度取决于几何，极性，极性和相互作用功能组的极性。现有的对接技术很难处理灵活性结合伙伴的结构可塑性未考虑到界面的结构可塑性。相信，如果可以克服某些技术限制，那么蛋白质配体界面的从头设计不仅可以扩展我们对分子识别涉及的基本力量的理解，而且还可以有助于蛋白质关键性的发展。该提案的目的是为蛋白质关键接口的从头设计开发计算方案。计算设计程序Rosetta将通过新的评分功能扩展，该功能使用基于知识的电位，该电位捕获蛋白质接口的部分共价相互作用（PCI-KBP）。此外，将实施一种基于碎片的小分子构象的方法。新的采样策略模型在结合界面处的配体柔韧性，并利用用于蛋白质设计的氨基酸旋转式采样速度。计算模型的准确性将通过重新设计和实验表征的16个蛋白突变体进行评估，每个突变体都优化，从16个相关化合物的聚焦库中结合一个小分子。将使用基于核磁共振（NMR）的筛选实验确定整个16x16 = 256组合的目标结合。 NMR允许检测弱结合，结合亲和力的确定以及在原子级细节处的结合位点的验证。这种方法创建了设计界面的详细图，并通过化学修饰（衍生化）以及蛋白质（突变）捕获了对结合的影响。实验确定的结合亲和力的矩阵将与罗塞塔（Rosetta）预测的亲密关系进行比较，从而提供了能量函数各个组件的准确性以及采样策略的效率的反馈。页：1 公共卫生相关性：该提案将为计算设计和实验验证蛋白质配体接口创造先进的方法。它将进一步探索使用这些方法来创建蛋白质理论来治疗前列腺癌，细菌感染和可卡因过量。页：1