Computational Design of Protein-Ligand Interaces - a Therapeutic Strategy

蛋白质-配体相互作用的计算设计 - 一种治疗策略

基本信息

批准号：
8551916
负责人：
Jens Meiler
金额：
$ 2.51万
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/8551916
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 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 cocaine overdose density design experience flexibility functional group improved in vivo knowledge base member molecular recognition mutant programs 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

描述（由申请人提供）：结合小分子的蛋白质可以通过隔离配体、刺激信号传导途径、将其他分子递送到作用位点以及用作体内诊断来充当治疗剂，尽管可以结合任何蛋白质的计算设计。给定配体尚不可能，最近从头酶设计的成功表明，即使在配体周围的第一个壳中，目前的方法仍然无法预测具有部分共价特征的最佳氨基酸。（例如氢键、盐桥和阳离子相互作用）对于在结合位点内实现精确定位通常至关重要，但很难建模，因为它们的强度由相互作用的轨道的几何形状、极性和极化率决定现有的对接技术难以处理灵活性。 W相信，蛋白质-配体界面的计算从头设计不仅可以扩展我们对分子识别所涉及的基本力的理解，而且还可以。如果能够克服某些技术限制，将有助于蛋白质疗法的发展。该提案的目标是开发一种用于蛋白质-配体界面从头设计的计算协议，该计算设计程序 ROSETTA 将通过新的评分功能此外，还将采用基于片段的小分子构象采样方法，对结合界面处的配体灵活性进行建模。利用用于蛋白质设计的氨基酸旋转异构体采样的速度，将通过重新设计和实验表征一组 16 个蛋白质突变体来评估计算模型的准确性，每个突变体都经过优化以结合重点库中的一个小分子。 16 种相关化合物的靶标结合将使用基于核磁共振 (NMR) 的筛选实验来确定，NMR 可以检测弱结合、确定结合亲和力并验证结合位点。这种方法创建了设计界面的详细图谱，并通过配体（衍生化）和蛋白质（突变）的化学修饰来捕获对结合的影响。实验确定的结合亲和力将与 ROSETTA 预测的结合亲和力进行比较，提供有关能量函数各个组成部分的准确性和采样策略的效率的反馈。