Computational Design of Protein-Ligand Interfaces - a Therapeutic Strategy

描述（由申请人提供）：结合小分子的蛋白质可通过螯合配体、刺激信号传导途径、将其它分子递送至作用位点和用作体内诊断剂而充当治疗剂。虽然计算机设计的蛋白质，可以结合到任何给定的配体是不可能的，最近在从头酶设计的成功表明，它是触手可及的。目前的方法仍然无法预测最佳的氨基酸，即使在配体周围的第一个壳。具有部分共价特征的短程相互作用（例如氢键、盐桥和阳离子-π-相互作用）通常对于实现结合位点内的精确定位至关重要，但难以建模，因为它们的强度由连接到相互作用官能团的轨道的几何形状、极性和极化率决定。现有的对接技术难以处理灵活性 的结合伙伴，所以界面的结构塑性不考虑。我们认为，蛋白质-配体界面的从头计算设计不仅可以扩展我们对分子识别中所涉及的基本作用力的理解，而且如果能够克服某些技术上的限制，还可以有助于蛋白质治疗学的发展。 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。计算设计程序ROSETTA将通过新的评分功能进行扩展，该功能使用基于知识的电位，在蛋白质-配体界面捕获部分共价相互作用（PCI-KBP）。此外，将实施基于片段的小分子构象采样方法。新的采样策略在结合界面配体的灵活性模型，并利用用于蛋白质设计的氨基酸旋转异构体采样的速度。计算模型的准确性将通过对一组16种蛋白质突变体的重新设计和实验表征来评估，每种突变体都被优化以结合16种相关化合物的集中文库中的一种小分子。将使用基于核磁共振（NMR）的筛选实验确定整组16 x16 =256种组合的靶结合。NMR允许检测弱结合，确定结合亲和力，并在原子级细节上验证结合位点。这种方法创建了设计界面的详细地图，并通过配体（衍生化）和蛋白质（突变）的化学修饰捕获对结合的影响。将实验确定的结合亲和力的矩阵与ROSETTA预测的结合亲和力进行比较，提供关于能量函数的各个分量的准确性和采样策略的效率的反馈。页面：1 公共卫生相关性：该提案将创建先进的方法来计算设计和实验验证蛋白质-配体界面。它将进一步探索 使用这些方法来产生用于治疗前列腺癌、细菌感染和可卡因过量的蛋白质治疗剂。页面：1

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