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以作为治疗药物，通过隔离配体，刺激信号通路，将其他分子传递到作用位点，并作为体内诊断。虽然计算设计可以与任何给定配体结合的蛋白质尚不可能，但最近在从头设计酶方面的成功表明，这是可以实现的。目前的方法仍然无法预测最佳氨基酸，甚至在配体周围的第一层。具有部分共价特征的短程相互作用（例如氢键、盐桥和阳离子相互作用）通常对实现结合位点内的精确定位至关重要，但很难建模，因为它们的强度取决于相互作用官能团上的轨道的几何形状、极性和极化率。现有的对接技术在处理灵活性方面存在困难

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