Enzyme Isoselective Inhibition ? a Novel Computational Approach to Drug Design

描述（由申请人提供）：本项目的长期目标是开发和实施一种用于虚拟筛选和设计新的共价过渡态（TS）类似物酶抑制剂的计算方法。传统的计算机辅助药物设计（CADD）方法侧重于优化酶抑制剂的非共价识别相互作用。不幸的是，由于感染性疾病和癌症的突变耐药性病例的发展，非共价抑制剂的这种良好优化的体外识别在体内迅速丧失。酶的催化残基不发生突变的事实使我们假设那些共价TS类似物抑制剂，具有与催化残基相互作用的显着结合贡献，应该遭受较少的突变抗性。因此，处理和设计共价TS类似物抑制剂的计算工具是非常重要的。我们考虑了一类特殊的同选择性过渡态类似物抑制剂，它们具有相同的识别位点（RS）和不同的化学位点（CS）。拟议的项目是基于这样的假设，即结合亲和力的趋势，以目标酶在一系列这样的抑制剂可以预测，通过一个高层次的量子力学模型-酶抑制剂趋势分析（EITA）。这一假设是基于我们早期的研究，分析了各种因素对酶抑制剂复合物稳定性的能量贡献。我们之前曾将跨学科的计算/实验方法应用于酶催化和抑制机制的研究。该模型包括相关的化学反应中心和一个特殊的协议占蛋白质/水的环境效应。通过实验数据对计算模型进行了标定，得到了真实、准确的计算结果。基于这些发现，我们建议开发一种新的可逆共价酶抑制剂的设计方法。我们的研究将集中在水解酶超家族的酶的抑制剂。首先，我们将使用各种医学上重要的酶来检查和验证所提出的模型。该模型将占酶抑制剂的共价键和溶剂抑制剂竞争反应的高层次量子力学DFT计算和结果将与实验动力学数据。然后，我们将通过考虑CS的非共价相互作用来扩展筛选能力。结果将作为一个数据库，可用作信息源或药物设计的工具。成熟的算法和数据库最终将被纳入，在我们未来的项目，到一个软件包，强调简单和清晰的日常使用的实际药物设计。该方法可用于设计新的先导化合物，以及开发不会因靶酶突变而失去活性的药物。公共卫生相关性：该项目旨在验证和进一步开发一种计算工具来预测结合亲和力，并进行新的共价过渡态（TS）类似物酶抑制剂的虚拟筛选。目前的项目将产生一个公开可用的算法和一个数据库，该数据库由表征医学上重要的靶点及其抑制剂之间结合趋势的各种参数组成。最终，我们的方法将被开发成一个软件包，帮助研究人员设计出不易受突变耐药性影响的新药。

DESCRIPTION (provided by applicant): The long-term goal of this project is to develop and implement a computational methodology for virtual screening and design of new covalent transition-state (TS) analog enzyme inhibitors. The conventional computer assisted drug design (CADD) methodologies focus on optimization of the enzyme-inhibitor noncovalent recognition interactions. Unfortunately, such a well optimized in vitro recognition of non-covalent inhibitors is rapidly lost in vivo due to the development of mutational drug resistance cases of infectious diseases and cancer. The fact that catalytic residues of enzymes are not subjected to mutations led us to hypothesize those covalent TS analog inhibitors, having significant binding contributions from interactions with catalytic residues, should suffer less from mutational resistance. Thus, a computational tool to handle and design covalent TS analog inhibitors is of major importance. We have considered a special class of isoselective transition state analog inhibitors a set with identical recognition site (RS) and different chemical site (CS). The proposed project is based on the hypothesis that the trend of binding affinity to a target enzyme in a series of such inhibitors can be predicted, by a high-level quantum mechanical model- Enzyme Inhibitor Trend Analysis (EITA). This hypothesis is based on our earlier studies that analyzed the energetic contribution of various factors to the stability of the enzyme-inhibitor complex. We have previously applied an interdisciplinary computational/experimental approach in the study of enzyme catalysis and inhibition mechanisms. The models included the relevant chemical reaction center and a special protocol accounting for the protein/water environmental effect. The calculated models were calibrated by experimental data, to provide realistic and accurate results. Based on these findings, we suggest developing a methodology for the design of new reversible covalent enzyme inhibitors. Our research will focus on inhibitors of enzymes of the hydrolases superfamily. First we will examine and validate the proposed model with various medicinally-important enzymes. The model will account for the enzyme-inhibitor covalent bond and for the solvent-inhibitor competing reaction by high-level quantum mechanical DFT calculations and the results will be correlated with experimental kinetic data. We will then expand the screening abilities by taking into account also non-covalent interactions of the CS. The results will be presented as a database that could be used as an information source or as a tool for drug design. The mature algorithm and the database will ultimately be incorporated, in our future project, into a software package, with emphasis on simplicity and clarity for routine use of practical drug design. The methodology can be beneficial in the design of new lead compounds, as well as in the development of drugs that will not lose their activity due to target enzyme mutations. PUBLIC HEALTH RELEVANCE: This project aims to validate and further develop a computational tool to predict binding affinities and to perform virtual screening of new covalent transition-state (TS) analog enzyme inhibitors. The current project will result in a publicly available algorithm and a database comprised of various parameters that characterize binding trends between medicinally important targets and their inhibitors. Ultimately our method will be developed into a software package that will assist researchers in the design of novel drugs that may be less susceptible to mutational drug resistance.