Development and Deployment of the Movable Type Method for Drug Discovery and Desi

用于药物发现和设计的可移动式方法的开发和部署

基本信息

批准号：
9032505
负责人：
Lance M Westerhoff
金额：
$ 56.13万
依托单位：
QUANTUMBIO, INC.
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-10 至 2018-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9032505
关键词：
Active Sites Address Affinity Binding Binding Proteins Biological Biotechnology Calorimetry Code Collaborations Complex Computational Biology Computer Simulation Computer software Coupled Databases Development Docking Drug Design Drug Interactions Environment Feedback Free Energy Freedom Goals Grant Hand Health Human Internet Laboratories Letters Life Ligand Binding Ligands Marketing Medicine Methodology Methods Modeling Molecular Molecular Bank Molecular Structure Nature Nuclear Magnetic Resonance Online Systems Performance Pharmacologic Substance Phase Physics Play Positioning Attribute Printing Probability Process Proteins Protocols documentation Publications Reporting Research Roentgen Rays Role Sampling Site Small Business Innovation Research Grant Statistical Mechanics Structure Surveys System Technology Titrations Validation Water Work X-Ray Crystallography base design drug discovery graphical user interface human disease improved insight interest intermolecular interaction knowledge base laboratory development next generation novel novel therapeutics phase 2 study protein structure receptor small molecule success tool validation studies web based interface

项目摘要

DESCRIPTION (provided by applicant): Our long-term goal is to provide a solution to the protein-ligand binding affinity and pose prediction problems. The protein-ligand docking and scoring problem is one of the central problems in computational biology because of its importance in understanding intermolecular interactions, and because of its practical payoff. The transformative impact molecular docking and scoring can have in the design of next generation medicines cannot be overstated. If we could routinely and accurately design molecules using these approaches it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort and scrutiny on highly active compounds. In this proposal we describe a novel method we call Movable Type (MT), which addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined with a novel way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the necessary partition functions we directly obtain binding free energies and the low free energy poses. Conceptually, the MT method is analogous to block and type set printing, which allows us to efficiently evaluate partition functions describing regions or systems of interest. In this approach we construct two databases that 1) describe the probability of certain pairwise interactions as a function of r obtained from a knowledge base (Protein Databank (PDB) or the Cambridge Structural Database (CSD)) and 2) the energetics of the pairwise interactions as a function of r obtained from empirical potentials, which can be either derived from the probabilities or can utilize extant pairwise potentials like AMBER. Overall, the MT method is a general one and can use a broad range of two-body potential functions and can be extended to higher-order interactions if so desired. In the present project we will extend and further validat the MT method and develop commercial quality software to deliver this methodology to users via the web and GUI. This will involve collaboration between the academic laboratory and the industrial laboratory, development of a new implementation of the method in order to commercially deploy the technology, construction of a graphical user interface based on MOE along with a web-based interface, and finally use of this software in real life structure-based drug discovery problems on-site with our pharmaceutical collaborators (see Letters of Support).

描述（由申请人提供）：我们的长期目标是提供蛋白质-配体结合亲和力的解决方案并提出预测问题。蛋白质-配体对接和评分问题是计算生物学的核心问题之一，因为它对于理解分子间相互作用非常重要，并且具有实际回报。分子对接和评分对下一代药物设计的变革性影响怎么强调也不为过。如果我们能够使用这些方法常规而准确地设计分子，那么它将通过筛选出没有活性的化合物，同时将更多的努力和审查集中在高活性化合物上，从而彻底改变药物发现。在本提案中，我们描述了一种称为可移动类型（MT）的新方法，该方法使用基本统计力学结合在蛋白质结合袋中生成配体整体的新方法来解决蛋白质配体结合和评分问题。通过快速组装必要的配分函数，我们直接获得结合自由能和低自由能位姿。从概念上讲，MT 方法类似于块和排版印刷，这使我们能够有效地评估描述感兴趣的区域或系统的分区函数。在通过这种方法，我们构建了两个数据库，1）将某些成对相互作用的概率描述为从知识库（蛋白质数据库（PDB）或剑桥结构数据库（CSD））获得的 r 的函数，2）成对相互作用的能量学作为从经验势获得的 r 的函数，经验势可以从概率导出，也可以利用现有的成对势（如 AMBER）。总的来说，MT 方法是一种通用方法，可以使用广泛的二体势函数，并且如果需要的话可以扩展到更高阶的相互作用。在当前项目中，我们将扩展并进一步验证 MT 方法，并开发商业质量软件，通过网络和 GUI 向用户提供该方法。这将涉及学术实验室和工业实验室之间的合作，开发该方法的新实现以便将该技术商业化部署，构建基于 MOE 的图形用户界面以及基于 Web 的界面，最后使用该软件与我们的制药合作者现场解决了现实生活中基于结构的药物发现问题（请参阅支持信）。