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：
8781973
负责人：
Lance M Westerhoff
金额：
$ 16.53万
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 2015-03-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8781973
关键词：
Active Sites Address Affinity Binding Binding Proteins Biological Biotechnology Build-it 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 Pharmaceutical Preparations Pharmacologic Substance Phase Physics Play Positioning Attribute Printing Probability Process Protein Binding Proteins Protocols documentation Publications Reporting Research Roentgen Rays Role Sampling Site Small Business Innovation Research Grant Solutions 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 next generation novel novel therapeutics phase 2 study protein structure public health relevance 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）：我们的长期目标是为蛋白质 - 配体结合亲和力和姿势预测问题提供解决方案。蛋白质配体对接和评分问题是计算生物学中的核心问题之一，因为它在理解分子间相互作用和实际收益方面具有重要意义。在下一代药物的设计中，分子对接和评分的变革性影响不能被夸大。如果我们可以使用这些方法常规，准确地设计分子，它将通过在没有活性的情况下抛弃化合物，同时将更多的精力和审查对高度活跃的化合物进行，从而彻底改变药物发现。在此提案中，我们描述了一种新的方法，我们称为可移动类型（MT），该方法使用基本统计力学结合了一种新型方法来解决蛋白质配体结合和评分问题，以在蛋白质结合口袋中生成配体的集合。通过快速组装必要的分区函数，我们直接获得结合自由能和低自由能姿势。从概念上讲，MT方法类似于阻止和类型设置打印，这使我们能够有效地评估描述区域或感兴趣系统的分区功能。在 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.总体而言，MT方法是一种通用方法，可以使用广泛的两体势函数，如果需要，可以扩展到高阶相互作用。在本项目中，我们将扩展并进一步验证MT方法并开发商业质量软件，以通过网络和GUI向用户提供此方法。这将涉及学术实验室与工业实验室之间的合作，开发该方法的新实施，以便在商业上部署技术，建立基于MOE的图形用户界面以及基于Web的界面，最后在现实生活结构的药物发现中使用该软件与我们的药品合作者在现实生活结构的药物发现问题中使用（请参阅支持的信件）。