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

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，因为它对理解分子间相互作用很重要，而且具有实际意义。分子对接和评分在下一代药物设计中的变革性影响怎么强调都不为过。如果我们能够常规地、准确地利用这些方法设计分子，它将彻底改变药物的发现，淘汰没有活性的化合物，同时把更多的精力和审查集中在高活性的化合物上。在这个提议中，我们描述了一种新的方法，我们称之为Movable Type (MT)，它解决了蛋白质配体结合和评分问题，使用基本的统计力学结合一种新的方法来生成配体在蛋白质结合口袋中的集合。通过必要配分函数的快速装配，我们直接得到了束缚自由能和低自由能位姿。从概念上讲，MT方法类似于块和字型集打印，它允许我们有效地评估描述感兴趣的区域或系统的配分函数。在

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).