Development, Validation, and Application of Structure-based Tools for Computational Molecular Design

项目总结/摘要 这项研究建议的中心目标是“开发”和“验证”方法， 在算法上编码潜在的物理可观测量，以改善小有机分子的设计， 生物目标及其在真实的世界系统中的“应用”。原子级计算建模 使人们能够理解驱动分子识别的因素，并实现可测试的预测， 可以被实验者证实。基于强有力的结果和数据，我们假设， 所述领域（即姿势准确性、富集、蛋白质柔性、特异性、位点互补性、易用性）可以 通过前瞻性的工具设计来改善采样，评分和搜索。一个主要 目前的任务是开发一个新的“从头”设计平台， 新分子的构建，这消除了只考虑那些先入为主的限制。 这将使化合物的设计高度“优化”和“特别定制”的目标蛋白质。 我们的方法采用从用户可定制的构建块库开始的分子构建 使用我们开发的算法并在程序DOCK 6中实现的片段。新的进展将是 通过公共发布沿着验证数据库和用户， 友好的在线教程。在没有发明性的配体发现方法的情况下，很有可能某些配体不能被发现。 化学空间区域可能无法通过标准筛选方法进行充分采样， 理性我们的预期结果是含有高度特异性和优化配体的集合。该提案 围绕着4个基本问题：（Q1）驱动分子运动的基本物理原理是什么？ 识别（结合，选择性，电阻）可以在原子级捕获，并用于设计改进的 软件和模拟协议的几何形状和能量的准确预测？(Q2)配体生长是否可以 通过“从无到有”组装小的有机物， 片段（从头设计）使用“分子模拟”的原则，以指导不断增长的合奏，因为它的演变？ (Q3)哪种抽样、评分和搜索方法对识别和设计最有效？ 验证有效的化合物，并能开发更有效的做法，以最大限度地提高整体“成功”， 与实验者合作？(Q4)对接和从头设计软件和协议是否可以设计 在不牺牲准确性或功率的情况下更用户友好？我们将与网络合作， 经验丰富的实验室，并利用我们的新工具进行预测。我们将识别小分子 探针和抑制剂，以回答基础研究问题，并提供生物学机制的理解， 与人类健康相关的系统包括：脂肪酸结合蛋白，nSMase 2，中性神经酰胺酶， HIVgp 41、GP 2、糖蛋白-E、ErbB家族突变体（EGFR、HER 2）、白色念珠菌Glx 3/Hsp 31和人 Tsg 101，其中。实验结果反过来将为我们进一步的方法开发工作提供信息。

PROJECT SUMMARY/ABSTRACT The central objectives of this research proposal are "development" and "validation" of methodologies to algorithmically encode underlying physical observables to improve design of small organic molecules for a biological target and their "application" to real world systems. Computational modeling at the atomic-level empowers understanding of the factors that drive molecular recognition and enables testable predictions that can be confirmed by experimentalists. Grounded in strong results and data, we hypothesize that major gaps in the field (i.e. pose accuracy, enrichment, protein flexibility, specificity, site complementarity, ease of use) can be bridged through forward-thinking design of tools that improve sampling, scoring, and searching. A major undertaking is development of a new platform for "de novo" design which will enable "from-scratch" construction of novel molecules, which removes the limitation of only considering those that are preconceived. This will enable design of compounds highly "optimized" and "specifically tailored" to the protein of interest. Our approach employs construction of molecules starting from user customizable libraries of building block fragments using algorithms we developed and implemented into the program DOCK6. New advances will be made available to the research community through public releases along with validation databases and user- friendly online tutorials. Without inventive approaches to ligand discovery, there is a high likelihood that certain areas of chemical space may not be adequately sampled by standard screening methods which provides the rational. Our expected outcomes are ensembles containing highly specific and optimized ligands. The proposal is framed around 4 fundamental questions: (Q1) What underlying physical principles that drive molecular recognition (binding, selectivity, resistance) can be captured at the atomic-level and used to design improved software and simulation protocols for accurate prediction of geometry and energy? (Q2) Can ligand growth be propelled to highly specific regions of chemical space through "from-scratch" assembly of small organic fragments (de novo design) using "molecular mimicry" principles to direct the growing ensemble as it evolves? (Q3) Which sampling, scoring, and searching methods are most effective for identification and design of verified-active compounds and can more effective practices be developed to maximize overall "success" in collaboration with experimentalist? (Q4) Can docking and de novo design software and protocols be designed to be more user friendly while not sacrificing accuracy or power? We will collaborate with a network of experienced experimental labs and employ our new tools to make predictions. We will identify small molecule probes and inhibitors to answer basic research questions and provide mechanistic understanding for biological systems of relevance to human health including: fatty acid binding protein, nSMase2, neutral ceramidase, HIVgp41, GP2, glycoprotein-E, ErbB-family mutants (EGFR, HER2), candid albicans Glx3/Hsp31, and human Tsg101, among others. Experimental outcomes in turn will inform our further method development efforts.