A Novel Probabilistic Engine for Virtual Screening

用于虚拟筛选的新型概率引擎

• 批准号: 6786885
• 负责人: RICHARD Masten FINE
• 金额: $ 15.34万
• 依托单位: BIOCOMPUTING GROUP, INC.
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2006-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6786885
• 关键词: binding sites; computational biology; computer program /software; computer system design /evaluation; ligands; statistics /biometry

DESCRIPTION (provided by applicant): The goal of this work is to provide a novel probabilistic computational engine for docking-based virtual screening. The engine is based on probabilistic model of Markov Random Fieds (MRF). MRF's have proven successful in other fields such as Computer Vision, and can be seen as a 3D analog of the successful 1D application of Hidden Markov Models to bioinformatics. The docking of a rigid ligand or ligand fragment into a protein active site is modeled as a weighted graphical match of an abstracted description of the ligand to an abstracted description of the active site. These abstracted descriptions are graphs, whose nodes are chemical entities (hydrogen bond acceptors/donors, hydrophobic spheres and etc.) and whose edges are associated distance constraints. The weighted graph-matching problem is expressed as an MRF, whose solution minimizes its associated free energy function. A fast, convergent message-passing scheme called Belief Propagation is used to solve the MRF. The result is a probability distribution that describes all possible placements of the ligand into the active site. Individual low-energy placements of the molecule are obtained by marginalizing this probability distribution. The method provides a fast and mathematically complete examination of possible fits of the ligand into the protein active site, and our prototype MRF application demonstrates excellent timing and completeness properties. The method also provides an attractive data structure enabling a variety of applications. The data structure intrinsically admits an enriched description of the active site. This description can incorporate an extended set of chemical substructures for matching at its nodes. It also can incorporate sets of probabilistic beliefs, expressed as probabilistic prior distributions. These can be used to bias matches according to known actives. Our goals in Phase I are to further develop our prototype into a robust MRF-based docking engine to positioning rigid molecules and molecular fragments into protein active sites. Our goals in Phase II will be to implement applications based on the MRF docking engine: (i) inclusion flexible ligand docking, (ii) incorporation of flexible side chains into docking, (iii) de-novo ligand design, and (iv) docking into multiple aligned proteins. We will seek corporate partners interested in collaborating on applying the technologies to specific problems in drug discovery in Phase I1. The technology developed will be sold as commercial software in Phase III.

描述（由申请人提供）：这项工作的目标是为基于对接的虚拟筛选提供一种新颖的概率计算引擎。该引擎基于马尔可夫随机场（MRF）的概率模型。 MRF 已被证明在计算机视觉等其他领域取得了成功，并且可以被视为隐马尔可夫模型在生物信息学中成功 1D 应用的 3D 模拟。刚性配体或配体片段与蛋白质活性位点的对接被建模为配体的抽象描述与活性位点的抽象描述的加权图形匹配。这些抽象的描述是图，其节点是化学实体（氢键受体/供体、疏水球等），其边是相关的距离约束。加权图匹配问题表示为 MRF，其解决方案最小化其相关的自由能函数。称为置信传播的快速、收敛消息传递方案用于解决 MRF。结果是描述配体进入活性位点的所有可能位置的概率分布。分子的各个低能位置是通过边缘化该概率分布来获得的。该方法提供了对配体与蛋白质活性位点的可能配合的快速且数学上完整的检查，并且我们的原型 MRF 应用程序展示了出色的定时和完整性特性。该方法还提供了一种有吸引力的数据结构，可实现多种应用。数据结构本质上允许对活性位点进行丰富的描述。该描述可以包含一组扩展的化学子结构，用于在其节点处进行匹配。它还可以包含一组概率信念，表示为概率先验分布。这些可用于根据已知的活性来偏置匹配。我们第一阶段的目标是将我们的原型进一步开发为基于 MRF 的强大对接引擎，以将刚性分子和分子片段定位到蛋白质活性位点。我们第二阶段的目标是实施基于 MRF 对接引擎的应用：(i) 包含灵活的配体对接，(ii) 将灵活的侧链纳入对接，(iii) 从头配体设计，以及 (iv) 对接到多个对齐的蛋白质中。我们将寻找有兴趣合作将这些技术应用于 I1 阶段药物发现的特定问题的企业合作伙伴。开发的技术将在第三阶段作为商业软件出售。