A Novel Probabilistic Engine for Virtual Screening

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的概率计算引擎。该引擎是基于马尔可夫随机场（MRF）的概率模型。MRF已经在其他领域（如计算机视觉）中证明是成功的，并且可以被视为隐马尔可夫模型在生物信息学中成功应用的3D模拟。将刚性配体或配体片段对接到蛋白质活性位点中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形，其节点是化学实体（氢键受体/供体，疏水球等）。并且其边缘是相关联的距离约束。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。一个快速，收敛的消息传递计划称为信念传播是用来解决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。通过边缘化这个概率分布，获得分子的个别低能量位置。该方法提供了一个快速和数学上完整的检查可能适合的配体到蛋白质的活性位点，我们的原型MRF应用程序表现出优异的时间和完整性。该方法还提供了一个有吸引力的数据结构，使各种应用程序。数据结构本质上允许对活性位点的丰富描述。该描述可以并入一组扩展的化学子结构以在其节点处进行匹配。它还可以包含概率信念集，表示为概率先验分布。这些可以用于根据已知的活动来偏置匹配。我们在第一阶段的目标是进一步发展我们的原型成为一个强大的基于MRF的对接引擎，定位刚性分子和分子片段到蛋白质活性位点。我们在第二阶段的目标是实现基于MRF对接引擎的应用：（i）包含柔性配体对接，（ii）将柔性侧链纳入对接，（iii）从头配体设计，以及（iv）对接到多个对齐的蛋白质中。我们将寻求有兴趣合作的企业合作伙伴，将这些技术应用于第一阶段药物发现的具体问题。开发的技术将在第三阶段作为商业软件出售。

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.