Method Development: Efficient Computer Vision Based Algo

方法开发：基于高效计算机视觉的算法

• 批准号: 7291814
• 负责人: Ruth Nussinov
• 金额: --
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7291814
• 关键词: Method; Development; Efficient; Computer; Vision

The rapid increase in experimental data along with recent progress in computational methods has brought modern biology a step closer toward solving one of the most challenging problems: prediction of protein function. Comprehension of protein function at its most basic level requires understanding of molecular interactions. Currently, it is becoming universally accepted that the scale of the accumulated data for analysis and for prediction necessitate highly efficient computational tools with appropriate application capabilities. We are developing computational methods for structural pattern discovery and for prediction of molecular associations. We focus on their applications toward a range of biological problems and the advantages of the combination of these methods and their integration with biological experiments. We synergistically merge structural modeling, rigid and flexible structural alignment and detection of conserved structural patterns and docking (rigid and flexible with hinge-bending movements). Our goal is toward a broader utilization of computational methods, and their cross-fertilization with experiment. The majority of proteins function when associated in multimolecular assemblies. Yet, prediction of the structures of multimolecular complexes has largely not been addressed, probably due to the magnitude of the combinatorial complexity of the problem. Docking applications have traditionally been used to predict pairwise interactions between molecules. We have developed an algorithm that extends the application of docking to multimolecular assemblies. We apply it to predict quaternary structures of both oligomers and multi-protein complexes. The algorithm predicted well a near-native arrangement of the input subunits for all cases in our data set, where the number of the subunits of the different target complexes varied from three to ten. In order to simulate a more realistic scenario, unbound cases were tested. In these cases the input conformations of the subunits are either unbound conformations of the subunits or a model obtained by a homology modeling technique. The successful predictions of the unbound cases, where the input conformations of the subunits are different from their conformations within the target complex, suggest that the algorithm is robust. We expect that this type of algorithm should be particularly useful to predict the structures of large macromolecular assemblies, which are difficult to solve by experimental structure determination. The flexible docking algorithm, FlexDock, is unique in its ability to handle any number of hinges in the flexible molecule, without degradation in run-time performance, as compared to rigid docking. Protein surface regions with similar physicochemical properties and shapes may perform similar functions and bind similar binding partners. We developed algorithms and software packages for recognition of the similarity of binding sites and interfaces. Both methods recognize local geometrical and physicochemical similarity, which can be present even in the absence of overall sequence or fold similarity. The first method, SiteEngine, receives as an input two protein structures and searches the complete surface of one protein for regions similar to the binding site of the other. The second, Interface-to-Interface (I2I)-SiteEngine, compares protein-protein interfaces, which are regions of interaction between two protein molecules. It receives as an input two structures of protein-protein complexes, extracts the interfaces and finds the three-dimensional transformation that maximizes the similarity between two pairs of interacting binding sites. The output consists of a superimposition in PDB file format and a list of physicochemical properties shared by the compared entities. The methods are highly efficient and the freely available software packages are suitable for large-scale database searches of the entire PDB.

实验数据的快速增加沿着计算方法的最新进展，使现代生物学朝着解决最具挑战性的问题之一：蛋白质功能预测又迈进了一步。在最基本的水平上理解蛋白质的功能需要理解分子间的相互作用。目前，人们普遍认为，分析和预测所积累数据的规模需要具有适当应用能力的高效计算工具。我们正在开发用于结构模式发现和分子缔合预测的计算方法。我们专注于他们的应用程序对一系列的生物学问题和这些方法的组合，并将其与生物实验的优势。我们协同合并结构建模，刚性和柔性结构对齐和检测保守的结构模式和对接（刚性和柔性铰链弯曲运动）。我们的目标是更广泛地利用计算方法，以及它们与实验的交叉受精。大多数蛋白质在多分子组装体中结合时起作用。然而，多分子复合物的结构预测在很大程度上还没有得到解决，可能是由于组合的复杂性的问题。对接应用传统上用于预测分子之间的成对相互作用。我们已经开发出一种算法，扩展应用对接多分子组件。我们将其应用于预测低聚物和多蛋白质复合物的四级结构。该算法很好地预测了我们数据集中所有情况下输入亚基的近天然排列，其中不同靶复合物的亚基数量从3到10不等。为了模拟更真实的场景，对未绑定的情况进行了测试。在这些情况下，亚基的输入构象是亚基的未结合构象或通过同源建模技术获得的模型。未结合的情况下，亚基的输入构象是不同的，从它们的构象内的目标复合物的成功预测表明，该算法是强大的。我们希望这种类型的算法应该是特别有用的预测大分子组装，这是很难解决的实验结构测定的结构。与刚性对接相比，柔性对接算法FlexDock的独特之处在于其能够处理柔性分子中任何数量的铰链，而不会降低运行时性能。具有相似物理化学性质和形状的蛋白质表面区域可以执行相似的功能并结合相似的结合伴侣。我们开发的算法和软件包识别的结合位点和接口的相似性。这两种方法都识别局部几何和物理化学相似性，即使在缺乏整体序列或折叠相似性的情况下也可以存在。第一种方法，SiteEngine，接收两个蛋白质结构作为输入，并在一个蛋白质的整个表面搜索与另一个蛋白质结合位点相似的区域。第二个，接口到接口（I2 I）-SiteEngine，比较蛋白质-蛋白质接口，这是两个蛋白质分子之间的相互作用区域。它接收两个蛋白质-蛋白质复合物的结构作为输入，提取界面并找到最大化两对相互作用的结合位点之间的相似性的三维变换。输出由PDB文件格式的叠加和比较实体共享的物理化学性质列表组成。这些方法效率很高，免费提供的软件包适合于整个PDB的大规模数据库搜索。