III: Small: Rapid screening of interacting ligands and proteins

从各种数据库中高效、准确地搜索生物分子的计算基础设施是任何现代生物学、生物化学、药理学和生物技术的基础。该项目的目标是开发计算方法和数据库，以无缝方式快速、实时地筛选各种类型的蛋白质及其相互作用分子的三维(3D)结构数据。要搜索的结构数据包括三维蛋白质结构和蛋白质复合体、预测的蛋白质结构、低温电子显微镜解析的低分辨率蛋白质复合体、小化学配体分子和药物分子。该项目采用了生物分子的数学表示法，可以快速比较和搜索与查询分子具有相似全局和局部表面形状和属性的生物分子。该项目将进一步扩大分子表示法的适用性，通过识别形状和表面属性的互补性来搜索相互作用的分子。该项目将开发的方法使生物学家能够快速识别潜在的与查询蛋白质相互作用的蛋白质，这将有助于通过构建分子网络来生成疾病分子机制的可检验假说。此外，这些方法还将使人们能够快速搜索适合目标蛋白质的配体分子和潜在的药物分子。生物学已经进入信息学时代，此时需要结合不同类型的大组学数据来达到对分子和细胞生物功能的系统水平的了解。为了有效地为生物学研究收集有用的结构数据，迫切需要能够快速、无缝地搜索不同类型的结构数据的计算方法。建立有效的方法来搜索生物分子的形状和物理化学性质，对于利用结构基因组学和其他项目为确定分子和细胞结构所做的大量努力至关重要。该项目将开发计算方法和数据库，以无缝和快速地筛选各种类型的蛋白质结构及其相互作用的分子。利用该项目中提出的分子表示法，可以非常快速地比较蛋白质和配体分子的全局和局部形状以及表面属性(静电势、疏水性)。与传统的生物分子三维结构搜索方法需要几个小时甚至一天多的时间才能完成数据库搜索不同，将要开发的方法将允许对大型数据库进行实时搜索。因此，对于生物学研究人员来说，结构分析将变得像序列数据库搜索一样方便。3D分子搜索方法将被应用于识别查询蛋白质的相互作用分子、将结合到查询蛋白质口袋区域的配体分子以及相互作用的蛋白质。了解分子间的相互作用对于理解蛋白质的功能至关重要。主要的创新包括：1)寻找与蛋白质相互作用的分子，即口袋-配体相互作用和蛋白质-蛋白质相互作用；2)功能注释的局部表面比对；所开发的方法将被应用到生物分子形状检索的一站式网站3D-Surfer中。所提出的方法可以应用于其他类型的快速形状和性质比较，如2D和3D医学图像、显微镜图像、地理景观和人脸识别。生物科学和计算机科学的研究生和本科生将在几个系之间交叉列出的课程中接受培训。普渡大学现有的几个招收少数族裔学生和本科生的计划将有助于广泛参与该项目。总体而言，拟议的项目利用了普渡大学S在跨学科计算生命科学和工程方面的努力。

Computational infrastructure for efficient and accurate searching of bio-molecules from various databases is foundation of any modern biology, biochemistry, pharmacology, and biotechnology. The goal of this project is to develop computational methods and databases that allow fast, real-time screening of various types of three dimensional (3D) structural data of proteins and their interacting molecules in a seamless fashion. The structure data to be searched include 3D protein structures and protein complexes, predicted protein structures, low-resolution protein complexes solved by cryo-electron microscopy, small chemical ligand molecules, and drug molecules. The project employs a mathematical representation of biomolecules that can quickly compare and search biomolecules that have similar global and local surface shape and properties with a query molecule. The project will further expand the applicability of the molecule representation for searching interacting molecules by identifying complementarity of shapes and surface properties. The methods to be developed in the project allow biologists to quickly identify potentially interacting proteins to a query protein, which will help generating testable hypothesis of molecular mechanisms of diseases through building molecular networks. Moreover, the methods will also enable quick searching of ligand molecules and potential drug molecules that fit to a target protein.Biology has entered the informatics era, when combining different types of big omics data are routinely required to reach a systems-level understanding of biological function of molecules and cells. In order to effectively glean useful structural data for biological studies, there is a strong need for computational methods that can quickly and seamlessly search for different types of structural data. Establishing efficient methods for searching biomolecular shape and physicochemical properties is essential for capitalizing on the large number of efforts directed towards determining molecular and cellular structures by structural genomics and other projects. The project will develop computational methods and databases to screen various types of protein structures and their interacting molecules seamlessly and quickly. Using the molecular representation proposed in the project, global and local shapes and surface properties (electrostatic potential, hydrophobicity) of proteins and ligand molecules can be compared ery fast. In contrast to conventional 3D structure search methods for biomolecules that take hours or even more than a day to finish a database search, the methods to be developed will allow real-time searches against large databases. Thus, structural analysis will become as convenient as sequence database searches for biology researchers. The 3D molecule search methods will be applied to identify interacting molecules for a query protein, ligand molecules that would bind to a pocket region of the query protein as well as interacting proteins. Knowing molecular interactions is critical for understanding functions of proteins. The key innovations include 1) finding interacting molecules to proteins, i.e. pocket-ligand interactions and protein-protein interactions; 2) local surface comparisons for functional annotations; Developed methods will be implemented into 3D-Surfer, a one-stop website for biomolecular shape retrieval.The proposed approach can be applied for other types of rapid shape and property comparisons, such as 2D and 3D medical images, microscope images, geographical landscapes, and face recognition. Graduate and undergraduate students in biological sciences and computer science will be trained in cross-listed courses among several departments. Several existing programs at Purdue for recruiting minority students and undergraduate students will contribute to broad participation in the project. Overall the proposed project leverages Purdue University?s efforts in interdisciplinary computational life science and engineering.