SEI: New Shape Analysis Methods for Structural Bioinformatics

要完全了解任何生物系统或疾病，就必须对其蛋白质如何与其他分子相互作用进行详细分析。大多数预测和理解蛋白质功能的方法都集中在确定氨基酸序列中的进化关系上。然而，蛋白质的分子功能也取决于它的三维结构(即原子如何在其活性部位内相互作用)，因此最近人们非常关注解决蛋白质的三维结构，希望计算机算法可以推断它们之间的功能关系。数万种蛋白质的3D原子坐标是可用的，而且在过去的几年里，这个数字一直在指数级增长。这个项目的目标是开发新的计算机算法来分析蛋白质结构，检测它们之间的相似性，可视化它们如何与其他分子相互作用，并自动为它们提供功能分类。例如，给定一种新的蛋白质结构，将使用新的几何算法来确定其活性部位的位置和形状。接下来，这些部位的结构和化学性质的模型将被用来搜索大型数据库中具有相似性的部位。最后，对最佳匹配进行比对，以便功能注释可以从一个蛋白质的活性部位转移到另一个蛋白质。这些算法不仅对分子生物学有用，还将推动对更广泛的计算方法的研究，这些方法用于检测噪声3D数据中的特征，匹配复杂3D结构的形状，以及搜索大型3D数据存储库。除了研究之外，该项目还将通过其跨学科合作、教育和推广计划以及信息的公开传播产生影响。该项目是一个跨不同学科的合作努力，帮助该项目促进不同领域之间的思想交叉授粉，并为学生提供新的教育机会，让他们在跨学科的环境中学习。作为这项提案的一部分开发的所有内容将通过讲座、研讨会、网页、课程笔记、软件库、书目和数据集免费向公众提供。

A complete understanding of any biological system or disease necessitates a detailed analysis of how its proteins interact with other molecules. Most methods for predicting and understanding protein function have focused on determining evolutionary relationships in amino acid sequences. However, the molecular function of a protein is determined also by its 3D structure (i.e., how atoms interact within its active sites), and thus a great deal of attention has recently been devoted towards solving the 3D structures of proteins with the hope that computer algorithms can infer functional relationships between them. 3D atomic coordinates are available for tens of thousands of proteins and the number has been increasing exponentially over the last several years. The goal of this project is to develop novel computer algorithms for analyzing protein structures, detecting similarities between them, visualizing how they interact with other molecules, and automatically providing functional classifications for them. For example, given a novel protein structure, new geometric algorithms will be used to determine the locations and shapes of its active sites. Next, the model of the structural and chemical properties of those sites will be used to search large databases for sites with similarities. Finally, the best matches are aligned so that functional annotations can be transferred from the active site of one protein to another. These algorithms will not only be useful for molecular biology, but they will drive research on a broader class of computation methods for detecting features in noisy 3D data, matching shapes of complex 3D structures, and searching large repositories of 3D data. Beyond the research, the project will have impact through its interdisciplinary collaborations, educational and outreach programs, and public dissemination of information. The project is a collaborative effort across diverse disciplines, aiding the project to promote cross-pollination of ideas between fields, and provide new educational opportunities for students to learn in an inter-disciplinary environment. Everything developed as part of this proposal will be made freely available to the public through talks, workshops, web pages, course notes, software libraries, bibliographies, and data sets.