Many biochemical and biomedical applications such as protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity, we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We demonstrate the feasibility of our approach using two data sets: an ensemble of hand-selected proteins with known similarities used for verification and an ensemble of ketolase enzymes, where we analyzed the individual domains using our method. Our method is integrated in an interactive visualization application, which allows users to explore and analyze the results. It visualizes the hierarchical clustering and offers linked views that provide details for a comparative data analysis.

许多生化和生物医学应用，如蛋白质工程或药物设计，都与寻找功能相似的蛋白质有关，然而，这仍然是一项具有挑战性的任务。我们提出了一种新的基于图像的方法，用于识别和视觉比较具有相似功能的蛋白质，该方法建立在分子表面图的层次聚类上。此类图是复杂分子表面的二维表示，可用于可视化蛋白质的拓扑结构和不同的物理化学特性。我们的方法基于这样一种观点，即视觉上相似的图也意味着映射蛋白质的功能相似。为了确定地图相似性，我们使用图像矩、颜色矩、或卷积神经网络，并将它们用于地图的层次聚类。我们使用两个数据集证明了我们的方法的可行性：一组用于验证的具有已知相似性的手工选择的蛋白质和一组酮醇酶，我们使用我们的方法分析了各个域。我们的方法集成在交互式可视化应用程序中，允许用户探索和分析结果。它可视化层次聚类并提供链接视图，为比较数据分析提供详细信息。我们的方法集成在交互式可视化应用程序中，允许用户探索和分析结果。它可视化层次聚类并提供链接视图，为比较数据分析提供详细信息。我们的方法集成在交互式可视化应用程序中，允许用户探索和分析结果。它可视化层次聚类并提供链接视图，为比较数据分析提供详细信息。