论文标题
原子邻里检查员
ProtoFold Neighborhood Inspector
论文作者
论文摘要
影响蛋白质残基（氨基酸）的翻译后修饰（PTM）会干扰其功能，从而导致疾病。 PTM是否是致病性的，取决于其类型和相邻残基的状态。在本文中，我们介绍了Protofold邻里检查员（PFNI），这是一种可视化系统，用于分析残基社区。主要的贡献是可视化习惯，即残基星座（RC），用于识别和比较基于占有特征和空间特征的三维社区。 RC利用该蛋白质三维结构的二维表示来克服诸如遮挡之类的问题，从而减轻了通常具有复杂空间排列的社区的分析。我们使用PFNI探索了蛋白质的结构PTM数据，这使我们能够识别可能与其致病状态有关的每个邻居PTM的分布和数量的模式。在下文中，我们定义指导PFNI开发的任务，并描述我们得出和使用的数据源。然后，我们介绍了PFNI，并通过分析工作流程的示例来说明其用法。最后，我们在使用该工具上使用该工具在提供的PFNI开发的提供的数据和未来方向上进行了初步发现来结束。
Post-translational modifications (PTMs) affecting a protein's residues (amino acids) can disturb its function, leading to illness. Whether or not a PTM is pathogenic depends on its type and the status of neighboring residues. In this paper, we present the ProtoFold Neighborhood Inspector (PFNI), a visualization system for analyzing residues neighborhoods. The main contribution is a visualization idiom, the Residue Constellation (RC), for identifying and comparing three-dimensional neighborhoods based on per-residue features and spatial characteristics. The RC leverages two-dimensional representations of the protein's three-dimensional structure to overcome problems like occlusion, easing the analysis of neighborhoods that often have complicated spatial arrangements. Using the PFNI, we explored proteins' structural PTM data, which allowed us to identify patterns in the distribution and quantity of per-neighborhood PTMs that might be related to their pathogenic status. In the following, we define the tasks that guided the development of the PFNI and describe the data sources we derived and used. Then, we introduce the PFNI and illustrate its usage through an example of an analysis workflow. We conclude by reflecting on preliminary findings obtained while using the tool on the provided data and future directions concerning the development of the PFNI.
