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.
翻译:影响蛋白质残留物(氨酸)的翻译后修改(PTMs)会扰乱其功能,导致疾病。PTM是否是病原体,取决于其类型和相邻残留物的状况。在本文件中,我们介绍了ProtoFold邻里检查员(PFNI),这是一个用于分析残留物附近的可视化系统。主要贡献是可视化定界,残余星座(RC),用于根据对应物特征和空间特征确定和比较三维邻区。RC利用蛋白质三维结构的二维表解,以克服诸如隔绝、简化对往往有复杂空间安排的邻区的分析等问题。我们利用PFNI,探讨了蛋白质的结构性PTM数据,从而使我们能够确定可能与其病原状态有关的近邻PTM的分布和数量模式。在下文中,我们界定了指导PFNI的发展并描述我们所产生和使用的数据源的任务。然后,我们介绍了通过PIMI数据的初步分析结果,并展示了它的未来发展趋势。我们随后通过PFNI工具分析了数据的发展趋势。