PMC:1867812 / 4044-5123 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1867812","sourcedb":"PMC","sourceid":"1867812","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1867812","text":"In this article, we propose a spatio-temporal mining approach to analyze folding trajectories. We extend the spatio-temporal data mining framework that we have developed earlier to analyze and manage such data [5]. This framework is designed to analyze spatio-temporal data produced in several scientific domains. Previously, we have applied this framework to analyze 8732 proteins taken from the Protein Data Bank to identify structural fingerprints for different protein classes (e.g., α-proteins) [6]. Each protein is associated with a set of objects that are extracted from its contact map. We then realize the notion of Spatial Object Association Pattern (SOAP) to effectively capture spatial relationships among such objects, Furthermore, by associating SOAPs with proteins in different protein classes, we have identified multiple types of SOAPs that can potentially function as the structural fingerprints for different protein classes. In this article, we extend such strategies to a new application domain: analyzing and characterizing the folding process of a protein.","tracks":[]}