Structural footprinting in protein structure comparison: the impact of structural fragments1 Department of Computer Science, University of Maryland, College Park, MD 20742, USA 2 Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA 3 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
BMC Structural Biology 2007, 7:53doi:10.1186/1472-6807-7-53
Additional filesAdditional file 3: The Python code for the SSEF and SEGF methods. An archive that contains the Python code and auxiliary files necessary to compute: (i) the SEGF footprint from a PDB file of a structure; (ii) the SSEF footprint from a PDB file of a structure; (iii) structural similarity score given PDB files of structures using either SSEF, SEGF or SSEF+SEGF method. Format: ZIP Size: 140KB Download file Additional file 4: The set of training examples used to learn linear combination coefficients. A text file that contains the set of training examples used to learn linear combination coefficients. Format: TXT Size: 101KB Download file Additional file 1: ROC scores for all superfamilies included in the study. An Excel file with performance, in terms of ROC300 scores, for all superfamilies included in the study and across different methods: SSEF, SEGF, LFF, voting, SSEF+SEGF, SSEF+LFF, SEGF+LFF, SSEF+SEGF+LFF. Format: XLS Size: 38KB Download file This file can be viewed with: Microsoft Excel Viewer Additional file 2: Affinity graphs and affinity scores for all three methods. An archive that contains the affinity graphs for all three methods. The graphs are given in Cytoscape's GML format. We also include a separate file for each method giving the affinity scores. Format: ZIP Size: 599KB Download file |




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