Protein Classification Based on Analysis of Local Sequence-Structure Correspondence
The goal of this project was to develop an algorithm to detect and calculate common structural motifs in compared structures, and define a set of numerical criteria to be used for fully automated motif based protein structure classification. The Protein Data Bank (PDB) contains more than 33,000 experimentally solved protein structures, and the Structural Classification of Proteins (SCOP) database, a manual classification of these structures, cannot keep pace with the rapid growth of the PDB. In our approach called STRALCP (STRucture Alignment based Clustering of Proteins), we generate detailed information about global and local similarities between given set of structures, identify similar fragments that are conserved within analyzed proteins, and use these conserved regions (detected structural motifs) to classify proteins.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 893991
- Report Number(s):
- UCRL-TR-218946; TRN: US200701%%92
- Country of Publication:
- United States
- Language:
- English
Similar Records
The value of protein structure classification information-Surveying the scientific literature
SCOPe: classification of large macromolecular structures in the structural classification of proteins—extended database