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 Laboratory (LLNL), Livermore, CA
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 893991
- Report Number(s):
- UCRL-TR-218946
- Country of Publication:
- United States
- Language:
- English
Similar Records
SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures
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
Journal Article
·
Mon Dec 02 19:00:00 EST 2013
· Nucleic Acids Research
·
OSTI ID:1625521
The value of protein structure classification information-Surveying the scientific literature
Journal Article
·
Wed Aug 26 20:00:00 EDT 2015
· Proteins
·
OSTI ID:1378622
SCOPe: classification of large macromolecular structures in the structural classification of proteins—extended database
Journal Article
·
Thu Nov 29 19:00:00 EST 2018
· Nucleic Acids Research
·
OSTI ID:1625572