Discovery of structural motifs using protein structural alphabets and 1D motif-finding methods.
Although the increasing number of available 3D proteins structures has made a wide variety of computational protein structure research possible, yet the success is still hindered by the high 3D computational complexity. Based on 3D information, several 1D protein structural alphabets have been developed, which can not only describe the global folding structure of a protein as a 1D sequence, but can also characterize local structures in proteins. Instead of applying computationally intensive 3D structure alignment tools, we introduce an approach that combines standard 1D motif detection methods with structural alphabets to discover locally conserved protein motifs. These 1D structural motifs can characterize protein groups at different levels, e.g., families, super families, and folds in SCOP, as group features.
Ku
Shih-Yen
SY
Department of Computer Science, Institute of Biological Engineering, National Chiao Tung Unversity, Hsinchu, Taiwan.
Hu
Yuh-Jyh
YJ
D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein / Q000706:statistics & numerical data*; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data; D020539:Sequence Analysis, Protein / Q000706:statistics & numerical data; D012985:Software Design
D011506:Proteins