DocumentCode
2687981
Title
Prediction of Protein Catalytic Residues by Local Structural Rigidity
Author
Chien, Yu-Tung ; Huang, Shao-Wei
Author_Institution
Dept. of Med. Inf., Tzu Chi Univ., Hualien, Taiwan
fYear
2012
fDate
4-6 July 2012
Firstpage
592
Lastpage
596
Abstract
Due to the large number of protein structures whose functions are unknown, it becomes increasing important to study the structural characteristics of catalytic residues. Here, we use a novel method to calculate the local structural rigidity (LSR) of protein. Based on a dataset of 760 proteins, the results show that catalytic residues have distinct structural properties. They are shown to be extremely rigid based on the calculation of LSR. Finally, we present a machine-learning based method to predict catalytic residues from protein structure using LSR as primary input feature. The prediction sensitivity and specificity are 0.86 and 0.86, respectively, and the Matthew´s correlation coefficient is 0.72.
Keywords
biology computing; catalysis; learning (artificial intelligence); molecular biophysics; molecular configurations; proteins; LSR; Matthew correlation coefficient; local structural rigidity; machine-learning based method; prediction sensitivity; protein catalytic residues; protein dataset; protein structure; structural characteristics; Amino acids; Bioinformatics; Proteins; Sensitivity; Solvents; Support vector machines; catalytic site prediction; local structural rigidity; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Complex, Intelligent and Software Intensive Systems (CISIS), 2012 Sixth International Conference on
Conference_Location
Palermo
Print_ISBN
978-1-4673-1233-2
Type
conf
DOI
10.1109/CISIS.2012.99
Filename
6245633
Link To Document