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 :
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