Predicting G-Protein-Coupled Receptor Classes Based on Adaptive K-nearest Neighbor Algorithm
G-Protein-Coupled Receptor (GPCRs) play a key role in cellular signaling networks that regulate various physiological processes. The functions of many of GPCRs are unknown, because they are difficult to crystallize and most of them will not dissolve in normal solvents. This difficulty has motivated and challenged the development of a computational method which can predict the classification of the families and subfamilies of GPCRs based on their primary sequence so as to help us classify drugs. In this paper the adaptive K-nearest neighbor algorithm and protein cellular automata image (CAI) is introduced. Based on the CAI, the complexity measure factors derived from each of the protein sequences concerned are adopted for its Pseudo amino acid composition. GPCRs were categorized into nine subtypes. The overall success rate in identifying GPCRs among their nine family classes was about 83.5％. The high success rate suggests that the adaptive k-nearest neighbor algorithm and protein CAI holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy.
GPCR Adaptive K-nearest Neighbor Algorithm Cellular Automata image
Xuan Xiao Wang-ren Qiu
Computer Department , Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333001, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
4411-4415
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）