Prediction Of The Coupling Specificity Of Neuropeptides And Their Receptors In C.elegans

To date, experimental data show that neuropeptide in C.elegans function eitherdirectly or indirectly to modulate behavior, among the knockout C.elegans analyzedthus far, defects in locomotion, dauer formation, egg laying, ethanol response, andsocial behavior have been reported.Unfortunately, though C.elegans neuropeptides have such important role, we stillknow little about their function. It’s obvious that directly identifying function ofneuropeptides is difficult, so the alternative strategy is to inactivate the receptors towhich the peptides bind then observe defects in behavior of C.elegans. This can beadvantage for determining the function of neuropeptides. In that way, prediction ofcoupling specificity of neuropeptides and their receptors become a key, butligand-receptor that have already been determined through the experiments remainspoorly. So bioinformatic and data mining can be a main method used to tackle thisimportant problem.Sequence data which is widely present in every field of social life is a special datain data mining, so it will be a new research project of data mining about how toexcavate useful information from these complex and mass sequence data, this projecthas important theoretical significance and actual value. This paper takes proteinsequence data for an example for studying the classification of this data set, meanwhile,it is also a subject of bioinformatics.Protein sequences investigated in this paper are neuropeptide receptor that are allG protein coupling receptors, this is a kind of important membrane proteincharacterized by seven-transmembrane domains. Apply bioinformatics strategy toresearch such membrane protein sequences for available information, that is to say, extracted sequence feature can be used to train Hidden Markov Models provided topredict new samples-neuropeptide receptors in C.elegans. Well-trained HMMs canrepresent protein sequence having common feature, it is need to select one bestmatched to a test sequence from these HMMs describing different protein families.With the result that realize classifying and predicting of neuropeptide receptors inC.elegans and providing reliable basis for design of biology experiment.Thereby, this paper will centre on classification and prediction of protein sequencedata, it uses sequence data of neuropeptide as data set which are divided into trainingset and test set. Training set intended for train HMM contain cholecystokinin receptors,galanin receptors, neurotensin receptors, neuropeptide Y receptors, somatostatinreceptors, tachykinin receptors, thyrotropin releasing hormone receptors, vasopressinreceptors of all species excluding nematode worms. Test set used to assess thesegenerated HMMs include17neuropeptide receptors verified by experiment inC.elegans. Every sequence among test set taken as query sequence against theseHMMs, for each HMM, there is a E-value for this query sequence. Selecting the proteinfamily to which the minimum E-value belongs as the prediction of target family. Thissubject has realized the aim of assortment prediction of neuropeptied receptors inC.elegans and provided reliable basis for design of biological experiment of explicitingthe function of neuropeptides.