Membrane Cholesterol Prediction from Human Receptor using Rough Set based Mean-Shift Approach
محورهای موضوعی : Machine learning
Rudra Kalyan Nayak
1
,
Ramamani Tripathy
2
,
Hitesh Mohapatra
3
,
Amiya Kumar Rath
4
,
Debahuti Mishra
5
1 - School of Computing Science and Engineering,VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, MP, India
2 - Department of Computer Science and Engineering, Chitkara University Himachal Pradesh Campus, Pinjore-Nalagarh National Highway, Dist-Baddi, Himachal Pradesh, India
3 - School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
4 - Department of Computer Science and Engineering,Veer Surendra Sai University of Technology, Burla, Odisha 768018, India
5 - Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India
کلید واژه: GPCR, CRAC, CARC, ANN, Decision Tree, Rough set, Mean shift,
چکیده مقاله :
In human physiology, cholesterol plays an imperative part in membrane cells which regulates the function of G-protein-coupled receptors (GPCR) family. Cholesterol is an individual type of lipid structure and about 90 percent of cellular cholesterol is present at plasma membrane region. Cholesterol Recognition/interaction Amino acid Consensus (CRAC) sequence, generally referred as the CRAC (L/V)-X1−5-(Y)-X1−5-(K/R) and the new cholesterol-binding domain is similar to the CRAC sequence, but exhibits the inverse orientation along the polypeptide chain i.e. CARC (K/R)-X1−5-(Y/F)-X1−5-(L/V). GPCR is treated as a biggest super family in human physiology and probably more than 900 protein genes included in this family. Among all membrane proteins GPCR is responsible for novel drug discovery in all pharmaceuticals industry. In earlier researches the researchers did not find the required number of valid motifs in terms of helices and motif types so they were lacking clinical relevance. The research gap here is that they were not able to predict the motifs effectively which are belonging to multiple motif types. To find out better motif sequences from human GPCR, we explored a hybrid computational model consisting of hybridization of Rough Set with Mean-Shift algorithm. In this paper we made comparison among our resulted output with other techniques such as fuzzy C-means (FCM), FCM with spectral clustering and we concluded that our proposed method targeted well on CRAC region in comparison to CARC region which have higher biological relevance in medicine industry and drug discovery.
In human physiology, cholesterol plays an imperative part in membrane cells which regulates the function of G-protein-coupled receptors (GPCR) family. Cholesterol is an individual type of lipid structure and about 90 percent of cellular cholesterol is present at plasma membrane region. Cholesterol Recognition/interaction Amino acid Consensus (CRAC) sequence, generally referred as the CRAC (L/V)-X1−5-(Y)-X1−5-(K/R) and the new cholesterol-binding domain is similar to the CRAC sequence, but exhibits the inverse orientation along the polypeptide chain i.e. CARC (K/R)-X1−5-(Y/F)-X1−5-(L/V). GPCR is treated as a biggest super family in human physiology and probably more than 900 protein genes included in this family. Among all membrane proteins GPCR is responsible for novel drug discovery in all pharmaceuticals industry. In earlier researches the researchers did not find the required number of valid motifs in terms of helices and motif types so they were lacking clinical relevance. The research gap here is that they were not able to predict the motifs effectively which are belonging to multiple motif types. To find out better motif sequences from human GPCR, we explored a hybrid computational model consisting of hybridization of Rough Set with Mean-Shift algorithm. In this paper we made comparison among our resulted output with other techniques such as fuzzy C-means (FCM), FCM with spectral clustering and we concluded that our proposed method targeted well on CRAC region in comparison to CARC region which have higher biological relevance in medicine industry and drug discovery.
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