Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

Frontiers in Bioengineering and Biotechnology
Xingyue GuDonghua Wang

Abstract

The G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical properties of GPCR redundancy. Matplotlib plots the coordinates to distinguish GPCRs from other protein sequences. The chart data show a clear distinction effect, and there is a well-defined boundary between the two. The experimental results show that our method can predict GPCRs.

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MRMD2
Matplotlab
mRMR
fasta
Matplotlib
DEKRADGSTAGPSTDGNPS

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