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

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

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 pr...

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Bibliographic Details
Main Authors: Xingyue Gu, Zhihua Chen, Donghua Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Bioengineering and Biotechnology
Subjects:
feature extraction
CTD
MRMD2.0
Matplotlib
predict GPCRs
Online Access:https://www.frontiersin.org/article/10.3389/fbioe.2020.00635/full
Description
Summary: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.
ISSN:2296-4185

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