iDRBP_MMC:

identifying DNA-binding proteins and RNA-binding proteins based on multi-label learning model and motif-based convolutional neural network

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Introduction

A new sequence-based computational predictor for identifying DNA-binding proteins and RNA-binding proteins. In which, multi-label learning and a novel motif-based convolution neural network were employed. It reduced the cross prediction between DNA-binding proteins and RNA-binding proteins and improved the predictive perfermance for identification of DNA-binding proteins and RNA-binding proteins. It is a useful tool for nucleic acid binding protein prediction. The framework and working process of iDRBP_MMC are shown in Fig.1.

iDRBP_MMC web-server
Figure 1. The framwork and working flowchar of iDRBP_MMC.

Data

The training dataset used in this study can be download from below link:

[Training dataset]

The test dataset TEST474 used in this study can be download from below link:

[Test dataset TEST474]

The test dataset PDB255 used in this study can be download from below link:

[Test dataset PDB255]

The dataset EZL used in this study can be download from below link:

[Dataset EZL]

The dataset DRBP206 used in this study can be download from below link:

[Dataset DRBP206]

The 467 structural motifs used in this study can be download from below link:

[Used structural motifs]

The predicted tomato DBPs and tomato RBPs can be download from below link:

[Predicted tomato nucleic acid binding proteins]

Source code

The source codes of iDRBP_MMC can be download from below link:

[Source codes]

Acknowledgments

We acknowledge with thanks the following softwares used in this server:

NCBI-BLAST--For generating PSSM profile;

Tensorflow--For constructing multi-label neural network;

Biopython--For data processing.



CONTACT US

Bin Liu
bliu@bliulab.net
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.

Copyright © by Liu Lab, Harbin Institute of Technology

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