scholarly journals DeepDBP: Deep neural networks for identification of DNA-binding proteins

2020 ◽  
Vol 19 ◽  
pp. 100318 ◽  
Author(s):  
Shadman Shadab ◽  
Md Tawab Alam Khan ◽  
Nazia Afrin Neezi ◽  
Sheikh Adilina ◽  
Swakkhar Shatabda
Keyword(s):  
Neural Networks ◽  
Dna Binding ◽  
Binding Proteins ◽  
Deep Neural Networks ◽  
Dna Binding Proteins

A Useful Tool for the Identification of DNA-binding Proteins Using Graph Convolutional Network

2020 ◽  
Vol 18 ◽  
Author(s):  
Dasheng Chen ◽  
Leyi Wei
Keyword(s):  
Neural Networks ◽  
Dna Binding ◽  
Protein Sequence ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Protein Classification ◽  
Sequence Information ◽  
Convolutional Network ◽  
Data Set ◽  
Graph Neural Networks

Background:: Both DNAs and proteins are important components of living organisms. DNA-binding proteins are a kind of helicase, which is a protein specifically responsible for binding to DNA single stranded regions. It plays a key role in the function of various biomolecules. Although there are some prediction methods for the DNA-binding proteins sequences, the use of graph neural networks in this research is still limited. Objective:: In this article, using graph neural networks, we developed a novel predictor GCN-DBP for protein classification prediction. Method:: Each protein sequence is treated as a document in this study, and then document is segmented according to the concept of k-mer. This research aims to use document word relationships and word co-occurrence as a corpus to construct a text graph. Then, the predictor learns protein sequence information by two-layer graph convolutional networks. Results:: In order to compare the proposed method with other four existing methods, we have conducted more experiments. Finally, we tested GCN-DBP on the independent data set PDB2272. Its accuracy reached 64.17% and MCC reached 28.32%. Conclusion:: The results show that the proposed method is superior to the other four methods and will be a useful tool for protein classification.

scholarly journals DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins

2019 ◽  
Author(s):  
Shadman Shadab ◽  
Md Tawab Alam Khan ◽  
Nazia Afrin Neezi ◽  
Sheikh Adilina ◽  
Swakkhar Shatabda
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dna Binding ◽  
Binding Proteins ◽  
Deep Neural Networks ◽  
Defense Mechanism ◽  
Dna Binding Proteins ◽  
Cellular Level ◽  
Test Accuracy ◽  
The Past

AbstractDNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body’s defense mechanism and oxygen transportation. They bind DNAs and interact with them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets.DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://github.com/antorkhan/DNABinding.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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