Identification of DNA-Binding Proteins via Fuzzy Multiple Kernel Model and Sequence Information

Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive. Objective: In past several years, various computational methods have been developed to detect DNAbinding proteins. However, most of them do not integrate multiple information. Methods: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, we extract several feature and construct multiple kernels. Then, multiple kernels are linear combined by Multiple Kernel Learning (MKL). At last, a final SVM model, constructed by combined kernel, is built to predict DNA-binding proteins. Results: The proposed method is tested on two benchmark data sets. Compared with other existing method, our approach is comparable, even better than other methods on some data sets. Conclusion: We can conclude that MK-SVM is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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

Current Bioinformatics ◽

10.2174/1574893615999200607173829 ◽

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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A Model Stacking Framework for Identifying DNA Binding Proteins by Orchestrating Multi-View Features and Classifiers

Genes ◽

10.3390/genes9080394 ◽

2018 ◽

Vol 9 (8) ◽

pp. 394 ◽

Cited By ~ 9

Author(s):

Xiu-Juan Liu ◽

Xiu-Jun Gong ◽

Hua Yu ◽

Jia-Hui Xu

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Structural Information ◽

Dna Binding Proteins ◽

Feature Representation ◽

Training Dataset ◽

Evolutionary Information ◽

Sequence Information ◽

Coupled Models ◽

Loosely Coupled

Nowadays, various machine learning-based approaches using sequence information alone have been proposed for identifying DNA-binding proteins, which are crucial to many cellular processes, such as DNA replication, DNA repair and DNA modification. Among these methods, building a meaningful feature representation of the sequences and choosing an appropriate classifier are the most trivial tasks. Disclosing the significances and contributions of different feature spaces and classifiers to the final prediction is of the utmost importance, not only for the prediction performances, but also the practical clues of biological experiment designs. In this study, we propose a model stacking framework by orchestrating multi-view features and classifiers (MSFBinder) to investigate how to integrate and evaluate loosely-coupled models for predicting DNA-binding proteins. The framework integrates multi-view features including Local_DPP, 188D, Position-Specific Scoring Matrix (PSSM)_DWT and autocross-covariance of secondary structures(AC_Struc), which were extracted based on evolutionary information, sequence composition, physiochemical properties and predicted structural information, respectively. These features are fed into various loosely-coupled classifiers such as SVM and random forest. Then, a logistic regression model was applied to evaluate the contributions of these individual classifiers and to make the final prediction. When performing on the training dataset PDB1075, the proposed method achieves an accuracy of 83.53%. On the independent dataset PDB186, the method achieves an accuracy of 81.72%, which outperforms many existing methods. These results suggest that the framework is able to orchestrate various predicted models flexibly with good performances.

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PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction

Molecules ◽

10.3390/molecules25010098 ◽

2019 ◽

Vol 25 (1) ◽

pp. 98 ◽

Cited By ~ 1

Author(s):

Changgeng Tan ◽

Tong Wang ◽

Wenyi Yang ◽

Lei Deng

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Dna Binding Proteins ◽

Computational Method ◽

Sequence Information ◽

Feature Subset ◽

Single Stranded Dna ◽

Cell Functions ◽

Double Stranded Dna ◽

Maximal Relevance

Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs.

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DNA-Prot: Identification of DNA Binding Proteins from Protein Sequence Information using Random Forest

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2009.10507281 ◽

2009 ◽

Vol 26 (6) ◽

pp. 679-686 ◽

Cited By ~ 70

Author(s):

K. Krishna. Kumar ◽

Ganesan Pugalenthi ◽

P. N. Suganthan

Keyword(s):

Random Forest ◽

Dna Binding ◽

Protein Sequence ◽

Binding Proteins ◽

Dna Binding Proteins ◽

Sequence Information

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PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method

BioMed Research International ◽

10.1155/2020/7297631 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Jun Wang ◽

Huiwen Zheng ◽

Yang Yang ◽

Wanyue Xiao ◽

Taigang Liu

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Transition Probability ◽

Dna Binding Proteins ◽

Computational Method ◽

Superior Performance ◽

Sequence Information ◽

Experimental Approaches ◽

Wet Lab ◽

Two Stages

DNA-binding proteins (DBPs) play vital roles in all aspects of genetic activities. However, the identification of DBPs by using wet-lab experimental approaches is often time-consuming and laborious. In this study, we develop a novel computational method, called PredDBP-Stack, to predict DBPs solely based on protein sequences. First, amino acid composition (AAC) and transition probability composition (TPC) extracted from the hidden markov model (HMM) profile are adopted to represent a protein. Next, we establish a stacked ensemble model to identify DBPs, which involves two stages of learning. In the first stage, the four base classifiers are trained with the features of HMM-based compositions. In the second stage, the prediction probabilities of these base classifiers are used as inputs to the meta-classifier to perform the final prediction of DBPs. Based on the PDB1075 benchmark dataset, we conduct a jackknife cross validation with the proposed PredDBP-Stack predictor and obtain a balanced sensitivity and specificity of 92.47% and 92.36%, respectively. This outcome outperforms most of the existing classifiers. Furthermore, our method also achieves superior performance and model robustness on the PDB186 independent dataset. This demonstrates that the PredDBP-Stack is an effective classifier for accurately identifying DBPs based on protein sequence information alone.

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Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/524502 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Xin Ma ◽

Jiansheng Wu ◽

Xiaoyun Xue

Keyword(s):

Support Vector Machine ◽

Dna Binding ◽

Binding Proteins ◽

Query Protein ◽

Dna Binding Proteins ◽

Evolutionary Information ◽

Support Vector ◽

Sequence Information ◽

Novel Approach ◽

Matthew’S Correlation Coefficient

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

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A sequence-based multiple kernel model for identifying DNA-binding proteins

BMC Bioinformatics ◽

10.1186/s12859-020-03875-x ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuqing Qian ◽

Limin Jiang ◽

Yijie Ding ◽

Jijun Tang ◽

Fei Guo

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Multiple Kernel Learning ◽

Dna Binding Proteins ◽

Kernel Learning ◽

Detection Methods ◽

Support Vector ◽

Data Sets ◽

System A ◽

Multiple Kernel

Abstract Background DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. Results In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ($$84.19\%$$ 84.19 % ) and PDB186 ($$83.7\%$$ 83.7 % ), respectively. Conclusion Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

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

Current Proteomics ◽

10.2174/1570164618999201210225354 ◽

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.

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