Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix

Waris, Muhammad Adeel; Ahmad, Khurshid; Kabir, Muhammad; Hayat, Maqsood

doi:10.1016/j.neucom.2016.03.025

Cited by 64 publications

(28 citation statements)

References 62 publications

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“…The existing SVM-based predictive methods differ in encoding schemes for protein sequences. A great number of sequence features have been applied to represent protein sequences into fixed-length numeric vectors, such as amino acid composition (AAC) [17], dipeptide composition [18], pseudo-AAC [19][20][21][22], position-specific score matrix (PSSM) profile [23][24][25][26][27], predicted secondary structure [28], and hidden Markov model (HMM) profile [29].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Kumar et al [24] first adopted the PSSM profile to identify DBPs and constructed an SVM model called DNAbinder. Waris et al [25] further developed a classifier by integrating the PSSM profile and other two protein representations, i.e., dipeptide composition and split AAC. Besides, the method of Wang et al [26] applied the discrete cosine transform and the discrete wavelet transform to compress the PSSM profile and achieved excellent prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Sang

Xiao

Zheng

et al. 2020

Computational and Mathematical Methods in Medicine

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Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto-and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Sang

Xiao

Zheng

et al. 2020

Computational and Mathematical Methods in Medicine

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“…In contrast, the sequence-based methods have become more popular since sequence features are usually easier to extract and more convenient to use. These sequence-based features of proteins are classified into three types: (1) composition-based features, such as amino acid composition (AAC) [9], dipeptide composition [10], and pseudo AAC [11][12][13]; (2) autocorrelation-based features, including autocross covariance [14,15], normalized Moreau-Broto autocorrelation [8], and physicochemical distance transformation [16]; and (3) profile-based features, including position-specific score matrix (PSSM) [17][18][19] and hidden markov model (HMM) [20]. Generally, autocorrelation-based features perform better than composition-based features, and profile-based features outperform autocorrelation-based features [21].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Kumar et al initially adopted evolutionary information embedded in the PSSM profile to identify DBPs and achieved a well-performed result [17]. Waris et al produced an ingenious classifier by integrating the PSSM profile with dipeptide composition and split AAC [18]. Zou et al proposed a fuzzy kernel ridge regression model to predict DBPs based on multiview sequence features [22].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zheng

Yang

et al. 2020

BioMed Research International

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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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“…Until now, several groups have published different studies based on either experimental or computational identification of DNA-binding proteins [1,[6][7][8][9][10][11] as well as residues in these proteins [12][13][14][15][16][17][18][19][20][21][22][23]. However, the usage of experimental approaches for the determination of binding sites is still challenging since they are often demanding, relatively expensive, and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

Dang

Meckbach

Tacke

et al. 2016

Entropy

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Abstract:The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure-or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen-Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC).

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Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix

Cited by 64 publications

References 62 publications

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

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

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

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