DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

Zhang, Xiaoyan; Lu, Long-jian; Song, Qi; Yang, Qianqian; Li, Dapeng; Sun, Jiangming; Li, Tong-hua; Peng, Cong

doi:10.1371/journal.pone.0060559

Cited by 11 publications

(10 citation statements)

References 47 publications

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“…A typical sequence labeling problem is to predict a sequence of categorical labels Y = (y 1 , y 2 ,…, y n ) given a sequence of observations X = (x 1 , x 2 ,…, x i ). CRFs are known to excel in sequence labeling tasks such as part-of-speech tagging and text segmentation in natural language sequences [ 36 , 37 ], image segmentation in video sequences [ 38 ], and gene finding [ 39 ], RNA secondary structural alignment [ 40 ], protein structural instability [ 41 ] and protein domain boundary in biological sequences [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

et al. 2015

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Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew’s correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

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Section: Methodsmentioning

confidence: 99%

Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

et al. 2015

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“… 2011 http://tuat.ac.jp/~domserv/DROP.html [37] DomHR Identify domain boundaries in proteins by defining the edge of domain and boundary regions as a hinge region. 2013 http://cal.tongji.edu.cn/domain/ [39] PDP-CON Combine predicted results from six single domain boundary prediction methods. 2016 https://cmaterju.org/cmaterbioinfo/ [38] ConDo Use long-range, coevolutionary features to train neural networks.…”

Section: Protein Domain Detection Methodsmentioning

confidence: 99%

Protein domain identification methods and online resources

Wang

Zhang

Zhong

et al. 2021

Computational and Structural Biotechnology Journal

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Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods.

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“…PSSM generated by PSI-BLAST has been widely used to represent the evolutionary information of a protein sequence, which was proved to be highly effective in a variety of prediction areas in protein structure bioinformatics, including the prediction of DNA-binding proteins [20] , [23] and sites [43] , function sites [41] , [44] , contact map [45] , [46] , disordered region [47] , domain boundary [48] , [49] , solvent accessibility [37] , to name just a few. The PSSM is a L ×20 matrix, where L is the length of the protein sequence and 20 is the number of amino acid types.…”

Section: Methodsmentioning

confidence: 99%

Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

et al. 2014

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Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.

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DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

Cited by 11 publications

References 47 publications

Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

Protein domain identification methods and online resources

Sequence Based Prediction of DNA-Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

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