Predicting Thermophilic Proteins with Pseudo Amino Acid Composition:Approached from Chaos Game Representation and Principal Component Analysis

Lu, Jin-Long; Hu, Xuehai

doi:10.2174/092986611797642661

Cited by 23 publications

(12 citation statements)

References 62 publications

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“…CTF is a fundamental group of features to recognize the interaction of RNA and proteins and was shown to be successful in the majority of published prediction tools [21,23,25,28]. Furthermore, CGR is an important group of features for protein studies and achieved remarkable results in many prediction tools [29][30][31][32][33]. Detailed comparisons with existing tools using RPI369 and RPI2241 demonstrated that the combinations of these two features indeed got achieved improvements, suggesting that our prediction model will be an important tool for RPI prediction.…”

Section: Introductionmentioning

confidence: 71%

“…2. Chaos game representation Chaos game representation (CGR) is another important method to formulate protein sequence and was also successfully used in many protein studies [29][30][31][32][33]. It originally applied the idea of Iterated Function System (IFS) from the fractal theory for generating CGR picture of DNA sequence in 1990 [35], and then was employed to generate CGR picture of protein sequence in 1997 [36].…”

Section: Features Of Proteins For Predictionmentioning

confidence: 99%

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Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation

Wang

2018

Bioengineered

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RNA-protein interactions (RPIs) play a very important role in a wide range of post-transcriptional regulations, and identifying whether a given RNA-protein pair can form interactions or not is a vital prerequisite for dissecting the regulatory mechanisms of functional RNAs. Currently, expensive and time-consuming biological assays can only determine a very small portion of all RPIs, which calls for computational approaches to help biologists efficiently and correctly find candidate RPIs. Here, we integrated a successful computing algorithm, conjoint triad feature (CTF), and another method, chaos game representation (CGR), for representing RNA-protein pairs and by doing so developed a prediction model based on these representations and random forest (RF) classifiers. When testing two benchmark datasets, RPI369 and RPI2241, the combined method (CTF+CGR) showed some superiority compared with four existing tools. Especially on RPI2241, the CTF+CGR method improved prediction accuracy (ACC) from 0.91 (the best record of all published works) to 0.95. When independently testing a newly constructed dataset, RPI1449, which only contained experimentally validated RPIs released between 2014 and 2016, our method still showed some generalization capability with an ACC of 0.75. Accordingly, we believe that our hybrid CTF+CGR method will be an important tool for predicting RPIs in the future.

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Section: Introductionmentioning

confidence: 71%

Section: Features Of Proteins For Predictionmentioning

confidence: 99%

Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation

Wang

2018

Bioengineered

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“…Therefore, it is natural to calculate box-counting dimensions of CGR pictures as an important feature. In fact, box-counting dimension has already been successfully employed as a significant fractal feature of DNA and protein sequences [19][20][21].…”

Section: Gpcr Familymentioning

confidence: 99%

“…In 1997, a similar CGR algorithm was presented by Basu, et al [16] to represent a protein sequence by using a 12-sided regular polygon, each vertex of which represents a class of amino acid residues on the basis of conservative substitutions. Up to present, CGR method has achieved some applications in the studies of bioinformatics [17][18][19][20][21]. Moreover, fractal dimensions (FD) are important features of highly irregular geometries.…”

Section: Introductionmentioning

confidence: 99%

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Nie

Wang

et al. 2015

BME

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Abstract. G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.

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“…CGR algorithm has many applications in the study of bioinformatics [14][15][16][17]. Among them, Yu et al [14] used CGR and multi-fractal analysis to construct a precise phylogenetic tree of bacteria; Yang et al [15] predicted protein structural classes by CGR algorithm and recurrence quantification analysis (RQA).…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein Solubility by the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Fractal Dimension

Niu

Hu²,

Shi³

et al. 2012

PPL

Self Cite

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Obtaining soluble proteins in sufficient concentrations is a major obstacle in various experimental studies. How to predict the propensity of targets in large-scale proteomics projects to be soluble is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) can investigate the patterns hiding in protein sequences, and can visually reveal previously unknown structure. Fractal dimensions are good tools to measure sizes of complex, highly irregular geometric objects. In this paper, we convert each protein sequence into a high-dimensional vector by CGR algorithm and fractal dimension, and then predict protein solubility by these fractal features together with Chou's pseudo amino acid composition features and support vector machine (SVM). We extract and study six groups of features computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test. As the results of comparisons, the group of 445-dimensional vector gets the best results, the average accuracy is 0.8741 and average MCC is 0.7358. The resulting predictor is also compared with existing methods and shows significant improvement.

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Predicting Thermophilic Proteins with Pseudo Amino Acid Composition:Approached from Chaos Game Representation and Principal Component Analysis

Cited by 23 publications

References 62 publications

Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation

Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Predicting Protein Solubility by the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Fractal Dimension

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