Probabilistic expression of spatially varied amino acid dimers into general form of Chou׳s pseudo amino acid composition for protein fold recognition

Saini, Harsh; Raicar, Gaurav; Sharma, Alok; Lal, Sunder; Dehzangi, Abdollah; Lyons, James; Paliwal, Kuldip K.; Imoto, Seiya; Miyano, Satoru

doi:10.1016/j.jtbi.2015.05.030

Cited by 25 publications

(18 citation statements)

References 80 publications

(79 reference statements)

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“…Until now, a number of feature extraction methods based on protein sequences have been proposed. Most of these methods are based on Chou's pseudoamino acid composition (PseAAC) [ 15 , 16 ]. PseAAC expends the simple amino acid composition (AAC) by considering and retaining the information of sequence order.…”

Section: Introductionmentioning

confidence: 99%

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

Huang

You

Gao

et al. 2015

BioMed Research International

100

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Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.

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Section: Introductionmentioning

confidence: 99%

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

Huang

You

Gao

et al. 2015

BioMed Research International

100

View full text Add to dashboard Cite

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“…Each fold has at least 11 training examples. The accuracy of our classifiers are shown in Table 1 along with the results reported by several other published methods121516171819. Since some of the sequences in these benchmarks are similar to the templates that make up our feature space (that is, similar to the sequences of the domains from which the templates are derived), we ran our classifiers both with and without filtering of these template-similar sequences (Table 1; “filtered” versions correspond to benchmarks where sequences with >25% pairwise identity with any template were removed; see Methods).…”

Section: Resultsmentioning

confidence: 99%

“…Dehzangi et al ., Saini et al ., and Lyons et al . all used a version of EDD that had the same 27 folds, but 21 extra domains151618. This is only a small fraction of the total number of domains in this dataset, so we do not expect this to have a major impact on the results.…”

Section: Methodsmentioning

confidence: 99%

Complete fold annotation of the human proteome using a novel structural feature space

Middleton

Illuminati

Kim

2017

Sci Rep

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“…Each fold has at least 11 training examples. The accuracy of our classifiers are shown in Table 1 along with several other published methods 12,[15][16][17][18][19][20] . Our SVM classifier performed the best on all three benchmarks, with the exception of the EDD dataset, where the best performance was from the method of Zakeri et al when it was used in combination with known Interpro functional annotations.…”

Section: Fold Recognition Performancementioning

confidence: 99%

Complete fold annotation of the human proteome using a novel structural feature space

Middleton

Illuminati

Kim

2016

Preprint

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Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.Although protein sequences can theoretically form a vast range of structures, the number of distinct three-dimensional topologies ("folds") actually observed in nature appears to be both finite and relatively small 1 : 1,221 folds are currently recognized in the SCOPe (Structural Classification of Proteins-extended) database 2 , and the rate of new fold discoveries has diminished greatly over the past two decades. Nevertheless, extending the catalog of protein fold diversity is still an important problem and fold classifying the entire proteome of an organism can lead to important insights about protein function [3][4][5] . Large-scale fold prediction typically involves computational methods, and the computational difficulty of ab initio structure prediction has led to template matching (e.g., using methods such as HHPred 6 ) as the most common method for predicting the structure. When sequence-based matching is difficult, other fold recognition approaches must be employed, such as protein threading. Threading-based methods, especially those that combine information from multiple templates, have been among the most successful algorithms in recent competitions for fold prediction 7,8 , but are bottlenecked by long run times. Machine learning-based methods have also been used, which can be designed either to recognize pairs of proteins with the same fold 9,10 or classify a protein into a fold 11,12 . Although these methods have shown promising results for a subset of folds, they have so far not been able to generalize to the full-scale fold recognition problem. This failure can mainly be attributed to the severe lack of training data available for most SCOPe folds, as well as the highly multi-class nature of the full problem, which requires distinguishing between over 1,000 different folds 12 . Here we introduce a method for full-scale fold recognition that integrates aspects of both threading and machine learning. At the core of our method is a novel feature space constructed by threading protein sequences against a relatively small set of structure templates. These templates...

show abstract

Probabilistic expression of spatially varied amino acid dimers into general form of Chou׳s pseudo amino acid composition for protein fold recognition

Cited by 25 publications

References 80 publications

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

Complete fold annotation of the human proteome using a novel structural feature space

Complete fold annotation of the human proteome using a novel structural feature space

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