Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information

An, Jiyong; You, Zhu-Hong; Chen, Xing; Huang, De-Shuang; Ge, Yan; Wang, Da-Fu

doi:10.1039/c6mb00599c

Cited by 18 publications

(6 citation statements)

References 30 publications

(35 reference statements)

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“…These algorithmic methods predict PPI from amino acid sequences and their collective information, for instance evolutionary background. Some examples include support vector machine (SVM) 13 , 14 , 23 , rotation forest and decision tree 24 , 25 , Bayesian classification 15 , 16 , Naïve Bayes 26 , relevance vector machine (RVM) 27 , 28 and weighted sparse representation (WSRC) 29 , 30 . These computational algorithms have contributed immensely to the study of PPI in a broad range of organisms, from bacteria 16 to humans 31 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of cassava protein interactome based on interolog method

Thanasomboon

Kalapanulak

Netrphan

et al. 2017

Sci Rep

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Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

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Section: Introductionmentioning

confidence: 99%

Prediction of cassava protein interactome based on interolog method

Thanasomboon

Kalapanulak

Netrphan

et al. 2017

Sci Rep

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show abstract

“…Recently, new feature extraction approaches for PPIs have been developed [30–33]. Among them, Li et al [30] proposed a new method for predicting self-interacting proteins (SIPs) based on amino acid sequences, achieving high precisions of 86.86 and 91.30% on the Saccharomyces cerevisiae and human SIPs datasets, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Li et al [32] developed a new hybrid method of physical chemistry and evolution-based feature extraction methods, which can capture discriminant features from evolution-based information and physicochemical features. An et al [33] explored a new feature representation method based on local binary pattern (LBP), which not only considers the amino acid sequence information but also the evolutionary information of multiple sequence alignments. The above studies show that effective feature extraction methods can mine useful information on protein pairs and improve the performance of PPIs prediction.…”

Section: Discussionmentioning

confidence: 99%

A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences

Wang

et al. 2019

PLoS ONE

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Protein-protein interactions (PPIs) play an important role in the life activities of organisms. With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequence coding methods have emerged, but the training of these methods is particularly time consuming. To solve this issue, we have proposed a novel matrix sequence coding method. Based on deep neural network (DNN) and a novel matrix protein sequence descriptor, we constructed a protein interaction prediction model for predicting PPIs. When performed on human PPIs data, the method achieved an accuracy of 94.34%, a recall of 98.28%, an area under the curve (AUC) of 97.79% and a loss of 23.25%. A non-redundant dataset was used to evaluate this prediction model, and the prediction accuracy is 88.29%. These results indicate that the matrix of sequence (MOS) descriptor can enhance the predictive power of PPIs and reduce training time, which can be a useful complement for future proteomics research. The experimental code and experimental results can be found at https://github.com/smalltalkman/hppi-tensorflow .

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“…The experiments have shown that a PCVM prediction model with a Zernike moments descriptor yields fantastic performance. By further contrast experiment, we found that our proposed method was superior to the state-of-the-art SVM, which clearly shows that the proposed approach is trustworthy in predicting PPIs [35,36,37,38,39]. …”

Section: Introductionmentioning

confidence: 89%

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Wang

You

Xiao

et al. 2017

IJMS

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Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

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Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information

Cited by 18 publications

References 30 publications

Prediction of cassava protein interactome based on interolog method

Prediction of cassava protein interactome based on interolog method

A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

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