An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics

Zhang, Lina; Zhang, Chengjin; Gao, Rui

doi:10.3390/ijms160921734

Cited by 41 publications

(46 citation statements)

References 63 publications

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“…Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4409/9/2/353/s1, Table S1. Performance comparisons between PVPred-SCM with existing methods on independent dataset derived from Zhang et al's method [11]. Table S2.…”

Section: Discussionmentioning

confidence: 99%

“…To validate the robustness of the proposed PVPred-SCM, we further tested and compared its performance using another independent dataset derived from Zhang et al's method [11] (containing 68 PVPs and 92 non-PVPs). Table S1 lists the results of this independent test.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…As noticed in Table 1, PVPred-SCM outperformed the two existing methods, i.e., Feng et al's method and PVPred. Meanwhile, Zhang et al's method obtained better prediction results than PVPred-SCM because the classifier employed was developed using a stack-based ensemble method in conjunction with various sequence features [11].…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Recently, many researchers have exploited various types of machine learning (ML) algorithms using sequence features to directly predict PVPs including Seguritan et al's method [8], Feng et al's method [9], PVPred [10], Zhang et al's method [11], PVP-SVM [12], PhagePred [13], Tan et al's method [14], Ru et al's method [15], and Pred-BVP-Unb [16], as summarized in Table 1. In 2012, Seguritan et al [8] proposed the first predictor to identify viral structural proteins using an artificial neural network cooperating with a feature combination of amino acid composition (AAC) and protein isoelectric points.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, few existing methods were not assessed using an independent dataset, indicating that these methods might provide misleading results with overestimated accuracy. Finally, no web server was provided for many methods in this area including Seguritan et al's method [8], Feng et al's method [9], Zhang et al's method [11], PhagePred [13], Tan et al's method [14], Ru et al's method [15], and Pred-BVP-Unb [16]. Although, these methods have demonstrated good prediction performances, they are not generalized or transferable to researchers with informatics background who can develop in-house prediction models.…”

Section: Introductionmentioning

confidence: 99%

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PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Charoenkwan

Kanthawong

Schaduangrat

et al. 2020

Cells

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Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Charoenkwan

Kanthawong

Schaduangrat

et al. 2020

Cells

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Identifying novel protein phenotype annotations by hybridizing protein–protein interactions and protein sequence similarities

et al. 2016

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Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.

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TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

Arif

Ahmad

Ali

et al. 2020

J Comput Aided Mol Des

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An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics

Cited by 41 publications

References 63 publications

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Identifying novel protein phenotype annotations by hybridizing protein–protein interactions and protein sequence similarities

TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

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