Analysis of protein phosphorylation site predictors with an independent dataset

Sikder, Abdur R; Zomaya, Albert Y.

doi:10.1504/ijbra.2009.022461

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…The independent benchmark dataset, named B in this study was collected from the article [17]. It contained 297 phospho-proteins and Table 6 shows the number of positive and negative sites in these proteins.…”

Section: Resultsmentioning

confidence: 99%

“…6 and 7) with 211, 85 and 97 phosphorylated sites of serine, threonine and tyrosine respectively in the article [17] and the performance of the five existing prediction systems (PPSP [6], DISPHOS [8], KinasePhos [11], NetPhosK [10] and Scansite 2.0 [12]) were tested with these 393 annotations. But from Table 6, it can be found that more phosphorylation annotations were done by Phospho.ELM server (ver.…”

Section: Resultsmentioning

confidence: 99%

“…The current set of phosphorylation site prediction systems has recently been analyzed [17]. The analysis also revealed that the existing systems are not generalized in a sense that they were trained mainly with a limited number of proteins having kinase annotations that add noise in the performance of prediction systems when no kinase information is known.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

2010

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BackgroundMost of the existing in silico phosphorylation site prediction systems use machine learning approach that requires preparing a good set of classification data in order to build the classification knowledge. Furthermore, phosphorylation is catalyzed by kinase enzymes and hence the kinase information of the phosphorylated sites has been used as major classification data in most of the existing systems. Since the number of kinase annotations in protein sequences is far less than that of the proteins being sequenced to date, the prediction systems that use the information found from the small clique of kinase annotated proteins can not be considered as completely perfect for predicting outside the clique. Hence the systems are certainly not generalized. In this paper, a novel generalized prediction system, PPRED (Phosphorylation PREDictor) is proposed that ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites.ResultsExperimental results based on cross validations and an independent benchmark reveal the significance of using the evolutionary information alone to classify phosphorylation sites from protein sequences. The prediction performance of the proposed system is better than those of the existing prediction systems that also do not incorporate kinase information. The system is also comparable to systems that incorporate kinase information in predicting such sites.ConclusionsThe approach presented in this paper provides an efficient way to identify phosphorylation sites in a given protein primary sequence that would be a valuable information for the molecular biologists working on protein phosphorylation sites and for bioinformaticians developing generalized prediction systems for the post translational modifications like phosphorylation or glycosylation. PPRED is publicly available at the URL http://www.cse.univdhaka.edu/~ashis/ppred/index.php.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

2010

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“…This dataset is originally from [33], where it was used to assess the performance of existing predictors. The annotations of this dataset are updated based on Release 9.0 of the Phospho.ELM database [11].…”

Section: Methodsmentioning

confidence: 99%

Probabilistic prediction of protein phosphorylation sites using classification relevance units machines

Menor

Baek

Poisson

2012

SIGAPP Appl. Comput. Rev.

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Phosphorylation is an important post-translational modification of proteins that is essential to the regulation of many cellular processes. Although most of the phosphorylation sites discovered in protein sequences have been identified experimentally, the in vivo and in vitro discovery of the sites is an expensive, time-consuming and laborious task. Therefore, the development of computational methods for prediction of protein phosphorylation sites has drawn considerable attention. In this work, we present a kernel-based probabilistic Classification Relevance Units Machine (CRUM) for in silico phosphorylation site prediction. In comparison with the popular Support Vector Machine (SVM) CRUM shows comparable predictive performance and yet provides a more parsimonious model. This is desirable since it leads to a reduction in prediction run-time, which is important in predictions on large-scale data. Furthermore, the CRUM training algorithm has lower run-time and memory complexity and has a simpler parameter selection scheme than the Relevance Vector Machine (RVM) learning algorithm. To further investigate the viability of using CRUM in phosphorylation site prediction, we construct multiple CRUM predictors using different combinations of three phosphorylation site features – BLOSUM encoding, disorder, and amino acid composition. The predictors are evaluated through cross-validation and the results show that CRUM with BLOSUM feature is among the best performing CRUM predictors in both cross-validation and benchmark experiments. A comparative study with existing prediction tools in an independent benchmark experiment suggests possible direction for further improving the predictive performance of CRUM predictors.1

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“…To our knowledge, different protein phosphorylation site predictors have not been intensively benchmarked via some independent datasets in the literature. Sikder and Zomaya (2009) developed an independent dataset, collected from Phospho.ELM, to compare the performances of KinasePhos, NetPhosk, DISPHOS, Scansite and PPSP [21]. However, this independent dataset contained mainly mammalian proteins.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Protein Phosphorylation Site Predictors

Que

Wang²,

Chen³

et al. 2010

PPL

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A series of elegant phosphorylation site prediction methods have been developed, which are playing an increasingly important role in accelerating the experimental characterization of phosphorylation sites in phosphoproteins. In this study, we selected six recently published methods (DISPHOS, NetPhosK, PPSP, KinasePhos, Scansite and PredPhospho) to evaluate their performance. First, we compiled three testing datasets containing experimentally verified phosphorylation sites for mammalian, Arabidopsis and rice proteins. Then, we present the prediction performance of the tested methods on these three independent datasets. Rather than quantitatively ranking the performance of these methods, we focused on providing an understanding of the overall performance of the predictors. Based on this evaluation, we found the following results: i) current phosphorylation site predictors are not effective for practical use and there is substantial need to improve phosphorylation site prediction; ii) current predictors perform poorly when used to predict phosphorylation sites in plant phosphoproteins, suggesting that a rice-specific predictor will be required to obtain confident computational annotation of phosphorylation sites in rice proteomics research; and iii) the tested predictors are complementary to some extent, implying that establishment of a meta-server might be a promising approach to developing an improved prediction system.

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Analysis of protein phosphorylation site predictors with an independent dataset

Cited by 4 publications

References 15 publications

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Probabilistic prediction of protein phosphorylation sites using classification relevance units machines

Evaluation of Protein Phosphorylation Site Predictors

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