GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network

Tang, Yurong; Chen, Yongzi; Canchaya, Carlos; Zhang, Ziding

doi:10.1093/protein/gzm035

Cited by 58 publications

(29 citation statements)

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“…Recently, Tang et al 9 proposed a new encoding scheme called position-specific amino acid propensity (PSAAP). However, this encoding scheme ignored the sequence-order effects, which would influence the PTM sites.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Prediction of posttranslational modification sites from sequences with kernel methods

2012

J Comput Chem

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Abstract:Posttranslational modification (PTM) is the chemical modification of a protein after its translation and one of the later steps in protein biosynthesis for many proteins. It plays an important role in modifying the end product of gene expression and contributes toward biological processes and diseased conditions. However, the experimental methods for identifying PTM sites are not only costly but also time consuming. Hence, computational methods are highly desired. In this article, a novel encoding method PSDP (position-specific dipeptide propensity), which is a modified version of position-specific amino acid propensity, is developed. Then, a support vector machine SVM with the kernel matrix computed by PSDP is applied to predict the PTM sites related to serine (S) and threonine (T) residues. The numerical results indicate that the performance of new method is better than the existing methods. Therefore, the new method is a useful computational resource for the identification of PTM sites. As the application, a software PTMPred is developed for predicting phosphorylation and O-glycosylation sites, and available at http://www.aporc.org/doc/wiki/PTMPred.

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Section: Introductionmentioning

confidence: 99%

Retracted: Prediction of posttranslational modification sites from sequences with kernel methods

2012

J Comput Chem

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“…To predict general phosphorylation sites, several tools have been developed, such as DISPHOS (2), NetPhos (3), NetPhosYeast (4), and GANNPhos (5). As the need for performing large scale predictions and constructing reliable phosphorylation networks evolves, robust prediction of kinase-specific phosphorylation sites has become necessary and challenging.…”

mentioning

confidence: 99%

GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy

Xue

Ren

Gao

et al. 2008

Molecular & Cellular Proteomics

595

610

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Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs have been developed, numerous PKs have been casually classified into subgroups without a standard rule. For large scale predictions, the false positive rate has also never been addressed. In this work, we adopted a well established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily, and single PK. In addition, we developed a simple approach to estimate the theoretically maximal false positive rates. The on-line service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in Java with the modified version of the Group-based Phosphorylation Scoring algorithm. As the first stand alone software for predicting phosphorylation, GPS 2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large scale prediction of more than 13,000 mammalian phosphorylation sites by GPS 2.0 was exhibited with great performance and remarkable accuracy. Using Aurora-B as an example, we also conducted a proteome-wide search and provided systematic prediction of Aurora-B-specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases and is freely available on line. Molecular & Cellular Proteomics 7:1598 -1608, 2008.Post-translational modification of proteins provides reversible means to regulate the function of a protein in space and time. Recently computational studies of post-translational modifications (PTMs) 1 of proteins have attracted much attention. Various PTMs regulate the functions and dynamics of proteins through specific modifications and are implicated in almost all cellular processes. In contrast to the labor-intensive and expensive experimental methods, in silico prediction of PTM-specific substrates with their sites has emerged as a popular alternative approach. To date, more than 32 computational prediction tools have been developed (1). In the field of computational PTMs, protein phosphorylation is the most studied example. To predict general phosphorylation sites, several tools have been developed, such as DISPHOS (2), NetPhos (3), NetPhosYeast (4), and GANNPhos (5). As the need for performing large scale predictions and constructing reliable phosphorylation networks evolves, robust prediction of kinase-specific phosphorylation sites has become necessary and challenging. For example, Neuberger et al. ) developed NetworKIN and constructed a human phosphorylation network, which has gained diversified interest not only for human phosphorylation network prediction but also for general implication in cell biology. To predict kinase-specific phosphorylation sites, several on-line Web services have been implemented using various algorithms, including our previ...

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“…a fragment of 11 amino acids), one 11-dimensional feature vector (X) was constructed by looking up the corresponding parameters from the above matrix, presented in Equation (6), which was further explained in the following example which was earlier used in [13].…”

Section: Position Specific Amino Acid Propensity (Psaap)mentioning

confidence: 99%

DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data

Islam¹

2017

AJBLS

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Protein Post Translation Modification identification is one of the important steps in conducting disease-associated mutation studies. Though multiple chemical alterations happen in a protein after translation, the addition of succinyl group to lysine residue plays a vital role in regulating cellular metabolism and thus disease. Use of a classification algorithm on some features, driven either from protein structural, physicochemical or even biochemical information becomes a common approach that can yield a satisfactory result up to a certain level. Although, researchers already developed many computational methods to identify whether a lysine residue modified with succinyl group after translation, most of them focused on the improvement either on a single decision using a single method or feature enrichment or even development of a benchmark dataset. Therefore, there still exists scope for further improvement to characterise lysine residues of a protein sequence by considering multiple predictors at a time. In this study, an ensemble based approach called DV-iSucLys has been designed to characterise the lysine residue by adapting three well known and conceptually different classifiers and ensembling their decisions. Also, a benchmark succinylation dataset was extracted from existing benchmark datasets and recently updated succinylation data from UniProt consortium to investigate the performance of the proposed approach as well as contribute to further research. Analysing rigorous cross-validation results show that DV-iSucLys can characterise succinyl lysine residue better than the existing predictors.

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GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network

Cited by 58 publications

References 0 publications

Retracted: Prediction of posttranslational modification sites from sequences with kernel methods

Retracted: Prediction of posttranslational modification sites from sequences with kernel methods

GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy

DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data

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