A Prediction Model for Membrane Proteins Using Moments Based Features

Butt, Ahmad Hassan; Khan, Sher Afzal; Hamza, J.; Rasool, Nouman; Khan, Yaser Daanial

doi:10.1155/2016/8370132

Cited by 61 publications

(33 citation statements)

References 38 publications

(36 reference statements)

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“…There are many feature extraction methods and algorithms developed for predicting biological features. For example, Butt et al used statistical moments to extract features and Multilayer Neural Network (MNN) to predict membrane proteins [ 38 ], Akmal et al extracted the protein feature with multiple methods and combined with MNN to identify glycosylation sites [ 39 ], and Ehsan et al used neuro network for classification of signal peptides [ 40 ]. With our dataset, the amino acid location feature extraction method and the SVM algorithm were employed to perform prediction.…”

Section: Discussionmentioning

confidence: 99%

Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria

Zhang

Liu

et al. 2018

PLoS ONE

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As an abundant post-translational modification, reversible phosphorylation is critical for the dynamic regulation of various biological processes. prkC, a critical serine/threonine-protein kinase in bacteria, plays important roles in regulation of signaling transduction. Identification of prkC-specific phosphorylation sites is fundamental for understanding the molecular mechanism of phosphorylation-mediated signaling. However, experimental identification of substrates for prkC is time-consuming and labor-intensive, and computational methods for kinase-specific phosphorylation prediction in bacteria have yet to be developed. In this study, we manually curated the experimentally identified substrates and phosphorylation sites of prkC from the published literature. The analyses of the sequence preferences showed that the substrate recognition pattern for prkC might be miscellaneous, and a complex strategy should be employed to predict potential prkC-specific phosphorylation sites. To develop the predictor, the amino acid location feature extraction method and the support vector machine algorithm were employed, and the methods achieved promising performance. Through 10-fold cross validation, the predictor reached a sensitivity of 91.67% at the specificity of 95.12%. Then, we developed freely accessible software, which is provided at http://free.cancerbio.info/prkc/. Based on the predictor, hundreds of potential prkC-specific phosphorylation sites were annotated based on the known bacterial phosphorylation sites. prkC-PSP was the first predictor for prkC-specific phosphorylation sites, and its prediction performance was promising. We anticipated that these analyses and the predictor could be helpful for further studies of prkC-mediated phosphorylation.

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Section: Discussionmentioning

confidence: 99%

Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria

Zhang

Liu

et al. 2018

PLoS ONE

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“…Raw moments are neither scale-invariant nor location-invariant [ 6 , 21 & 22 ]. The central moments also provide similar information, but these moments are computed along the centroid of the data, which makes it location invariant with respect to the centroid nonetheless it is still scale-variant [ 21 , 22 & 23 ]. Hahn moments are based on Hahn polynomials; these moments are neither scale-invariant nor location-variant.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers have made numerous contributions in developing several computational models to predict an attribute of a protein [ 8 ]. Studies showed that attributes of a protein are reliant not only on the composition of amino acids but also on the sequence in which amino acids occur in the polypeptide chain [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of N-linked glycosylation sites using position relative features and statistical moments

2017

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Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen’s recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites. In this study, a comprehensive technique for the identification of N-linked glycosylation sites has been proposed using machine learning. The proposed predictor was trained using an up-to-date dataset through back propagation algorithm for multilayer neural network. The results of ten-fold cross-validation and other performance measures such as accuracy, sensitivity, specificity and Mathew’s correlation coefficient inferred that the accuracy of proposed tool is far better than the existing systems such as Glyomine, GlycoEP, Ensemble SVM and GPP.

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“…Several properties of data are described by different orders of moments. Some moments are used to reveal eccentricity and orientation of data while some are used to estimate the data size [45]- [48]. Several moments have been formed by various mathematicians and statisticians based on famous distribution functions and polynomials.…”

Section: Statistical Momentsmentioning

confidence: 99%

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Ah¹,

Alkhalaf

Iqbal

et al. 2020

Preprint

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Enhancers are DNA fragments that do not encode RNA molecules and proteins, but they act critically in the production of RNAs and proteins by controlling gene expression. Prediction of enhancers and their strength plays significant role in regulating gene expression. Prediction of enhancer regions, in sequences of DNA, is considered a difficult task due to the fact that they are not close to the target gene, have less common motifs and are mostly tissue/cell specific. In recent past, several bioinformatics tools were developed to discriminate enhancers from other regulatory elements and to identify their strengths as well.However the need for improvement in the quality of its prediction method requires enhancements in its application value practically. In this study, we proposed a new method that builds on nucleotide composition and statistical moment based features to distinguish between enhancers and non-enhancers and additionally determine their strength. Our proposed method achieved accuracy better than current state-of-the-art methods using 5-fold and 10-fold cross-validation. The outcomes from our proposed method suggest that the use of statistical moments based features could bear more efficient and effective results. For the accessibility of the scientific community, we have developed a user-friendly web server for EnhancerP-2L which will increase the impact of bioinformatics on medicinal chemistry and drive medical science into an unprecedented resolution. Web server is freely accessible at http://www.biopred.org/enpred.

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A Prediction Model for Membrane Proteins Using Moments Based Features

Cited by 61 publications

References 38 publications

Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria

Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria

Prediction of N-linked glycosylation sites using position relative features and statistical moments

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

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