Prediction of sumoylation sites in proteins using linear discriminant analysis

Xu, Yan; Ding, Ya-Xin; Deng, Ning; Liu, Liming

doi:10.1016/j.gene.2015.09.072

Cited by 21 publications

(17 citation statements)

References 36 publications

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“…Then, an important shift in direction was proposed by [25], who were the first to use protein structural features for prediction of sumoylation sites, in particular, predicted disorder and confirmation flexibility. And, finally, the most recent methods are pSumo-CD [26] and SUMO_LDA [27]. The former applied a covariance discriminant algorithm in combination with pseudo amino acid composition model.…”

Section: Introductionmentioning

confidence: 99%

HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues

et al. 2019

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Background: Post-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratorybased techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research. Results: This work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew's correlation coefficient (0.78-0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset. Conclusions: The proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method. The extracted data of this study can be accessed at https://github.com/YosvanyLopez/HseSUMO.

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Section: Introductionmentioning

confidence: 99%

HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues

et al. 2019

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“…( 1, 2, , ) [15] is to obtain the frequency difference of kspace amino acids pairs at different positions, and the method reduces the feature dimension relative to CKSAAP [14]. Inspired by MCKSAAP, we propose the PKSAAP (pertinence of k-space amino acid pair) method, which can get the pertinence difference of k-space amino acids pairs between the positive and negative training set.…”

Section: Psaap Featuresmentioning

confidence: 99%

“…The composition of k-spaced amino acid pairs (CKSAAP) method was used to predict Ubiquitination sites by Chen et al [14]. Xu et al [15] developed SUMO_LDA based on position-specific amino acid propensity (PSAAP) and modification of composition of k-space amino acid pair (MCKSAAP) feature extraction methods to predict sumoylation sites. Those methods are also applicable to the amidation sites prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting protein amidation sites by orchestrating amino acid sequence features

Zhao

Gong

2017

J. Phys.: Conf. Ser.

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Abstract. Amidation is the fourth major category of post-translational modifications, which plays an important role in physiological and pathological processes. Identifying amidation sites can help us understanding the amidation and recognizing the original reason of many kinds of diseases. But the traditional experimental methods for predicting amidation sites are often time-consuming and expensive. In this study, we propose a computational method for predicting amidation sites by orchestrating amino acid sequence features. Three kinds of feature extraction methods are used to build a feature vector enabling to capture not only the physicochemical properties but also position related information of the amino acids. An extremely randomized trees algorithm is applied to choose the optimal features to remove redundancy and dependence among components of the feature vector by a supervised fashion. Finally the support vector machine classifier is used to label the amidation sites. When tested on an independent data set, it shows that the proposed method performs better than all the previous ones with the prediction accuracy of 0.962 at the Matthew's correlation coefficient of 0.89 and area under curve of 0.964.

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“…To address this issue, new studies have been introduced to determine PTM sites using computational methods and in particular, machine learning techniques [19,20,21,22,23,24,25,26,27,28,29]. However, there have been a limited focus on predicting sumoylation sites prediction because of its novelty.…”

Section: Introductionmentioning

confidence: 99%

“…Most recent studies employed the concept of pseudo amino acid composition (e.g., pSumo-CD and SUMO-LDA) for extracting evolutionary information. This concept revolutionized PTM research and achieved the best results in predicting sumoylation sites among the state-of-the-art studies [26,35].…”

Section: Introductionmentioning

confidence: 99%

SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure

et al. 2018

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Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: .

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Prediction of sumoylation sites in proteins using linear discriminant analysis

Cited by 21 publications

References 36 publications

HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues

HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues

Predicting protein amidation sites by orchestrating amino acid sequence features

SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure

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