Multi‐iPPseEvo: A Multi‐label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou′s General PseAAC via Grey System Theory

Qiu, Wang-Ren; Zheng, Quan-Shu; Sun, Bo; Xiao, Xuan

doi:10.1002/minf.201600085

Cited by 35 publications

(7 citation statements)

References 87 publications

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“…Also, our result manifested that it appears that using FDAs is essential for the prediction of acetylation functional class, which had been reported in previous research (Qiu et al, 2016a,b, 2017b), and the information related to subcellular is also important for identifying the PTM proteins. As the growing demand of verification of acetylation sites, we argue that more effort should be input in developing organism-specific predictors for this issue.…”

Section: Resultssupporting

confidence: 77%

“…In fact, we have made some preliminary exploration and attempt on identifying phosphorylated proteins. In Qiu et al (2017a,b), we presented a method for identifying human phosphorylated proteins and a multi-label classifying model for different type of phosphorylated proteins with the help of the General PseAAC concept and gray system theory. Although the results are not so perfect, we still argue that the formulations and models can be applied to this issue, and it may be more powerful when some structure, function or localization information of proteins were added into the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation

Qiu¹,

Xu²,

Xu³

et al. 2019

Front. Bioeng. Biotechnol.

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Acetylation is one of post-translational modification (PTM), which often reacts with acetic acid and brings an acetyl radical to an organic compound. It is helpful to identify acetylation protein correctly for understanding the mechanism of acetylation in biological systems. Although many acetylation sites have been identified by high throughput experimental studies via mass spectrometry, there still are lots of acetylation sites need to be discovered. Computational methods have showed their power for identifying acetylation sites with informatics techniques which usually reduce experiment cost and improve the effectiveness and efficiency. In fact, if there is an approach can distinguish the acetylated proteins from the non-acetylated ones, it is no doubt a very meaningful and effective method for this issue. Here, we proposed a novel computational method for identifying acetylation proteins by extracting features from the conservation information of sequence via gray system model and KNN scores based on the information of functional domain annotation and subcellular localization. The authors have performed the 5-fold cross-validation on three datasets along with much analysis of features and the Relief feature selection algorithm. The obtained accuracies are all satisfactory, as the mean performance, the accuracy is 77.10%, the Matthew's correlation coefficient is 0.5457, and the AUC value is 0.8389. These works might provide useful insights for the related experimental validation, and further studies of other PTM process. For the convenience of related researchers, the web-server named “iACetyP” was established and is accessible at http://www.jci-bioinfo.cn/iAcetyP.

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Section: Resultssupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation

Qiu¹,

Xu²,

Xu³

et al. 2019

Front. Bioeng. Biotechnol.

Self Cite

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“…negative site in total 24669) were also extracted from the same protein sequences to maintain consistency. For formulating any post-translational modification site (PTM), a de-facto standard used by the researchers [19][20][21][22] is to extract the PTM site centred (in this experiment, lysine centred) peptide segments of an optimal size. These peptide segments can be expressed as-…”

Section: Datasetsmentioning

confidence: 99%

“…By doing this, a set of benchmark datasets have been obtained. In PTM Site prediction, the presence of homology and redundancy in the peptide segments may bias or overestimate the performance of the predictors, which is also mentioned by different PTM site researchers [19][20][21][22][23]. This study investigates this issue by considering less than 40% pairwise sequence identity in different initial benchmark datasets (for different segment size) using clustering.…”

Section: S = S ∪ Smentioning

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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“…In order to launch a useful sequence-based statistical predictor for a biological system as demonstrated in a series of recent publications [8,15,[28][29][30][31][32][33][34][35], the Chou's five-step rules [36] should be followed: 1) construct or select a valid benchmark dataset to train and test the predictor, 2) formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted, 3) introduce or develop a powerful algorithm (or engine) to operate the prediction, 4) properly perform cross-validation tests to objectively evaluate its anticipated accuracy, and 5) establish a user-friendly webserver that is accessible to the public.…”

Section: Introductionmentioning

confidence: 99%

mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

Hasan¹,

Ahmad²

2018

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Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as "acetylation", "crotonylation", "methylation" and "succinylation" and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.

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Multi‐iPPseEvo: A Multi‐label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou′s General PseAAC via Grey System Theory

Cited by 35 publications

References 87 publications

Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation

Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation

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

mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

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