PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations

Auliah, Firda Nurul; Nilamyani, Andi Nur; Shoombuatong, Watshara; Alam, Md. Ashad; Hasan, Md. Mehedi; Kurata, Hiroyuki

doi:10.3390/ijms22042120

Cited by 7 publications

(9 citation statements)

References 63 publications

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“…This article used the same data set as that of Aulia ( 15 ). The data set was retrieved and obtained from the publications of PupDB ( 31 ) and contained 233 pupylation proteins which were subject to a cutting of redundancy treatment to remove those sequences that had ≥80% pairwise sequence identity with any other.…”

Section: Methodsmentioning

confidence: 99%

“…Judging from the analysis of the pupylation protein sequence preferred by FN Aulia et al. ( 15 ), it can be seen that the prediction is the best when the window size is 57 with δ = 28.…”

Section: Methodsmentioning

confidence: 99%

“…For predicting pupylation sites, TPC ( 15 , 21 ), AAI ( 22 , 23 ), One-Hot ( 21 , 37 ), PseAAC ( 25 , 26 ), CKSAAP ( 21 , 27 , 28 ), and Word Embedding ( 17 , 18 ) coding schemes were involved in extracting protein fragment [for example, formula (2)] information and are briefly described as follows.…”

Section: Feature Extraction and Methodsmentioning

confidence: 99%

“…Hasan established a web server named pbPUP ( 14 ), which was a profile-based feature method to predict pupylation sites. Recently, FN Auliah developed PUP-Fuse web server for predicting pupylation sites ( 15 ); this algorithm was based on a variety of sequence features to predict pupylation sites. Although these algorithms could output higher specificity, their sensitivity scores are much lower.…”

Section: Introductionmentioning

confidence: 99%

“…In predicting the pupylation protein model, the KNN scoring matrix, the Word Embedding model ( 16 – 18 ), the Synthetic Minority Oversampling Technique(SMOTE) ( 19 ), and Random Under-sampling(RUS) ( 20 ) were applied to enhance the operation engine. Moreover, in the pupylation site prediction model, TPC ( 15 , 21 ), AAI ( 22 , 23 ), One-Hot ( 24 ), PseAAC ( 25 , 26 ), CKSAAP ( 21 , 27 , 28 ), and Word Embedding ( 16 – 18 ) were used for feature extraction, and the chi-square test ( 15 , 29 , 30 ) was used to reduce the dimensionality of the feature space. Both these two models were verified with 10-fold cross-validation and compared with other existing predictors, the performance proved that this work is promising for the issue.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods

et al. 2022

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Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew’s correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.

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Section: Methodsmentioning

confidence: 99%

“…Judging from the analysis of the pupylation protein sequence preferred by FN Aulia et al. ( 15 ), it can be seen that the prediction is the best when the window size is 57 with δ = 28.…”

Section: Methodsmentioning

confidence: 99%

Section: Feature Extraction and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods

et al. 2022

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A tool for feature extraction from biological sequences

Amerifar

Norouzi

Ghandi³

2022

Briefings in Bioinformatics

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With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The methods, however, are practical only when the sequences are converted into feature vectors. Many tools target this task including iLearnPlus, a Python-based tool which supports a rich set of features. In this paper, we propose a holistic tool that extracts features from biological sequences (i.e. DNA, RNA and Protein). These features are the inputs to machine learning models that predict properties, structures or functions of the input sequences. Our tool not only supports all features in iLearnPlus but also 30 additional features which exist in the literature. Moreover, our tool is based on R language which makes an alternative for bioinformaticians to transform sequences into feature vectors. We have compared the conversion time of our tool with that of iLearnPlus: we transform the sequences much faster. We convert small nucleotides by a median of 2.8X faster, while we outperform iLearnPlus by a median of 6.3X for large sequences. Finally, in amino acids, our tool achieves a median speedup of 23.9X.

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PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection

Sohrawordi

Hossain

Hasan

2022

Briefings in Bioinformatics

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A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular mechanism behind the phosphoglycerylation process to develop the drugs for related diseases. But accurately identifying of phosphoglycerylation site from a protein sequence in a laboratory is a very difficult and challenging task. Hence, the construction of an efficient computation model is greatly sought for this purpose. A little number of computational models are currently available for identifying the phosphoglycerylation sites, which are not able to reach their prediction capability at a satisfactory level. Therefore, an effective predictor named PLP_FS has been designed and constructed to identify phosphoglycerylation sites in this study. For the training purpose, an optimal number of feature sets was obtained by fusion of multiple F_Score feature selection techniques from the features generated by three types of sequence-based feature extraction methods and fitted with the support vector machine classification technique to the prediction model. On the other hand, the k-neighbor near cleaning and SMOTE methods were also implemented to balance the benchmark dataset. The suggested model in 10-fold cross-validation obtained an accuracy of 99.22%, a sensitivity of 98.17% and a specificity of 99.75% according to the experimental findings, which are better than other currently available predictors for accurately identifying the phosphoglycerylation sites.

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PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations

Cited by 7 publications

References 63 publications

Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods

Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods

A tool for feature extraction from biological sequences

PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection

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