Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

Kao, Hui-Ju; Hsu, Justin Bo-Kai; Weng, Shun-Long; Lee, Tzong-Yi

doi:10.1186/s12859-018-2394-9

Cited by 25 publications

(28 citation statements)

References 54 publications

(70 reference statements)

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“…CKSAAP reflects the composition of K-spaced amino acid pairs that have been successfully applied in many PTM predictions with a competitive performance [ 30 – 35 ]. CKSAAP counts the occurrence frequencies of the k-spaced amino acid pairs in a peptide sequence.…”

Section: Methodsmentioning

confidence: 99%

Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration

Liu

Wang

et al. 2020

BMC Genomics

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Background Malonylation is a recently discovered post-translational modification that is associated with a variety of diseases such as Type 2 Diabetes Mellitus and different types of cancers. Compared with experimental identification of malonylation sites, computational method is a time-effective process with comparatively low costs. Results In this study, we proposed a novel computational model called Mal-Prec (Malonylation Prediction) for malonylation site prediction through the combination of Principal Component Analysis and Support Vector Machine. One-hot encoding, physio-chemical properties, and composition of k-spaced acid pairs were initially performed to extract sequence features. PCA was then applied to select optimal feature subsets while SVM was adopted to predict malonylation sites. Five-fold cross-validation results showed that Mal-Prec can achieve better prediction performance compared with other approaches. AUC (area under the receiver operating characteristic curves) analysis achieved 96.47 and 90.72% on 5-fold cross-validation of independent data sets, respectively. Conclusion Mal-Prec is a computationally reliable method for identifying malonylation sites in protein sequences. It outperforms existing prediction tools and can serve as a useful tool for identifying and discovering novel malonylation sites in human proteins. Mal-Prec is coded in MATLAB and is publicly available at https://github.com/flyinsky6/Mal-Prec, together with the data sets used in this study.

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Section: Methodsmentioning

confidence: 99%

Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration

Liu

Wang

et al. 2020

BMC Genomics

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“…As in our previous study, the sequence-based features including amino acid composition (AAC), amino acid pair composition (AAPC), BLOSUM62 scoring matrix (B62) and position-specific scoring matrix (PSSM) that were used for the identification of protein carbonylation sites [14]. Note that all these sequence-based features have widely been employed for analysis and prediction of various types of PTM sites in the enormous amount of research [15][16][17]. In this study, the phosphoglycerylated sequences should be transformed into numeric vectors based on the above features to construct a supervised learning model.…”

Section: Features Extraction and Encodingmentioning

confidence: 99%

iDPGK: Characterization and Identification of Lysine Phosphoglycerylation Sites Based on Sequence-Based Features

Hung

Kao

Lau

et al. 2020

Preprint

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Background:Protein phosphoglycerylation, the addition of a 1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus to form a 3-phosphoglyceryl-lysine (pgK), is a reversible and non-enzymatic post-translational modification (PTM) and plays a regulatory role in glucose metabolism and glycolytic process. As the number of experimentally verified phosphoglycerylated sites has increased significantly, statistical or machine learning methods are imperative for investigating the characteristics of phosphoglycerylation sites. Currently, research into phosphoglycerylation is very limited, and only a few resources are available for the computational identification of phosphoglycerylation sites. Result: We present a bioinformatics investigation of phosphoglycerylation sites based on sequence-based features. The TwoSampleLogo analysis reveals that the regions surrounding the phosphoglycerylation sites contain a high relatively of positively charged amino acids, especially in the upstream flanking region. Additionally, the non-polar and aliphatic amino acids are more abundant surrounding phosphoglycerylated lysine following the results of PTM-Logo, which may play a functional role in discriminating between phosphoglycerylation and non- phosphoglycerylation sites. Many types of features were adopted to build the prediction model on the training dataset, including amino acid composition, amino acid pair composition, positional weighted matrix and position-specific scoring matrix. Further, to improve the predictive power, numerous top features ranked by F-score were considered as the final combination for classification, and thus the predictive models were trained using DT, RF and SVM classifiers. Evaluation by five-fold cross-validation showed that the selected features was most effective in discriminating between phosphoglycerylated and non- phosphoglycerylated sites.Conclusion: The SVM model trained with the selected sequence-based features performed well, with a sensitivity of 77.5%, a specificity of 73.6%, an accuracy of 74.9%, and a Matthews Correlation Coefficient value of 0.49. Furthermore, the model also consistently provides the effective performance in independent testing set, yielding sensitivity of 75.7% and specificity of 64.9%. Finally, the model has been implemented as a web-based system, namely iDPGK, which is now freely available at http://mer.hc.mmh.org.tw/iDPGK/.

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“…Later on, we applied CD-HIT [31] over the negative sequences to remove sequences with high sequential similarity. In this case, we use 40% similarity cut-off as it is widely used in the literature [23][24][25]. Due to the limited availability of positive samples compared to the negative samples, the peptides with positive sites remain untouched.…”

Section: Datasetmentioning

confidence: 99%

“…During the past few years, a wide range of methods has been proposed to predict Glutarylation sites using many machine learning approaches [20][21][22][23][24][25]. Recently, many deep learning models have been used to predict different types of PTMs [6,[26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features

Arafat

Ahmad

Shovan

et al. 2020

Genes

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Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew’s Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.

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Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

Cited by 25 publications

References 54 publications

Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration

Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration

iDPGK: Characterization and Identification of Lysine Phosphoglycerylation Sites Based on Sequence-Based Features

Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features

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