PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids

Chandra, Abel Avitesh; Sharma, Alok; Dehzangi, Abdollah; Ranganathan, Shoba; Jokhan, Anjeela D.; Chou, Kuo‐Chen; Tsunoda, Tatsuhiko

doi:10.1038/s41598-018-36203-8

Cited by 35 publications

(15 citation statements)

References 92 publications

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“…To avoid such a bias, it is necessary to balance the employed dataset. To deal with this issue, various balancing schemes have been introduced in the literature [42][43][44].…”

Section: Handling Imbalanced Datasetmentioning

confidence: 99%

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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“…To avoid such a bias, it is necessary to balance the employed dataset. To deal with this issue, various balancing schemes have been introduced in the literature [42][43][44].…”

Section: Handling Imbalanced Datasetmentioning

confidence: 99%

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

Arafat

Ahmad

Shovan

et al. 2020

Genes

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“…In some other studies, K-Nearest Neighbors (KNN) strategy, and Neighborhood Cleaning Rule (NCR) were adopted to balance the data [59], [61], [63]. In order to calculate their Euclidean distance, they have tuned the value of k with several thresholds in simultaneous iterations.…”

Section: Addressing Imbalanced Dataset Issuementioning

confidence: 99%

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

et al. 2020

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Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/) INDEX TERMS Cluster centroid based majority under-sampling technique, evolutionary information, light gradient boosting, lysine malonylation, machine learning, post translational modifications.

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“…The identification of phosphoglycerylation and non-phosphoglycerylation sites using computational techniques is becoming popular [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. This shift has been due to the high cost, time-consumption, and inefficiency involved while carrying out experimental methods in laboratories, such as mass spectrometry [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…Later, three more methods for predicting protein phosphoglycerylation sites emerged. They are PhoglyStruct [ 35 ], EvolStruct-Phogly [ 41 ], and Bigram-PGK [ 42 ]. The most recent method, with a web server that allows users to query protein sequences for phosphoglycerylation sites, is called iPGK-PseAAC [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix

Chandra

Sharma

Dehzangi

et al. 2020

Genes

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Background: Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent years, out of which, lysine phosphoglycerylation has emerged as one of the recent developments. The traditional methods of identifying phosphoglycerylated residues, which are experimental procedures such as mass spectrometry, have shown to be time-consuming and cost-inefficient, despite the abundance of proteins being sequenced in this post-genomic era. Due to these drawbacks, computational techniques are being sought to establish an effective identification system of phosphoglycerylated lysine residues. The development of a predictor for phosphoglycerylation prediction is not a first, but it is necessary as the latest predictor falls short in adequately detecting phosphoglycerylated and non-phosphoglycerylated lysine residues. Results: In this work, we introduce a new predictor named RAM-PGK, which uses sequence-based information relating to amino acid residues to predict phosphoglycerylated and non-phosphoglycerylated sites. A benchmark dataset was employed for this purpose, which contained experimentally identified phosphoglycerylated and non-phosphoglycerylated lysine residues. From the dataset, we extracted the residue adjacency matrix pertaining to each lysine residue in the protein sequences and converted them into feature vectors, which is used to build the phosphoglycerylation predictor. Conclusion: RAM-PGK, which is based on sequential features and support vector machine classifiers, has shown a noteworthy improvement in terms of performance in comparison to some of the recent prediction methods. The performance metrics of the RAM-PGK predictor are: 0.5741 sensitivity, 0.6436 specificity, 0.0531 precision, 0.6414 accuracy, and 0.0824 Mathews correlation coefficient.

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PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids

Cited by 35 publications

References 92 publications

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

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

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix

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