Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine

Zhao, Xiaowei; Zhao, Xiaosa; Bao, Lingling; Zhang, Yonggang; Dai, Jiangyan; Yin, Minghao

doi:10.3390/molecules22111891

Cited by 19 publications

(13 citation statements)

References 36 publications

(46 reference statements)

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“…Among other recent methods, the webservers for glycation, PreGly [40] and iProtGly-SS [42] were not functional when accessed to test their method. In addition, the published codes for Glypre [41] could not be executed in the absence of a guide. Both Glypre and iProtGly-SS employed GlyPse-AAC data for training their classifier and used GlyNN data as comparator dataset.…”

Section: Comparison With Benchmark Prediction Methodsmentioning

confidence: 99%

“…They have considered features from position-specific amino acid propensity (PSAAP) scheme. More recently, Zhao et al proposed Glypre predictor [41] using a combination of features like position conservation, amino acid index and CKSAAP. In addition, Islam et al [42] investigated an even larger set of features that included propensity based features, amino acid composition, physicochemical features and secondary structure motifs for their predictor iProtGly-SS.…”

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confidence: 99%

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GlyStruct: glycation prediction using structural properties of amino acid residues

et al. 2019

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Background: Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Results: We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew's correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold crossvalidation.Conclusion: Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and nonglycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods.

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Section: Comparison With Benchmark Prediction Methodsmentioning

confidence: 99%

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confidence: 99%

GlyStruct: glycation prediction using structural properties of amino acid residues

et al. 2019

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“…The CKSAAP reflects the short-range interactions of residues within the sequence surrounding the PTM site. [52,60,95,96] LC For the sites located in the N-terminal, C-terminal or the middle of a sequence, LC used 3-bit binary to encode this terminal information, i.e. N-terminal for (1, 0, 0), C-terminal for (0, 0, 1) and middle for (0, 1, 0).…”

Section: Cksaapmentioning

confidence: 99%

“…Stand-alone tools are available for KA-predictor [52], Glycation sites Predictor (Glypre) [96], PMeS [119], GPS-MSP [48], SUMOsp [76], UbPred [123], CKSAAP UbSite [60], UbiProber [98] and hCKSAAP UbSite [61]-all of which are reviewed in this study. Providing detailed software installation instructions, with information about dependencies and runtime environment, is therefore strongly suggested, especially considering that it is generally challenging for biologists to use these stand-alone tools on their local machines.…”

Section: Spcmentioning

confidence: 99%

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Chen

Liu

et al. 2018

Briefings in Bioinformatics

110

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Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

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“…Then, machine learning algorithms were adopted to train models. The published predictors about seven types of lysine modified sites are as follows: (1) Acetylation: NetAcet [ 2 ], PAIL [ 3 ], BRABSB-PHKA [ 4 ], PSKAcePred [ 5 ], LAceP [ 6 ], N-Ace [ 7 ], ASEB [ 8 ], ProAcePred [ 9 ] and DeepAcet [ 10 ]; (2) Glycation: GlyNN [ 11 ], PreGly [ 12 ], Gly-PseAAC [ 13 ], Glypre [ 14 ], BPB_GlySite [ 15 ], and iProtGly-SS [ 16 ]; (3) Succinylation: SucPred [ 17 ], iSuc-PseAAC [ 18 ], iSuc-PseOpt [ 19 ], SuccFind [ 20 ], SuccinSite [ 21 ], pSuc-Lys [ 22 ], SSEvol-Suc [ 23 ], and PSuccE [ 24 ]; (4) Ubiquitination: UbPred [ 25 ], CKSAAP_UbSite [ 26 ], UbiProber [ 27 ], UbiNet [ 28 ] and DeepUbi [ 29 ]; (5) SUMO: SUMOpre [ 30 ], SUMmOn [ 31 ] and seeSUMO [ 32 ]; (6) Methylation: AutoMotif Server [ 33 ], MASA [ 34 ], and PSSMe [ 35 ]; (7) Malonylation: MaloPred [ 36 ] and Mal-Lys [ 37 ]. However, these tools cannot implement classification of all potential lysine modified PTMs, only focusing on a single type, which limits the possibility of mining more information and ignores the interconnections of multiple PTMs.…”

Section: Introductionmentioning

confidence: 99%

Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

Yang

Wang

et al. 2021

BMC Bioinformatics

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Background Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein’s function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. Method We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. Results In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN. Conclusions The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

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Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine

Cited by 19 publications

References 36 publications

GlyStruct: glycation prediction using structural properties of amino acid residues

GlyStruct: glycation prediction using structural properties of amino acid residues

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

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