Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix
Abstract Background The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of th...
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Molecular and Cell Biology |
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| Online Access: | https://doi.org/10.1186/s12860-019-0240-1 |
| _version_ | 1819275003515895808 |
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| author | Abel Chandra Alok Sharma Abdollah Dehzangi Daichi Shigemizu Tatsuhiko Tsunoda |
| author_facet | Abel Chandra Alok Sharma Abdollah Dehzangi Daichi Shigemizu Tatsuhiko Tsunoda |
| author_sort | Abel Chandra |
| collection | DOAJ |
| description | Abstract Background The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification. Results We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively. Conclusions The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK. |
| first_indexed | 2024-12-23T23:17:25Z |
| format | Article |
| id | doaj.art-c428b4bc6e0a461098c849082c25c3a7 |
| institution | Directory Open Access Journal |
| issn | 2661-8850 |
| language | English |
| last_indexed | 2024-12-23T23:17:25Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Molecular and Cell Biology |
| spelling | doaj.art-c428b4bc6e0a461098c849082c25c3a72022-12-21T17:26:28ZengBMCBMC Molecular and Cell Biology2661-88502019-12-0120S21910.1186/s12860-019-0240-1Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrixAbel Chandra0Alok Sharma1Abdollah Dehzangi2Daichi Shigemizu3Tatsuhiko Tsunoda4School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South PacificSchool of Engineering and Physics, Faculty of Science Technology and Environment, University of the South PacificDepartment of Computer Science, Morgan State UniversityDepartment of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU)Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU)Abstract Background The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification. Results We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively. Conclusions The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK.https://doi.org/10.1186/s12860-019-0240-1Post-translational modificationPhosphoglycerylationLysine residueComputational techniqueEvolutionary information |
| spellingShingle | Abel Chandra Alok Sharma Abdollah Dehzangi Daichi Shigemizu Tatsuhiko Tsunoda Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix BMC Molecular and Cell Biology Post-translational modification Phosphoglycerylation Lysine residue Computational technique Evolutionary information |
| title | Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix |
| title_full | Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix |
| title_fullStr | Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix |
| title_full_unstemmed | Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix |
| title_short | Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix |
| title_sort | bigram pgk phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix |
| topic | Post-translational modification Phosphoglycerylation Lysine residue Computational technique Evolutionary information |
| url | https://doi.org/10.1186/s12860-019-0240-1 |
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