DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction
Abstract Background Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge...
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| Format: | Article |
| Language: | English |
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BMC
2020-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-020-3342-z |
| _version_ | 1819021950409768960 |
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| author | Niraj Thapa Meenal Chaudhari Sean McManus Kaushik Roy Robert H. Newman Hiroto Saigo Dukka B. KC |
| author_facet | Niraj Thapa Meenal Chaudhari Sean McManus Kaushik Roy Robert H. Newman Hiroto Saigo Dukka B. KC |
| author_sort | Niraj Thapa |
| collection | DOAJ |
| description | Abstract Background Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to − 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. Results Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. Conclusion Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation. |
| first_indexed | 2024-12-21T04:15:15Z |
| format | Article |
| id | doaj.art-4917ff7995944b89bb81b388a0ebb863 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T04:15:15Z |
| publishDate | 2020-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-4917ff7995944b89bb81b388a0ebb8632022-12-21T19:16:20ZengBMCBMC Bioinformatics1471-21052020-04-0121S311010.1186/s12859-020-3342-zDeepSuccinylSite: a deep learning based approach for protein succinylation site predictionNiraj Thapa0Meenal Chaudhari1Sean McManus2Kaushik Roy3Robert H. Newman4Hiroto Saigo5Dukka B. KC6Department of Computational Science and Engineering, North Carolina A&T State UniversityDepartment of Computational Science and Engineering, North Carolina A&T State UniversityDepartment of Computational Science and Engineering, North Carolina A&T State UniversityDepartment of Computer Science, North Carolina A&T State UniversityDepartment of Biology, North Carolina A&T State UniversityFaculty of Information Science and Electrical Engineering, Kyushu UniversityElectrical Engineering and Computer Science Department, Wichita State UniversityAbstract Background Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to − 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. Results Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. Conclusion Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.http://link.springer.com/article/10.1186/s12859-020-3342-zSuccinylationDeep learningConvolutional neural networkRecurrent neural networkLong short-term memoryEmbedding |
| spellingShingle | Niraj Thapa Meenal Chaudhari Sean McManus Kaushik Roy Robert H. Newman Hiroto Saigo Dukka B. KC DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction BMC Bioinformatics Succinylation Deep learning Convolutional neural network Recurrent neural network Long short-term memory Embedding |
| title | DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction |
| title_full | DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction |
| title_fullStr | DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction |
| title_full_unstemmed | DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction |
| title_short | DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction |
| title_sort | deepsuccinylsite a deep learning based approach for protein succinylation site prediction |
| topic | Succinylation Deep learning Convolutional neural network Recurrent neural network Long short-term memory Embedding |
| url | http://link.springer.com/article/10.1186/s12859-020-3342-z |
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