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Article

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

1
Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
2
Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111, Australia
3
Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
4
School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
5
Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
6
Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
7
Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
8
Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
*
Authors to whom correspondence should be addressed.
Genes 2020, 11(12), 1431; https://doi.org/10.3390/genes11121431
Received: 22 October 2020 / Revised: 23 November 2020 / Accepted: 23 November 2020 / Published: 28 November 2020
Post-translational modification (PTM) is a critical biological reaction which adds to the diversification of the proteome. With numerous known modifications being studied, pupylation has gained focus in the scientific community due to its significant role in regulating biological processes. The traditional experimental practice to detect pupylation sites proved to be expensive and requires a lot of time and resources. Thus, there have been many computational predictors developed to challenge this issue. However, performance is still limited. In this study, we propose another computational method, named PupStruct, which uses the structural information of amino acids with a radial basis kernel function Support Vector Machine (SVM) to predict pupylated lysine residues. We compared PupStruct with three state-of-the-art predictors from the literature where PupStruct has validated a significant improvement in performance over them with statistical metrics such as sensitivity (0.9234), specificity (0.9359), accuracy (0.9296), precision (0.9349), and Mathew’s correlation coefficient (0.8616) on a benchmark dataset. View Full-Text
Keywords: post-translational modification (PTM); lysine pupylation; structural features; protein sequences; amino acids; prediction post-translational modification (PTM); lysine pupylation; structural features; protein sequences; amino acids; prediction
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MDPI and ACS Style

Singh, V.; Sharma, A.; Dehzangi, A.; Tsunoda, T. PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids. Genes 2020, 11, 1431. https://doi.org/10.3390/genes11121431

AMA Style

Singh V, Sharma A, Dehzangi A, Tsunoda T. PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids. Genes. 2020; 11(12):1431. https://doi.org/10.3390/genes11121431

Chicago/Turabian Style

Singh, Vineet, Alok Sharma, Abdollah Dehzangi, and Tatushiko Tsunoda. 2020. "PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids" Genes 11, no. 12: 1431. https://doi.org/10.3390/genes11121431

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