Abstract：
: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.
journal_name
Genes (Basel)journal_title
Genesauthors
Singh V,Sharma A,Dehzangi A,Tsunoda Tdoi
10.3390/genes11121431subject
Has Abstractpub_date
2020-11-28 00:00:00issue
12issn
2073-4425pii
genes11121431journal_volume
11pub_type
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