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dc.contributor.authorSingh, V
dc.contributor.authorSharma, A
dc.contributor.authorChandra, A
dc.contributor.authorDehzangi, A
dc.contributor.authorShigemizu, D
dc.contributor.authorTsunoda, T
dc.date.accessioned2020-04-16T21:30:30Z
dc.date.available2020-04-16T21:30:30Z
dc.date.issued2019
dc.identifier.isbn9783030298937
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-29894-4_39
dc.identifier.urihttp://hdl.handle.net/10072/393004
dc.description.abstractPost-transcriptional modification (PTM) in a form of covalently attached proteins like ubiquitin (Ub) are considered an exclusive feature of eukaryotic organisms. Pupylation, a crucial type of PTM of prokaryotic proteins, is modification of lysine residues with a prokaryotic ubiquitin-like protein (Pup) tagging functionally to ubiquitination used by certain bacteria in order to target proteins for proteasomal degradation. Pupylation plays an important role in regulating many biological processes and accurate identification of pupylation sites contributes in understanding the molecular mechanism of pupylation. The experimental technique used in identification of pupylated lysine residues is still a costly and time-consuming process. Thus, several computational predictors have been developed based on protein sequence information to tackle this crucial issue. However, the performance of these predictors are still unsatisfactory. In this work, we propose a new predictor, PSSM-PUP that uses evolutionary information of amino acids to predict pupylated lysine residues. Each lysine residue is defined through its profile bigrams extracted from position specific scoring matrices (PSSM). PSSM-PUP has demonstrated improvement in performance compared to other existing predictors using the benchmark dataset from Pupdb Database. The proposed method achieves highest performance in 10-fold PSSM-PUP with accuracy value of 0.8975, sensitivity value of 0.8731, specificity value of 0.9222, precision value of 0.9222 and Matthews correlation coefficient value of 0.801.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofconferencename16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science
dc.relation.ispartofdatefrom2019-08-26
dc.relation.ispartofdateto2019-08-30
dc.relation.ispartoflocationCuvu, Fiji
dc.relation.ispartofpagefrom488
dc.relation.ispartofpageto500
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.ispartofvolume11672
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleComputational Prediction of Lysine Pupylation Sites in Prokaryotic Proteins Using Position Specific Scoring Matrix into Bigram for Feature Extraction
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationSingh, V; Sharma, A; Chandra, A; Dehzangi, A; Shigemizu, D; Tsunoda, T, Computational Prediction of Lysine Pupylation Sites in Prokaryotic Proteins Using Position Specific Scoring Matrix into Bigram for Feature Extraction, Lecture Notes in Computer Science , 2019, 11672, pp. 488-500
dc.date.updated2020-04-06T23:44:07Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© Springer Nature Switzerland AG 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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