PMID- 33297954
OWN - NLM
STAT- MEDLINE
DCOM- 20201223
LR  - 20201223
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 21
IP  - 1
DP  - 2020 Dec 9
TI  - iDPGK: characterization and identification of lysine phosphoglycerylation sites 
      based on sequence-based features.
PG  - 568
LID - 10.1186/s12859-020-03916-5 [doi]
LID - 568
AB  - BACKGROUND: Protein phosphoglycerylation, the addition of a 
      1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus 
      to form a 3-phosphoglyceryl-lysine, is a reversible and non-enzymatic 
      post-translational modification (PTM) and plays a regulatory role in glucose 
      metabolism and glycolytic process. As the number of experimentally verified 
      phosphoglycerylated sites has increased significantly, statistical or machine 
      learning methods are imperative for investigating the characteristics of 
      phosphoglycerylation sites. Currently, research into phosphoglycerylation is very 
      limited, and only a few resources are available for the computational 
      identification of phosphoglycerylation sites. RESULT: We present a bioinformatics 
      investigation of phosphoglycerylation sites based on sequence-based features. The 
      TwoSampleLogo analysis reveals that the regions surrounding the 
      phosphoglycerylation sites contain a high relatively of positively charged amino 
      acids, especially in the upstream flanking region. Additionally, the non-polar 
      and aliphatic amino acids are more abundant surrounding phosphoglycerylated 
      lysine following the results of PTM-Logo, which may play a functional role in 
      discriminating between phosphoglycerylation and non-phosphoglycerylation sites. 
      Many types of features were adopted to build the prediction model on the training 
      dataset, including amino acid composition, amino acid pair composition, 
      positional weighted matrix and position-specific scoring matrix. Further, to 
      improve the predictive power, numerous top features ranked by F-score were 
      considered as the final combination for classification, and thus the predictive 
      models were trained using DT, RF and SVM classifiers. Evaluation by five-fold 
      cross-validation showed that the selected features was most effective in 
      discriminating between phosphoglycerylated and non-phosphoglycerylated sites. 
      CONCLUSION: The SVM model trained with the selected sequence-based features 
      performed well, with a sensitivity of 77.5%, a specificity of 73.6%, an accuracy 
      of 74.9%, and a Matthews Correlation Coefficient value of 0.49. Furthermore, the 
      model also consistently provides the effective performance in independent testing 
      set, yielding sensitivity of 75.7% and specificity of 64.9%. Finally, the model 
      has been implemented as a web-based system, namely iDPGK, which is now freely 
      available at http://mer.hc.mmh.org.tw/iDPGK/ .
FAU - Huang, Kai-Yao
AU  - Huang KY
AD  - Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsinchu City 
      300, Taiwan.
AD  - Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan.
FAU - Hung, Fang-Yu
AU  - Hung FY
AD  - Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, 
      Hsinchu City 300, Taiwan.
FAU - Kao, Hui-Ju
AU  - Kao HJ
AD  - Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsinchu City 
      300, Taiwan.
FAU - Lau, Hui-Hsuan
AU  - Lau HH
AD  - Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan. 
      huihsuan1220@gmail.com.
AD  - Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, 
      Hsinchu City 300, Taiwan. huihsuan1220@gmail.com.
AD  - Department of Obstetrics and Gynecology, Mackay Memorial Hospital, Taipei City 
      104, Taiwan. huihsuan1220@gmail.com.
FAU - Weng, Shun-Long
AU  - Weng SL
AD  - Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan. 
      4467@mmh.org.tw.
AD  - Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, 
      Hsinchu City 300, Taiwan. 4467@mmh.org.tw.
AD  - Mackay Junior College of Medicine, Medicine, Nursing and Management College, 
      Taipei City 112, Taiwan. 4467@mmh.org.tw.
LA  - eng
GR  - MOST109-2320-B-195-001/Ministry of Science and Technology, Taiwan/
PT  - Journal Article
DEP - 20201209
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (Proteins)
RN  - K3Z4F929H6 (Lysine)
SB  - IM
MH  - Amino Acid Sequence
MH  - Computational Biology/*methods
MH  - Glycosylation
MH  - Internet
MH  - Lysine/chemistry/*metabolism
MH  - Machine Learning
MH  - Position-Specific Scoring Matrices
MH  - Protein Processing, Post-Translational
MH  - Proteins/chemistry
MH  - ROC Curve
MH  - Reproducibility of Results
MH  - *Software
MH  - Support Vector Machine
PMC - PMC7727188
OTO - NOTNLM
OT  - 3-Phosphoglyceryl-lysine (pgK)
OT  - Post-translational modification (PTM)
OT  - Protein phosphoglycerylation
OT  - Sequence-based features
COIS- The authors have declared that no competing interests exist.
EDAT- 2020/12/11 06:00
MHDA- 2020/12/29 06:00
PMCR- 2020/12/09
CRDT- 2020/12/10 05:35
PHST- 2020/08/10 00:00 [received]
PHST- 2020/11/30 00:00 [accepted]
PHST- 2020/12/10 05:35 [entrez]
PHST- 2020/12/11 06:00 [pubmed]
PHST- 2020/12/29 06:00 [medline]
PHST- 2020/12/09 00:00 [pmc-release]
AID - 10.1186/s12859-020-03916-5 [pii]
AID - 3916 [pii]
AID - 10.1186/s12859-020-03916-5 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2020 Dec 9;21(1):568. doi: 10.1186/s12859-020-03916-5.