290 related articles for article (PubMed ID: 27104833)
21. Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features.
Weng SL; Huang KY; Kaunang FJ; Huang CH; Kao HJ; Chang TH; Wang HY; Lu JJ; Lee TY
BMC Bioinformatics; 2017 Mar; 18(Suppl 3):66. PubMed ID: 28361707
[TBL] [Abstract][Full Text] [Related]
22. Using deep neural networks and biological subwords to detect protein S-sulfenylation sites.
Do DT; Le TQT; Le NQK
Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32613242
[TBL] [Abstract][Full Text] [Related]
23. iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features.
Xu Y; Yang Y; Ding J; Li C
IEEE Trans Nanobioscience; 2018 Oct; 17(4):394-401. PubMed ID: 29994125
[TBL] [Abstract][Full Text] [Related]
24. S-SulfPred: A sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique.
Jia C; Zuo Y
J Theor Biol; 2017 Jun; 422():84-89. PubMed ID: 28411111
[TBL] [Abstract][Full Text] [Related]
25. iPreny-PseAAC: Identify C-terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC.
Xu Y; Wang Z; Li C; Chou KC
Med Chem; 2017; 13(6):544-551. PubMed ID: 28425870
[TBL] [Abstract][Full Text] [Related]
26. DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning.
Xie Y; Luo X; Li Y; Chen L; Ma W; Huang J; Cui J; Zhao Y; Xue Y; Zuo Z; Ren J
Genomics Proteomics Bioinformatics; 2018 Aug; 16(4):294-306. PubMed ID: 30268931
[TBL] [Abstract][Full Text] [Related]
27. GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity.
Chen YJ; Lu CT; Huang KY; Wu HY; Chen YJ; Lee TY
PLoS One; 2015; 10(4):e0118752. PubMed ID: 25849935
[TBL] [Abstract][Full Text] [Related]
28. Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.
Cai B; Jiang X
BMC Bioinformatics; 2016 Mar; 17():116. PubMed ID: 26940649
[TBL] [Abstract][Full Text] [Related]
29. Cy-preds: An algorithm and a web service for the analysis and prediction of cysteine reactivity.
Soylu İ; Marino SM
Proteins; 2016 Feb; 84(2):278-91. PubMed ID: 26685111
[TBL] [Abstract][Full Text] [Related]
30. iDPGK: characterization and identification of lysine phosphoglycerylation sites based on sequence-based features.
Huang KY; Hung FY; Kao HJ; Lau HH; Weng SL
BMC Bioinformatics; 2020 Dec; 21(1):568. PubMed ID: 33297954
[TBL] [Abstract][Full Text] [Related]
31. iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC.
Liu LM; Xu Y; Chou KC
Med Chem; 2017; 13(6):552-559. PubMed ID: 28521678
[TBL] [Abstract][Full Text] [Related]
32. Prediction of posttranslational modification sites from amino acid sequences with kernel methods.
Xu Y; Wang X; Wang Y; Tian Y; Shao X; Wu LY; Deng N
J Theor Biol; 2014 Mar; 344():78-87. PubMed ID: 24291233
[TBL] [Abstract][Full Text] [Related]
33. PredHydroxy: computational prediction of protein hydroxylation site locations based on the primary structure.
Shi SP; Chen X; Xu HD; Qiu JD
Mol Biosyst; 2015 Mar; 11(3):819-25. PubMed ID: 25534958
[TBL] [Abstract][Full Text] [Related]
34. Cysteine-Mediated Redox Regulation of Cell Signaling in Chondrocytes Stimulated With Fibronectin Fragments.
Wood ST; Long DL; Reisz JA; Yammani RR; Burke EA; Klomsiri C; Poole LB; Furdui CM; Loeser RF
Arthritis Rheumatol; 2016 Jan; 68(1):117-26. PubMed ID: 26314228
[TBL] [Abstract][Full Text] [Related]
35. Structural, redox, and mechanistic parameters for cysteine-sulfenic acid function in catalysis and regulation.
Claiborne A; Mallett TC; Yeh JI; Luba J; Parsonage D
Adv Protein Chem; 2001; 58():215-76. PubMed ID: 11665489
[TBL] [Abstract][Full Text] [Related]
36. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC.
Ju Z; Cao JZ; Gu H
J Theor Biol; 2016 May; 397():145-50. PubMed ID: 26908349
[TBL] [Abstract][Full Text] [Related]
37. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.
Qiu WR; Xiao X; Xu ZC; Chou KC
Oncotarget; 2016 Aug; 7(32):51270-51283. PubMed ID: 27323404
[TBL] [Abstract][Full Text] [Related]
38. Physicochemical sequence characteristics that influence S-palmitoylation propensity.
Reddy KD; Malipeddi J; DeForte S; Pejaver V; Radivojac P; Uversky VN; Deschenes RJ
J Biomol Struct Dyn; 2017 Aug; 35(11):2337-2350. PubMed ID: 27498722
[TBL] [Abstract][Full Text] [Related]
39. iCysMod: an integrative database for protein cysteine modifications in eukaryotes.
Wang P; Zhang Q; Li S; Cheng B; Xue H; Wei Z; Shao T; Liu ZX; Cheng H; Wang Z
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33406221
[TBL] [Abstract][Full Text] [Related]
40. SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties.
Hasan MM; Yang S; Zhou Y; Mollah MN
Mol Biosyst; 2016 Mar; 12(3):786-95. PubMed ID: 26739209
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
