126 related articles for article (PubMed ID: 25492406)
1. Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements.
Raimondi D; Orlando G; Vranken WF
Bioinformatics; 2015 Apr; 31(8):1219-25. PubMed ID: 25492406
[TBL] [Abstract][Full Text] [Related]
2. An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model.
Raimondi D; Orlando G; Vranken WF
PLoS One; 2015; 10(7):e0131792. PubMed ID: 26161671
[TBL] [Abstract][Full Text] [Related]
3. Cysteine separations profiles on protein sequences infer disulfide connectivity.
Zhao E; Liu HL; Tsai CH; Tsai HK; Chan CH; Kao CY
Bioinformatics; 2005 Apr; 21(8):1415-20. PubMed ID: 15585533
[TBL] [Abstract][Full Text] [Related]
4. Predicting disulfide bond connectivity in proteins by correlated mutations analysis.
Rubinstein R; Fiser A
Bioinformatics; 2008 Feb; 24(4):498-504. PubMed ID: 18203772
[TBL] [Abstract][Full Text] [Related]
5. Predicting disulfide connectivity patterns.
Lu CH; Chen YC; Yu CS; Hwang JK
Proteins; 2007 May; 67(2):262-70. PubMed ID: 17285623
[TBL] [Abstract][Full Text] [Related]
6. Disulfide connectivity prediction using recursive neural networks and evolutionary information.
Vullo A; Frasconi P
Bioinformatics; 2004 Mar; 20(5):653-9. PubMed ID: 15033872
[TBL] [Abstract][Full Text] [Related]
7. Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.
Song J; Yuan Z; Tan H; Huber T; Burrage K
Bioinformatics; 2007 Dec; 23(23):3147-54. PubMed ID: 17942444
[TBL] [Abstract][Full Text] [Related]
8. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.
Yang J; He BJ; Jang R; Zhang Y; Shen HB
Bioinformatics; 2015 Dec; 31(23):3773-81. PubMed ID: 26254435
[TBL] [Abstract][Full Text] [Related]
9. Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization.
Savojardo C; Fariselli P; Alhamdoosh M; Martelli PL; Pierleoni A; Casadio R
Bioinformatics; 2011 Aug; 27(16):2224-30. PubMed ID: 21715467
[TBL] [Abstract][Full Text] [Related]
10. Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy.
Yaseen A; Li Y
BMC Bioinformatics; 2013; 14 Suppl 13(Suppl 13):S9. PubMed ID: 24267383
[TBL] [Abstract][Full Text] [Related]
11. Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations.
Savojardo C; Fariselli P; Martelli PL; Casadio R
BMC Bioinformatics; 2013; 14 Suppl 1(Suppl 1):S10. PubMed ID: 23368835
[TBL] [Abstract][Full Text] [Related]
12. A simplified approach to disulfide connectivity prediction from protein sequences.
Vincent M; Passerini A; Labbé M; Frasconi P
BMC Bioinformatics; 2008 Jan; 9():20. PubMed ID: 18194539
[TBL] [Abstract][Full Text] [Related]
13. DISULFIND: a disulfide bonding state and cysteine connectivity prediction server.
Ceroni A; Passerini A; Vullo A; Frasconi P
Nucleic Acids Res; 2006 Jul; 34(Web Server issue):W177-81. PubMed ID: 16844986
[TBL] [Abstract][Full Text] [Related]
14. A novel database of disulfide patterns and its application to the discovery of distantly related homologs.
van Vlijmen HW; Gupta A; Narasimhan LS; Singh J
J Mol Biol; 2004 Jan; 335(4):1083-92. PubMed ID: 14698301
[TBL] [Abstract][Full Text] [Related]
15. DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification.
Ferrè F; Clote P
Nucleic Acids Res; 2006 Jul; 34(Web Server issue):W182-5. PubMed ID: 16844987
[TBL] [Abstract][Full Text] [Related]
16. Prediction of the disulfide-bonding state of cysteine in proteins.
Muskal SM; Holbrook SR; Kim SH
Protein Eng; 1990 Aug; 3(8):667-72. PubMed ID: 2217140
[TBL] [Abstract][Full Text] [Related]
17. diSBPred: A machine learning based approach for disulfide bond prediction.
Mishra A; Kabir MWU; Hoque MT
Comput Biol Chem; 2021 Apr; 91():107436. PubMed ID: 33550156
[TBL] [Abstract][Full Text] [Related]
18. DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines.
Lin HH; Tseng LY
Nucleic Acids Res; 2010 Jul; 38(Web Server issue):W503-7. PubMed ID: 20530534
[TBL] [Abstract][Full Text] [Related]
19. Disulfide connectivity prediction with 70% accuracy using two-level models.
Chen BJ; Tsai CH; Chan CH; Kao CY
Proteins; 2006 Jul; 64(1):246-52. PubMed ID: 16615141
[TBL] [Abstract][Full Text] [Related]
20. Improving the accuracy of predicting disulfide connectivity by feature selection.
Zhu L; Yang J; Song JN; Chou KC; Shen HB
J Comput Chem; 2010 May; 31(7):1478-85. PubMed ID: 20127740
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
