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Items: 1 to 20 of 96

1.

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 15;27(16):2224-30. doi: 10.1093/bioinformatics/btr387.

PMID:
21715467
2.

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 1;23(23):3147-54.

PMID:
17942444
3.

Prediction of disulfide connectivity in proteins.

Fariselli P, Casadio R.

Bioinformatics. 2001 Oct;17(10):957-64.

PMID:
11673241
4.

Disulfide connectivity prediction using recursive neural networks and evolutionary information.

Vullo A, Frasconi P.

Bioinformatics. 2004 Mar 22;20(5):653-9.

PMID:
15033872
5.

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:S10. doi: 10.1186/1471-2105-14-S1-S10.

6.

Cysteine separations profiles on protein sequences infer disulfide connectivity.

Zhao E, Liu HL, Tsai CH, Tsai HK, Chan CH, Kao CY.

Bioinformatics. 2005 Apr 15;21(8):1415-20.

PMID:
15585533
7.

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. doi: 10.1002/jcc.21433.

PMID:
20127740
8.

Prediction of the disulfide-bonding state of cysteine in proteins.

Muskal SM, Holbrook SR, Kim SH.

Protein Eng. 1990 Aug;3(8):667-72.

PMID:
2217140
9.

Predicting disulfide connectivity patterns.

Lu CH, Chen YC, Yu CS, Hwang JK.

Proteins. 2007 May 1;67(2):262-70.

PMID:
17285623
10.

Disulfide connectivity prediction with 70% accuracy using two-level models.

Chen BJ, Tsai CH, Chan CH, Kao CY.

Proteins. 2006 Jul 1;64(1):246-52.

PMID:
16615141
11.

DISULFIND: a disulfide bonding state and cysteine connectivity prediction server.

Ceroni A, Passerini A, Vullo A, Frasconi P.

Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W177-81.

12.

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 15;31(8):1219-25. doi: 10.1093/bioinformatics/btu794.

PMID:
25492406
13.

A simplified approach to disulfide connectivity prediction from protein sequences.

Vincent M, Passerini A, Labbé M, Frasconi P.

BMC Bioinformatics. 2008 Jan 14;9:20. doi: 10.1186/1471-2105-9-20.

14.

On the relevance of sophisticated structural annotations for disulfide connectivity pattern prediction.

Becker J, Maes F, Wehenkel L.

PLoS One. 2013;8(2):e56621. doi: 10.1371/journal.pone.0056621.

15.

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. doi: 10.1093/nar/gkq514.

16.

Prediction of disulfide connectivity from protein sequences.

Chen YC, Hwang JK.

Proteins. 2005 Nov 15;61(3):507-12.

PMID:
16170781
17.

Improving disulfide connectivity prediction with sequential distance between oxidized cysteines.

Tsai CH, Chen BJ, Chan CH, Liu HL, Kao CY.

Bioinformatics. 2005 Dec 15;21(24):4416-9.

PMID:
16223789
18.

Predicting disulfide bond connectivity in proteins by correlated mutations analysis.

Rubinstein R, Fiser A.

Bioinformatics. 2008 Feb 15;24(4):498-504. doi: 10.1093/bioinformatics/btm637.

PMID:
18203772
19.

DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification.

Ferrè F, Clote P.

Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W182-5.

20.

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 1;31(23):3773-81. doi: 10.1093/bioinformatics/btv459.

PMID:
26254435

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