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Items: 1 to 20 of 63

1.

AmylPepPred: Amyloidogenic Peptide Prediction tool.

Nair SS, Reddy NS, Hareesha K.

Bioinformation. 2012;8(20):994-5. doi: 10.6026/97320630008994. Epub 2012 Oct 13.

2.

Exploiting heterogeneous features to improve in silico prediction of peptide status - amyloidogenic or non-amyloidogenic.

Nair SS, Subba Reddy NV, Hareesha KS.

BMC Bioinformatics. 2011;12 Suppl 13:S21. doi: 10.1186/1471-2105-12-S13-S21. Epub 2011 Nov 30.

3.

Prediction of amyloid fibril-forming segments based on a support vector machine.

Tian J, Wu N, Guo J, Fan Y.

BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S45. doi: 10.1186/1471-2105-10-S1-S45.

4.
5.

Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential.

Zhang Z, Chen H, Lai L.

Bioinformatics. 2007 Sep 1;23(17):2218-25. Epub 2007 Jun 28.

PMID:
17599928
6.

Is it possible to predict amyloidogenic regions from sequence alone?

Galzitskaya OV, Garbuzynskiy SO, Lobanov MY.

J Bioinform Comput Biol. 2006 Apr;4(2):373-88.

PMID:
16819789
7.

Consensus prediction of amyloidogenic determinants in amyloid fibril-forming proteins.

Hamodrakas SJ, Liappa C, Iconomidou VA.

Int J Biol Macromol. 2007 Aug 1;41(3):295-300. Epub 2007 Mar 25.

PMID:
17477968
8.

Sequence determinants of amyloid fibril formation.

López de la Paz M, Serrano L.

Proc Natl Acad Sci U S A. 2004 Jan 6;101(1):87-92. Epub 2003 Dec 22.

9.

Switch-peptides: design and characterization of controllable super-amyloid-forming host-guest peptides as tools for identifying anti-amyloid agents.

Camus MS, Dos Santos S, Chandravarkar A, Mandal B, Schmid AW, Tuchscherer G, Mutter M, Lashuel HA.

Chembiochem. 2008 Sep 1;9(13):2104-12. doi: 10.1002/cbic.200800245.

PMID:
18683159
10.

Lysozyme amyloidogenesis is accelerated by specific nicking and fragmentation but decelerated by intact protein binding and conversion.

Mishra R, Sörgjerd K, Nyström S, Nordigården A, Yu YC, Hammarström P.

J Mol Biol. 2007 Feb 23;366(3):1029-44. Epub 2006 Dec 2.

PMID:
17196616
12.

FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence.

Garbuzynskiy SO, Lobanov MY, Galzitskaya OV.

Bioinformatics. 2010 Feb 1;26(3):326-32. doi: 10.1093/bioinformatics/btp691. Epub 2009 Dec 17.

PMID:
20019059
13.

NetCSSP: web application for predicting chameleon sequences and amyloid fibril formation.

Kim C, Choi J, Lee SJ, Welsh WJ, Yoon S.

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W469-73. doi: 10.1093/nar/gkp351. Epub 2009 May 25.

14.

Fibril formation of hsp10 homologue proteins and determination of fibril core regions: differences in fibril core regions dependent on subtle differences in amino acid sequence.

Yagi H, Sato A, Yoshida A, Hattori Y, Hara M, Shimamura J, Sakane I, Hongo K, Mizobata T, Kawata Y.

J Mol Biol. 2008 Apr 11;377(5):1593-606. doi: 10.1016/j.jmb.2008.02.012. Epub 2008 Feb 14.

PMID:
18329043
16.

Classification of protein quaternary structure with support vector machine.

Zhang SW, Pan Q, Zhang HC, Zhang YL, Wang HY.

Bioinformatics. 2003 Dec 12;19(18):2390-6.

PMID:
14668222
17.

Amyloid fibril formation in the context of full-length protein: effects of proline mutations on the amyloid fibril formation of beta2-microglobulin.

Chiba T, Hagihara Y, Higurashi T, Hasegawa K, Naiki H, Goto Y.

J Biol Chem. 2003 Nov 21;278(47):47016-24. Epub 2003 Sep 4.

18.
20.

The structure of a fibril-forming sequence, NNQQNY, in the context of a globular fold.

Guo Z, Eisenberg D.

Protein Sci. 2008 Sep;17(9):1617-23. doi: 10.1110/ps.036368.108. Epub 2008 Jun 13.

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