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Items: 1 to 20 of 94

1.

Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications.

Brusic V, Bajic VB, Petrovsky N.

Methods. 2004 Dec;34(4):436-43. Review.

PMID:
15542369
2.

Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens.

Srinivasan KN, Zhang GL, Khan AM, August JT, Brusic V.

Bioinformatics. 2004 Aug 4;20 Suppl 1:i297-302.

PMID:
15262812
3.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network.

Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L.

Bioinformatics. 1998;14(2):121-30.

PMID:
9545443
4.

Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE.

Bian H, Hammer J.

Methods. 2004 Dec;34(4):468-75.

PMID:
15542373
5.
6.

Cutting edge: identification of novel T cell epitopes in Lol p5a by computational prediction.

de Lalla C, Sturniolo T, Abbruzzese L, Hammer J, Sidoli A, Sinigaglia F, Panina-Bordignon P.

J Immunol. 1999 Aug 15;163(4):1725-9.

7.

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding.

Brusic V, Bucci K, Schönbach C, Petrovsky N, Zeleznikow J, Kazura JW.

J Mol Graph Model. 2001;19(5):405-11, 467.

PMID:
11552688
8.

A structure-based approach for prediction of MHC-binding peptides.

Altuvia Y, Margalit H.

Methods. 2004 Dec;34(4):454-9.

PMID:
15542371
9.

Toward the atomistic simulation of T cell epitopes automated construction of MHC: peptide structures for free energy calculations.

Todman SJ, Halling-Brown MD, Davies MN, Flower DR, Kayikci M, Moss DS.

J Mol Graph Model. 2008 Feb;26(6):957-61. Epub 2007 Jul 28.

PMID:
17766153
10.
11.

T-cell epitopes of the La/SSB autoantigen: prediction based on the homology modeling of HLA-DQ2/DQ7 with the insulin-B peptide/HLA-DQ8 complex.

Kosmopoulou A, Vlassi M, Stavrakoudis A, Sakarellos C, Sakarellos-Daitsiotis M.

J Comput Chem. 2006 Jul 15;27(9):1033-44.

PMID:
16639700
12.

New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

Hattotuwagama CK, Guan P, Doytchinova IA, Flower DR.

Org Biomol Chem. 2004 Nov 21;2(22):3274-83. Epub 2004 Sep 16.

PMID:
15534705
13.

Class II HLA-peptide binding prediction using structural principles.

Mohanapriya A, Lulu S, Kayathri R, Kangueane P.

Hum Immunol. 2009 Mar;70(3):159-69. doi: 10.1016/j.humimm.2008.12.012. Epub 2009 Jan 31.

PMID:
19187794
14.

The use of bioinformatics for identifying class II-restricted T-cell epitopes.

Bian H, Reidhaar-Olson JF, Hammer J.

Methods. 2003 Mar;29(3):299-309. Review.

PMID:
12725795
15.

Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties.

Cui J, Han LY, Lin HH, Zhang HL, Tang ZQ, Zheng CJ, Cao ZW, Chen YZ.

Mol Immunol. 2007 Feb;44(5):866-77. Epub 2006 Jun 27.

PMID:
16806474
16.
17.

Efficient peptide-MHC-I binding prediction for alleles with few known binders.

Jacob L, Vert JP.

Bioinformatics. 2008 Feb 1;24(3):358-66. Epub 2007 Dec 14.

PMID:
18083718
18.

Clinical validation of the "in silico" prediction of immunogenicity of a human recombinant therapeutic protein.

Koren E, De Groot AS, Jawa V, Beck KD, Boone T, Rivera D, Li L, Mytych D, Koscec M, Weeraratne D, Swanson S, Martin W.

Clin Immunol. 2007 Jul;124(1):26-32. Epub 2007 May 9.

PMID:
17490912
19.

Customized predictions of peptide-MHC binding and T-cell epitopes using EPIMHC.

Molero-Abraham M, Lafuente EM, Reche P.

Methods Mol Biol. 2014;1184:319-32. doi: 10.1007/978-1-4939-1115-8_18.

PMID:
25048133
20.

Quantitative online prediction of peptide binding to the major histocompatibility complex.

Hattotuwagama CK, Guan P, Doytchinova IA, Zygouri C, Flower DR.

J Mol Graph Model. 2004 Jan;22(3):195-207.

PMID:
14629978

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