Biomedical Engineering Reference
In-Depth Information
the genet ic backgrounds of most humans worldwi de [32].
Likewise, epitope s are gener ally limited to 9-21 amino acids
in leng th, mak ing their analysis man ageable. Databases are
available, such as the Immune Epitope Databa se Analys is
Resour ce (IEDB; www.tools.im munoepi tope.org ), which
manual ly curate experimentally characterize d immune epit-
opes [33]. Taken toge ther, amino aci d prefere nces for inter-
acting with differ ent parts of the MH C binding groove can
be obser ved and then extrapolated to predict interact ions
between unknown epitope s and certai n MH C molecu les.
Many compute r-based algo rithms create d to identify
MHC-re stricted T-cell epitope s h ave emerged from the
research com munity over the last 10 or more years (Table
5.1). These in sil ico methods include fre quency analysis,
support- vector machin es, Markov models , and neural net-
works [34-36 ]. The common ground between these tools is
the ability to quic kly scre en large data sets, including whole
genomes, for predict ion of T-cell epitope s. These type s of
predictio ns have successf ully been applied both to vaccine
development [37-41 ] and to the iden tification of key
epitope s trigge ring autoimm unity [42] .
Additiona l tools for immu nogenici ty screeni ng that go
beyond these standard predictio n methods have been devel-
oped by De Groot et al. [9,36,4 3]. Using their EpiM atrix
algori thm, prot ein sequences are parsed into overlapping
9-mer peptide frames, each of which is then evalua ted for
binding pote ntial to each of the eight common MH C class II
HLA alleles that general ly represe nt the natural breadth of
genetic diversity in human HLA [32]. Th is algori thm uses
MHC “pocke t profil es” that descr ibe HLA-bi nding g roove
coeffi cients and applies these coeffi cients to the predict
whethe r a given 9-mer pept ide wi ll bind to a g iven MHC
allele. Th e method initially descr ibed by Sturniolo and
Hamm er [44] has been adapted b y De Groot et al. to generat e
class II predictio n tools [45]. One of the unique features of
this algorithm is that allel e-specifi c scor es are n ormalized,
making it possibl e to com pare scores of any 9-mer acro ss
multipl e HLA allel es and enablin g a more broad asse ssment
of predict ed immu nogenicity [36] . The predictive value of
this algo rithm has been extensively tested and is suppor ted
by publishe d in vitr o and in vivo stud ies [40,45 -50].
Mapping putative e pitopes w ithin a candi da te protein
therapeutic is a s tart ing point for a ssessing t he potential
immunogeni city of a w hole pr otein. It provides informa-
t i o n a b o u t t h e n u m b e r o f e p i t o p e s i n a g iven p r o t e i n a s w e l l
as the tendency of these epitop es to clust er w ithin particu-
lar r egions of a protein. Subregi ons of densely packed
high-scoring
frames
or
“clusters”
of
potential
TABLE 5.1 Epitope Prediction Tools
Name
Developer/Institution
Type
Website
EpiScreen
M. Baker and F. Carr
Antitope, Ltd, Cambridge, UK
Commercial
www.antitope.co.uk/
Epibase
Lasters and P. Stas
Algonomics NV/Lonza, Inc., Gent, Belgium
Commercial
www.lonza.com
EpiMatrix
A.S. De Groot and W.D. Martin
EpiVax, Inc., Providence, RI, USA
Collaborative/
commercial
www.epivax.com
IEDB
Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N,
Damle R, Sette A, Peters B.
The immune epitope database 2.0. Nucleic Acids Res. (2010) January;
38, D854-D862.
Public
www.immuneepitope.com
MHC2PRED
G.P.S. Raghava
Bioinformatics Center, Institute of Microbial Technology,
Chandigarh, India
Public
www.imtech.res.in/
raghava/mhc2pred/
MHCPRED
D.R. Flower
The Jenner Institute, Oxford, UK
Public
www.darrenflower.info/
MHCPRED/
PROPRED/TEPITOPE G.P.S. Raghava and H. Singh
Bioinformatics Center, Institute of Microbial Technology,
Chandigarh, India
Public
www.imtech.res.in/
raghava/propred/
RANKPEP
P.A. Reche
Harvard Medical School, Cambridge, MA, USA
Public
http://bio.dfci.harvard
.edu/RANKPEP/
SVRMHC
P. Donnes, A. Elofsson
Division for Simulation of Biological Systems, University of
Tubingen, Germany
Public
http://svrmhc.biolead.org/
SYFPEITHI
H.G. Rammensee
Department of Immunology, Tubingen, Germany
Public
www.syfpeithi.de/home
.htm
SMM-
Align/NetMHCII-2.2
M. Nielsen, C.Lundegaard, and O. Lund
Center for Biological Sequence Analysis, Department of Systems
Biology, Technical University of Denmark
Public
www.cbs.dtu.dk/services/
NetMHCII-2.2/
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