Reliable prediction of T-cell epitopes using neural networks with novel sequence representations
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. / Nielsen, Morten; Lundegaard, Claus; Worning, Peder; Lauemøller, Sanne Lise; Lamberth, Kasper; Buus, Søren; Brunak, Søren; Lund, Ole.
I: Protein Science, Bind 12, Nr. 5, 2003, s. 1007-17.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Reliable prediction of T-cell epitopes using neural networks with novel sequence representations
AU - Nielsen, Morten
AU - Lundegaard, Claus
AU - Worning, Peder
AU - Lauemøller, Sanne Lise
AU - Lamberth, Kasper
AU - Buus, Søren
AU - Brunak, Søren
AU - Lund, Ole
N1 - Keywords: Amino Acid Sequence; Epitopes, T-Lymphocyte; Genome, Viral; HLA-A2 Antigen; Hepacivirus; Histocompatibility Antigens Class I; Humans; Markov Chains; Models, Molecular; Neural Networks (Computer); Peptides; Protein Binding
PY - 2003
Y1 - 2003
N2 - In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
AB - In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
M3 - Journal article
C2 - 12717023
VL - 12
SP - 1007
EP - 1017
JO - Protein Science
JF - Protein Science
SN - 0961-8368
IS - 5
ER -
ID: 9944078