Reliable prediction of T-cell epitopes using neural networks with novel sequence representations

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

SN - 0961-8368

VL - 12

SP - 1007

EP - 1017

JO - Protein Science

JF - Protein Science

IS - 5

ER -