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

Abstract

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.

An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

“…For the A1, A2, A3, A24, B7, B8, B27, B44, B58, and B62 supertype the affinity predictors are based on ANN [12,13]. This prediction method is available at http://cbs.dtu.dk/ services/NetMHC.…”

“…Accordingly, many attempts have been made to predict the outcome of the steps involved in antigen presentation. A number of methods have been developed that very reliably predict the binding affinity of peptides to the different MHC class I alleles [11][12][13][14]. Likewise, methods have been developed that predict the efficiency with which peptides of arbitrary length will be transported by TAP [15,16].…”

“…The majority of these peptides have successfully passed the steps involved in antigen presentation. We used this dataset to develop an improved method for CTL epitope identification by combining the prediction methods for MHC class I affinity, TAP transport efficiency, and C-terminal cleavage, and have demonstrated that the integrative approach has a predictive performance that is superior to predictions of MHC class I affinity alone using either artificial neural networks (ANN) [13], BIMAS [22], or SYFPEITHI [23]. Internal cleavage of peptides may result in the destruction of epitopes.…”

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

“…To identify peptides encoded by CSGs suitable for a targeted immunotherapy, we implemented the artificial neural network (ANN) algorithm 30,31 provided by the immune epitope database IEDB 3.0. 32 RAVEN can apply this ANN algorithm to predict peptide-affinities for different peptide lengths and the most common human and murine MHC-subtypes.…”

“…Therefore, RAVEN uses artificial neural networks (ANN) and a prediction algorithm developed by NetMHC. 30,31 The peptide search service 36 of UniProt is queried via a RESTful web service which API is provided and integrated by Protein Information Resource (PIR) using ApacheLucene for peptide text searches. 36,37 In RAVEN, this approach is available for the most common alleles in human and mouse.…”