A neural network model for the prediction of membrane‐spanning amino acid sequences

Lohmann, Reinhard; Schneider, Gisbert; Behrens, Dirk; Wrede, Paul

doi:10.1002/pro.5560030924

Cited by 46 publications

(17 citation statements)

References 41 publications

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“…Second, the high prediction accuracy (seven confirmed out of seven predicted epitopes) shows again that ANNs trained with evolutionary algorithms are efficient tools for pattern-recognition tasks [31,41,42]. Despite the random selection of negative training data from the p53 protein sequence, the specificity was improved compared to the reference tools mentioned here.…”

Section: Discussionmentioning

confidence: 87%

A Strategy for the Identification of Canonical and Non‐canonical MHC I‐binding Epitopes Using an ANN‐based Epitope Prediction Algorithm

et al. 2006

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Small peptides bound by Major Histocompatibility Complex (MHC) class I molecules and recognized in this context by the T-cell receptor of CD8 þ T cells are known as T-cell epitopes and are of extraordinary importance for the development of new vaccines against cancer and viral infections. Several algorithms predicting a peptides binding capability to a given MHC class I molecule are currently available and have been successfully applied in the identification of new T-cell epitopes within proteins. Most of these newly identified epitopes obey to the empirically determined anchor residue patterns that are specific for the different MHC I alleles. However, in recent studies an increasing number of weakly binding T-cell epitopes could be identified that do not fit to these canonical amino acid patterns. Therefore there is a need for new prediction algorithms improving the prediction accuracy for weakly binding epitopes that are biologically relevant as they are presented by, e.g. antigen presenting cells. Here we describe the development and application of an Artificial Neural Network (ANN)-based T-cell epitope prediction strategy for the MHC class I allele HLA-A*0201, leading to the identification of seven new melanoma-associated T-cell epitopes, that do not fit to the canonical HLA-A*0201 recognition motif. In this strategy the ANN-based epitope prediction is combined with a filter algorithm that incorporates knowledge of peptide chemistry and a ranking procedure that enables the selection of candidate epitopes according to protein family and phylogenetic conservation.

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Section: Discussionmentioning

confidence: 87%

A Strategy for the Identification of Canonical and Non‐canonical MHC I‐binding Epitopes Using an ANN‐based Epitope Prediction Algorithm

et al. 2006

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“…Neural networks have been largely employed in biochemistry and correlated research fields such as protein, DNA/RNA and molecular biology sciences [121][122][123][124][125][126][127].…”

Section: Biochemistrymentioning

confidence: 99%

Applications of Artificial Neural Networks in Chemical Problems

Matarollo¹,

Honório²,

Silva³

2013

Artificial Neural Networks - Architectures and Applications

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“…Prior to application of neural networks to amino acid sequence analysis, the data must be treated with special care to reduce the danger of network 'overlearning'. Often cross-validation experiments can help to evaluate the usefulness of the features extracted (Hirst and Sternberg 1992;Lohmann et al 1994;Rost et al 1993;Schneider et al 1995).…”

Section: Classification Of Dipeptides By Different Types Of Neural Nementioning

confidence: 99%

Development of simple fitness landscapes for peptides by artificial neural filter systems

1995

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The applicability of artificial neural filter systems as fitness functions for sequence-oriented peptide design was evaluated. Two example applications were selected: classification of dipeptides according to their hydrophobicity and classification of proteolytic cleavage-sites of protein precursor sequences according to their mean hydrophobicities and mean side-chain volumes. The cleavage-sites covered 12 residues. In the dipeptide experiments the objective was to separate a selected set of molecules from all other possible dipeptide sequences. Perceptrons, feedforward networks with one hidden layer, and a hybrid network were applied. The filters were trained by a (1, lambda) evolution strategy. Two types of network units employing either a sigmoidal or a unimodal transfer function were used in the feedforward filters, and their influence on classification was investigated. The two-layer hybrid network employed gaussian activation functions. To analyze classification of the different filter systems, their output was plotted in the two-dimensional sequence space. The diagrams were interpreted as fitness landscapes qualifying the markedness of a characteristic peptide feature which can be used as a guide through sequence space for rational peptide design. It is demonstrated that the applicability of neural filter systems as a heuristic method for sequence optimization depends on both the appropriate network architecture and selection of representative sequence data. The networks with unimodal activation functions and the hybrid networks both led to a number of local optima. However, the hybrid networks produced the best prediction results. In contrast, the filters with sigmoidal activation produced good reclassification results leading to fitness landscapes lacking unreasonable local optima. Similar results were obtained for classification of both dipeptides and cleavage-site sequences.

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A neural network model for the prediction of membrane‐spanning amino acid sequences

Cited by 46 publications

References 41 publications

A Strategy for the Identification of Canonical and Non‐canonical MHC I‐binding Epitopes Using an ANN‐based Epitope Prediction Algorithm

A Strategy for the Identification of Canonical and Non‐canonical MHC I‐binding Epitopes Using an ANN‐based Epitope Prediction Algorithm

Applications of Artificial Neural Networks in Chemical Problems

Development of simple fitness landscapes for peptides by artificial neural filter systems

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