OnionMHC: A deep learning model for peptide — HLA-A*02:01 binding predictions using both structure and sequence feature sets

Shikhar Saxena,Sambhavi Animesh,Melissa J Fullwood,Yuguang Mu

doi:10.1142/s2424913020500095

Abstract

The peptide binding to Major Histocompatibility Complex (MHC) proteins is an important step in the antigen-presentation pathway. Thus, predicting the binding potential of peptides with MHC is essential for the design of peptide-based therapeutics. Most of the available machine learning-based models predict the peptide-MHC binding based on the sequence of amino acids alone. Given the importance of structural information in determining the stability of the complex, here we have utilized both the complex structure and the peptide sequence features to predict the binding affinity of peptides to human receptor HLA-A*02:01. To our knowledge, no such model has been developed for the human HLA receptor before that incorporates both structure and sequence-based features. Results: We have applied machine learning techniques through the natural language processing (NLP) and convolutional neural network to design a model that performs comparably with the existing state-of-the-art models. Our model shows that the information from both sequence and structure domains results in enhanced performance in the binding prediction compared to the information from one domain alone. The testing results in 18 weekly benchmark datasets provided by the Immune Epitope Database (IEDB) as well as experimentally validated peptides from the whole-exome sequencing analysis of the breast cancer patients indicate that our model has achieved state-of-the-art performance. Conclusion: We have developed a deep-learning model (OnionMHC) that incorporates both structure as well as sequence-based features to predict the binding affinity of peptides with human receptor HLA-A*02:01. The model demonstrates state-of-the-art performance on the IEDB benchmark dataset as well as the experimentally validated peptides. The model can be used in the screening of potential neo-epitopes for the development of cancer vaccines or designing peptides for peptide-based therapeutics. OnionMHC is freely available at https://github.com/shikhar249/OnionMHC .

Highlights

OnionMHC: a deep learning model for peptide - HLA-A*02:01 binding predictions using both structure and sequence feature sets
The Long Short-Term Memory (LSTM)-based neural network in the sequence module achieved better performance with our structure module compared to the CNN-based neural network in the sequence module as shown in Table 2 (Table S1, Additional File 2)
Our model achieved a better performance both in terms of Area Under Receiver Operating Curve (AUC) and Spearman Rank Correlation Coefficient (SRCC) compared to NetMHCpan4-L, convMHC, and HLAthena and the performance was comparable to NetMHCpan4-B (Table 3)

Summary

Results

We have applied machine learning techniques through the natural language processing (NLP) and convolutional neural network to design a model that performs comparably with the existing state-of-theart models. Our model shows that the information from both sequence and structure domains results in enhanced performance in the binding prediction compared to the information from one domain alone. The testing results in 18 weekly benchmark datasets provided by the Immune Epitope Database (IEDB) as well as experimentally validated peptides from the whole-exome sequencing analysis of the breast cancer patients indicate that our model has achieved state-of-the-art performance

Conclusion

Methods:

Results and Discussions

Experimental results