Machine Learning & Data Science

MHCAttnNet: Predicting MHC-Peptide Bindings for MHC Alleles Classes I and II Using an Attention-Based Deep Neural Model

This new deep learning model, MHCAttnNet, uses Bi-LSTMs to predict the MHC-peptide binding more accurately than existing methods.

Content provided by Aayush Grover, the co-author of the paper MHCAttnNet: Predicting MHC-Peptide Bindings for MHC Alleles Classes I and II Using an Attention-Based Deep Neural Model.

According to the World Health Organization (WHO), cancer is the second leading cause of death worldwide and was responsible for death of an estimated 9.6 million people in 2018. Research is now focused on personalized cancer vaccines, an approach to help a patient’s own immune system to learn to fight cancer, as a promising weapon in the fight against the disease. The immune system cannot by itself easily distinguish between a healthy and cancerous cell. The way personalized cancer vaccines work is that they externally synthesize a peptide that when passed into the patient helps the immune system identify cancerous cells. This is done by forming a bond between the injected peptide and cancerous cells in the body. Since cancerous cells differ from person to person, such an approach requires analysis to choose the right peptides that can trigger an appropriate immune response. One of the major steps in the synthesis of personalized cancer vaccines is to computationally predict whether a given peptide will bind with the patient’s Major Histocompatibility Complex (MHC) allele. Peptides and MHC alleles are sequences of amino-acids; peptides are shorter versions of proteins and MHC alleles are proteins essential for the adaptivity of the immune system.

A barrier to the easy development of personalized cancer vaccines is the lack of understanding among the scientific community about how exactly the MHC- peptide binding takes place (https://doi.org/10.1186/1471-2105-8-459). Another difficulty is with the need to clinically test different molecules before the vaccine is built, which is resource-intensive task.

This new deep learning model, which we call MHCAttnNet, uses Bi-LSTMs to predict the MHC-peptide binding more accurately than existing methods.

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What’s New: Our model is unique in the way that it not only predicts the binding more accurately, but also highlights the subsequences of amino-acids that are likely to be important in order to make a prediction. This is the first work that works towards understanding the underlying binding mechanism to make a prediction.

How It Works: MHCAttnNet also uses the attention mechanism, a technique from natural language processing, to highlight the important subsequences from the amino-acid sequences of peptides and MHC alleles that were used by the MHCAttnNet model to make the binding prediction. If we see how many times a particular subsequence of the allele gets highlighted with a particular amino-acid of peptide, we can learn a lot about the relationship between the peptide and allele subsequences. This would provide insights on how the MHC-peptide binding actually takes place. The computational model used in the study has predicted that the number of trigrams of amino-acids of the MHC allele that could be of significance for predicting the binding, corresponding to an amino-acid of a peptide, is plausibly around 3% of the total possible trigrams. This reduced list is enabled by what the authors call “sequence reduction,” and will help reduce the work and expense required for clinical trials of vaccines to a large extent.

Key Insights: This work will help researchers develop personalized cancer vaccines by improving the understanding of the MHC-peptide binding mechanism. The higher accuracy of this model will improve the performance of the computational verification step of personalized vaccine synthesis. This, in turn, would improve the likelihood of a personalized cancer vaccine that works on a given patient. Sequence reduction will help focus on a particular few amino acid sequences, which can further facilitate a better understanding of the underlying binding mechanism. This work is also an illustration of how artificial intelligence and machine learning research using cloud-based solutions (we used AWS machines) can make a mark in different domains including medicine, in a much shorter time and at a fraction of the usual cost.

The paper MHCAttnNet: Predicting MHC-Peptide Bindings for MHC Alleles Classes I and II Using an Attention-Based Deep Neural Model is on Oxford Academic.


Meet the authors Aayush Grover from International Institute of Information Technology Bangalore.


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