New Algorithm Accurately Predicts Immune Response to Peptides.
Listeria monocytogenes. A machine learning-based algorithm could predict the potential of peptides as immune activators.
A team of Georgian Technical University researchers have developed a deep neural network-based algorithm dubbed BOTA (Bacteria Originated T cell Antigen) that can predict — based on a bacterial genome data — the peptides with the best chance to trigger an immune response.
In the immune system, T cells are kept under control by regulating precisely when they are able to respond to a pathogen. For example helper T cells will only turn on if other immune cells — like antigen-presenting cells (APCs) — present bacterial peptides on their surface in a protein complex called MHC (The major histocompatibility complex is a set of cell surface proteins essential for the acquired immune system to recognize foreign molecules in vertebrates, which in turn determines histocompatibility) class II.
Not every bacterial peptide is immunodominant—where they get loaded into MHC (The major histocompatibility complex is a set of cell surface proteins essential for the acquired immune system to recognize foreign molecules in vertebrates, which in turn determines histocompatibility) II and present to T cells. In addition not every peptide bound to the complex antigenic is capable of provoking an immune response.
How these systems operate is not yet full known making efforts to better understand the relationship between humans as hosts, the pathogens that can infect the body and microbiomes, difficult to achieve.
However BOTA (Bacteria Originated T cell Antigen) is built and trained to recognize potential antigens by running a “peptidomic” study of MHC (The major histocompatibility complex is a set of cell surface proteins essential for the acquired immune system to recognize foreign molecules in vertebrates, which in turn determines histocompatibility) II collecting and characterizing every MHC (The major histocompatibility complex is a set of cell surface proteins essential for the acquired immune system to recognize foreign molecules in vertebrates, which in turn determines histocompatibility) II-bound peptide natively found in antigen-presenting cells (APCs) in mice. The system then formulates a list of features underlying immunodominance and antigenicity.
“Identifying immunodominant T cell epitopes remains a significant challenge in the context of infectious disease autoimmunity and immuno-oncology” the authors write. “To address the challenge of antigen discovery we developed a quantitative proteomic approach that enabled unbiased identification of major histocompatibility complex class II (MHCII)–associated peptide epitopes and biochemical features of antigenicity”.
BOTA (Bacteria Originated T cell Antigen) was then benchmarked with two of mouse models — Listeria monocytogenes infection and colitis — to assess its predictions using a high-throughput, single-cell RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) – sequencing screening test that measure whether T cells could see predicted peptides and how strongly they reacted.
The new algorithm was able to accurately predict which bacterial peptides bound to MCH (The major histocompatibility complex is a set of cell surface proteins essential for the acquired immune system to recognize foreign molecules in vertebrates, which in turn determines histocompatibility) II in both models. The researchers also found that the RNA (Ribonucleic acid is a polymeric molecule essential in various biological roles in coding, decoding, regulation and expression of genes. RNA and DNA are nucleic acids, and, along with lipids, proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life) – sequencing data helped to identify the peptides that sparked the strongest T cell responses in the Listeria model.
“Collectively these studies provide a framework for defining the immunodominance landscape across a broad range of immune pathologies” the study states.
The results suggest that the new system could ultimately help researchers in a number of ways including discovering previously unknown bacterial antigens improving vaccine designs and illuminating how the microbiome tunes the immune system to understand how that tuning breaks down in inflammatory conditions.