Immunopeptidomics Data Analysis: Challenges, Advances, and Applications - Biognosys

Immunopeptidomics Data Analysis: Challenges, Advances, and Applications

The Challenge 

 

As immunotherapies become increasingly sophisticated, researchers need reliable ways to understand how drugs influence antigen presentation and immune recognition. Immunopeptidomics enables this level of insight and is a valuable tool for connecting molecular drug effects with biological and clinical outcomes.

However, generating robust immunopeptidomics data often requires large amounts of input material, which can be limiting in both preclinical models and clinical samples. At the same time, the complexity of the search space makes data analysis computationally demanding, with long processing times slowing iteration and decision-making. Together, these challenges can constrain how broadly and efficiently immunopeptidomics is applied across a drug development pipeline.

The Solution 

 

Recent advances in proteomics software are changing what’s possible. With Spectronaut® 20, immunopeptidomics workflows can now achieve dramatically faster data analysis while delivering higher sensitivity and confidence in peptide identification.

In the interview below, Daniel Green, Head of Bioinformatics at Greywolf Therapeutics, shares how these advances are helping his team integrate immunopeptidomics across the drug development pipeline.

User Perspective: Daniel Green Reflects on Addressing Immunopeptidomics Challenges with Spectronaut

 

What value does immunopeptidomics bring for your research teams at Greywolf Therapeutics?

Immunopeptidomics for us is quite central to our mechanism of action. We develop drugs that modulate the MHC Class I antigen repertoire via inhibiting ERAP1 and ERAP2. So, we really use immunopeptidomics to study the effect that our drug is having in cells.

 

Where in your development pipeline does immunopeptidomics play the biggest role?

We use immunopeptidomics all through our development pipeline, so anything from pre-clinical development of our drugs – so really understanding what target engagement looks like in cells – all the way through to monitoring target engagement in the clinic for real-life patients.

  

What recent successes has your team achieved?

Greywolf have had a really successful couple of years. A few years ago we opened our first clinical trial in oncology, and just this year we opened our first-of-its-kind Phase 1-2 trial in autoimmunity, treating a condition called axial spondyloarthritis.

  

What major challenges has your research team encountered?

There are a few things for us. One is the requirements of the assay, so needing quite a lot of input material to generate the peptides that we’re able to find. And then secondly, probably the complexity of the search space for immunopeptidomics. Because it’s an unspecific search, the time it takes to search that data is always a factor for us.

  

How has Spectronaut 20 helped you overcome these challenges?

Spectronaut 20 has helped us address both of those issues quite well. From a search perspective, it is incredibly fast relative to Spectronaut 19, so we can now search the same size of data in a much, much quicker time.

And secondly, from an identification perspective, we can now have much greater sensitivity in Spectronaut 20. So, we need to worry less about input for the same rate of recovery of peptides.

Advance Your Immunopeptidomics Research with Spectronaut 20

 

 

Do you want to learn in more detail how Greywolf Therapeutics leverages Spectronaut for immunopeptidomics data analysis?

 

Watch our on-demand HUPO seminar to see how Spectronaut 20 enables efficient immunopeptidomics workflows and hear practical insights from experts working at the forefront of immunopeptidomics and plasma research.

 

 

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