So, good day here.
In my talk today, I would like to present our work on prediction of MHC presentation,
and how we can apply those developed models to assess, in silico, the immunogenicity of
biologics.
Historically, MHC class 2 binding prediction algorithms have had a poor reputation of being
over predictive, achieving many false positive predictions.
This is illustrated here in this benchmark paper from a few years back, where series
of the publicly available methods for benchmark against some independent benchmark data, and
show here that even the best methods, the ones shown here to the left, have a performance
in this benchmark of an AUC around 0.8, which is lower, much lower than what we observed,
generally, for MHC class 1 binding prediction algorithms where the performance values are
in the range 0.9, 0.95.
But this has changed, in my view, over the last couple of years, by introducing novel
prediction training methods and novel data to train the methods on.
If you look into the public domain, for instance, the IDB database, you see a huge amount of
MHC-like and data being made available.
This number has increased dramatically over the last year by the inclusion of peptides,
obtained from mass spec MHC elution studies, where you elute all peptides from the cell
surface in combination with MHC.
And those peptides have that really high information content because they are containing information,
not only about the MHC binding, but also all the steps leading up to MHC presentation,
including processing, and all the interactions with the chaperones interacting with the MHC
in the binding event.
So, by training on mixed data types, we're able to move forward.
So, historically, we were training on these in vitro MHC binding data, where we have peptides,
showing the MHC class 1 context, with a binding affinity associated to individual MHC molecules.
And from these, we could train conventional algorithms, be challenged by the fact that
the MHC molecule has an open end in the binding clip, allowing peptides of highly variable
length to bind to the molecule.
But since the binding core, binding motif, also for MHC class 2, is, in most cases, a
nightmare.
We could design an algorithm that scan through all these peptides, identified during the
training algorithm, a likely binding core, and then from that training, the method arriving
at a tool that can both predict the binding core and the interaction swings between the
peptide and the MHC.
This model is kind of old now, but it was developed to solve this problem, and it's
called in NNMi.
And it proved to be very effective training on binding affinity data.
We could characterize the binding motif for all of these ADRP to one molecule and showed
that they were very different, again, leading to this high diversity of the MHC peptide
space presented on each cell line.
The challenge of developing prediction algorithms for, you may see, is as you know, the high
polymorphism of the MHC space, with now, it's a bit outdated slide, but close to 25,000
different alleles being characterized in the MITT database.
So, to characterize each of them is an impossible undertaking.
And we have designed this band specific approach, where we train not only on the peptide sequence,
but also on the MHC binding environment, shown here in blue below, where we can kind of learn
this interrelationship between peptide and MHC molecule that results in a given binding
affinity.
So, again, we're showing these five peptides, all the same peptides measured in different
MHC class 1 molecules, in this case, and the binding affinity value shown here to the right.
These variations are dictated by variations in the binding environment of the MHC molecule.
But by training this combined, we can make a path specific method that can now make predictions
and extrapolate from the alleles, where we have data to the complete MHC binding space.
And now back to the data I mentioned before, these novel, eluded data sets that we get
from mass spec elution studies.
And here these data have a different nature, compared to what we had originally, the MHC
binding data, in that they are binary they are binders, and then we can make artificial
non binding peptides by assuming that random peptides do not bind to the HLAs.
Then we have this quantitative yes/no data to train on.
And since we now have a mixture of data, both of these binding affinity with build value,
continuous target values in these binary, how could we mix them so that we can benefit
from both data types when we train our model?
We designed this kind of pipeline here, but we train on a normal machine framework, but
now have two prediction values for each peptide.
One is the binding affinity and one is the (inaudible) core.
Then during training, depending on the color of the peptide, we train the corresponding
weights in the network.
And that allows us to mix these two datas together and get the synergy of boosting performance
beyond what each of the two data types contain by themselves.
We can then, from these models, predict binding preferences and run predictions, and predict
high binding peptides in a given protein context.
That leads us into the next challenge of dealing with these mass spec you rely on data.
In this case here, we have the data, assigned to binary one and zero with a single allele
associated with each peptide.
In most cases, these mass spec data have a different nature.
They are multi allele in the sense that each of the peptides we observed, seen here on
the right in this picture, can originate from multiple MHC molecules, all the MHC molecules
expressed on the surface of the given cell on the investigation.
That makes it challenging to learn the binding rules, and the processing that leads to presentation.
We need to de convolute those peptides and map them back to single specificities, in
order to characterize what each MHC molecule, what type of peptide space it can present.
So, you have to kind of find a way to de convolute and cluster these peptides into sub motifs
so each sub motif will then correspond to one MHC specificity.
This is a far from a trivial task.
We designed an algorithm, a few years ago, called inner line, MA.
MA for multi allele.
It is very simple in concept, but really powerful.
So, it builds on the idea that you can train in a pan specific methods on single allele
data, meaning data, either binding affinity or mass spec, you relied data from cell lines,
expressing only one single MHC molecule.
We can train that model for some time to get some pan specific predictive power.
And then we can use that model, subsequently, to annotate these MA data, so that we get
each peptide in these MA data assigned to one single allele.
Now, when they are a single allele by a notation, we can feed them into the training data here
and merge them and then train again.
Then we can do that over and over again, we can re annotate these MA data during the simulation.
To show how this works, after this pre training, some alleles are already highly and accurately
characterized, because there are a lot of binding affinity, a lot of single deal data
available for those.
So, after just this short pre training, we have already captured the motif, accurately.
For other deals, that are not part of this assay data set, we get a motif out.
And then as we continue our training, as shown here below, this motif is changed dramatically,
and we end up having a motif that only to a light degree resembles what was captured
in the ESA data.
This is kind of the power of this MA method that you extrapolate and get you extend the
data set, beyond what is characterized by the ESA data, and are now able to make integrate
data from these MA datasets with a much broader allele coverage.
And this can also be applied to MHC class 2, maybe again, can apply the method and de
convolute finding motifs and difference in line datasets.
We had in context, which are these amino acids flanking the peptide (inaudible) sequence
and show that actually leads to boost in performance, seen here to the upper right, where we have
the old models that, before adding in these mass spec data with the performance of SharePoint
Aid, I mentioned before.
Now, by adding in these which allele data and this content information, we get a boosted
performance.
And that's this performance gain also holds for independent individual data sets.
This is the tool, (inaudible).
So, to conclude this first part, we have boosted performance dramatically by MHC class 2, and
if you add in signal processing from the context, you'll get an even further increase in predictive
power.
So now, how can we apply these tools to assess the immunogenicity of a protein drop?
Protein drop can often induce the error response, as we all know, and the error response is
correlated to the ability to induce a TCD for Hilger (phonetic) response to a peptide
in the protein drop.
So, most people would like to assess MHC class 2 binding potential as a mimicry to assess
if the protein can induce an error response, and most companies and people doing this would
do this by running these mass assay the MHC eluded, like in assays where you feed these
proteins to anti immunogenic cells, and then you elude all peptides from these protein
cell surfaces, and that could be a mimicry or an idea of what is available to stimulate
a T cell response.
And doing this to get these profiles back, as shown here, mass profile for a given protein
drug, we have regions here, with a high chance of inducing a T cell response in other regions,
where there's hardly any potential to bind to the HLAs of the given population tested.
Then, we would like to say can we use our prediction algorithms as a complement to these
highly expensive and complex mass assays.
So, we tested this approach, using the (inaudible) tool pad for consumer model compared to the
earlier (inaudible) pan model that trained on binding affinity data only.
And then in other methods, in which you may see MHC 2 (inaudible), a model also trained
on these mass spec, lightened data.
And what we observe in general, across many, many proteins, is that we get a higher correspondence
between the mass data shown here in gray, and the predicted profiles image use these
new methods trained on mass data, and we remove many of these false positive predicted peaks
that we saw for the earlier methods like an MNC 2 pan.
We can now go further and investigate.
We see often certain peaks remain in these predicted mass profiles, as shown here in
peak two, and in peak four in this example.
Then, we can make take peptides from those regions and test them for T cell responses
in a simulation assay and doing this, we find that actually that peak two and peak four,
both of them, have responses.
It's a very small study, only six donors were tested, but in half of the donors was response
against peptide two, and in one out of 6 donors there were responses, again, peptides around
peak four and received no responses against peptides from regions with no predictive presentation,
and we see responses for regions where both the predictions, and the mass assays do agree.
That suggests to us that you can complement the mass assays by these in silico models
to get a more comprehensive picture of where you have regions of potential T cell hotspot
features.
So, the last thing I would like to mention on this is that we have not only dealt with
the MHC binding potential, but we all know that self similarity, like peptides from this
host, the patient's own genome, that have been showing to T cells during final elucation
have a low chance of being immunogenic.
So, could we somehow make a simple model to take into account the self similarity of the
peptides to the self proteome and thereby down way, the predicted potential generating
sn immune response to those regions?
And we are working on this for some time now, trying to get our hands on this.
It's very complex problem, because what defines self similarity is highly non-trivial, but
we can make a simple model by just saying, if a given peptide in your protein drop sequence
is found with an identical match in the reference of the human proteome or a database of known
B and T cell receptor sequences, then we can make profiles showing the sort of isolation
likelihood in that region.
This is only shown here in this plot with the green bars.
And we see here that the written two was immunogenic and predicted to be MHC binding has no polarization.
So, this is really a foreign region, in this protein, and also we found a response here.
In contrast, the region five here, right, where there is some kind of peak, but we didn't
see a response, we go back here to this region five here.
There was no response at all against that peak.
That can be explained, maybe by this region here is highly similar to self.
And if you can then go back and test this idea on a bigger set of protein, drop sequences
maybe have from the publications to clinical immunity, the proportion of donors between
inducing and add a response to the proteins, then we see if we just take the area under
this group, the red curve, without taking into account polarization, you see a very
poor correlation between what you see in the clinic on the Y axis and the predicted immunity,
on the X axis.
But if you take into account this polarization and down way, the importance of peptides that
are found to be identical matches to the human proteome, then we see a positive quality,
not perfect, but still positive.
This is kind of the way forward, finding a way to take into account, self similarity,
internalization into the models to really be able to predict the clinical response against
protein drops.
And once we have that in place, we can now start to play around and make assessments
of ranking proteins, based on this predicted area under these curves here.
And we can rank proteins based on this area, we can visualize, where, in the protein, to
receive the hotspots and which alleles are involved in those hotspots, and we can start
to de immunize by potentially mutating positions in the protein in those regions, and thereby
hopefully be able de immunize and lower the area under this curve, and making the protein
drop less immunogenic.
So, in summary, I think we have shown that MHC class 2 presentation tools have been improved
dramatically over the last years; we can start to use those prediction algorithms to guide
the assessments of protein immunogenicity and performed immunization to lower that immunogenicity.
So, with this, I would like to thank you and take questions later.