Hello, to everyone, first I would like to thank the guys for giving me the opportunity to talk in this excellent conference.
My name is Jose Spinozacarrasco, I'm working in the LAP of Notre Dame's LAP at the CERG in Barcelona. This is the LAP where NEXO was originally developed.
I'm also an NFC or team member, and today I'm going to introduce you an NFC or platform, which is a standardized pipeline for HAP for Plotting Statue of Nature Methods.
So maybe if you don't know already, you may be wondering why it's interesting to predict Plotting Statue and the thing is that once you have the Statue of Plotting you can infer its function.
And once we know the function of this Plotting, we can do some applications, like for instance, the Stabilized Statue for Biatenological Applications, so that they work in higher temperatures with higher performance.
We can do things like design, for acting in its active side, in a intelligent way, or we can do more basic signs and we can study the evolution of this given Plotting, comparing its structure with other Plotting families and species.
And it's possible to get the Statue of Plotting using a experimental method, but the thing is that as you may imagine, this is very time consuming and also very expensive.
And that's why it has been a long-standing program in computational structure biology, how to predict structures using computational methods.
So that there's even a competition, which is called the CAS contest, and during this contest, the Stabilized Statue of Plotting families gather and they compare their methods, and one of them, up to the other, and it wins in different data sets.
And this is in the framework of this contest, it was when, for the first time, a photo show that AI-based methods can outperform existing methods and not only that, but they can, alpha photo was able to achieve almost experimental accuracy.
So this was a big breakthrough in biology, and it was awarded the 2024 chemist Nobel Prize, as you can see here, but more in practical terms, what I'm showing you here in this ball or in this circle is how many structures were available in the PDV database, which is the database where people were were the position, the structures of the Plottings that were played at the experimentally.
And after the alpha photo, inception, you can see how the number of structures a bio has employed.
And indeed, also the number of tools that can predict structures using a-based methods has employed.
Here, you have several of them, and we have mainly two categories, most of them, as you can see, are based in evolutionary research, DBM models.
And this means that these tools, what they do is that they search for similar sequences in databases, and then they perform multiple sequence alignment.
And with these multiple sequence alignment, what they want to achieve is to know which parts of the protein of the sequence of the protein are evolutionary concerns.
These parts of the protein are important for the function, and they tend to be maintained in evolution for being absorbed at the protein can still have the function.
And after this, with this information, and with this sequence, this is applied to an neural network, which is the one that infers the structure, and after several steps of refinement.
We have for their tools that use large language models, like ESM-4, and in this case, these tools are faster, but sometimes dispenses of accuracy, mainly when dealing with long sequences.
So, as always, in competition, there is a trade-off between speed, accuracy, and those cost.
Yes, I wanted to say something else. I got lost with it, I mean, sorry.
And of course, users, depending on what they want to do, they will use one of the tools, or they will like to use several of these tools, and so on and so forth.
But actually running these tools is not to stay forward, because they try to provide you the images to make it done, but even though it's hard.
And why it's hard, because all of these tools, they rely in several libraries, different versions of the libraries, so you have to make all them available in your environment, and also the tools that are based, that need the MSA step, they also rely in very big databases that you have to have in your environment.
And this is why we implemented NF Core Plotting Fold, here you have the typical NF Core Metal Map, and you can see here in this first, to go through that this pipeline allows you to download all the databases that you need for these tools.
And this is a one-shot workflow, this means that you don't need to run it each time that you run the pipeline, you run it once, and then you can provide it to the pipeline with the human parameters databases.
And then you can see the alternative paths that you can follow in this pipeline, this is the development version, by the way.
So here on the top, you have tools that are based in evolutionary driven tools.
In this case, these tools perform both the MSA step and the prediction step in the same process, so you can not split them.
Then here you have these two other paths, the one in green, and the one in yellow, the green, it's alpha-fold 2, alpha-fold 2 doesn't allow to do it natively, this separation between the MSA step and the prediction step, but we have modified to be able to do it.
And also, call up for MSA, call up for Lamboz, that in this case this is native, and we just take advantage of it.
And here you have ESM Fold, which is the one that it's using the large language model approach.
So the pipeline is giving you the PDVs, the structures of the proteins, but within this part is also very interesting, this is something that we have been working on is that for each tool you get a report with all the information of the accuracy.
But also you also get another report which allows you to compare the performance of the different tools that you have used in your run.
And here I am just summarizing some of the futures of these pipelines as I show before it allows you to plan on load the databases and the parameters of the models.
Everything is containerized, so it means that you don't need to install any of these tools or its dependencies.
It's easy to configure, and this is important for these tools that allow you to run the prediction step, separated from the MSA step, because MSA step does not get any advantage of being run in CPUs.
This way you can configure it to run in CPU and CPU.
And this is something that it's actually, can be done thanks to NXO because NXO separates the configuration from the logic of the pipeline.
So it's just need to provide a configuration file and you get it.
And as I said, as I mentioned before, it allows you to do methods of benchmarking.
And actually this is also interesting because this is something that we are pushing in our lab is half pipelines.
This means pipelines that perform that where you have several tools that perform the same thing.
This will allow users to compare these tools among them, but also the developers of the tools or people that are interested in comparing the performance of these tools to it using these pipelines.
It have two reporting capabilities that I already mentioned, and it's part of NF Core which have several embedded as I will show you.
Here in this slide, I'm showing the comparison report.
Each of the colors depends one of the tools that we have used to predict this structure.
This is a sequence that we use for the testing of the pipeline.
So it's a single sequence and several tools to get the structures.
You can see how you can spin them.
You can choose which one you want to show.
Then you can see here the accuracy of each of the tools.
The average accuracy.
What you can see afterwards is that the accuracy along the sequence.
And then finally, you can see the coverage along the sequence as well.
Let's see if I...
So...
Yes.
I will stay in this one.
Ah.
Is it opposite?
Okay.
What is that?
Sorry about that.
Okay.
So this is a real application of the pipeline that has been done by Kiran Roewell in Sydney.
What he has done is that he wanted to prove these hypotheses, whether a sales evolved via San Bios is between
two studies in bacteria and in the same producer in Arcaya.
So he got all the...
He's... they sequenced all the genome of one of these Arcaya.
They then use... in a corporate info to predict all the structures of these... of these Arcaya.
And for this, he was using alternative methods because for some of them it was easy.
He was using just ESM fault.
And that's all.
But when he was dealing with more long sequences, sometimes it was not working.
Then he moved to Alphafall 2 or in some ways that weren't even longer.
He used or more difficult to infer he used Alphafall 3.
And of course it was very interesting having these reports to compare these performance.
And then he used Faultsick to see whether these structures actually fall close together with the bacterial sequences.
But this is... I think I'm a little bit out of time.
You can beat the paper here.
This is not the last version of the... Sorry.
Well, I will move here.
And another interesting thing of NF Core is that...
NF Core makes that the pipelines can last longer.
And why is this?
Because sometimes you work in your lab.
You have a very nice pipeline.
But at some point in the funding stops.
Or maybe you don't have more in-setific interest on this pipeline.
But this pipeline is a very nice pipeline.
And it's useful for a community.
And what happens in NF Core is that as you can see with the sample of this...
This smaller-nastic pipeline is that the people that have started the pipeline
is the life-love at some point.
They even stop working on it.
But all the groups were from...
Both from industry and from...
Academia were already contributing to it.
And they just took over.
So this means that you...
Did that you pipeline last...
Not forever.
I would say that longer than if you are in...
Isolated in your isolated lab.
And here you have all the contributors.
That have contributed to the NF Core.
But in full pipeline.
From the Sierra Lea in Barcelona.
Also UPF in Barcelona.
We are technically working with people in Sydney.
In the UN's.
The UN's.
And also from Australia.
Come on.
And then your university.
University of Korea.
So if you want to join us, here you have...
S7 out of the ways.
How you can do it.
So you can join the NF Core.
There are several ways to do it.
You can also join a Slack, which is the main communication channel of the NF Core.
And there we have the protein-full channel, which is for the users of the pipeline.
And the protein-full web channel, which is for the developers of the pipeline.
And you are very welcome to come here.
And also the GitHub repository.
We have a paper in preparation that we want to submit soon.
More importantly, we have all the tools that have shown you are not yet in the release version of the pipeline.
They are still in development.
But we are planning this release for, I think, in two weeks should be ready.
I wanted to be ready for this presentation.
But it was not.
And here you have other things that we want to optimize on the pipeline.
But you can take a look.
And maybe we can discuss afterwards.
And finally, I just want to finish thinking my supervisors,
I think Notre Dame, of the people, of my lab,
especially Athanasios Vatsis, who was the original developer of the pipeline.
Also the people in Australia, marketing from Korea.
The people from Zekera, both the NF Core and the XO community.
And also the AWS.
Open data-sports and simpler.
Who is allowing us to host all the data-basis in, in, in, in,
in, in, in, in, in, in, in, in, in, in, in, in, in, in, in, in, in.
So thank you very much.
And I am open to questions.
Thank you very much.