Possible reason for a counter-intuitive result about ppiformer

[amin-sagar]

Thanks @anton-bushuiev
This makes perfect sense.
Another issue could be that I was trying to predict the changes for a protein-peptide interface.
The stability and expression learnings from proteins might not apply to peptides.
I look forward to the finetuning scripts to finetune the model for protein-peptide interactions.
Feel free to close this issue.

[anton-bushuiev]

Hi Amin!

Thank you very much for your feedback! I am glad to see that most of your predictions made sense.

There may be two explanations for the unintuitive behaviour you describe:

1. The second asparagine (that does not interact with the other chain) may be important for some other basic function (e.g. stability or protein expression). PPIformer is trained in two steps. First, it is pre-trained to fill in masked amino acids in protein-protein interfaces. During this step, the model learns most likely patterns in natural protein interfaces. As shown, for example, in this paper, this kind of pretraining may lead to the model making predictions towards more stable and easier expressible proteins. In the second step of the training, PPIformer is fine-tuned for ddG from the small available data.
2. The model is not perfect yet. As Table 2 in our paper shows, there is still a gap for improving PPIformer. Specifically, Flex ddG, a five orders of magnitude slower Rosetta-based protocol, achieves better performance on the SKEMPI v2.0 benchmark dataset. It means that PPIformer may still fail in certain cases, and we are currently working to understand them better.

Please let me know if you have other questions.

Best,
Anton