Mamp-ml was built upon two decades of foundational research. To generate the training data required, I manually pulled receptor and epitope sequences from every paper I could find, small or large. In total, we were able to capture over 1,300+ combinations across 11 receptors and 91 plant species.
Compared to other state-of-the-art models such as AlphaFold3, mamp-ml can predict these outcomes with higher confidence, even when a solved structure is not available. We anticipate users can now screen for immunogenic outcomes of receptor-ligand variants before spending time and $$ in the lab.
Mamp-ml harnesses the power of protein language models (ESM-2) to build a classifier model for immunogenic outcomes.
Previously immunogenicity was primarily characterized in the lab but was a expensive bottleneck considering the variation captured computationally!
We then generated a pipeline that combines AlphaFold2 and LRR-Annotation to precisely extract the LRR ectodomain as well as generated features to improve model training. This even includes tracking which residues interface with the ligand!
Finally, we also tested its ability to predict outcomes for convergently evolved receptors. SCORE, a csp22 receptor found in Citrus and relatives, was recently discovered by @brunongou.bsky.social. We preliminarily found mamp-ml can zero-shot predict immunogenic outcomes!
#2025ISMPMI 📣 In silico screening of PRR-epitope interactions is now possible!
Here, we developed mamp-ml to predict their immunogenic outcomes without structural context. Let's accelerate engineering plant receptors for robust resistance! 🚀🌱 Small 🧵
www.biorxiv.org/content/10.1...
While there's lots of work to be done, mamp-ml is a critical advancement in plant immunology for accelerating receptor-epitope characterization and engineering resistance. Mamp-ml is on Github and we implemented a version on Google Colab for easy use. Please check it out!
github.com/DanielleMSte...
An artificial intelligence tool developed by @microsoft.com researchers can predict the multiple conformational states of proteins in minutes with a fraction of the resources required by other techniques. cen.acs.org/biological-c... #chemsky 🧪
Great presentation from @gittacoaker.bsky.social on kinase mediated defense responses and harnessing the power of language models for receptor/ ligand prediction. These LMs will help us in engineering more resilient crops #2025ISMPMI
While I'm not going to be at #2025ISMPMI this year (🥲),
@kseniakrasileva.bsky.social will be there presenting on some of our latest work that was submitted to BioRxiv today. Teaser: Plant immunity + large language models will transform receptor discovery and engineering for disease resistance. 🌱🚀
