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Dani Stevens, Ph.D.

@danimstevens.bsky.social
946 followers 558 following 19 posts

Plant Immunologist x Machine Learning, occasional biochemist | 🌱-🧫 interactions | UC Berkeley Postdoc | 2 x USDA NIFA Fellow

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Dani Stevens, Ph.D. @danimstevens.bsky.social · 16/07/2025
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!
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Dani Stevens, Ph.D. @danimstevens.bsky.social · 16/07/2025
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.
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Dani Stevens, Ph.D. @danimstevens.bsky.social · 16/07/2025
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!
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Dani Stevens, Ph.D. @danimstevens.bsky.social · 16/07/2025
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!
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Dani Stevens, Ph.D. @danimstevens.bsky.social · 16/07/2025
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.
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Dani Stevens, Ph.D. @danimstevens.bsky.social · 13/11/2024
Sometimes the smallest things warm the soul ❤️ so thankful to be part of such a great lab group!
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