Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences
Thompson, M.; Martin, M.; Olmo, T. S.; Rajesh, C.; Koo, P.; Bolognesi, B.; Lehner, B.
Show abstract
Protein aggregation is a pathological hallmark of more than fifty human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the amyloid nucleation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts amyloid nucleation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYAs decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict amyloid nucleation.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- PSICHIC: physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data 96%
- Predicting functional effect of missense variants using graph attention neural networks 95%
- Self-iterative multiple instance learning enables the prediction of CD4+ T cell immunogenic epitopes 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.