Previously, in PiCAP we uncovered the protein-sugar interactome, identifying over 7.5k human proteins that likely bind to carbohydrates. Since AF3 and other methods do well 85% of the time - can we use AF3 to predict these structures? Simple answer: No, not just yet on a high throughput scale

But, can these models tell us when they don't perform well - using their confidence metrics like ipTM or pLDDT? The simple answer - kinda. AF3 is incredibly overconfident, but there is a moderate trend in most models from prediction confidence and prediction accuracy.

Across the board, all models perform great at protein-carbohydrate docking, achieving 85% of top-5 structures with at least acceptable quality! However, the largest limitation is that overall performance decreases as saccharide length increases.

In this work, we developed a unique test set of 20 proteins to evaluate the performance of AF3, Boltz-1, Chai-1, DiffDock, and RFAA at protein-carbohydrate docking. We additionally made a new metric: DockQC, inspired by protein-protein docking to evaluate their performances across many test cases!

My latest manuscript is now on BioRxiv: Evaluation of De Novo Deep Learning Models on the Protein-Sugar Interactome. where we benchmark AF3 at protein-carbohydrate docking. This work was only possible with Dr. Lei Lu @takeshita-sho.bsky.social and @jeffreyjgray.bsky.social
doi.org/10.1101/2025...

If you’d like to use CAPSIF2 or PiCAP, you can do so freely at our github link: github.com/Graylab/picap or you can use our server on ROSIE: r2.graylab.jhu.edu/apps/ !

We also provide an updated model of our CAPSIF, a model for predicting carbohydrate binding regions of proteins. Comparing CAPSIF2 with @parthbibekar.bsky.social’s PeSTo-Carbs, finding their model outperforms CAPSIF2 on the TS90 dataset, but, CAPSIF2 modestly outperforms P-C on a larger dataset.

Now that the stage is set: what proteins bind to carbohydrates? We used PiCAP on three proteomes: E. Coli, mice, and humans and found that PiCAP predicts 35-40% of proteins bind to carbohydrates. And we provide a table of all proteins and their predictions for open scientific use and validation!

PiCAP does well on the test set -- but how does it do on other datasets that aren’t manually sculpted? We compare PiCAP to LectomeXplore finding a 99.5% agreement between the methods. We also interrogated the ganglioside interactome finding that PiCAP has a strong correlation with the experiments!

To do this we developed a deep learning (DL) model named Protein interaction of CArbohydrate Predictor (PiCAP). We find that PiCAP has approximately a 90% accuracy on identifying carbohydrate binding proteins, with limitations primarily on proteins that have reduced evolutionarily evidence (Abs).

It all started by asking just a simple question: “What proteins bind carbohydrates?” Lectins bind carbs, but, there are myriads of other proteins that bind carbohydrates (e.g. RTKs). And so we set out to find that out: can we find all the proteins in the human proteome that bind carbohydrates.

I’m happy to announce my latest paper has been released as a preprint on BioRxiv: Predictions from Deep Learning Propose Substantial Protein-Carbohydrate Interplay. This paper was only able to happen thanks to both @jeffreyjgray.bsky.social and Dr. Ronald L Schnaar

www.biorxiv.org/content/10.1...