Alissa M. Hummer

The paper describes a Kaggle competition for RNA 3D structure prediction, showing that template-based methods can outperform deep learning approaches.

Across 42 CASP16 nucleic acid targets, accuracy stayed tied to template availability; human experts (Vfold first) led servers and AlphaFold 3, though servers now improve on templates on average.

The paper evaluates the need for more diverse and larger datasets to improve antibody-antigen ΔΔG prediction using Graphinity, showing current experimental data is insufficient.

PLAbDab-nano is a database of 5000 annotated VHH and VNAR nanobody sequences from camelids and sharks, providing a resource for designing libraries and finding similar sequences.

The paper shows that simple computational methods like BLOSUM can create diverse, binder-enriched antibody libraries better than deep learning approaches.