Marina A. Pak

Bento: Benchmarking Classical and AI Docking on Drug Design-Relevant Data

This study benchmarks 11 protein-ligand interaction prediction tools, showing classical and deep learning methods perform similarly on drug-like ligands, while co-folding tools excel on complex ligands and all struggle with generalization to unseen pockets.

MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding

The paper introduces MULAN, a multimodal protein language model that integrates sequence and structure information, showing improved performance on downstream tasks compared to sequence-only and structure-aware models.

In silico simulations reveal molecular mechanism of uranyl ion toxicity towards DNA-binding domain of PARP-1 protein

The paper uses computational simulations to reveal how uranyl ion disrupts the DNA-binding activity of PARP-1 by altering its zinc finger domains.

Using AlphaFold to predict the impact of single mutations on protein stability and function

This study assesses AlphaFold's ability to predict the impact of single mutations on protein stability and function, finding weak or no correlation with experimental data.

The new mega dataset combined with a deep neural network makes progress in predicting the impact of single mutations on protein stability

The paper uses a deep neural network with a Light Attention mechanism to predict protein stability changes from mutations, achieving high accuracy on a new mega dataset.

Best templates outperform homology models in predicting the impact of mutations on protein stability

The paper compares homology models and best templates for predicting mutation impact on protein stability, finding best templates significantly improve predictions over homology models.