Terra Sztain

Mechanism of molecular recognition revealed through dynamic drug binding pathways to SARS-CoV-2 main protease

This study reveals the molecular mechanism of nirmatrelvir binding to SARS-CoV-2 main protease using combined NMR titrations and weighted ensemble simulations, identifying two distinct binding pathways and key residues involved in resistance.

Assessing the Generalizability of Machine Learning and Physics-Based Methods with DNA-Encoded Libraries

The paper evaluates ML and physics-based methods for predicting protein-ligand binding using DNA-encoded libraries, finding that no single method consistently generalizes to out-of-distribution chemical space.

Parameterization of the PA endonuclease bimetallic center reveals the dynamics of clinically relevant mutations

This study benchmarks metal-parameterization strategies for simulating the PAN endonuclease domain to understand resistance mutations in influenza virus.

Machine Learning Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments

The paper trains machine learning models on deep mutational scanning data to predict SARS-CoV-2 RBD variant fitness and outperforms point mutation averaging in extrapolating to Omicron variants.

Adaptation to high pressure; insights from the genome of an evolved Escherichia coli strain with increased piezotolerance

This study identifies genome-level mutations in an evolved Escherichia coli strain adapted to high pressure, revealing potential genetic factors contributing to piezotolerance.