P. Barth

BioKinema generates physically accurate all-atom biomolecular trajectories at lower computational cost than traditional MD simulations, enabling dynamic analysis of protein and protein-ligand systems.

This paper introduces TMDiffusion, a deep learning model for designing membrane proteins, achieving high experimental success and accurate structural predictions.

This study investigates ADGRG1 activation mechanisms through mechanical and allosteric pathways, showing force direction determines signaling via GAIN domain dynamics and introducing engineered variants with tailored sensitivity.

This study designs GPCR variants with reprogrammed ligand-selective signaling using computational methods, showing sequence variations impact ligand potency and efficacy.

This paper designs light-regulated dynamic proteins using deep learning, showing precise optogenetic control of cellular processes.

This paper develops T-SenSER, a synthetic biosensor for T cells that enhances anti-tumor responses by sensing TME factors like VEGF and CSF1, showing improved efficacy in lung cancer and multiple myeloma models.

The paper designs and validates six minimal integrating shuttle vectors for Saccharomyces cerevisiae with reduced size and restriction sites outside the MCS.

AlloPool predicts dynamic allosteric networks in proteins using a graph neural network, outperforming existing models in trajectory reconstruction.

This study investigates the mechanical properties of the ADGRG1 GAIN domain using single-molecule force spectroscopy and simulations, revealing structural mechanisms that enhance mechanical resistance and informing potential drug design.

The paper designs dynamic receptor--peptide signaling complexes for chemotaxis, achieving potent signaling and chemotaxis in human T cells through computational strategies.

The paper computationally reprograms GPCR responses to ligands by rewiring allosteric pathways, revealing distinct signaling mechanisms for different agonists.