G. R. Bowman

The paper uses simulations to study G protein activation mechanisms, finding conserved allosteric pathways and validating their dominance through experimental mutation analysis.

The paper introduces PopShift, a method to estimate binding free energies by accounting for ligand-induced population shifts in protein conformations using Markov state models, showing improved accuracy over traditional methods.

This study shows that two point mutations at the same residue in beta-myosin heavy chain cause distinct cardiomyopathies through different molecular mechanisms.

The paper shows that using dynamic simulations with an AlphaFold structure improves the prediction of allosteric inhibitor binding to PPM1D compared to static structures.

This paper introduces Sculptor, a deep generative algorithm for designing epitope-specific binding proteins by optimizing loop conformations and backbone structures.

This study uses AlphaFold to generate structures for cryptic pockets, showing it can sample open states and aid in discovering drug targets, though some remain challenging.

This paper shows that the probability of cryptic pocket opening in myosin motor domains predicts drug specificity and affinity, using molecular dynamics simulations and Markov state models.

The paper introduces PocketMiner, a graph neural network that predicts cryptic pockets in proteins, identifying them accurately and rapidly, expanding the druggable proteome.

The paper identifies a nepenthesin insert essential for plasmepsin V function in malaria parasites and shows it allosterically regulates catalysis.

This study uses single-molecule spectroscopy and molecular dynamics simulations to show that ApoE4 has extensive conformational heterogeneity in both lipid-free and lipid-bound states, differing from previous models.

This study identifies how the parasite Toxoplasma gondii uses the disordered protein TgIST to block STAT1-mediated gene expression by altering STAT1 dimer conformation and preventing activator recruitment.

This study screens bexarotene derivatives to identify compounds with enhanced anti-leukemic activity in AML, finding that dual RARA/RXRA activity and structural modifications affect potency and metabolic stability.

The paper investigates how a cryptic pocket in β-lactamase, formed under the Ω-loop, enhances enzyme activity against penicillin, suggesting excited states can improve function.

This paper investigates how Nsp10 activates Nsp16 and identifies a cryptic pocket in SARS-CoV-2 Nsp16 that could be targeted for pan-coronavirus antivirals.

The paper investigates the subcellular localization of VirB, a transcriptional regulator of virulence genes in Shigella flexneri, showing that DNA-binding site interactions are required for its formation of discrete cytoplasmic foci.

DiffNets identifies structural features predicting biochemical differences between protein variants using self-supervised learning.

The paper uses exascale simulations to study SARS-CoV-2 protein dynamics, revealing cryptic epitopes and conformational changes that inform antiviral target design.

This study investigates the SARS-CoV-2 nucleocapsid protein's dynamics, disorder, and RNA phase separation using single-molecule spectroscopy and simulations, revealing its multivalent RNA binding properties and phase separation behavior.

This study identifies a cryptic pocket in Ebola VP35 that allosterically controls RNA binding, validated through simulations, experiments, and drug-like modifications.

This study identifies three spatiotemporal patterns of cysteine oxidation in response to EGF stimulation, revealing that cryptic cysteines become redox regulated through structural changes induced by EGF.

The paper models conformational distributions of myosin motor domains using molecular dynamics simulations to predict mechanochemical properties better than sequence or structure alone.

Enspara improves the scalability of Markov state models by introducing novel algorithms and data structures for efficient computation.

The paper introduces a method to identify functionally-relevant conformational changes in proteins by analyzing cooperative solvent exposure changes, demonstrating its utility in discovering cryptic allosteric sites.

The paper simulates G protein activation to identify a new intermediate that drives GDP unbinding, revealing tilting of a helix as the rate-limiting step.

The paper identifies an evolutionary switch in the specificity of a CORVET tether in Tetrahymena thermophila that enables the formation of regulated secretory vesicles called mucocysts.