Nagarajan Vaidehi

The paper shows that a second G protein couples with higher affinity to a GPCR dimer than the first, due to conformational stabilization by the first G protein.

The paper identifies allosteric pathways that determine G protein coupling selectivity in promiscuous GPCRs using a combination of mutagenesis, simulations, and machine learning.

The paper develops an interpretable machine learning model to identify allosteric modulators of the AT1R receptor and discovers a negative allosteric modulator.

This study investigates how odorant receptors regulate selectivity and antagonism through allosteric gating mechanisms using simulations and experiments.

The paper identifies subtype-specific conserved residues in G protein subtypes that modulate Gβγ coupling, showing conservation does not guarantee functional equivalence.

DRUMBEAT is a machine learning model that identifies time-resolved cooperative events in protein dynamics, revealing folding pathways and critical residues.

The paper uses machine learning and simulations to show that distant residue interactions in the G protein core influence GPCR selectivity and can be engineered to change signaling preferences.

This study investigates how different detergents affect the stability and ligand binding of neurotensin receptor 1, revealing a trade-off between structural stabilization and conformational flexibility.

This study uses NMR and MD simulations to analyze conformational entropy changes in GPCRs, showing how dynamic fluctuations influence allosteric signaling and receptor function.

This paper develops an interpretable machine learning model to identify residue communities involved in GPCR conformational transitions, with applications to dopamine receptor subtype-specific drug design.

This study shows that frustration in the SMARCA2-VHL protein-protein interface correlates with PROTAC cooperativity, offering a framework for structure-based PROTAC design.

The paper identifies frustrated residues in GPCR:G protein interfaces and engineered degrader complexes, showing higher frustration in engineered interfaces.

BaNDyT uses Bayesian network modeling to analyze molecular dynamics trajectories, providing data-driven insights into protein function and interactions.

This study uses protein frustration analysis to identify key residues distinguishing strong from weak BRD4-Cereblon degraders.

This study shows that lipids like PIP2 and GM3 modulate the conformation of ICL3 in beta-2AR, affecting G protein coupling through membrane tilting and allosteric effects.

This study uses Bayesian network modeling to identify cooperative GPCR:G protein interactions and their allosteric effects, revealing subnetworks that contribute to G protein coupling selectivity.

This paper develops a PROTAC-based strategy to selectively degrade activated STAT3 in specific cell types, showing efficacy in reducing STAT3 levels and tumor growth in lymphoma models.

The paper uses engineered odorant receptors to uncover structural principles of odor discrimination by solving cryoEM structures of consensus ORs and comparing their ligand recognition properties.

The paper determines the cryo-EM structure of mTAAR7f bound to DMCH, revealing its unique binding site and activation mechanism distinct from ORs.