Outstanding Poster Award at 2025 MCB/BCB Retreat Satyaki Saha

We are delighted to congratulate Satyaki Saha, a member of the Bahar Lab, for receiving the Outstanding Poster Award at the 2025 MCB/BCB Retreat.

His presentation, “How to Distinguish between Positive Allosteric Modulators (PAMs) and Negative Allosteric Modulators (NAMs): Insights from Molecular Simulations,” highlights a novel computational approach using the weighted ensemble (WE) path sampling strategy coupled with molecular dynamics simulations. This method successfully predicts how small molecule modulators can either activate or inhibit target proteins, providing key insights into the mechanisms underlying drug action.

Event: 2025 MCB/BCB Retreat
Date & Location: October 14, 2025 · The Old Field Club, East Setauket, NY

Title: How to Distinguish between Positive Allosteric Modulators (PAMs) and Negative Allosteric Modulators (NAMs): Insights from Molecular Simulations

Abstract: Although many computational methods can identify small molecule modulators (SMMs, including drugs) of target proteins, determining the precise pharmacological effects of SMMs remains a grand challenge. This challenge arises because activity is dictated by the mechanism of how exactly the protein carries out its function, and how the SMM alters this mechanism. To our knowledge, current methods cannot accurately predict whether an SMM has an inhibitory or activating effect on the target protein, even if the binding poses and affinities are known or predicted with high confidence. To address this, we used the weighted ensemble (WE) path sampling strategy with MD simulations. As a case study, we first focused on the activation of a Class A GPCR, β2-adrenergic receptor. We then extended our efforts to the more challenging Class B1 GPCRs, specifically PTH1R (parathyroid hormone type 1 receptor) and GLP1R (glucagon-like peptide 1 receptor), in the presence of a negative allosteric modulator (NAM) and positive allosteric modulator (PAM), respectively. Our all-atom WE simulations applied to these two Class B1 GPCRs accurately predict the opposite behavior of the corresponding PAM or NAM.