Biophysical Society Thematic Meeting | Bucharest 2026

Biophysics of Membrane Reactions in Brian

Thursday Speaker Abstracts

ROBOSAMPLE: ACCELERATING PROTEIN CONFORMATIONAL LANDSCAPE EXPLORATION VIA ROBOT MECHANICS SIMULATIONS Laurentiu Spiridon ; Victor G Ungureanu; Teodor A ?ulea; Andrei J Petrescu; Institute of Biochemistry of The Romanian Academy, Bioinformatics and Structural Biochemistry, Bucuresti, Romania The rugged potential energy landscapes of biomolecules pose a significant challenge for traditional molecular dynamics (MD) and Markov Chain Monte Carlo (MCMC) methods. While enhanced sampling techniques have made strides, they often struggle with the high dimensionality and topological constraints of complex systems. Here, we introduce Robosample, a software framework that integrates multi-layered enhanced sampling—including Hamiltonian Monte Carlo (HMC), Replica Exchange (REMC) and non-equilibrium trial moves —with generalized coordinates simulations derived from multibody dynamics. By treating molecular substructures as rigid bodies connected by kinematic joints, Robosample enables the design of highly versatile guidance Hamiltonians that significantly improve sampling efficiency (Spiridon et al., 2020). Our methodology combines HMC with Gibbs sampling, where Gibbs blocks are defined by degrees of freedom mapped onto robotic joints. By leveraging recursive kinematics and dynamics algorithms, Robosample achieves several orders of magnitude higher performance than conventional atomistic simulation suites. We ensure ergodicity through a joint-probability block-sampling scheme implemented via Cartesian HMC. To demonstrate its utility, we applied Robosample to G protein-coupled receptors (GPCRs), among other complex systems. Analysis of coupled mechanical and geometric descriptors successfully identified pivot regions and preferred displacement pathways that regulate binding site accessibility. This mechanistic framework explicitly connects inter-helical forces and helix geometry to ligand accommodation. Our rigid-body dynamics approach offers a scalable, efficient strategy to probe collective motions, providing a powerful complement to atomistic simulations for characterizing large scale conformational transitions.

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