arXiv subjects

An Ghysels

Publications and source records attributed to An Ghysels.

4 recordsLinked to original sources

arXiv2026

Collaborate to decorrelate in path space: Hamiltonian replica exchange transition interface sampling (HRETIS)

We present Hamiltonian Replica Exchange Transition Interface Sampling (HRETIS), a path sampling framework designed to efficiently sample rare events in systems with complex potential energy landscapes. HRETIS introduces a helper potential within a Hamiltonian replica exchange scheme, which enhances exploration of path space when the underlying potential is not well suited for conventional path sampling approaches. This is particularly advantageous for systems exhibiting multiple pathways separated by orthogonal barriers such as in drug (un)binding, where standard algorithms often show slow convergence since they become trapped within specific pathways. By exchanging Hamiltonians between the path ensembles, HRETIS overcomes these limitations and increases the decorrelation between subsequent paths in the Monte Carlo chain. We demonstrate that HRETIS provides robust and accurate kinetics in several systems, including coarse-grained simulations of amino acid permeation through a dipalmitoylphosphatidylcholine (DPPC) membrane. Moreover, HRETIS is found to improve sampling efficiency and convergence, illustrating its potential as a powerful tool for rare event sampling in complex molecular systems.

physics.comp-ph↗

arXiv2026

Estimating Full Path Lengths and Kinetics from Partial Path Transition Interface Sampling Simulations

Assessing the time scale of biological processes using molecular dynamics (MD) simulations with sufficient statistical accuracy is a challenging task, as processes are often rare and/or slow events, which may extend largely beyond the time scale of what is accessible with modern day high performance computational infrastructure. Recently, the replica exchange partial path transition interface sampling (REPPTIS) algorithm was developed to study rare and slow events involving metastable states along their reactive pathways. REPPTIS is a path sampling method where paths are cut short to reduce the computational cost, while combining this with the efficiency offered by replica exchange between the partial path ensembles. However, REPPTIS still lacks a formalism to extract time-dependent properties, such as mean first passage times, fluxes, and rates, from the short partial paths. In this work, we introduce a Markov state model (MSM) framework to estimate full path lengths and kinetic properties from the overlapping partial paths generated by REPPTIS. The framework results in newly derived closed formulas for the REPPTIS crossing probability, mean first passage times (MFPTs), flux, and rate constant. Our approach is then validated using simulations of Brownian and Langevin particles on a series of one-dimensional potential energy profiles as well as the dissociation of KCl in solution, demonstrating that REPPTIS accurately reproduces the exact kinetics benchmark. The MSM framework is further applied to the trypsin-benzamidine complex to compute the dissociation rate as a test case of a biological system, albeit the computed rate underestimates the experimental value. In conclusion, our MSM framework equips REPPTIS simulations with a robust theoretical and practical foundation for extracting kinetic information from computationally efficient partial paths.

physics.comp-ph↗

arXiv2026

Generalized Path Reweighting and History-Dependent Free Energies

Transition interface sampling (TIS) and replica exchange TIS (RETIS) are powerful methods for computing rates of rare events inaccessible to straightforward molecular dynamics (MD) simulations. Path reweighting extends their output, enabling the evaluation of diverse thermodynamic and kinetic quantities, including reaction prediction metrics, activation barriers, committor functions, and free energies. The recently developed Infinity-RETIS algorithm boosts parallel efficiency through asynchronous replica exchanges in the infinite-swap limit, eliminating the wall-time bottlenecks of conventional RETIS. This approach introduces fractional samples and biased sampling distributions, requiring a generalized path reweighting framework, for which we derive expressions demonstrating how exact dynamic and thermodynamic variables can be computed. We then focus on a special class of free energy surfaces defined by history-dependent conditions, whose values are influenced by kinetic factors such as particle mass and friction, unlike standard unconditional free energy surfaces. Even with suboptimal reaction coordinates, these conditional free energies can reveal kinetically relevant barriers that may be misrepresented by standard unconditional free energies, thereby providing a rigorous and versatile tool for characterizing complex molecular transitions.

physics.chem-ph↗

arXiv2025

Path sampling challenges in large biomolecular systems: RETIS and REPPTIS for ABL-imatinib kinetics

Predicting the kinetics of drug-protein interactions is crucial for understanding drug efficacy, particularly in personalized medicine, where protein mutations can significantly alter drug residence times. This study applies Replica Exchange Transition Interface Sampling (RETIS) and its Partial Path variant (REPPTIS) to investigate the dissociation kinetics of imatinib from Abelson nonreceptor tyrosine kinase (ABL) and mutants relevant to chronic myeloid leukemia therapy. These path-sampling methods offer a bias-free alternative to conventional approaches requiring qualitative predefined reaction coordinates. Nevertheless, the complex free-energy landscape of ABL-imatinib dissociation presents significant challenges. Multiple metastable states and orthogonal barriers lead to parallel unbinding pathways, complicating convergence in TIS-based methods. Despite employing computational efficiency strategies such as asynchronous replica exchange, full convergence remained elusive. This work provides a critical assessment of path sampling in high-dimensional biological systems, discussing the need for enhanced initialization strategies, advanced Monte Carlo path generation moves, and machine learning-derived reaction coordinates to improve kinetic predictions of drug dissociation with minimal prior knowledge.

physics.bio-ph↗