Path sampling with memory reduction and replica exchange to reach long permeation timescales

PyRETIS 3: Conquering rare and slow events without boundaries

“…Akin to RETIS enhancing the transition interface sampling (TIS) 28 efficiency through replica exchange, [29][30][31] similar advancements in PPTIS can lead to the REPPTIS method. 18 The inclusion of replica exchange in REPPTIS relies on MC moves designed to exchange path segments among adjacent path ensembles. These segments are then extended until they meet the acceptance criteria of a neighbouring ensemble, an example of which is shown in Figure 3C.…”

How far can we stretch the timescale with RETIS?

“…It is interesting to note that while the idea of performing replica exchange moves between path ensembles was initially proposed for PPTIS before RETIS [42], it was only recently that a replica exchange variant of PPTIS (REPP-TIS) was realized and demonstrated [43]. Although PP-TIS and REPPTIS are no longer exact, they still retain some memory, unlike milestoning or Markov state models.…”

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

“…15,16 For example, an external bias potential can be added to the Hamiltonian (as in umbrella sampling and metadynamics (MetaD)), or the system can be coupled to higher temperatures (as in replica exchange MD) to effectively reduce energy barriers and thus sample transition regions, or the transition ensemble can be selectively sampled with path sampling approaches, such as transition interface sampling or its combination with replica exchange with or without memory effects. 14,17 The success of enhanced free energy sampling methods involving Hamiltonian bias requires the selection of a proper set of collective variables (CVs). CVs are user-defined functions of atomic (Cartesian) coordinates that provide a low-dimensional projection of conformational phase space while, in principle, retaining "important" information.…”

“…To circumvent this sampling issue, many of the existing computational methods used to evaluate membrane permeation rely on enhanced free energy sampling techniques. − Impressively, membrane permeation has been directly observed for a few small molecules in canonical MD simulations on the nano- and microsecond time scales. For instance, Krämer et al performed unbiased MD simulations to evaluate the permeability coefficients of oxygen, water, and ethanol using counting methods and maximum likelihood estimation for the inhomogeneous solubility-diffusion (ISD) model. ,, They found that counting methods yield nearly model-free estimates for all of the three permeants, whereas the ISD model causes large uncertainties for water due to insufficient sampling and overestimates for ethanol due to collective effects in the membrane. , For larger molecules with slower permeation, however, enhanced sampling can be essential to increase the occurrence of the slowest dynamic motions that enable permeation. This can be done by biasing the potential energy surface or altering the probability density of sampled conformations. , For example, an external bias potential can be added to the Hamiltonian (as in umbrella sampling and metadynamics (MetaD)), or the system can be coupled to higher temperatures (as in replica exchange MD) to effectively reduce energy barriers and thus sample transition regions, or the transition ensemble can be selectively sampled with path sampling approaches, such as transition interface sampling or its combination with replica exchange with or without memory effects. , …”

“…6,11,12 They found that counting methods 13 yield nearly model-free estimates for all of the three permeants, whereas the ISD model causes large uncertainties for water due to insufficient sampling and overestimates for ethanol due to collective effects in the membrane. 10,14 For larger molecules with slower permeation, however, enhanced sampling can be essential to increase the occurrence of the slowest dynamic motions that enable permeation. This can be done by biasing the potential energy surface or altering the probability density of sampled conformations.…”