Evaluation of Unrestrained Replica-Exchange Simulations Using Dynamic Walkers in Temperature Space for Protein Structure Refinement

Abstract

A central problem of computational structural biology is the refinement of modeled protein structures taken from either comparative modeling or knowledge-based methods. Simulations are commonly used to achieve higher resolution of the structures at the all-atom level, yet methodologies that consistently yield accurate results remain elusive. In this work, we provide an assessment of an adaptive temperature-based replica exchange simulation method where the temperature clients dynamically walk in temperature space to enrich their population and exchanges near steep energetic barriers. This approach is compared to earlier work of applying the conventional method of static temperature clients to refine a dataset of conformational decoys. Our results show that, while an adaptive method has many theoretical advantages over a static distribution of client temperatures, only limited improvement was gained from this strategy in excursions of the downhill refinement regime leading to an increase in the fraction of native contacts. To illustrate the sampling differences between the two simulation methods, energy landscapes are presented along with their temperature client profiles.

Experimental accuracy in protein structure refinement via molecular dynamics simulations

“…refinement protocols commonly apply positional restraints with respect to the initial homology models (28). Without such restraints, initial models tend to move away from the native state (44) without returning, even during simulations over 100 μs (40) and with enhanced sampling methods (24). However, the restraints also limit progress toward the native state.…”

“…Structure refinement methods aim at improving the accuracy of homology models toward experimental quality (17). A common approach is to initiate extensive conformational sampling from a given homology model to search for structures that are closer to the true native state and identify those via suitable scoring functions (18)(19)(20)(21)(22)(23)(24)(25)(26). Refinement is achieved when the sampling generates conformations closer to the native state and when the scoring protocol can discriminate such conformations (19).…”

Protein structure refinement via molecular‐dynamics simulations: What works and what does not?

“…Another strategy is the application of physics‐based sampling in the refinement of approximate template‐based models. The idea of refinement of template‐based models via MD simulations is not new and anecdotal success stories have appeared in the literature for a while . However, consistent success with MD‐based structure refinement for a large set of targets was only recently demonstrated .…”

On the Helix Propensity in Generalized Born Solvent Descriptions of Modeling the Dark Proteome

“…An alternative to Equation (1) is an adaptive replica exchange method of allowing the clients to dynamically walk in temperature space (Katzgraber et al, 2006 ; Trebst et al, 2006 ; Lee and Olson, 2011 ; Olson and Lee, 2014 ; Olson et al, 2016 ). In implementing the adaptive algorithm, each client is tagged as either “cold” or “hot” depending on the last temperature extreme it visited (Lee and Olson, 2011 ).…”

“…One of the sampling methods is the conventional application of T-ReX with a static set of temperatures to explore the conformational landscape. The other technique is an adaptive T-ReX where the replica clients dynamically walk in temperature space in search of the optimal population density on a modeled energy function (Katzgraber et al, 2006 ; Trebst et al, 2006 ; Lee and Olson, 2011 ; Olson and Lee, 2014 ; Olson et al, 2016 ). The GB models analyzed are GBMV2 (generalized Born molecular volume; Lee et al, 2002 , 2003 ) and the GBSW2 (generalized Born smoothing window; Im et al, 2003 ).…”