Generalized Born Implicit Solvent Models Do Not Reproduce Secondary Structures of De Novo Designed Glu/Lys Peptides

Abstract

We test a range of standard generalized Born (GB) models and protein force fields for a set of five experimentally characterized, designed peptides comprising alternating blocks of glutamate and lysine, which have been shown to differ significantly in α-helical content. Sixty-five combinations of force fields and GB models are evaluated in >800 μs of molecular dynamics simulations. GB models generally do not reproduce the experimentally observed α-helical content, and none perform well for all five peptides. These results illustrate that these models are not usefully predictive in this context. These peptides provide a useful test set for simulation methods.

Simulation Reveals the Chameleonic Behavior of Macrocycles

“…Monte Carlo conformational sampling in implicit solvent, followed by DFT optimization, provided an excellent description of the conformations populated in chloroform, but not so in the polar DMSO. The lack of predictivity of implicit solvent models in polar environments has been noted earlier for macrocycles ,, and was comprehensively illustrated in a most recent study of linear peptides . However, we found that MD simulations using explicit solvation followed by DFT optimization described the conformations of the macrocycles correctly in both environments.…”

“…The lack of predictivity of implicit solvent models in polar environments has been noted earlier for macrocycles 16 , 17 , 21 and was comprehensively illustrated in a most recent study of linear peptides. 62 However, we found that MD simulations using explicit solvation followed by DFT optimization described the conformations of the macrocycles correctly in both environments. These observations indicate that hydrogen bonding dictates the use of explicit solvent models for correct predictions in polar solvents.…”

Uncovering the contributions of charge regulation to the stability of single alpha helices

“…These ideas and usages might need to account for the effects of charge state heterogeneity and accommodate the observation that for finite-sized systems, such as the (E 4 K 4 ) 3 peptide studied here, the helical rod is unlikely to be the dominant conformation ( Figure 7 ). This has been recently critiqued as an artefact of a specific brand of implicit solvation models 61 . In that study, the authors set up an expectation that (E 4 K 4 ) n systems should be nearly 100% helical and perfectly rod-like.…”

“…Of course, one might be inclined to critique the observed conformational heterogeneity we report in Figure 7. Indeed, recent work suggests that the failure to observe a rod-like alpha helical conformation in 100% of the ensemble represents a failure of a specific brand of implicit solvation models 62 . In that study, the authors set up the expectation that (E 4 K 4 ) n systems should be nearly 100% alpha helical and perfectly rod-like.…”

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

“…39 In MM-based modeling, implicit solvent model based simulations do not always accurately reproduce folded [40][41][42] and unfolded structural distributions. 8,38,43,44 For QM-based modeling, pKa predictions of the solute can sometimes be off by a couple of pH units, if the hydrogen bonding between solute and the nearby solvent molecules is completely ignored in implicit solvent calculations. 45 In principle, such deciencies in implicit solvent based MM and QM modelings can be mitigated by including one or multiple solvent molecules or the entire rst solvation shell into the solute region of implicit solvent calculations.…”