Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution

Abstract

<div>The rational design of foldable and functionalizable peptidomimetic scaffolds requires the concerted application of both computational and experimental methods. Recently, a new class of designed peptoid macrocycle incorporating spiroligomer proline mimics (Q-prolines) has been found to pre-organize when bound by monovalent metal cations. To determine the solution-state structure of these cation-bound macrocycles, we employ a Bayesian inference method (BICePs) to reconcile enhanced-sampling molecular simulations with sparse ROESY correlations from experimental NMR studies. The BICePs approach circumvents the need for bespoke force field parameterization, instead relying on experimental restraints to help narrow the possible set of <i>cis</i>/<i>trans</i> amide isomers in solution. Conformations predicted to be most populated in solution were then simulated in the presence of explicit cations to yield trajectories with observed binding events, revealing a highly-preorganized all-<i>trans</i> amide conformation, whose formation is likely limited by the slow rate of <i>cis</i>/<i>trans</i> isomerization. Interestingly, this conformation differs from a racemic crystal structure solved in the absence of cation. Free energies of cation binding computed from distance-dependent potentials of mean force suggest Na<sup>+</sup> has higher affinity to the macrocycle than K<sup>+</sup>, with both cations binding much more strongly in acetonitrile than water. The simulated affinities are able to correctly rank the extent to which different macrocycle sequences exhibit preorganization in the presence of different metal cations and solvents, suggesting our approach is suitable for solution-state computational design.</div>

Model selection using replica averaging with Bayesian inference of conformational populations

“…The Bayesian Inference of Conformational Populations (BICePs) algorithm 19,[27][28][29][30][31] is a related (see SI Text) but unique MaxEnt method that addresses both of these shortcomings: it is a post-processing reweighting method that does not require knowledge of experimental errors, and instead infers this from the data.…”