Monitoring of the Conformational Space of Dipeptides by Generative Topographic Mapping

This work describes a procedure to build generative topographic maps (GTM) as 2D representation of the conformational space (CS) of dipeptides. GTMs with excellent propensities to support highly predictive landscapes of various conformational properties were reported for three dipeptides (AA, KE and KR). CS monitoring via GTMproceeds through the projection of conformer ensembles on the map, producing cumulated responsibility (CR) vectors characteristic of the CS areas covered by the ensemble. Overlap of the CS areas visited by two distinct simulations can be expressed by the Tanimoto coefficient Tc of the associated CRs. This idea was used to monitor the reproducibility of the stochastic evolutionary conformer generation process implemented in S4MPLE. It could be shown that conformers produced by <500 S4MPLE runs reproducibly cover the relevant CS zone at given setup of the driving force field. The propensity of a simulation to visit the native CS zone can thus be quantitatively estimated, as the Tc score with respect to the “native“ CR, as defined by the ensemble of dipeptide geometries extracted from PDB proteins. It could be shown that low‐energy CS regions were indeed found to fall within the native zone. The Tc overlap score behaved as a smooth function of force field parameters. This opens the perspective of a novel force field parameter tuning procedure, bound to simultaneously optimize the behavior of the in Silico simulations for every possible dipeptide.