Unknown

Dataset Information

0

Improved Modeling of Peptidic Foldamers Using a Quantum Chemical Parametrization Based on Torsional Minimum Energy Path Matching.


ABSTRACT: The increasing interest in novel foldamer constructs demands an accurate computational treatment on an extensive timescale. However, it is still a challenge to derive a force field (FF) that can reproduce the experimentally known fold while also allowing the spontaneous exploration of other structures. Here, aiming at a realistic reproduction of backbone torsional barriers, the relevant proper dihedrals of acyclic ?2-, ?3- and ?2,3-amino acids were added to the CHARMM FF and optimized using a novel, self-consistent iterative procedure based on quantum chemical relaxed scans. The new FF was validated by molecular dynamics simulations on three acyclic peptides. While they resided most of the time in their preferred fold (>80?% in helices and >50?% in hairpin), they also visited other conformations. Owing to the CHARMM36m-consistent parametrization, the proposed extension is suitable for exploring new foldamer structures and assemblies, and their interactions with diverse biomolecules.

SUBMITTER: Wacha A 

PROVIDER: S-EPMC6686720 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

Improved Modeling of Peptidic Foldamers Using a Quantum Chemical Parametrization Based on Torsional Minimum Energy Path Matching.

Wacha András A   Beke-Somfai Tamás T   Nagy Tibor T  

ChemPlusChem 20190704 7


The increasing interest in novel foldamer constructs demands an accurate computational treatment on an extensive timescale. However, it is still a challenge to derive a force field (FF) that can reproduce the experimentally known fold while also allowing the spontaneous exploration of other structures. Here, aiming at a realistic reproduction of backbone torsional barriers, the relevant proper dihedrals of acyclic β<sup>2</sup>-, β<sup>3</sup>- and β<sup>2,3</sup>-amino acids were added to the C  ...[more]

Similar Datasets

| S-EPMC5108092 | biostudies-literature
| S-EPMC8041298 | biostudies-literature
| S-EPMC4338430 | biostudies-other
| S-EPMC10302482 | biostudies-literature
| S-EPMC4718141 | biostudies-literature
| S-EPMC9814141 | biostudies-literature
| S-EPMC2736613 | biostudies-other
| S-EPMC3264638 | biostudies-literature
| S-EPMC11331531 | biostudies-literature
| S-EPMC3225126 | biostudies-literature