PEPCONF, a diverse data set of peptide conformational energies

Viki Kumar Prasad,Alberto Otero-de-la-Roza,Gino A. DiLabio

doi:10.1038/sdata.2018.310

Abstract

We present an extensive and diverse database of peptide conformational energies. Our database contains five different classes of model geometries: dipeptides, tripeptides, and disulfide-bridged, bioactive, and cyclic peptides. In total, the database consists of 3775 conformational energy data points and 4530 conformer geometries. All the reference energies have been calculated at the LC-ωPBE-XDM/aug-cc-pVTZ level of theory, which is shown to yield conformational energies with an accuracy in the order of tenths of a kcal/mol when compared to complete-basis-set coupled-cluster reference data. The peptide conformational data set (PEPCONF) is presented as a high-quality reference set for the development and benchmarking of molecular-mechanics and semi-empirical electronic structure methods, which are the most commonly used techniques in the modeling of medium to large proteins.

Highlights

Background & SummaryThe structure and function of proteins are governed by the intermolecular interactions between their building blocks, amino acids
Results obtained from the computational methods commonly employed to model proteins, such as force field and semi-empirical electronic structure methods, are usually compared to, and parametrized against, those obtained from higher-level computational methods
MPCONF196 is the only set in the literature that considers cyclic peptides

Summary

Introduction

Background & SummaryThe structure and function of proteins are governed by the intermolecular interactions between their building blocks, amino acids. These structures were used to generate a new conformational energy database of isolated amino acid monomers containing 466 data points. A comprehensive set of data that contains reference conformational energies on a diversity of small peptides would provide valuable information to those engaged in the development of atomistic computational methods for protein modeling.

Results

Conclusion