Literature DB >> 29461829

Toward Accurate Conformational Energies of Smaller Peptides and Medium-Sized Macrocycles: MPCONF196 Benchmark Energy Data Set.

Jan Řezáč1, Daniel Bím1, Ondrej Gutten1, Lubomír Rulíšek1.   

Abstract

A carefully selected set of acyclic and cyclic model peptides and several other macrocycles, comprising 13 compounds in total, has been used to calibrate the accuracy of the DFT(-D3) method for conformational energies, employing BP86, PBE0, PBE, B3LYP, BLYP, TPSS, TPSSh, M06-2X, B97-D, OLYP, revPBE, M06-L, SCAN, revTPSS, BH-LYP, and ωB97X-D3 functionals. Both high- and low-energy conformers, 15 or 16 for each compound adding to 196 in total, denoted as the MPCONF196 data set, were included, and the reference values were obtained by the composite protocol, yielding the CCSD(T)/CBS extrapolated energies or their DLPNO-CCSD(T)/CBS equivalents in the case of larger systems. The latter was shown to be in near-quantitative (∼0.10-0.15 kcal·mol-1) agreement with the canonical CCSD(T), provided the TightPNO setting is used, and, therefore, can be used as the reference for larger systems (likely up to 150-200 atoms) for the problem studied here. At the same time, it was found that many D3-corrected DFT functionals provide results of ∼1 kcal·mol-1 accuracy, which we consider as quite encouraging. This result implies that DFT-D3 methods can be, for example, safely used in efficient conformational sampling algorithms. Specifically, the DFT-D3/DZVP-DFT level of calculation seems to be the best trade-off between computational cost and accuracy. Based on the calculated data, we have not found any cheaper variant for the treatment of conformational energies, since the semiempirical methods (including DFTB) provide results of inferior accuracy (errors of 3-5 kcal·mol-1).

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Year:  2018        PMID: 29461829     DOI: 10.1021/acs.jctc.7b01074

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  9 in total

1.  Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.

Authors:  Yudong Qiu; Daniel G A Smith; Simon Boothroyd; Hyesu Jang; David F Hahn; Jeffrey Wagner; Caitlin C Bannan; Trevor Gokey; Victoria T Lim; Chaya D Stern; Andrea Rizzi; Bryon Tjanaka; Gary Tresadern; Xavier Lucas; Michael R Shirts; Michael K Gilson; John D Chodera; Christopher I Bayly; David L Mobley; Lee-Ping Wang
Journal:  J Chem Theory Comput       Date:  2021-09-22       Impact factor: 6.578

2.  Better force fields start with better data: A data set of cation dipeptide interactions.

Authors:  Xiaojuan Hu; Maja-Olivia Lenz-Himmer; Carsten Baldauf
Journal:  Sci Data       Date:  2022-06-17       Impact factor: 8.501

3.  Analysis of Density Functional Tight Binding with Natural Bonding Orbitals.

Authors:  Xiya Lu; Juan Duchimaza-Heredia; Qiang Cui
Journal:  J Phys Chem A       Date:  2019-08-15       Impact factor: 2.781

4.  Mechanistic analysis of light-driven overcrowded alkene-based molecular motors by multiscale molecular simulations.

Authors:  Mudong Feng; Michael K Gilson
Journal:  Phys Chem Chem Phys       Date:  2021-03-25       Impact factor: 3.676

5.  Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.

Authors:  Roman Zubatyuk; Justin S Smith; Jerzy Leszczynski; Olexandr Isayev
Journal:  Sci Adv       Date:  2019-08-09       Impact factor: 14.136

6.  Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications.

Authors:  László Gyevi-Nagy; Mihály Kállay; Péter R Nagy
Journal:  J Chem Theory Comput       Date:  2021-01-05       Impact factor: 6.006

7.  Benchmark assessment of molecular geometries and energies from small molecule force fields.

Authors:  Victoria T Lim; David F Hahn; Gary Tresadern; Christopher I Bayly; David L Mobley
Journal:  F1000Res       Date:  2020-12-03

8.  Optimization of the r2SCAN-3c Composite Electronic-Structure Method for Use with Slater-Type Orbital Basis Sets.

Authors:  Thomas Gasevic; Julius B Stückrath; Stefan Grimme; Markus Bursch
Journal:  J Phys Chem A       Date:  2022-06-02       Impact factor: 2.944

9.  PEPCONF, a diverse data set of peptide conformational energies.

Authors:  Viki Kumar Prasad; Alberto Otero-de-la-Roza; Gino A DiLabio
Journal:  Sci Data       Date:  2019-01-22       Impact factor: 6.444

  9 in total

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