Toward Reliable Conformational Energies of Amino Acids and DipeptidesThe DipCONFS Benchmark and DipCONL Datasets

Simulating peptides
and proteins is becoming increasingly
important,
leading to a growing need for efficient computational methods. These
are typically semiempirical quantum mechanical (SQM) methods, force
fields (FFs), or machine-learned interatomic potentials (MLIPs), all
of which require a large amount of accurate data for robust training
and evaluation. To assess potential reference methods and complement
the available data, we introduce two sets, DipCONFL and DipCONFS,
which cover large parts of the conformational space of 17 amino acids
and their 289 possible dipeptides in aqueous solution. The conformers
were selected from the exhaustive PeptideCS dataset by Andris et al.
[J. Phys. Chem. B 2022, 126, 5949–5958]. The structures, originally generated with
GFN2-xTB, were reoptimized using the accurate r2SCAN-3c
density functional theory (DFT) composite method including the implicit
CPCM water solvation model. The DipCONFS benchmark set contains 918
conformers and is one of the largest sets with highly accurate coupled
cluster conformational energies so far. It is employed to evaluate
various DFT and wave function theory (WFT) methods, especially regarding
whether they are accurate enough to be used as reliable reference
methods for larger datasets intended for training and testing more
approximated SQM, FF, and MLIP methods. The results reveal that the
originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies
are not sufficiently accurate. Among the DFT methods tested as an
alternative reference level, the revDSD-PBEP86-D4 double hybrid performs
best with a mean absolute error (MAD) of 0.2 kcal mol–1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient
r2SCAN-3c composite method also shows excellent results,
with an MAD of 0.3 kcal mol–1, similar to the best-tested
hybrid ωB97M-D4. With these findings, we compiled the large
DipCONFL set, which includes over 29,000 realistic conformers in solution
with reasonably accurate r2SCAN-3c reference conformational
energies, gradients, and further properties potentially relevant for
training MLIP methods. This set, also in comparison to DipCONFS, is
used to assess the performance of various SQM, FF, and MLIP methods
robustly and can complement training sets for those.