Unbiased disentanglement of conformational baths with the help of microwave spectroscopy, quantum chemistry, and artificial intelligence: The puzzling case of homocysteine.

An integrated experimental-computational strategy for the accurate characterization of the conformational landscape of flexible biomolecule building blocks is proposed. This is based on the combination of rotational spectroscopy with quantum-chemical computations guided by artificial intelligence tools. The first step of the strategy is the conformer search and relative stability evaluation performed by means of an evolutionary algorithm. In this step, last generation semiempirical methods are exploited together with hybrid and double-hybrid density functionals. Next, the barriers ruling the interconversion between the low-lying conformers are evaluated in order to unravel the possible fast relaxation paths. The relative stabilities and spectroscopic parameters of the "surviving" conformers are then refined using state-of-the-art composite schemes. The reliability of the computational procedure is further improved by the inclusion of vibrational and thermal effects. The final step of the strategy is the comparison between experiment and theory without any ad hoc adjustment, which allows an unbiased assignment of the spectroscopic features in terms of different conformers and their spectroscopic parameters. The proposed approach has been tested and validated for homocysteine, a highly flexible non-proteinogenic α-amino acid. The synergism of the integrated strategy allowed for the characterization of five conformers stabilized by bifurcated N-H 2 ⋯O=C hydrogen bonds, together with an additional conformer involving a more conventional HN⋯H-O hydrogen bond. The stability order estimated from the experimental intensities as well as the number and type of conformers observed in the gas phase are in full agreement with the theoretical predictions. Analogously, a good match has been found for the spectroscopic parameters.