Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully ‘selected’ to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground state faster than the harmful reactions can occur; however, such photochemistry is not fully understood, in part because theoretical simulations of such systems are extremely expensive—with only smaller chromophores accessible. Here, we study the excited-state dynamics of tyrosine using a method based on deep neural networks that leverages the physics underlying quantum chemical data and combines different levels of theory. We reveal unconventional and dynamically controlled ‘roaming’ dynamics in excited tyrosine that are beyond chemical intuition and compete with other ultrafast deactivation mechanisms. Our findings suggest that the roaming atoms are radicals that can lead to photodamage, offering a new perspective on the photostability and photodamage of biological systems.
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The molecular coordinates of the conformers used in this study that are shown in Supplementary Fig. 1 and Supplementary Fig. 9 are available in the Supplementary Information. Additionally, the dataset is made available at https://doi.org/10.6084/m9.figshare.15132081 (ref. 66) in the Atomic Simulation Environment (ase)67 database format including the initial conditions (geometries and velocities) to set up the dynamics. A detailed description of the ML models, training set generation, training procedure, dynamics simulations and analysis is provided in the Supplementary Information. For reproduction of this work, replace the example dataset provided in the tutorial with the provided dataset and set the necessary parameters indicated in the Supplementary Information and listed in the tutorial instructions. For dynamics, set up the trajectories with SHARC68 using the initial conditions file available at https://doi.org/10.6084/m9.figshare.15132081 (ref. 66) and follow the tutorial on how to run dynamics with SchNarc. Source data are provided with this paper.
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This work was financially supported by the Austrian Science Fund (W 1232 (MolTag doctoral program “Ion channels and transporters as molecular drug targets”) and J 4522-N (J.W.)) and the uni:docs programme of the University of Vienna (J.W.). The computational results presented have been achieved in part using the Vienna Scientific Cluster. P.M. and L.G. thank the University of Vienna for continuous support, and also support in the frame of the research platform ViRAPID (Vienna Research Platform on Accelerating Photoreaction Discovery). J.W. and P.M. are grateful for an Nvidia Hardware Grant. M.G. works at the BASLEARN Joint Lab for Machine Learning, cofinanced by the Technische Universität Berlin and BASF SE.
The authors declare no competing interests.
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Extended Data Fig. 1 Validation of our method to combine ADC(2) and CASPT2, the generation of ad hoc data, and their influence on neural network training.
(a) Potential energy curves along the PhO-H bond using CASSCF(12,11) (left panel, solid lines), CASPT2(12,11) (left panel, dotted lines), and ADC(2) (right panel). (b) Absorption spectra of tyrosine computed with ADC(2), TD-DFT/PBE0/SV(P), CASSCF(12,11), and CASPT(12,11) spectra using 1000 Wigner-sampled conformations. For CASPT2(12,11) and CASSCF(12,11) 758 calculations converged. The full width at half maximum for the Gaussian convolution was 0.2 eV in ADC(2) and TD-DFT spectra and 0.5 eV in CASPT2(12,11) and CASSCF(12,11) spectra and absorption peaks are scaled such that the lowestenergy peak has the same height as the energetically lowest-lying experimental absorption peak that was extracted from ref. 46. (c) Spectrum obtained from ADC(2) redshifted by 0.3 eV with contributions from different excited states. (d) Tyrosine shown with circled hydrogen atoms that were detached to generate ad hoc data (Supplementary Table 3 and 4). (e) The Ph-OH and (f) the N-H bond as example reaction coordinates to show the differences of ADC(2) (solid and dashed lines), CASPT2(12,11) (circles), and MP2 calculations (crosses) that manifest the degeneracy of singlet and triplet states. Second plots illustrates the data used for training and the third and fourth plots show neural network predictions using a simple multi-layer feed-forward neural network (Supplementary section 7.A) faster trainable than SchNarc models that were trained without ad hoc data points and with ad hoc data points, respectively. S refers to singlet and T to triplet states.
Supplementary Figs. 1–17, Tables 1–6 and Discussion.
Roaming atoms in tyrosine.
Recoiling atoms in tyrosine.
Code for SchNarc dynamics as available on github at https://github.com/schnarc/SchNarc/tree/develop.
The xyz structures of tyrosine conformers, roaming geometries and dissociation data points.
Source data for the Supplementary Information.
Structures of the first trajectory, in which roaming atoms were observed.
Analysis of dynamics simulations.
Potential energy curves and UV/visible absorption spectra to support the method validation and the generation of ad hoc data points.
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Westermayr, J., Gastegger, M., Vörös, D. et al. Deep learning study of tyrosine reveals that roaming can lead to photodamage. Nat. Chem. 14, 914–919 (2022). https://doi.org/10.1038/s41557-022-00950-z