Excitation of nucleobases from a computational perspective I: reaction paths
Abstract
The main intrinsic photochemical events in nucleobases can be described on theoretical grounds within the realm of non-adiabatic computational photochemistry. From a static standpoint, the photochemical reaction path approach (PRPA), through the computation of the respective minimum energy path (MEP), can be regarded as the most suitable strategy in order to explore the electronically excited isolated nucleobases. Unfortunately, the PRPA does not appear widely in the studies reported in the last decade. The main ultrafast decay observed experimentally for the gas-phase excited nucleobases is related to the computed barrierless MEPs from the bright excited state connecting the initial Franck-Condon region and a conical intersection involving the ground state. At the highest level of theory currently available (CASPT2//CASPT2), the lowest excited (1)(ππ*) hypersurface for cytosine has a shallow minimum along the MEP deactivation pathway. In any case, the internal conversion processes in all the natural nucleobases are attained by means of interstate crossings, a self-protection mechanism that prevents the occurrence of photoinduced damage of nucleobases by ultraviolet radiation. Many alternative and secondary paths have been proposed in the literature, which ultimately provide a rich and constructive interplay between experimentally and theoretically oriented research.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.