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Updated: Apr 20, 2026

Analyzing and Building Nucleic Acid Structures with 3DNA
Published on: April 26, 2013
Towards biochemically relevant QM computations on nucleic acids: controlled electronic structure geometry
1CEITEC - Central European Institute of Technology, Campus Bohunice, Kamenice 5, 625 00 Brno, Czech Republic. kruse@ceitec.muni.cz.
Abstract:
Recent developments in dispersion-corrected density functional theory methods allow for the first time the description of large fragments of nucleic acids (hundreds of atoms) with an accuracy clearly surpassing the accuracy of common biomolecular force fields. Such calculations can significantly improve the description of the potential energy surface of nucleic acid molecules, which may be useful for studies of molecular interactions and conformational preferences of nucleic acids, as well as verification and parameterization of other methods. The first of such studies, however, demonstrated that successful applications of accurate QM calculations to larger nucleic acid building blocks are hampered by difficulties in obtaining geometries that are biochemically relevant and are not biased by non-native structural features. We present an approach that can greatly facilitate large-scale QM studies on nucleic acids, namely electronic structure geometry optimization of nucleic acid fragments utilizing a penalty function to restrain key internal coordinates with a specific focus on the torsional backbone angles. This work explores the viability of these restraint optimizations for DFT-D3, PM6-D3H and HF-3c optimizations on a set of examples (a UpA dinucleotide, a DNA G-quadruplex and a B-DNA fragment). Evaluation of different penalty function strengths reveals only a minor system-dependency and reasonable restraint values range from 0.01 to 0.05 Eh rad(-2) for the backbone torsions. Restraints are crucial to perform the QM calculations on biochemically relevant conformations in implicit solvation and gas phase geometry optimizations. The reasons for using restrained instead of constrained or unconstrained optimizations are explained and an open-source external optimizer is provided.
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