QuantumDNA: A python package for analyzing quantum charge dynamics in DNA and exploring its biological relevance

The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine, and plays a critical role in processes such as DNA damage detection, protein-DNA interactions, and DNA-based nanotechnology. However, despite significant progress in each of these areas, knowledge often remains inaccessible to researchers in other scientific communities, limiting the broader impact of advances across disciplines. To bridge this gap, we present QuantumDNA, an open-source Python package for simulating DNA charge transfer and excited state dynamics using quantum physical methods. QuantumDNA combines an efficient Linear Combination of Atomic Orbitals (LCAO) approach combined with tight-binding models and incorporates open quantum systems techniques to account for environmental effects. This approach allows for a rapid yet sufficiently accurate analysis of large DNA ensembles, enabling statistical studies of genetic and epigenetic phenomena. To ensure accessibility, the package features a graphical user interface, making it suitable for researchers across disciplines. Program Title: QuantumDNA CPC Library link to program files: https://doi.org/10.17632/5mw48c7gbb.1 Developer's repository link: https://github.com/dehe1011/QuantumDNA Licensing provisions: BSD 3-clause Programming language: Python Nature of the Problem: Over the past 60 years, a variety of advanced simulation methods have been employed to explore charge dynamics of DNA. However, most of these approaches are computationally too expensive for the large-scale statistical screening required in genetics and epigenetics. Therefore, theoretical and computational results are often restricted to specialized fields, limiting their accessibility and reproducibility to researchers across disciplines. Solution Method: QuantumDNA combines computational methods from quantum physics and theoretical chemistry to facilitate high-throughput analysis of DNA charge dynamics with sufficient accuracy. Unlike computationally expensive ab initio methods, it utilizes the efficiency of LCAO and TB models to simulate charge dynamics while considering environmental effects, making simulations more accessible and encouraging interdisciplinary research. Additional comments: QuantumDNA is an open-source package featuring a graphical user interface, tutorial Jupyter notebooks, and a dedicated documentation website. The package also supports CPU parallel computing.

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Publication Details

Journal
Computer Physics Communications
Published
2025-04-22
DOI
https://doi.org/10.1016/j.cpc.2025.109626
Citations
1
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
0.81

Funders

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article

QuantumDNA: A python package for analyzing quantum charge dynamics in DNA and exploring its biological relevance

Marco Trenti, Marilena Mantela, Constantinos Simserides, Joachim Ankerhold et al.
1 citations
Computer Physics Communications
Advanced biosensing and bioanalysis techniques
0.81
article

QuantumDNA: A python package for analyzing quantum charge dynamics in DNA and exploring its biological relevance

Marco Trenti, Marilena Mantela, Constantinos Simserides, Joachim Ankerhold, Mirko Rossini, Dennis Herb
article en
1 citations

Abstract

The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine, and plays a critical role in processes such as DNA damage detection, protein-DNA interactions, and DNA-based nanotechnology. However, despite significant progress in each of these areas, knowledge often remains inaccessible to researchers in other scientific communities, limiting the broader impact of advances across disciplines. To bridge this gap, we present QuantumDNA, an open-source Python package for simulating DNA charge transfer and excited state dynamics using quantum physical methods. QuantumDNA combines an efficient Linear Combination of Atomic Orbitals (LCAO) approach combined with tight-binding models and incorporates open quantum systems techniques to account for environmental effects. This approach allows for a rapid yet sufficiently accurate analysis of large DNA ensembles, enabling statistical studies of genetic and epigenetic phenomena. To ensure accessibility, the package features a graphical user interface, making it suitable for researchers across disciplines. Program Title: QuantumDNA CPC Library link to program files: https://doi.org/10.17632/5mw48c7gbb.1 Developer's repository link: https://github.com/dehe1011/QuantumDNA Licensing provisions: BSD 3-clause Programming language: Python Nature of the Problem: Over the past 60 years, a variety of advanced simulation methods have been employed to explore charge dynamics of DNA. However, most of these approaches are computationally too expensive for the large-scale statistical screening required in genetics and epigenetics. Therefore, theoretical and computational results are often restricted to specialized fields, limiting their accessibility and reproducibility to researchers across disciplines. Solution Method: QuantumDNA combines computational methods from quantum physics and theoretical chemistry to facilitate high-throughput analysis of DNA charge dynamics with sufficient accuracy. Unlike computationally expensive ab initio methods, it utilizes the efficiency of LCAO and TB models to simulate charge dynamics while considering environmental effects, making simulations more accessible and encouraging interdisciplinary research. Additional comments: QuantumDNA is an open-source package featuring a graphical user interface, tutorial Jupyter notebooks, and a dedicated documentation website. The package also supports CPU parallel computing.

Computer Physics CommunicationsVol. 313
National and Kapodistrian University of Athens (GR), Center for Integrated Quantum Science and Technology (DE), Roth and Rau (Germany) (DE)
Bundesministerium für Bildung und Forschung, Center for Integrated Quantum Science and Technology
Openalex Percentile: Top 29%
Advanced biosensing and bioanalysis techniques
0.81
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