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Cited by 294

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Molecular flexibility in ab initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations

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Rohs, R.·2005·Nucleic Acids Research

Abstract

The dynamics of biological processes depend on the structure and flexibility of the interacting molecules. In particular, the conformational diversity of DNA allows for large deformations upon binding. Drug-DNA interactions are of high pharmaceutical interest since the mode of action of anticancer, antiviral, antibacterial and other drugs is directly associated with their binding to DNA. A reliable prediction of drug-DNA binding at the atomic level by molecular docking methods provides the basis for the design of new drug compounds. Here, we propose a novel Monte Carlo (MC) algorithm for drug-DNA docking that accounts for the molecular flexibility of both constituents and samples the docking geometry without any prior binding-site selection. The binding of the antimalarial drug methylene blue at the DNA minor groove with a preference of binding to AT-rich over GC-rich base sequences is obtained in MC simulations in accordance with experimental data. In addition, the transition between two drug-DNA-binding modes, intercalation and minor-groove binding, has been achieved in dependence on the DNA base sequence. The reliable ab initio prediction of drug-DNA binding achieved by our new MC docking algorithm is an important step towards a realistic description of the structure and dynamics of molecular recognition in biological systems.

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Metadata

Authors
Rohs, R.
Venue
Nucleic Acids Research
Published
2005-12-09
Volume
33
Issue
22
Pages
7048-7057
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
OpenAlex
W2087752336
Cited by
294
References
35
FWCI
4.71
Type
journal article
Language
en
Open access
gold

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Rohs, R. (2005). Molecular flexibility in ab initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations. Nucleic Acids Research. https://doi.org/10.1093/nar/gki1008

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