Skip to Main content Skip to Navigation
Journal articles

Recognition of a tandem lesion by DNA bacterial formamidopyrimidine glycosylases explored combining molecular dynamics and machine learning

Abstract : The combination of several closely spaced DNA lesions, which can be induced by a single radical hit, constitutes a hallmark in the DNA damage landscape and radiation chemistry. The occurrence of such a tandem base lesion gives rise to a strong coupling with the double helix degrees of freedom and induces important structural deformations, in contrast to DNA strands containing a single oxidized nucleobase. Although such complex lesions are known to be refractory to repair by DNA glycosylases, there is still a lack of structural evidence to rationalize these phenomena. In this contribution, we explore, by numerical modeling and molecular simulations, the behavior of the bacterial glycosylase responsible for base excision repair (MutM), specialized in excising oxidatively-damaged defects such as 7,8-dihydro-8-oxoguanine (8-oxoG). The difference in lesion recognition between a simple damage and a tandem lesion featuring an additional abasic site is assessed at atomistic resolution owing to microsecond molecular dynamics simulations and machine learning postprocessing, allowing to extensively pinpoint crucial differences in the interaction patterns of the damaged bases. Our results reveal substantial changes in the interaction network surrounding the 8-oxoG upon addition of an adjacent abasic site, leading to the perturbation of the intercalation triad which is crucial for lesion recognition and processing. The recognition process might also be impacted by a more constrained MutM-DNA binding upon tandem damage, as shown by the machine learning post-processing. This work advocates for the use of such high throughput numerical simulations for exploring the complex combinatorial chemistry of tandem DNA lesions repair and more generally local multiple damaged sites of the utmost significance in radiation chemistry.
Document type :
Journal articles
Complete list of metadata
Contributor : Béatrice Rayet Connect in order to contact the contributor
Submitted on : Friday, November 12, 2021 - 12:26:37 PM
Last modification on : Sunday, June 26, 2022 - 3:19:04 AM
Long-term archiving on: : Sunday, February 13, 2022 - 7:06:40 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Emmanuelle Bignon, Natacha Gillet, Chen-Hui Chan, Tao Jiang, Antonio Monari, et al.. Recognition of a tandem lesion by DNA bacterial formamidopyrimidine glycosylases explored combining molecular dynamics and machine learning. Computational and Structural Biotechnology Journal, Elsevier, 2021, 19, pp.2861-2869. ⟨10.1016/j.csbj.2021.04.055⟩. ⟨hal-03326585⟩



Record views


Files downloads