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Lesion Sensing during Initial Binding by Yeast XPC/Rad4: Toward Predicting Resistance to Nucleotide Excision Repair
journal contribution
posted on 2018-10-04, 00:00 authored by Hong Mu, Yingkai Zhang, Nicholas E. Geacintov, Suse BroydeNucleotide excision
repair (NER) excises a variety of environmentally
derived DNA lesions. However, NER efficiencies for structurally different
DNA lesions can vary by orders of magnitude; yet the origin of this
variance is poorly understood. Our goal is to develop computational
strategies that predict and identify the most hazardous, repair-resistant
lesions from the plethora of such adducts. In the present work, we
are focusing on lesion recognition by the xeroderma pigmentosum C
protein complex (XPC), the first and required step for the subsequent
assembly of factors needed to produce successful NER. We have performed
molecular dynamics simulations to characterize the initial binding
of Rad4, the yeast orthologue of human XPC, to a library of 10 different
lesion-containing DNA duplexes derived from environmental carcinogens.
These vary in lesion chemical structures and conformations in duplex
DNA and exhibit a wide range of relative NER efficiencies from repair
resistant to highly susceptible. We have determined a promising set
of structural descriptors that characterize initial binding of Rad4
to lesions that are resistant to NER. Key initial binding requirements
for successful recognition are absent in the repair-resistant cases:
There is little or no duplex unwinding, very limited interaction between
the β-hairpin domain 2 of Rad4 and the minor groove of the lesion-containing
duplex, and no conformational capture of a base on the lesion partner
strand. By contrast, these key binding features are present to different
degrees in NER susceptible lesions and correlate to their relative
NER efficiencies. Furthermore, we have gained molecular understanding
of Rad4 initial binding as determined by the lesion structures in
duplex DNA and how the initial binding relates to the repair efficiencies.
The development of a computational strategy for identifying NER-resistant
lesions is grounded in this molecular understanding of the lesion
recognition mechanism.