a, b, A 10 nM solution of Cy5–15-bp-DNATHF was mixed with 10 nM UV-DDB and counter-titrated with nucleosome or 145-bp DNA containing 6–4PP (a) or THF2 (b) at the −1 (−22/−23) position. Undamaged 145-bp DNA was used as a negative control. All data are shown as mean ± s.d. of three technical replicates. c, Gel electrophoretic mobility shift assays were carried out by mixing 40 nM of nucleosomes containing THF2 at different positions, −5∗ (−16/−17) to +3 (−18/−19), with increasing amounts of UV-DDB (0 to 320 nM). Gels were imaged by Alexa Fluor 488 with Typhoon Image Analyzer. d, As in b, but counter-titrated with different 145-bp DNA containing THF2 at the −5∗ (−16/−17) to +3 (−18/−19) positions. e, Estimation of EC 50 from the counter-titration experiments (a, b and Fig. 2e). Data are shown as mean ± s.d. of three technical replicates. Lesions placed at position zero show the highest binding affinity, with an EC 50 around 1.1-fold higher than those at the −1 (−22/−23) position used for structure determination in Fig. 1. However, the EC 50 decreased approximately 2.5-fold for nucleosomes with THF2 lesions at the +1 (−20/−21) site, around eightfold at the +2 (−19−20) site and around 15-fold at the +3 (−18/−19) site. A similar drop in affinity is found for lesions placed successively in the other direction from the −2 (−23/−24) site through to the −3 (−24/−25) and −4 (−25/−26) sites (Fig. 2e). f, Representative VPP cryo-EM micrographs (left) and reference-free 2D class averages (right) for the NCPTHF2(−3)–UV-DDB complex. g, Ab initio model generated with RELION for the complex shown in f. h, Two different microscope datasets were collected under identical imaging conditions leading to 3,890 micrographs. All dose-fractionated micrograph stacks were subjected to beam-induced motion correction with MotionCor255. All frames (1–40) were included during this step. Further processing was carried out using MotionCor2-corrected sums that were filtered according to exposure dose (1 e− Å−2 per frame). A small dataset was manually picked to obtain 2D class averages used for autopicking within RELION. The model shown in g was low-pass-filtered to 60 Å and used as initial model for the first round of 3D classification. Several rounds of 2D and 3D classification were necessary to obtain homogeneous datasets. The last 3D classification divided the dataset into six models. Refinement of the best particles with a soft mask around the entire complex led to a 4.1 Å resolution map. i, Representative conventional (no VPP) cryo-EM micrograph (left) and reference-free 2D class averages (right) for the isolated NCPTHF2(−3). j, A total of 2,433 micrographs were collected and a small dataset was manually picked to obtain initial 2D class averages followed by autopicking in RELION. Four rounds of 2D classification led to a homogeneous dataset. The density for UV-DDB was removed from the model shown in g. The resulting map was low-pass-filtered to 60 Å and used as initial model for the first round of 3D refinement leading to a map at 4.1 Å resolution after polishing. Given the accumulated dose of 40 e− Å−2 spanning 40 frames, frames 1–28 were included during movie refinement and particle polishing in RELION. To improve the resolution, we performed 3D classification into three classes. Refinement with a mask of class II led to a 3.6 Å resolution map. Per particle CTF refinement improved the map to 3.5 Å resolution. k, l, Local-resolution-filtered map for NCPTHF2(−3)–UV-DDB (k) and NCPTHF2(−3) (l). m, Gold-standard FSC curves for NCPTHF2(−3)–UV-DDB (blue) and NCPTHF2(−3) (orange). n, o, Angular distribution for NCPTHF2(−3)–UV-DDB (n) and NCPTHF2(−3) (o). p, Overlay of the predicted NCPTHF2(−3)–UV-DDB model (red) with its cryo-EM structure (yellow), the difference between the two is reconciled by nucleosomal register shifting. Source data