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Abstract Transcription factor IIH (TFIIH) is a protein assembly essential for transcription initiation and nucleotide excision repair (NER). Yet, understanding of the conformational switching underpinning these diverse TFIIH functions remains fragmentary. TFIIH mechanisms critically depend on two translocase subunits, XPB and XPD. To unravel their functions and regulation, we build cryo-EM based TFIIH models in transcription- and NER-competent states. Using simulations and graph-theoretical analysis methods, we reveal TFIIH’s global motions, define TFIIH partitioning into dynamic communities and show how TFIIH reshapes itself and self-regulates depending on functional context. Our study uncovers an internal regulatory mechanism that switches XPB and XPD activities making them mutually exclusive between NER and transcription initiation. By sequentially coordinating the XPB and XPD DNA-unwinding activities, the switch ensures precise DNA incision in NER. Mapping TFIIH disease mutations onto network models reveals clustering into distinct mechanistic classes, affecting translocase functions, protein interactions and interface dynamics. 

Abstract Nucleotide excision repair (NER) is critical for removing bulky DNA base lesions and avoiding diseases. NER couples lesion recognition by XPC to strand separation by XPB and XPD ATPases, followed by lesion excision by XPF and XPG nucleases. Here, we describe key regulatory mechanisms and roles of XPG for and beyond its cleavage activity. Strikingly, by combing single-molecule imaging and bulk cleavage assays, we found that XPG binding to the 7-subunit TFIIH core (coreTFIIH) stimulates coreTFIIH-dependent double-strand (ds)DNA unwinding 10-fold, and XPG-dependent DNA cleavage by up to 700-fold. Simultaneous monitoring of rates for coreTFIIH single-stranded (ss)DNA translocation and dsDNA unwinding showed XPG acts by switching ssDNA translocation to dsDNA unwinding as a likely committed step. Pertinent to the NER pathway regulation, XPG incision activity is suppressed during coreTFIIH translocation on DNA but is licensed when coreTFIIH stalls at the lesion or when ATP hydrolysis is blocked. Moreover, ≥15 nucleotides of 5′-ssDNA is a prerequisite for efficient translocation and incision. Our results unveil a paired coordination mechanism in which key lesion scanning and DNA incision steps are sequentially coordinated, and damaged patch removal is only licensed after generation of ≥15 nucleotides of 5′-ssDNA, ensuring the correct ssDNA bubble size before cleavage. 

Abstract Small angle X‐ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS‐assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography‐SAXS (SEC‐SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS‐assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.