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The development of methyl-transverse relaxation-optimized spectroscopy (methyl-TROSY)–based NMR methods, in concert with robust strategies for incorporation of methyl-group probes of structure and dynamics into the protein of interest, has facilitated quantitative studies of high-molecular-weight protein complexes. Here we develop a one-pot in vitro reaction for producing NMR quantities of methyl-labeled DNA at the C5 and N6 positions of cytosine (5mC) and adenine (6mA) nucleobases, respectively, enabling the study of high-molecular-weight DNA molecules using TROSY approaches originally developed for protein applications. Our biosynthetic strategy exploits the large number of naturally available methyltransferases to specifically methylate DNA at a desired number of sites that serve as probes of structure and dynamics. We illustrate the methodology with studies of the 153-base pair Widom DNA molecule that is simultaneously methyl-labeled at five sites, showing that high-quality 13C-¹H spectra can be recorded on 100 μM samples in a few minutes. NMR spin relaxation studies of labeled methyl groups in both DNA and the H2B histone protein component of the 200-kDa nucleosome core particle (NCP) establish that methyl groups at 5mC and 6mA positions are, in general, more rigid than Ile, Leu, and Val methyl probes in protein side chains. Studies focusing on histone H2B of NCPs wrapped with either wild-type DNA or DNA methylated at all 26 CpG sites highlight the utility of NMR in investigating the structural dynamics of the NCP and how its histone core is affected through DNA methylation, an important regulator of transcription.

Background Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. Results Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. Conclusion Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

DNA replication restart, the essential process that reinitiates prematurely terminated genome replication reactions, relies on exquisitely specific recognition of abandoned DNA replication-fork structures. The PriA DNA helicase mediates this process in bacteria through mechanisms that remain poorly defined. We report the crystal structure of a PriA/replication-fork complex, which resolves leading-strand duplex DNA bound to the protein. Interaction with PriA unpairs one end of the DNA and sequesters the 3′-most nucleotide from the nascent leading strand into a conserved protein pocket. Cross-linking studies reveal a surface on the winged-helix domain of PriA that binds to parental duplex DNA. Deleting the winged-helix domain alters PriA’s structure-specific DNA unwinding properties and impairs its activity in vivo. Our observations lead to a model in which coordinated parental-, leading-, and lagging-strand DNA binding provide PriA with the structural specificity needed to act on abandoned DNA replication forks.

Single-molecule total internal reflection fluorescence (TIRF) microscopy allows for the real-time visualization of macromolecular dynamics and complex assembly. Prism-based TIRF microscopes (prismTIRF) are relatively simple to operate and can be easily modulated to fit the needs of a wide variety of experimental applications. While building a prismTIRF microscope without expert assistance can pose a significant challenge, the components needed to build a prismTIRF microscope are relatively affordable and, with some guidance, the assembly can be completed by a determined novice. Here, we provide an easy-to-follow guide for the design, assembly, and operation of a three-color prismTIRF microscope which can be utilized for the study of macromolecular complexes, including the multi-component protein–DNA complexes responsible for DNA repair, replication, and transcription. Our hope is that this article can assist laboratories that aspire to implement single-molecule TIRF techniques, and consequently expand the application of this technology.

Transcriptional activation of the interferon‐β (IFN‐β) gene requires assembly of an enhanceosome containing the transcription factors ATF‐2/c‐Jun, IRF‐3/IRF‐7, NF‐κB and HMGI(Y). These factors cooperatively bind a composite DNA site and activate expression of the IFN‐β gene. The 3.0 Å crystal structure of the DNA‐binding domains of ATF‐2/c‐Jun and two IRF‐3 molecules in a complex with 31 base pairs (bp) of the PRDIV–PRDIII region of the IFN‐β enhancer shows that association of the four proteins with DNA creates a continuous surface for the recognition of 24 bp. The structure, together with in vitro binding studies and protein mutagenesis, shows that protein–protein interactions are not critical for cooperative binding. Instead, cooperativity arises mainly through nucleotide sequence‐dependent structural changes in the DNA that allow formation of complementary DNA conformations. Because the binding sites overlap on the enhancer, the unit of recognition is the entire nucleotide sequence, not the individual subsites.

The Evening Complex (EC), composed of the DNA binding protein LUX ARRHYTHMO (LUX) and two additional proteins EARLY FLOWERING 3 (ELF3) and ELF4, is a transcriptional repressor complex and a core component of the plant circadian clock. In addition to maintaining oscillations in clock gene expression, the EC also participates in temperature and light entrainment, acting as an important environmental sensor and conveying this information to growth and developmental pathways. However, the molecular basis for EC DNA binding specificity and temperature-dependent activity were not known. Here, we solved the structure of the DNA binding domain of LUX in complex with DNA. Residues critical for high-affinity binding and direct base readout were determined and tested via site-directed mutagenesis in vitro and in vivo. Using extensive in vitro DNA binding assays of LUX alone and in complex with ELF3 and ELF4, we demonstrate that, while LUX alone binds DNA with high affinity, the LUX-ELF3 complex is a relatively poor binder of DNA. ELF4 restores binding to the complex. In vitro, the full EC is able to act as a direct thermosensor, with stronger DNA binding at 4 °C and weaker binding at 27 °C. In addition, an excess of ELF4 is able to restore EC binding even at 27 °C. Taken together, these data suggest that ELF4 is a key modulator of thermosensitive EC activity.

The Staphylococcus aureus multidrug‐binding protein QacR represses transcription of the qacA multidrug transporter gene and is induced by multiple structurally dissimilar drugs. QacR is a member of the TetR/CamR family of transcriptional regulators, which share highly homologous N‐terminal DNA‐binding domains connected to seemingly non‐homologous ligand‐binding domains. Unlike other TetR members, which bind ∼15 bp operators, QacR recognizes an unusually long 28 bp operator, IR1, which it appears to bind cooperatively. To elucidate the DNA‐binding mechanism of QacR, we determined the 2.90 Å resolution crystal structure of a QacR–IR1 complex. Strikingly, our data reveal that the DNA recognition mode of QacR is distinct from TetR and involves the binding of a pair of QacR dimers. In this unique binding mode, recognition at each IR1 half‐site is mediated by a complement of DNA contacts made by two helix–turn–helix motifs. The inferred cooperativity does not arise from cross‐dimer protein–protein contacts, but from the global undertwisting and major groove widening elicited by the binding of two QacR dimers.

The Escherichia coli AlkA protein is a base excision repair glycosylase that removes a variety of alkylated bases from DNA. The 2.5 Å crystal structure of AlkA complexed to DNA shows a large distortion in the bound DNA. The enzyme flips a 1‐azaribose abasic nucleotide out of DNA and induces a 66° bend in the DNA with a marked widening of the minor groove. The position of the 1‐azaribose in the enzyme active site suggests an S N 1‐type mechanism for the glycosylase reaction, in which the essential catalytic Asp238 provides direct assistance for base removal. Catalytic selectivity might result from the enhanced stacking of positively charged, alkylated bases against the aromatic side chain of Trp272 in conjunction with the relative ease of cleaving the weakened glycosylic bond of these modified nucleotides. The structure of the AlkA–DNA complex offers the first glimpse of a helix–hairpin–helix (HhH) glycosylase complexed to DNA. Modeling studies suggest that other HhH glycosylases can bind to DNA in a similar manner.

The large T antigen (T‐ag) protein binds to and activates DNA replication from the origin of DNA replication ( ori ) in simian virus 40 (SV40). Here, we determined the crystal structures of the T‐ag origin‐binding domain (OBD) in apo form, and bound to either a 17 bp palindrome (sites 1 and 3) or a 23 bp ori DNA palindrome comprising all four GAGGC binding sites for OBD. The T‐ag OBDs were shown to interact with the DNA through a loop comprising Ser147–Thr155 (A1 loop), a combination of a DNA‐binding helix and loop (His203–Asn210), and Asn227. The A1 loop traveled back‐and‐forth along the major groove and accounted for most of the sequence‐determining contacts with the DNA. Unexpectedly, in both T‐ag‐DNA structures, the T‐ag OBDs bound DNA independently and did not make direct protein–protein contacts. The T‐ag OBD was also captured bound to a non‐consensus site ATGGC even in the presence of its canonical site GAGGC. Our observations taken together with the known biochemical and structural features of the T‐ag–origin interaction suggest a model for origin unwinding.

Background Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots , which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods. Results Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Pre dicting Hot s pots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones. Conclusions PreHots , which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .