Specific interactions between proteins and DNA are fundamental to many biological processes. In this review, we provide a revised view of protein-DNA interactions that emphasizes the importance of the threedimensional structures of both macromolecules. We divide proteinDNA interactions into two categories: those when the protein recognizes the unique chemical signatures of the DNA bases (base readout) and those when the protein recognizes a sequence-dependent DNA shape (shape readout). We further divide base readout into those interactions that occur in the major groove from those that occur in the minor groove. Analogously, the readout of the DNA shape is subdivided into global shape recognition (for example, when the DNA helix exhibits an overall bend) and local shape recognition (for example, when a base pair step is kinked or a region of the minor groove is narrow). Based on the >1500 structures of protein-DNA complexes now available in the Protein Data Bank, we argue that individual DNA-binding proteins combine multiple readout mechanisms to achieve DNA-binding specificity. Specificity that distinguishes between families frequently involves base readout in the major groove, whereas shape readout is often exploited for higher resolution specificity, to distinguish between members within the same DNA-binding protein family.

/pdf/origins-of-specificity-in-protein-dna-recognition-2wh7lbzegq.pdf

Origins of Specificity in Protein-DNA Recognition

We present parmbsc1, a force field for DNA atomistic simulation, which has been parameterized from high-level quantum mechanical data and tested for nearly 100 systems (representing a total simulation time of ~140 μs) covering most of DNA structural space. Parmbsc1 provides high-quality results in diverse systems. Parameters and trajectories are available at http://mmb.irbbarcelona.org/ParmBSC1/.

/pdf/parmbsc1-a-refined-force-field-for-dna-simulations-1mh4mmx0d1.pdf

Parmbsc1: a refined force field for DNA simulations

The small GTPase Ran enzyme regulates critical eukaryotic cellular functions including nuclear transport and mitosis through the creation of a RanGTP gradient around the chromosomes. This concentration gradient is created by the chromatin-bound RCC1 (regulator of chromosome condensation) protein, which recruits Ran to nucleosomes and activates Ran’s nucleotide exchange activity. Although RCC1 has been shown to bind directly with the nucleosome, the molecular details of this interaction were not known. Here we determine the crystal structure of a complex of Drosophila RCC1 and the nucleosome core particle at 2.9 A resolution, providing an atomic view of how a chromatin protein interacts with the histone and DNA components of the nucleosome. Our structure also suggests that the Widom 601 DNA positioning sequence present in the nucleosomes forms a 145-base-pair nucleosome core particle, not the expected canonical 147-base-pair particle. The small GTPase Ran regulates nuclear transport and mitosis by creating a RanGTP gradient around the chromosomes. The chromatin-bound RCC1 protein, which is a regulator for the onset of chromosome condensation, recruits Ran to nucleosomes and activates Ran's nucleotide-exchange activity. Here, the crystal structure of the complex between RCC1 and the nucleosome core particle provides an atomic view of how a chromatin protein interacts with the histone and DNA components of the nucleosome. The small GTPase Ran regulates nuclear transport and cell division by creating a gradient of RanGTP around chromosomes. The RCC1 protein recruits Ran to nucleosomes and activates Ran's nucleotide exchange activity. Here, the crystal structure of the complex between RCC1 and the nucleosome core particle is revealed. It provides an atomic view of how a chromatin protein interacts with the histone and DNA components of the nucleosome.

/pdf/structure-of-rcc1-chromatin-factor-bound-to-the-nucleosome-3k82157j3c.pdf

Structure of RCC1 chromatin factor bound to the nucleosome core particle

It is well recognized that base sequence exerts a significant influence on the properties of DNA and plays a significant role in protein– DNA interactions vital for cellular processes. Understanding and predicting base sequence effects requires an extensive structural and dynamic dataset which is currently unavailable from experiment. A consortium of laboratories was consequently formed to obtain this information using molecular simulations. This article describes results providing information not only on all 10 unique base pair steps, but also on all possible nearest-neighbor effects on these steps. These results are derived from simulations of 50–100ns on 39 different DNA oligomers in explicit solvent and using a physiological salt concentration. We demonstrate that the simulations are converged in terms of helical and backbone parameters. The results show that nearest-neighbor effects on base pair steps are very significant, implying that dinucleotide models are insufficient for predicting sequence-dependent behavior. Flanking base sequences can notably lead to base pair step parameters in dynamic equilibrium between two conformational sub-states. Although this study only provides limited data on next-nearestneighbor effects, we suggest that such effects should be analyzed before attempting to predict the sequence-dependent behavior of DNA.

/pdf/a-systematic-molecular-dynamics-study-of-nearest-neighbor-4lyk6k8xy7.pdf

A systematic molecular dynamics study of nearest-neighbor effects on base pair and base pair step conformations and fluctuations in B-DNA

Insight into the three-dimensional architecture of RNA is essential for understanding its cellular functions. However, even the classic transfer RNA structure contains features that are overlooked by existing bioinformatics tools. Here we present DSSR (Dissecting the Spatial Structure of RNA), an integrated and automated tool for analyzing and annotating RNA tertiary structures. The software identifies canonical and noncanonical base pairs, including those with modified nucleotides, in any tautomeric or protonation state. DSSR detects higher-order coplanar base associations, termed multiplets. It finds arrays of stacked pairs, classifies them by base-pair identity and backbone connectivity, and distinguishes a stem of covalently connected canonical pairs from a helix of stacked pairs of arbitrary type/linkage. DSSR identifies coaxial stacking of multiple stems within a single helix and lists isolated canonical pairs that lie outside of a stem. The program characterizes ‘closed’ loops of various types (hairpin, bulge, internal, and junction loops) and pseudoknots of arbitrary complexity. Notably, DSSR employs isolated pairs and the ends of stems, whether pseudoknotted or not, to define junction loops. This new, inclusive definition provides a novel perspective on the spatial organization of RNA. Tests on all nucleic acid structures in the Protein Data Bank confirm the efficiency and robustness of the software, and applications to representative RNA molecules illustrate its unique features. DSSR and related materials are freely available at http://x3dna.org/.

DSSR: an integrated software tool for dissecting the spatial structure of RNA

We describe Curves+, a new nucleic acid conformational analysis program which is applicable to a wide range of nucleic acid structures, including those with up to four strands and with either canonical or modified bases and backbones. The program is algorithmically simpler and computationally much faster than the earlier Curves approach, although it still provides both helical and backbone parameters, including a curvilinear axis and parameters relating the position of the bases to this axis. It additionally provides a full analysis of groove widths and depths. Curves+ can also be used to analyse molecular dynamics trajectories. With the help of the accompanying program Canal, it is possible to produce a variety of graphical output including parameter variations along a given structure and time series or histograms of parameter variations during dynamics.

https://lcvmwww.epfl.ch/publications/data/articles/120/gkp608v1.pdf

K. Zakrzewska

Papers

Conformational analysis of nucleic acids revisited: Curves+