Bending in the right direction

Targeted chemical wedges reveal the role of allosteric DNA modulation in protein-DNA assembly

The cooperative assembly of multiprotein complexes results from allosteric modulations of DNA structure as well as direct intermolecular contacts between proteins. Such cooperative binding plays a critical role in imparting exquisite sequence specificity on the homeobox transcription factor (Hox) family of developmental transcription factors. A well-characterized example includes the interaction of Hox proteins with extradenticle (Exd), a highly conserved DNA binding transcription factor. Although direct interactions are important, the contribution of indirect interactions toward cooperative assembly of Hox and Exd remains unresolved. Here we use minor groove binding polyamides as structural wedges to induce perturbations at specific base steps within the Exd binding site. We find that allosteric modulation of DNA structure contributes nearly 1.5 kcal/mol to the binding of Exd to DNA, even in the absence of direct Hox contacts. In contrast to previous studies, the sequence-targeted chemical wedges reveal the role of DNA geometry in cooperative assembly of Hox-Exd complexes. Programmable polyamides may well serve as general probes to investigate the role of DNA modulation in the cooperative and highly specific assembly of other protein-DNA complexes.

CSI-Tree: a regression tree approach for modeling binding properties of DNA-binding molecules based on cognate site identification (CSI) data

The identification and characterization of binding sites of DNA-binding molecules, including transcription factors (TFs), is a critical problem at the interface of chemistry, biology and molecular medicine. The Cognate Site Identification (CSI) array is a high-throughput microarray platform for measuring comprehensive recognition profiles of DNA-binding molecules. This technique produces datasets that are useful not only for identifying binding sites of previously uncharacterized TFs but also for elucidating dependencies, both local and nonlocal, between the nucleotides at different positions of the recognition sites. We have developed a regression tree technique, CSI-Tree, for exploring the spectrum of binding sites of DNA-binding molecules. Our approach constructs regression trees utilizing the CSI data of unaligned sequences. The resulting model partitions the binding spectrum into homogeneous regions of position specific nucleotide effects. Each homogeneous partition is then summarized by a position weight matrix (PWM). Hence, the final outcome is a binding intensity rank-ordered collection of PWMs each of which spans a different region in the binding spectrum. Nodes of the regression tree depict the critical position/nucleotide combinations. We analyze the CSI data of the eukaryotic TF Nkx-2.5 and two engineered small molecule DNA ligands and obtain unique insights into their binding properties. The CSI tree for Nkx-2.5 reveals an interaction between two positions of the binding profile and elucidates how different nucleotide combinations at these two positions lead to different binding affinities. The CSI trees for the engineered DNA ligands exhibit a common preference for the dinucleotide AA in the first two positions, which is consistent with preference for a narrow and relatively flat minor groove. We carry out a reanalysis of these data with a mixture of PWMs approach. This approach is an advancement over the simple PWM model and accommodates position dependencies based on only sequence data. Our analysis indicates that the dependencies revealed by the CSI-Tree are challenging to discover without the actual binding intensities. Moreover, such a mixture model is highly sensitive to the number and length of the sequences analyzed. In contrast, CSI-Tree provides interpretable and concise summaries of the complete recognition profiles of DNA-binding molecules by utilizing binding affinities.

Molecular flexibility in ab initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations

The dynamics of biological processes depend on the structure and flexibility of the interacting molecules. In particular, the conformational diversity of DNA allows for large deformations upon binding. Drug–DNA interactions are of high pharmaceutical interest since the mode of action of anticancer, antiviral, antibacterial and other drugs is directly associated with their binding to DNA. A reliable prediction of drug–DNA binding at the atomic level by molecular docking methods provides the basis for the design of new drug compounds. Here, we propose a novel Monte Carlo (MC) algorithm for drug–DNA docking that accounts for the molecular flexibility of both constituents and samples the docking geometry without any prior binding-site selection. The binding of the antimalarial drug methylene blue at the DNA minor groove with a preference of binding to AT-rich over GC-rich base sequences is obtained in MC simulations in accordance with experimental data. In addition, the transition between two drug–DNA-binding modes, intercalation and minor-groove binding, has been achieved in dependence on the DNA base sequence. The reliable ab initio prediction of drug–DNA binding achieved by our new MC docking algorithm is an important step towards a realistic description of the structure and dynamics of molecular recognition in biological systems.