Semiflexible Oligomer−Polymer Binding: Combinatorial and Conditional Probability Analyses and Stochastic Simulation

Effect of Counterions on the Soft Ionization Mass Spectra of Analytes with Multiple Permanent Charges

Multiply permanently charged analytes (MPCAs) are of great interest for various applications. MPCA soft ionization mass spectra (MS) strongly depend on the counterions of MPCA. We have studied thoroughly this effect to expand the use of MS in MPCA characterization. To this end, β-cyclodextrin-based MPCAs with 7 (MIM7NBCD) and 14 (MIM14BCD) quaternary ammonium charges with a series of monovalent counterions were prepared and their MS were measured using two of the most popular soft ionization techniques, electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI). MALDI MS of both analytes were well resolved, with signals assignable to the analytes only with the two least basic tested counterions (ClO4- and TfO-). Similarly, analyte-assignable signals were observed in ESI MS of MIM14BCD only with ClO4- and TfO-. The situation was opposite with ESI MS of MIM7NBCD where assignable signals were observed with Cl- but not with TfO-. Thus, to get high-quality MS, binding between the MPCA permanent charges and the counterions must be of the optimal effective strength, given also by the number of analyte permanent charges as shown by the simple combinatorial model of binding. Of practical interest is the observation that unsuitable counterions can be replaced in situ by an excess of corresponding acid. The findings form a coherent framework for interpreting and improving MPCA mass spectra.

Calculating transcription factor binding maps for chromatin

Current high-throughput experiments already generate enough data for retrieving the DNA sequence-dependent binding affinities of transcription factors (TF) and other chromosomal proteins throughout the complete genome. However, the reverse task of calculating binding maps in a chromatin context for a given set of concentrations and TF affinities appears to be even more challenging and computationally demanding. The problem can be addressed by considering the DNA sequence as a one-dimensional lattice with units of one or more base pairs. To calculate protein occupancies in chromatin, one needs to consider the competition of TF and histone octamers for binding sites as well as the partial unwrapping of nucleosomal DNA. Here, we consider five different classes of algorithms to compute binding maps that include the binary variable, combinatorial, sequence generating function, transfer matrix and dynamic programming approaches. The calculation time of the binary variable algorithm scales exponentially with DNA length, which limits its use to the analysis of very small genomic regions. For regulatory regions with many overlapping binding sites, potentially applicable algorithms reduce either to the transfer matrix or dynamic programming approach. In addition to the recently proposed transfer matrix formalism for TF access to the nucleosomal organized DNA, we develop here a dynamic programming algorithm that accounts for this feature. In the absence of nucleosomes, dynamic programming outperforms the transfer matrix approach, but the latter is faster when nucleosome unwrapping has to be considered. Strategies are discussed that could further facilitate calculations to allow computing genome-wide TF binding maps.

Statistical-mechanical lattice models for protein-DNA binding in chromatin

Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibria measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical-mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.

Condensed DNA: condensing the concepts

DNA is stored in vivo in a highly compact, so-called condensed phase, where gene regulatory processes are governed by the intricate interplay between different states of DNA compaction. These systems often have surprising properties, which one would not predict from classical concepts of dilute solutions. The mechanistic details of DNA packing are essential for its functioning, as revealed by the recent developments coming from biochemistry, electrostatics, statistical mechanics, and molecular and cell biology. Different aspects of condensed DNA behavior are linked to each other, but the links are often hidden in the bulk of experimental and theoretical details. Here we try to condense some of these concepts and provide interconnections between the different fields. After a brief description of main experimental features of DNA condensation inside viruses, bacteria, eukaryotes and the test tube, main theoretical approaches for the description of these systems are presented. We end up with an extended discussion of the role of DNA condensation in the context of gene regulation and mention potential applications of DNA condensation in gene therapy and biotechnology.