Calculating transcription factor binding maps for chromatin

Random sampling of ligand arrangements on a one-dimensional lattice

We introduce a transfer-matrix-based sequential sampling scheme for generating random samples of ligand arrangements on one-dimensional templates. The number of ligand types is arbitrary, the binding constants can have positional dependence, and cooperativity parameters are included. From the random arrangements, any (linear or nonlinear) observable can be calculated using sample averaging. As an example case study, we investigate the competitive binding of three ligand types (the sequence-specific binder netropsin, YOYO-1, and ethidium bromide) to a DNA molecule. We also employ our random sampling method of ligands to determine the quality of synthetically generated DNA barcodes as a function of concentration of a ligand (e.g., netropsin) in optical DNA mapping (ODM) experiments. We provide publically available softwares, with a computational time that scales linearly with the lattice size, for generating random ligand arrangements and for generating synthetic barcodes.

A Predictive Model of Antibody Binding in the Presence of IgG-Interacting Bacterial Surface Proteins

Many bacteria can interfere with how antibodies bind to their surfaces. This bacterial antibody targeting makes it challenging to predict the immunological function of bacteria-associated antibodies. The M and M-like proteins of group A streptococci (GAS) exhibit IgGFc-binding regions, which they use to reverse IgG binding orientation depending on the host environment. Unraveling the mechanism behind these binding characteristics may identify conditions under which bound IgG can drive an efficient immune response. Here, we have developed a biophysical model for describing these complex protein-antibody interactions. We show how the model can be used as a tool for studying the binding behavior of various IgG samples to M protein by performing in silico simulations and correlating this data with experimental measurements. Besides its use for mechanistic understanding, this model could potentially be used as a tool to aid in the development of antibody treatments. We illustrate this by simulating how IgG binding to GAS in serum is altered as specified amounts of monoclonal or pooled IgG is added. Phagocytosis experiments link this altered antibody binding to a physiological function and demonstrate that it is possible to predict the effect of an IgG treatment with our model. Our study gives a mechanistic understanding of bacterial antibody targeting and provides a tool for predicting the effect of antibody treatments in the presence of bacteria with IgG-modulating surface proteins.

Theory of Site-Specific DNA-Protein Interactions in the Presence of Nucleosome Roadblocks

We show that nucleosomes exert a maximal amount of hindrance to the one-dimensional diffusion of transcription factors (TFs) when they are present between TFs and their cognate sites on DNA. The effective one-dimensional diffusion coefficient of TFs (χ_TF) decreases with a rise in the free-energy barrier (μ_NU) of the sliding of nucleosomes as χ_TF∝exp(-μ_NU). The average time (η_L) required by TFs to slide over L sites on DNA increases with μ_NU as η_L∝exp(μ_NU). When TFs move close to nucleosomes, then they exhibit typical subdiffusion. Nucleosomes can enhance the search dynamics of TFs when TFs are present between nucleosomes and TF binding sites. These results suggest that nucleosome-depleted regions around the cognate sites of TFs are mandatory for efficient site-specific binding of TFs. Remarkably, the genome-wide in vivo positioning pattern of TFs shows a maximum at their specific binding sites where the occupancy of nucleosomes shows a minimum. This could be a consequence of an increasing level of breathing dynamics of nucleosome cores and decreasing levels of fluctuations in the DNA binding domains of TFs as they move across TF binding sites. The dynamics of TFs becomes slow as they approach their cognate sites so that TFs form a tight site-specific complex, whereas the dynamics of nucleosomes becomes rapid so that they quickly pass through the cognate sites of TFs. Several in vivo data sets on the genome-wide positioning pattern of nucleosomes and TFs agree well with our arguments. The retarding effects of nucleosomes can be minimized when the degree of condensation of DNA is such that it can permit a jump size associated with the dynamics of TFs beyond ∼160-180 bp.

Hetero-association models of non-covalent molecular complexation

The present review discusses the current state-of-the-art in building models enabling the description of non-covalent equilibrium complexation of different types of molecules in solution, which results in the formation of supramolecular structures different in length and composition (hetero-association or supramolecular multicomponent co-polymerisation). The description is focused on standard physical and chemical quantities such as experimental observables and equilibrium parameters of interaction (equilibrium constants and concentrations). The major partial cases of the hetero-association models, such as finite and indefinite isodesmic and cooperative complexations, and Benesi-Hildebrand and Langmuir adsorption models are considered. Future challenges in the development of the hetero-association models are provided.

Nucleosome positioning: resources and tools online

Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this review is to cover a practical aspect of this field, namely, to provide a guide to the multitude of nucleosome positioning resources available online. These include almost 300 experimental datasets of genome-wide nucleosome occupancy profiles determined in different cell types and more than 40 computational tools for the analysis of experimental nucleosome positioning data and prediction of intrinsic nucleosome formation probabilities from the DNA sequence. A manually curated, up to date list of these resources will be maintained at http://generegulation.info.

Conditions for positioning of nucleosomes on DNA

Positioning of nucleosomes along a eukaryotic genome plays an important role in its organization and regulation. There are many different factors affecting the location of nucleosomes. Some can be viewed as preferential binding of a single nucleosome to different locations along the DNA and some as interactions between neighboring nucleosomes. In this study, we analyze positioning of nucleosomes and derive conditions for their good positioning. Using analytic and numerical approaches we find that, if the binding preferences are very weak, an interplay between the interactions and the binding preferences is essential for a good positioning of nucleosomes, especially on correlated energy landscapes. Analyzing the empirical energy landscape, we conclude that good positioning of nucleosomes in vivo is possible only if they strongly interact. In this case, our model, predicting long-length-scale fluctuations of nucleosomes' occupancy along the DNA, accounts well for the empirical observations.

Competitive binding-based optical DNA mapping for fast identification of bacteria--multi-ligand transfer matrix theory and experimental applications on Escherichia coli

We demonstrate a single DNA molecule optical mapping assay able to resolve a specific Escherichia coli strain from other strains. The assay is based on competitive binding of the fluorescent dye YOYO-1 and the AT-specific antibiotic netropsin. The optical map is visualized by stretching the DNA molecules in nanofluidic channels. We optimize the experimental conditions to obtain reproducible barcodes containing as much information as possible. We implement a multi-ligand transfer matrix method for calculating theoretical barcodes from known DNA sequences. Our method extends previous theoretical approaches for competitive binding of two types of ligands to many types of ligands and introduces a recursive approach that allows long barcodes to be calculated with standard computer floating point formats. The identification of a specific E. coli strain (CCUG 10979) is based on mapping of 50-160 kilobasepair experimental DNA fragments onto the theoretical genome using the developed theory. Our identification protocol introduces two theoretical constructs: a P-value for a best experiment-theory match and an information score threshold. The developed methods provide a novel optical mapping toolbox for identification of bacterial species and strains. The protocol does not require cultivation of bacteria or DNA amplification, which allows for ultra-fast identification of bacterial pathogens.

Electrostatic effect of H1-histone protein binding on nucleosome repeat length

Within a simple biophysical model we describe the effect of electrostatic binding of H1 histone proteins on the nucleosome repeat length in chromatin. The length of wrapped DNA optimizes its binding energy to the histone core and the elastic energy penalty of DNA wrapping. The magnitude of the effect predicted from our model is in agreement with the systematic experimental data on the linear variation of nucleosome repeat lengths with H1/nucleosome ratio (Woodcock C L et al 2006 Chromos. Res. 14 17-25). We compare our model to the data for different cell types and organisms, with a widely varying ratio of bound H1 histones per nucleosome. We underline the importance of this non-specific histone-DNA charge-balance mechanism in regulating the positioning of nucleosomes and the degree of compaction of chromatin fibers in eukaryotic cells.

Regulation of the nucleosome repeat length in vivo by the DNA sequence, protein concentrations and long-range interactions

The nucleosome repeat length (NRL) is an integral chromatin property important for its biological functions. Recent experiments revealed several conflicting trends of the NRL dependence on the concentrations of histones and other architectural chromatin proteins, both in vitro and in vivo, but a systematic theoretical description of NRL as a function of DNA sequence and epigenetic determinants is currently lacking. To address this problem, we have performed an integrative biophysical and bioinformatics analysis in species ranging from yeast to frog to mouse where NRL was studied as a function of various parameters. We show that in simple eukaryotes such as yeast, a lower limit for the NRL value exists, determined by internucleosome interactions and remodeler action. For higher eukaryotes, also the upper limit exists since NRL is an increasing but saturating function of the linker histone concentration. Counterintuitively, smaller H1 variants or non-histone architectural proteins can initiate larger effects on the NRL due to entropic reasons. Furthermore, we demonstrate that different regimes of the NRL dependence on histone concentrations exist depending on whether DNA sequence-specific effects dominate over boundary effects or vice versa. We consider several classes of genomic regions with apparently different regimes of the NRL variation. As one extreme, our analysis reveals that the period of oscillations of the nucleosome density around bound RNA polymerase coincides with the period of oscillations of positioning sites of the corresponding DNA sequence. At another extreme, we show that although mouse major satellite repeats intrinsically encode well-defined nucleosome preferences, they have no unique nucleosome arrangement and can undergo a switch between two distinct types of nucleosome positioning.

Learning protein-DNA interaction landscapes by integrating experimental data through computational models

MOTIVATION

Transcriptional regulation is directly enacted by the interactions between DNA and many proteins, including transcription factors (TFs), nucleosomes and polymerases. A critical step in deciphering transcriptional regulation is to infer, and eventually predict, the precise locations of these interactions, along with their strength and frequency. While recent datasets yield great insight into these interactions, individual data sources often provide only partial information regarding one aspect of the complete interaction landscape. For example, chromatin immunoprecipitation (ChIP) reveals the binding positions of a protein, but only for one protein at a time. In contrast, nucleases like MNase and DNase can be used to reveal binding positions for many different proteins at once, but cannot easily determine the identities of those proteins. Currently, few statistical frameworks jointly model these different data sources to reveal an accurate, holistic view of the in vivo protein-DNA interaction landscape.

RESULTS

Here, we develop a novel statistical framework that integrates different sources of experimental information within a thermodynamic model of competitive binding to jointly learn a holistic view of the in vivo protein-DNA interaction landscape. We show that our framework learns an interaction landscape with increased accuracy, explaining multiple sets of data in accordance with thermodynamic principles of competitive DNA binding. The resulting model of genomic occupancy provides a precise mechanistic vantage point from which to explore the role of protein-DNA interactions in transcriptional regulation.

AVAILABILITY AND IMPLEMENTATION

The C source code for compete and Python source code for MCMC-based inference are available at http://www.cs.duke.edu/∼amink.

CONTACT

amink@cs.duke.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development

During differentiation of embryonic stem cells, chromatin reorganizes to establish cell type-specific expression programs. Here, we have dissected the linkages between DNA methylation (5mC), hydroxymethylation (5hmC), nucleosome repositioning, and binding of the transcription factor CTCF during this process. By integrating MNase-seq and ChIP-seq experiments in mouse embryonic stem cells (ESC) and their differentiated counterparts with biophysical modeling, we found that the interplay between these factors depends on their genomic context. The mostly unmethylated CpG islands have reduced nucleosome occupancy and are enriched in cell type-independent binding sites for CTCF. The few remaining methylated CpG dinucleotides are preferentially associated with nucleosomes. In contrast, outside of CpG islands most CpGs are methylated, and the average methylation density oscillates so that it is highest in the linker region between nucleosomes. Outside CpG islands, binding of TET1, an enzyme that converts 5mC to 5hmC, is associated with labile, MNase-sensitive nucleosomes. Such nucleosomes are poised for eviction in ESCs and become stably bound in differentiated cells where the TET1 and 5hmC levels go down. This process regulates a class of CTCF binding sites outside CpG islands that are occupied by CTCF in ESCs but lose the protein during differentiation. We rationalize this cell type-dependent targeting of CTCF with a quantitative biophysical model of competitive binding with the histone octamer, depending on the TET1, 5hmC, and 5mC state.

Systems biophysics of gene expression

Gene expression is a process central to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges to the use of traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses.

Modeling nucleosome position distributions from experimental nucleosome positioning maps

MOTIVATION

Recent experimental advancements allow determining positions of nucleosomes for complete genomes. However, the resulting nucleosome occupancy maps are averages of heterogeneous cell populations. Accordingly, they represent a snapshot of a dynamic ensemble at a single time point with an overlay of many configurations from different cells. To study the organization of nucleosomes along the genome and to understand the mechanisms of nucleosome translocation, it is necessary to retrieve features of specific conformations from the population average.

RESULTS

Here, we present a method for identifying non-overlapping nucleosome configurations that combines binary-variable analysis and a Monte Carlo approach with a simulated annealing scheme. In this manner, we obtain specific nucleosome configurations and optimized solutions for the complex positioning patterns from experimental data. We apply the method to compare nucleosome positioning at transcription factor binding sites in different mouse cell types. Our method can model nucleosome translocations at regulatory genomic elements and generate configurations for simulations of the spatial folding of the nucleosome chain.

AVAILABILITY

Source code, precompiled binaries, test data and a web-based test installation are freely available at http://bioinformatics.fh-stralsund.de/nucpos/

Taking into account nucleosomes for predicting gene expression

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147 bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin.