345 research outputs found
Simulation and analysis of in vitro DNA evolution
We study theoretically the in vitro evolution of a DNA sequence by binding to
a transcription factor. Using a simple model of protein-DNA binding and
available binding constants for the Mnt protein, we perform large-scale,
realistic simulations of evolution starting from a single DNA sequence. We
identify different parameter regimes characterized by distinct evolutionary
behaviors. For each regime we find analytical estimates which agree well with
simulation results. For small population sizes, the DNA evolutional path is a
random walk on a smooth landscape. While for large population sizes, the
evolution dynamics can be well described by a mean-field theory. We also study
how the details of the DNA-protein interaction affect the evolution.Comment: 11 pages, 11 figures. Submitted to PNA
Recognition models to predict DNA-binding specificities of homeodomain proteins
Motivation: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C2H2 zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes
A modified bacterial one-hybrid system yields improved quantitative models of transcription factor specificity
We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis—GRaMS (Growth Rate Modeling of Specificity)—that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well-characterized C2H2 zinc-finger TF on both a 28 bp randomized library for the standard B1H method and on 6 bp randomized library for the CV-B1H method for which 45 different experimental conditions were tested: five time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media
Exploring the DNA-recognition potential of homeodomains
The recognition potential of most families of DNA-binding domains (DBDs) remains relatively unexplored. Homeodomains (HDs), like many other families of DBDs, display limited diversity in their preferred recognition sequences. To explore the recognition potential of HDs, we utilized a bacterial selection system to isolate HD variants, from a randomized library, that are compatible with each of the 64 possible 3′ triplet sites (i.e., TAANNN). The majority of these selections yielded sets of HDs with overrepresented residues at specific recognition positions, implying the selection of specific binders. The DNA-binding specificity of 151 representative HD variants was subsequently characterized, identifying HDs that preferentially recognize 44 of these target sites. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in specificity. This information was used to create more explicit HD recognition models, which can inform the prediction of transcriptional regulatory networks for extant HDs or the engineering of HDs with novel DNA-recognition potential. The diversity of recovered HD recognition sequences raises important questions about the fitness barrier that restricts the evolution of alternate recognition modalities in natural systems
Bayesian Centroid Estimation for Motif Discovery
Biological sequences may contain patterns that are signal important
biomolecular functions; a classical example is regulation of gene expression by
transcription factors that bind to specific patterns in genomic promoter
regions. In motif discovery we are given a set of sequences that share a common
motif and aim to identify not only the motif composition, but also the binding
sites in each sequence of the set. We present a Bayesian model that is an
extended version of the model adopted by the Gibbs motif sampler, and propose a
new centroid estimator that arises from a refined and meaningful loss function
for binding site inference. We discuss the main advantages of centroid
estimation for motif discovery, including computational convenience, and how
its principled derivation offers further insights about the posterior
distribution of binding site configurations. We also illustrate, using
simulated and real datasets, that the centroid estimator can differ from the
maximum a posteriori estimator.Comment: 24 pages, 9 figure
Using defined finger-finger interfaces as units of assembly for constructing zinc-finger nucleases
Zinc-finger nucleases (ZFNs) have been used for genome engineering in a wide variety of organisms; however, it remains challenging to design effective ZFNs for many genomic sequences using publicly available zinc-finger modules. This limitation is in part because of potential finger-finger incompatibility generated on assembly of modules into zinc-finger arrays (ZFAs). Herein, we describe the validation of a new set of two-finger modules that can be used for building ZFAs via conventional assembly methods or a new strategy-finger stitching-that increases the diversity of genomic sequences targetable by ZFNs. Instead of assembling ZFAs based on units of the zinc-finger structural domain, our finger stitching method uses units that span the finger-finger interface to ensure compatibility of neighbouring recognition helices. We tested this approach by generating and characterizing eight ZFAs, and we found their DNA-binding specificities reflected the specificities of the component modules used in their construction. Four pairs of ZFNs incorporating these ZFAs generated targeted lesions in vivo, demonstrating that stitching yields ZFAs with robust recognition properties
The cis-regulatory map of Shewanella genomes
While hundreds of microbial genomes are sequenced, the challenge remains to define their cis-regulatory maps. Here, we present a comparative genomic analysis of the cis-regulatory map of Shewanella oneidensis, an important model organism for bioremediation because of its extraordinary abilities to use a wide variety of metals and organic molecules as electron acceptors in respiration. First, from the experimentally verified transcriptional regulatory networks of Escherichia coli, we inferred 24 DNA motifs that are conserved in S. oneidensis. We then applied a new comparative approach on five Shewanella genomes that allowed us to systematically identify 194 nonredundant palindromic DNA motifs and corresponding regulons in S. oneidensis. Sixty-four percent of the predicted motifs are conserved in at least three of the seven newly sequenced and distantly related Shewanella genomes. In total, we obtained 209 unique DNA motifs in S. oneidensis that cover 849 unique transcription units. Besides conservation in other genomes, 77 of these motifs are supported by at least one additional type of evidence, including matching to known transcription factor binding motifs and significant functional enrichment or expression coherence of the corresponding target genes. Using the same approach on a more focused gene set, 990 differentially expressed genes derived from published microarray data of S. oneidensis during exposure to metal ions, we identified 31 putative cis-regulatory motifs (16 with at least one type of additional supporting evidence) that are potentially involved in the process of metal reduction. The majority (18/31) of those motifs had been found in our whole-genome comparative approach, further demonstrating that such an approach is capable of uncovering a large fraction of the regulatory map of a genome even in the absence of experimental data. The integrated computational approach developed in this study provides a useful strategy to identify genome-wide cis-regulatory maps and a novel avenue to explore the regulatory pathways for particular biological processes in bacterial systems
Purifying Selection in Deeply Conserved Human Enhancers Is More Consistent than in Coding Sequences
(c) 2014 De Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Formation of regulatory modules by local sequence duplication
Turnover of regulatory sequence and function is an important part of
molecular evolution. But what are the modes of sequence evolution leading to
rapid formation and loss of regulatory sites? Here, we show that a large
fraction of neighboring transcription factor binding sites in the fly genome
have formed from a common sequence origin by local duplications. This mode of
evolution is found to produce regulatory information: duplications can seed new
sites in the neighborhood of existing sites. Duplicate seeds evolve
subsequently by point mutations, often towards binding a different factor than
their ancestral neighbor sites. These results are based on a statistical
analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome,
and a comparison set of intergenic regulatory sequence in Saccharomyces
cerevisiae. In fly regulatory modules, pairs of binding sites show
significantly enhanced sequence similarity up to distances of about 50 bp. We
analyze these data in terms of an evolutionary model with two distinct modes of
site formation: (i) evolution from independent sequence origin and (ii)
divergent evolution following duplication of a common ancestor sequence. Our
results suggest that pervasive formation of binding sites by local sequence
duplications distinguishes the complex regulatory architecture of higher
eukaryotes from the simpler architecture of unicellular organisms
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