Predicting Transcription Factor Specificity with All-Atom Models

The binding of a transcription factor (TF) to a DNA operator site can initiate or repress the expression of a gene. Computational prediction of sites recognized by a TF has traditionally relied upon knowledge of several cognate sites, rather than an ab initio approach. Here, we examine the possibility of using structure-based energy calculations that require no knowledge of bound sites but rather start with the structure of a protein-DNA complex. We study the PurR E. coli TF, and explore to which extent atomistic models of protein-DNA complexes can be used to distinguish between cognate and non-cognate DNA sites. Particular emphasis is placed on systematic evaluation of this approach by comparing its performance with bioinformatic methods, by testing it against random decoys and sites of homologous TFs. We also examine a set of experimental mutations in both DNA and the protein. Using our explicit estimates of energy, we show that the specificity for PurR is dominated by direct protein-DNA interactions, and weakly influenced by bending of DNA.Comment: 26 pages, 3 figure

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

A flexible integrative approach based on random forest improves prediction of transcription factor binding sites

Transcription factor binding sites (TFBSs) are DNA sequences of 6-15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding

TFinDit: transcription factor-DNA interaction data depository

BACKGROUND: One of the crucial steps in regulation of gene expression is the binding of transcription factor(s) to specific DNA sequences. Knowledge of the binding affinity and specificity at a structural level between transcription factors and their target sites has important implications in our understanding of the mechanism of gene regulation. Due to their unique functions and binding specificity, there is a need for a transcription factor-specific, structure-based database and corresponding web service to facilitate structural bioinformatics studies of transcription factor-DNA interactions, such as development of knowledge-based interaction potential, transcription factor-DNA docking, binding induced conformational changes, and the thermodynamics of protein-DNA interactions. DESCRIPTION: TFinDit is a relational database and a web search tool for studying transcription factor-DNA interactions. The database contains annotated transcription factor-DNA complex structures and related data, such as unbound protein structures, thermodynamic data, and binding sequences for the corresponding transcription factors in the complex structures. TFinDit also provides a user-friendly interface and allows users to either query individual entries or generate datasets through culling the database based on one or more search criteria. CONCLUSIONS: TFinDit is a specialized structural database with annotated transcription factor-DNA complex structures and other preprocessed data. We believe that this database/web service can facilitate the development and testing of TF-DNA interaction potentials and TF-DNA docking algorithms, and the study of protein-DNA recognition mechanisms

Benchmarks for flexible and rigid transcription factor-DNA docking

<p>Abstract</p> <p>Background</p> <p>Structural insight from transcription factor-DNA (TF-DNA) complexes is of paramount importance to our understanding of the affinity and specificity of TF-DNA interaction, and to the development of structure-based prediction of TF binding sites. Yet the majority of the TF-DNA complexes remain unsolved despite the considerable experimental efforts being made. Computational docking represents a promising alternative to bridge the gap. To facilitate the study of TF-DNA docking, carefully designed benchmarks are needed for performance evaluation and identification of the strengths and weaknesses of docking algorithms.</p> <p>Results</p> <p>We constructed two benchmarks for flexible and rigid TF-DNA docking respectively using a unified non-redundant set of 38 test cases. The test cases encompass diverse fold families and are classified into easy and hard groups with respect to the degrees of difficulty in TF-DNA docking. The major parameters used to classify expected docking difficulty in flexible docking are the conformational differences between bound and unbound TFs and the interaction strength between TFs and DNA. For rigid docking in which the starting structure is a bound TF conformation, only interaction strength is considered.</p> <p>Conclusions</p> <p>We believe these benchmarks are important for the development of better interaction potentials and TF-DNA docking algorithms, which bears important implications to structure-based prediction of transcription factor binding sites and drug design.</p

FootprintDB: Analysis of plant cis-regulatory elements, transcription factors, and binding interfaces

28 Pags.- 8 Figs. The definitive version is available at: http://link.springer.com/bookseries/7651 and http://link.springer.com/book/10.1007/978-1-4939-6396-6.FootprintDB is a database and search engine that compiles regulatory sequences from open access libraries of curated DNA cis-elements and motifs, and their associated transcription factors (TFs). It systematically annotates the binding interfaces of the TFs by exploiting protein-DNA complexes deposited in the Protein Data Bank. Each entry in footprintDB is thus a DNA motif linked to the protein sequence of the TF(s) known to recognize it, and in most cases, the set of predicted interface residues involved in specific recognition. This chapter explains step-by-step how to search for DNA motifs and protein sequences in footprintDB and how to focus the search to a particular organism. Two real-world examples are shown where this software was used to analyze transcriptional regulation in plants. Results are described with the aim of guiding users on their interpretation, and special attention is given to the choices users might face when performing similar analyzes.This work was funded by grant Euroinvestigación EUI2008-03612 under the framework of the Transnational (Germany, France, Spain) Cooperation within the PLANT-KBBE Initiative.Peer reviewe

Specificity Determination by paralogous winged helix-turn-helix transcription factors

Transcription factors (TFs) localize to regulatory regions throughout the genome, where they exert physical or enzymatic control over the transcriptional machinery and regulate expression of target genes. Despite the substantial diversity of TFs found across all kingdoms of life, most belong to a relatively small number of structural families characterized by homologous DNA-binding domains (DBDs). In homologous DBDs, highly-conserved DNA-contacting residues define a characteristic ‘recognition potential’, or the limited sequence space containing high-affinity binding sites. Specificity-determining residues (SDRs) alter DNA binding preferences to further delineate this sequence space between homologous TFs, enabling functional divergence through the recognition of distinct genomic binding sites. This thesis explores the divergent DNA-binding preferences among dimeric, winged helix-turn-helix (wHTH) TFs belonging to the OmpR sub-family. As the terminal effectors of orthogonal two-component signaling pathways in Escherichia coli, OmpR paralogs bind distinct genomic sequences and regulate the expression of largely non-overlapping gene networks. Using high-throughput SELEX, I discover multiple sources of variation in DNA-binding, including the spacing and orientation of monomer sites as well as a novel binding ‘mode’ with unique half-site preferences (but retaining dimeric architecture). Surprisingly, given the diversity of residues observed occupying positions in contact with DNA, there are only minor quantitative differences in sequence-specificity between OmpR paralogs. Combining phylogenetic, structural, and biological information, I then define a comprehensive set of putative SDRs, which, although distributed broadly across the protein:DNA interface, preferentially localize to the major groove of the DNA helix. Direct specificity profiling of SDR variants reveals that individual SDRs impact local base preferences as well as global structural properties of the protein:DNA complex. This study demonstrates clearly that OmpR family TFs possess multiple ‘axes of divergence’, including base recognition, dimeric architecture, and structural attributes of the protein:DNA complex. It also provides evidence for a common structural ‘code’ for DNA-binding by OmpR homologues, and demonstrates that surprisingly modest residue changes can enable recognition of highly divergent sequence motifs. Importantly, well-characterized genomic binding sites for many of the TFs in this study diverge substantially from the presented de novo models, and it is unclear how mutations may affect binding in more complex environments. Further analysis using native sequences is required to build combined models of cis- and trans-evolution of two-component regulatory networks

Predicting Target DNA Sequences of DNA-Binding Proteins Based on Unbound Structures

DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential functions can be applied on protein-DNA co-crystallized structures to generate PWMs that are considerably consistent with experimental data. However, this success has not been extended to DNA-binding proteins lacking co-crystallized structures. This study aims at investigating the possibility of predicting the DNA sequences bound by DNA-binding proteins from the proteins' unbound structures (structures of the unbound state). Given an unbound query protein and a template complex, the proposed method first employs structure alignment to generate synthetic protein-DNA complexes for the query protein. Once a complex is available, an atomic-level knowledge-based potential function is employed to predict PWMs characterizing the sequences to which the query protein can bind. The evaluation of the proposed method is based on seven DNA-binding proteins, which have structures of both DNA-bound and unbound forms for prediction as well as annotated PWMs for validation. Since this work is the first attempt to predict target sequences of DNA-binding proteins from their unbound structures, three types of structural variations that presumably influence the prediction accuracy were examined and discussed. Based on the analyses conducted in this study, the conformational change of proteins upon binding DNA was shown to be the key factor. This study sheds light on the challenge of predicting the target DNA sequences of a protein lacking co-crystallized structures, which encourages more efforts on the structure alignment-based approaches in addition to docking- and homology modeling-based approaches for generating synthetic complexes

Use of structural DNA properties for the prediction of transcription-factor binding sites in Escherichia coli

Recognition of genomic binding sites by transcription factors can occur through base-specific recognition, or by recognition of variations within the structure of the DNA macromolecule. In this article, we investigate what information can be retrieved from local DNA structural properties that is relevant to transcription factor binding and that cannot be captured by the nucleotide sequence alone. More specifically, we explore the benefit of employing the structural characteristics of DNA to create binding-site models that encompass indirect recognition for the Escherichia coli model organism. We developed a novel methodology [Conditional Random fields of Smoothed Structural Data (CRoSSeD)], based on structural scales and conditional random fields to model and predict regulator binding sites. The value of relying on local structural-DNA properties is demonstrated by improved classifier performance on a large number of biological datasets, and by the detection of novel binding sites which could be validated by independent data sources, and which could not be identified using sequence data alone. We further show that the CRoSSeD-binding-site models can be related to the actual molecular mechanisms of the transcription factor DNA binding, and thus cannot only be used for prediction of novel sites, but might also give valuable insights into unknown binding mechanisms of transcription factors