Systematic characterization of protein-DNA interactions

Spatiotemporal and global profiling of DNA-protein interactions enables discovery of low-affinity transcription factors

Precise dissection of DNA-protein interactions is essential for elucidating the recognition basis, dynamics and gene regulation mechanism. However, global profiling of weak and dynamic DNA-protein interactions remains a long-standing challenge. Here, we establish the light-induced lysine (K) enabled crosslinking (LIKE-XL) strategy for spatiotemporal and global profiling of DNA-protein interactions. Harnessing unique abilities to capture weak and transient DNA-protein interactions, we demonstrate that LIKE-XL enables the discovery of low-affinity transcription-factor/DNA interactions via sequence-specific DNA baits, determining the binding sites for transcription factors that have been previously unknown. More importantly, we successfully decipher the dynamics of the transcription factor subproteome in response to drug treatment in a time-resolved manner, and find downstream target transcription factors from drug perturbations, providing insight into their dynamic transcriptional networks. The LIKE-XL strategy offers a complementary method to expand the DNA-protein profiling toolbox and map accurate DNA-protein interactomes that were previously inaccessible via non-covalent strategies, for better understanding of protein function in health and disease.

Transcription factors in the pathogenesis of pulmonary arterial hypertension-Current knowledge and therapeutic potential

Pulmonary arterial hypertension (PAH) is a disease characterized by elevated pulmonary vascular resistance and pulmonary artery pressure. Mortality remains high in severe cases despite significant advances in management and pharmacotherapy. Since currently approved PAH therapies are unable to significantly reverse pathological vessel remodeling, novel disease-modifying, targeted therapeutics are needed. Pathogenetically, PAH is characterized by vessel wall cell dysfunction with consecutive remodeling of the pulmonary vasculature and the right heart. Transcription factors (TFs) regulate the process of transcribing DNA into RNA and, in the pulmonary circulation, control the response of pulmonary vascular cells to macro- and microenvironmental stimuli. Often, TFs form complex protein interaction networks with other TFs or co-factors to allow for fine-tuning of gene expression. Therefore, identification of the underlying molecular mechanisms of TF (dys-)function is essential to develop tailored modulation strategies in PAH. This current review provides a compendium-style overview of TFs and TF complexes associated with PAH pathogenesis and highlights their potential as targets for vasculoregenerative or reverse remodeling therapies.

Regulation of Nir gene in Lactobacillus plantarum WU14 mediated by GlnR

Nitrogen (N) is an essential element in the biosynthesis of key cellular components, such as proteins and nucleic acids, in all living organisms. Nitrite, as a form of nitrogen utilization, is the main nutrient for microbial growth. However, nitrite is a potential carcinogen that combines with secondary amines, which are breakdown products of proteins, to produce N-nitroso compounds that are strongly carcinogenic. Nitrite reductase (Nir) produced by microorganisms can reduce nitrite. Binding of GlnR to the promoter of nitrogen metabolism gene can regulate the expression of Nir operon. In this study, nitrite-resistant Lactobacillus plantarum WU14 was isolated from Pickles and its protease Nir was analyzed. GlnR-mediated regulation of L. plantarum WU14 Nir gene was investigated in this study. New GlnR and Nir genes were obtained from L. plantarum WU14. The regulation effect of GlnR on Nir gene was examined by gel block test, yeast two-hybrid system, bacterial single hybrid system and qRT-RCR. Detailed analysis showed that GlnR ound to the Nir promoter region and interacted with Nir at low nitrite concentrations, positively regulating the expression of NIR. However, the transcription levels of GlnR and Nir decreased gradually with increasing nitrite concentration. The results of this study improve our understanding of the function of the Nir operon regulatory system and serve as the ground for further study of the signal transduction pathway in lactic acid bacteria.

DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences

Efforts to elucidate protein-DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie's outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie's webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Motivation

Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

Results

We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

Availability and implementation

SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

I-Block: a simple Escherichia coli-based assay for studying sequence-specific DNA binding of proteins

We have developed a simple method called I-Block assay, which can detect sequence-specific binding of proteins to DNA in Escherichia coli. The method works by detecting competition between the protein of interest and RNA polymerase for binding to overlapping target sites in a plasmid-borne lacI promoter variant. The assay utilizes two plasmids and an E. coli host strain, from which the gene of the Lac repressor (lacI) has been deleted. One of the plasmids carries the lacI gene with a unique NheI restriction site created in the lacI promoter. The potential recognition sequences of the tested protein are inserted into the NheI site. Introduction of the plasmids into the E. coliΔlacI host represses the constitutive β-galactosidase synthesis of the host bacterium. If the studied protein expressed from a compatible plasmid binds to its target site in the lacI promoter, it will interfere with lacI transcription and lead to increased β-galactosidase activity. The method was tested with two zinc finger proteins, with the lambda phage cI857 repressor, and with CRISPR-dCas9 targeted to the lacI promoter. The I-Block assay was shown to work with standard liquid cultures, with cultures grown in microplate and with colonies on X-gal indicator plates.

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

Prediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as one of the major bottlenecks in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for <50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline (https://github.com/mutwil/LSTrAP-Lite) and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package.

Options and Considerations When Using a Yeast One-Hybrid System

Comprehensive mapping of protein-DNA interactions is essential to uncover the mechanisms involved in gene regulation. However, the data generated has been sparse given the number of regulatory elements and transcription factors (TFs) encoded in the genomes of metazoan organisms. Yeast one-hybrid (Y1H) assays provide a powerful "DNA-centered" method, complementary to "TF-centered" methods such as chromatin immunoprecipitation, to identify the TFs that can bind a DNA sequence of interest. Here, we present different technical variations that should be considered when using a Y1H system, including the type of DNA sequence to test, source of TF clones, as well as types of vectors and screening format. Finally, we discuss limitations of the assay and future challenges.

Specific dsDNA recognition by a mimic of the DNA binding domain of the c-Myc/Max transcription factor

We here report on the synthesis of the first mimic of the DNA binding domain of the c-Myc/Max-bHLH-ZIP transcription factor able to selectively recognize its cognate E-box sequence 5'-CACGTG-3' through the major groove of the double-stranded DNA. The designed peptidosteroid conjugate was shown to be effective as DNA binder in the presence of excess competitor DNA.

DNA-protein interactions explored by atomic force microscopy

DNA-protein interactions play an important role in all living organisms on Earth. The advent of atomic force microscopy permitted for the first time to follow and to characterize interaction forces between these two molecular species. After a short description of the AFM and its imaging modes we review, in a chronological order some of the studies that we think importantly contributed to the field.

Sequence specific detection of restriction enzymes at DNA-modified carbon nanotube field effect transistors

Protein-DNA interactions play a central role in many cellular processes, and their misregulation has been implicated in a number of human diseases. Thus, there is a pressing need for the development of analytical strategies for interrogating the binding of proteins to DNA. Herein, we report the electrical monitoring of a prototypical DNA-binding protein, the PvuII restriction enzyme, at microfluidic-encapsulated, DNA-modified carbon nanotube field effect transistors. Our integrated platform enables the sensitive, sequence specific detection of PvuII at concentrations as low as 0.5 pM in a volume of 0.025 μL (corresponding to ~7500 proteins). These figures of merit compare favorably to state of the art values reported for alternative fluorescent and electrical assays. The overall detection strategy represents a step toward the massively parallel electrical monitoring, identification, and quantification of protein-DNA interactions at arrayed nanoscale devices.

On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions

Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.

Latest developments in experimental and computational approaches to characterize protein-lipid interactions

Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein-protein) or between classes (e.g. protein-DNA). While the technologies for analyzing protein-protein and protein-DNA interactions are well established, the field of protein-lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein-lipid interactions. Experimental technologies fall into two principal categories, namely solution-based and array-based methods. Computational methods include large-scale data-driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein-lipid interactions and their importance in biological systems.

A brief introduction to tiling microarrays: principles, concepts, and applications

Technological achievements have always contributed to the advancement of biomedical research. It has never been more so than in recent times, when the development and application of innovative cutting-edge technologies have transformed biology into a data-rich quantitative science. This stunning revolution in biology primarily ensued from the emergence of microarrays over two decades ago. The completion of whole-genome sequencing projects and the advance in microarray manufacturing technologies enabled the development of tiling microarrays, which gave unprecedented genomic coverage. Since their first description, several types of application of tiling arrays have emerged, each aiming to tackle a different biological problem. Although numerous algorithms have already been developed to analyze microarray data, new method development is still needed not only for better performance but also for integration of available microarray data sets, which without doubt constitute one of the largest collections of biological data ever generated. In this chapter we first introduce the principles behind the emergence and the development of tiling microarrays, and then discuss with some examples how they are used to investigate different biological problems.

Nanobody(R)-based chromatin immunoprecipitation/micro-array analysis for genome-wide identification of transcription factor DNA binding sites

Nanobodies® are single-domain antibody fragments derived from camelid heavy-chain antibodies. Because of their small size, straightforward production in Escherichia coli, easy tailoring, high affinity, specificity, stability and solubility, nanobodies® have been exploited in various biotechnological applications. A major challenge in the post-genomics and post-proteomics era is the identification of regulatory networks involving nucleic acid-protein and protein-protein interactions. Here, we apply a nanobody® in chromatin immunoprecipitation followed by DNA microarray hybridization (ChIP-chip) for genome-wide identification of DNA-protein interactions. The Lrp-like regulator Ss-LrpB, arguably one of the best-studied specific transcription factors of the hyperthermophilic archaeon Sulfolobus solfataricus, was chosen for this proof-of-principle nanobody®-assisted ChIP. Three distinct Ss-LrpB-specific nanobodies®, each interacting with a different epitope, were generated for ChIP. Genome-wide ChIP-chip with one of these nanobodies® identified the well-established Ss-LrpB binding sites and revealed several unknown target sequences. Furthermore, these ChIP-chip profiles revealed auxiliary operator sites in the open reading frame of Ss-lrpB. Our work introduces nanobodies® as a novel class of affinity reagents for ChIP. Taking into account the unique characteristics of nanobodies®, in particular, their short generation time, nanobody®-based ChIP is expected to further streamline ChIP-chip and ChIP-Seq experiments, especially in organisms with no (or limited) possibility of genetic manipulation.

Functional protein microarray as molecular decathlete: a versatile player in clinical proteomics

Functional protein microarrays were developed as a high-throughput tool to overcome the limitations of DNA microarrays and to provide a versatile platform for protein functional analyses. Recent years have witnessed tremendous growth in the use of protein microarrays, particularly functional protein microarrays, to address important questions in the field of clinical proteomics. In this review, we will summarize some of the most innovative and exciting recent applications of protein microarrays in clinical proteomics, including biomarker identification, pathogen-host interactions, and cancer biology.

Proven in vitro evolution of protease cathepsin E-inhibitors and -activators at pH 4.5 using a paired peptide method

Improving a particular function of molecules is often more difficult than identifying such molecules ab initio. Here, a method to acquire higher affinity and/or more functional peptides was developed as a progressive library selection method. The primary library selection products were utilized to build a secondary library composed of blocks of 4 amino acids, of which selection led to peptides with increased activity. These peptides were further converted to randomly generate paired peptides. Cathepsin E-inhibitors thus obtained exhibited the highest activities and affinities (pM order). This was also the case with cathepsin E-activating peptides, proving the methodological effectiveness. The primary, secondary, and tertiary library selections can be regarded as module-finding, module-shuffling, and module-pairing, respectively, which resembles the progression of the natural evolution of proteins. The mode of peptide binding to their target proteins is discussed in analogy to antibodies and epitopes of an antigen.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Experimental approaches to identify cellular G-quadruplex structures and functions

Guanine-rich nucleic acids can fold into non-canonical DNA secondary structures called G-quadruplexes. The formation of these structures can interfere with the biology that is crucial to sustain cellular homeostases and metabolism via mechanisms that include transcription, translation, splicing, telomere maintenance and DNA recombination. Thus, due to their implication in several biological processes and possible role promoting genomic instability, G-quadruplex forming sequences have emerged as potential therapeutic targets. There has been a growing interest in the development of synthetic molecules and biomolecules for sensing G-quadruplex structures in cellular DNA. In this review, we summarise and discuss recent methods developed for cellular imaging of G-quadruplexes, and the application of experimental genomic approaches to detect G-quadruplexes throughout genomic DNA. In particular, we will discuss the use of engineered small molecules and natural proteins to enable pull-down, ChIP-Seq, ChIP-chip and fluorescence imaging of G-quadruplex structures in cellular DNA.

Direct iterative protein profiling (DIPP) - an innovative method for large-scale protein detection applied to budding yeast mitosis

The budding yeast Saccharomyces cerevisiae is a major model organism for important biological processes such as mitotic growth and meiotic development, it can be a human pathogen, and it is widely used in the food-, and biotechnology industries. Consequently, the genomes of numerous strains have been sequenced and a very large amount of RNA profiling data is available. Moreover, it has recently become possible to quantitatively analyze the entire yeast proteome; however, efficient and cost-effective high-throughput protein profiling remains a challenge. We report here a new approach to direct and label-free large-scale yeast protein identification using a tandem buffer system for protein extraction, two-step protein prefractionation and enzymatic digestion, and detection of peptides by iterative mass spectrometry. Our profiling study of diploid cells undergoing rapid mitotic growth identified 86% of the known proteins and its output was found to be widely concordant with genome-wide mRNA concentrations and DNA variations between yeast strains. This paves the way for comprehensive and straightforward yeast proteome profiling across a wide variety of experimental conditions.