DNA Shape Research Articles

Deep flanking sequence engineering for efficient promoter design using DeepSEED

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Potential of implementing DNA shape in heat transfer applications: A numerical study

Potential of implementing DNA shape in heat transfer applications: A numerical study

Taxonomic reassessment of salamanders (genus Hynobius) from Tsushima Islands, Japan, with a resurrection of Hynobius tagoi Dunn, 1923 (Amphibia: Caudata).

To clarify the taxonomic status of salamanders from Tsushima Islands, Japan, we examined two species of salamanders, Hynobius tsuensis and Hynobius sp. from Tsushima Islands, and compared them with H. nebulosus from Kyushu. We found that the three taxa differ from each other in nuclear DNA, adult morphology, and egg-sac shape, and consider them to be independent species. Based on the investigation of type specimens of the synonyms, we identified Hynobius sp. as H. tagoi Dunn, 1923 and redescribe it herein. Also, we designate a neotype for H. tsuensis and redescribe H. tsuensis. The distribution areas of the two species partly overlap but they were distinguishable by their body colorations: H. tsuensis has uniformly blackish tail sides and a distinct yellow stripe on the upper tail edge; whereas H. tagoi has brown tail sides with numerous dark stippling and without a distinct yellow stripe on the upper tail edge.

Noncanonical binding of transcription factors: time to revisit specificity?

Transcription factors (TFs) are one of the most studied classes of DNA-binding proteins that have a direct functional impact on gene transcription and thus, on human physiology and disease. The mechanisms that TFs use for recognizing target DNA binding sites have been studied for nearly five decades, yet they remain poorly understood. It is classically assumed that a TF recognizes a specific sequence pattern, or motif, as its binding sites. However, recent studies are consistently finding examples of noncanonical binding, that is, TFs binding at sites that do not resemble their sequence motifs. Here we review the current literature on four major types of noncanonical TF binding, namely binding based on DNA shape readout, at Guanine-quadruplex structures, at repeat sequences, and bispecific binding. These examples point to a critical need for studies to unify our current observations, many of which are at odds with the "one TF, one motif" view, into a more comprehensive definition of the DNA-binding specificity of TFs.

Genomic hotspots of chromosome rearrangements explain conserved synteny despite high rates of chromosome evolution in a holocentric lineage.

Holocentric organisms, unlike typical monocentric organisms, have kinetochore activity distributed along almost the whole length of the chromosome. Because of this, chromosome rearrangements through fission and fusion are more likely to become fixed in holocentric species, which may account for the extraordinary rates of chromosome evolution that many holocentric lineages exhibit. Long blocks of genome synteny have been reported in animals with holocentric chromosomes despite high rates of chromosome rearrangements. Nothing is known from plants, however, despite the fact that holocentricity appears to have played a key role in the diversification of one of the largest angiosperm genera, Carex (Cyperaceae). In the current study, we compared genomes of Carex species and a distantly related Cyperaceae species to characterize conserved and rearranged genome regions. Our analyses span divergence times ranging between 2 and 50 million years. We also compared a C. scoparia chromosome-level genome assembly with a linkage map of the same species to study rearrangements at a population level and suppression of recombination patterns. We found longer genome synteny blocks than expected under a null model of random rearrangement breakpoints, even between very distantly related species. We also found repetitive DNA to be non-randomly associated with holocentromeres and rearranged regions of the genome. The evidence of conserved synteny in sedges despite high rates of chromosome fission and fusion suggests that conserved genomic hotspots of chromosome evolution related to repetitive DNA shape the evolution of recombination, gene order and crossability in sedges. This finding may help explain why sedges are able to maintain species cohesion even in the face of high interspecific chromosome rearrangements.

Structural insights into human TFIIIC promoter recognition.

Transcription factor (TF) IIIC recruits RNA polymerase (Pol) III to most of its target genes. Recognition of intragenic A- and B-box motifs in transfer RNA (tRNA) genes by TFIIIC modules τA and τB is the first critical step for tRNA synthesis but is mechanistically poorly understood. Here, we report cryo-electron microscopy structures of the six-subunit human TFIIIC complex unbound and bound to a tRNA gene. The τB module recognizes the B-box via DNA shape and sequence readout through the assembly of multiple winged-helix domains. TFIIIC220 forms an integral part of both τA and τB connecting the two subcomplexes via a ~550-amino acid residue flexible linker. Our data provide a structural mechanism by which high-affinity B-box recognition anchors TFIIIC to promoter DNA and permits scanning for low-affinity A-boxes and TFIIIB for Pol III activation.

Structural underpinnings of mutation rate variations in the human genome.

Single nucleotide mutation rates have critical implications for human evolution and genetic diseases. Importantly, the rates vary substantially across the genome and the principles underlying such variations remain poorly understood. A recent model explained much of this variation by considering higher-order nucleotide interactions in the 7-mer sequence context around mutated nucleotides. This model's success implicates a connection between DNA shape and mutation rates. DNA shape, i.e. structural properties like helical twist and tilt, is known to capture interactions between nucleotides within a local context. Thus, we hypothesized that changes in DNA shape features at and around mutated positions can explain mutation rate variations in the human genome. Indeed, DNA shape-based models of mutation rates showed similar or improved performance over current nucleotide sequence-based models. These models accurately characterized mutation hotspots in the human genome and revealed the shape features whose interactions underlie mutation rate variations. DNA shape also impacts mutation rates within putative functional regions like transcription factor binding sites where we find a strong association between DNA shape and position-specific mutation rates. This work demonstrates the structural underpinnings of nucleotide mutations in the human genome and lays the groundwork for future models of genetic variations to incorporate DNA shape.

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance. We present DeepSTF, a unique deep-learning architecture for predicting TFBSs by integrating DNA sequence and shape profiles. We use the improved transformer encoder structure for the first time in the TFBSs prediction approach. DeepSTF extracts DNA higher-order sequence features using stacked convolutional neural networks(CNNs), whereas rich DNA shape profiles are extracted by combining improved transformer encoder structure and bidirectional long short-term memory(Bi-LSTM), and, finally, the derived higher-order sequence features and representative shape profiles are integrated into the channel dimension to achieve accurate TFBSs prediction. Experiments on 165 ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) datasets show that DeepSTF considerably outperforms several state-of-the-art algorithms in predicting TFBSs, and we explain the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features. In addition, this paper examines the significance of DNA shape features predicting TFBSs. The source code of DeepSTF is available at https://github.com/YuBinLab-QUST/DeepSTF/.

DNA structural properties of DNA binding sites for 21 transcription factors in the mycobacterial genome.

Mycobacterium tuberculosis, the causative agent of tuberculosis, has evolved over time into a multidrug resistance strain that poses a serious global pandemic health threat. The ability to survive and remain dormant within the host macrophage relies on multiple transcription factors contributing to virulence. To date, very limited structural insights from crystallographic and NMR studies are available for TFs and TF-DNA binding events. Understanding the role of DNA structure in TF binding is critical to deciphering MTB pathogenicity and has yet to be resolved at the genome scale. In this work, we analyzed the compositional and conformational preference of 21 mycobacterial TFs, evident at their DNA binding sites, in local and global scales. Results suggest that most TFs prefer binding to genomic regions characterized by unique DNA structural signatures, namely, high electrostatic potential, narrow minor grooves, high propeller twist, helical twist, intrinsic curvature, and DNA rigidity compared to the flanking sequences. Additionally, preference for specific trinucleotide motifs, with clear periodic signals of tetranucleotide motifs, are observed in the vicinity of the TF-DNA interactions. Altogether, our study reports nuanced DNA shape and structural preferences of 21 TFs.

Structural insights into CodY activation and DNA recognition.

Virulence factors enable pathogenic bacteria to infect host cells, establish infection, and contribute to disease progressions. In Gram-positive pathogens such as Staphylococcus aureus (Sa) and Enterococcus faecalis (Ef), the pleiotropic transcription factor CodY plays a key role in integrating metabolism and virulence factor expression. However, to date, the structural mechanisms of CodY activation and DNA recognition are not understood. Here, we report the crystal structures of CodY from Sa and Ef in their ligand-free form and their ligand-bound form complexed with DNA. Binding of the ligands-branched chain amino acids and GTP-induces conformational changes in the form of helical shifts that propagate to the homodimer interface and reorient the linker helices and DNA binding domains. DNA binding is mediated by a non-canonical recognition mechanism dictated by DNA shape readout. Furthermore, two CodY dimers bind to two overlapping binding sites in a highly cooperative manner facilitated by cross-dimer interactions and minor groove deformation. Our structural and biochemical data explain how CodY can bind a wide range of substrates, a hallmark of many pleiotropic transcription factors. These data contribute to a better understanding of the mechanisms underlying virulence activation in important human pathogens.

Prediction of mammalian tissue-specific CLOCK–BMAL1 binding to E-box DNA motifs

The Brain and Muscle ARNTL-Like 1 protein (BMAL1) forms a heterodimer with either Circadian Locomotor Output Cycles Kaput (CLOCK) or Neuronal PAS domain protein 2 (NPAS2) to act as a master regulator of the mammalian circadian clock gene network. The dimer binds to E-box gene regulatory elements on DNA, activating downstream transcription of clock genes. Identification of transcription factor binding sites and genomic features that correlate to DNA binding by BMAL1 is a challenging problem, given that CLOCK–BMAL1 or NPAS2–BMAL1 bind to several distinct binding motifs (CANNTG) on DNA. Using three different types of tissue-specific machine learning models with features based on (1) DNA sequence, (2) DNA sequence plus DNA shape, and (3) DNA sequence and shape plus histone modifications, we developed an interpretable predictive model of genome-wide BMAL1 binding to E-box motifs and dissected the mechanisms underlying BMAL1–DNA binding. Our results indicated that histone modifications, the local shape of the DNA, and the flanking sequence of the E-box motif are sufficient predictive features for BMAL1–DNA binding. Our models also provide mechanistic insights into tissue specificity of DNA binding by BMAL1.

Extraction & Purification of Plasmid from Some Spices of E. coli in Different Ways

This study involved the use of multiple methods to separate plasmids from bacterial cell DNA for some isolates of pathogenic E. coli through several steps, starting with the analysis of the bacterial cell using lysozymes to remove the outer wall, followed by centrifugation to isolate plasmids found in the solution from the rest of the proteins and other forms of DNA. Many sequential methods were used to separate plasmids. The first method used was the basal denaturation sodium hydroxide-based, which led to the denaturation of the chromosomal DNA without affecting the plasmid DNA, followed by the addition of sodium acetate, which led to the preservation of the shape and structure of the plasmid DNA. Second, using cesium chloride gradient density to isolate the protein cell components and the rest of the DNA forms. The different densities of these components led to the appearance of sequential bundles depending on their different molecular weights. Ethidium bromide, which gave the plasmid bundles a fluorescent dye, was added using ultraviolet rays. The last purification method was using the boiling method using a water bath. Plasmid samples extracted from the previous methods were taken to perform the purification and separation process using the high electrophoresis method. Akarose gel was used to separate the high molecular weight protein fragments. Standard proteins and plasmids were migrated to determine the volumes of purified plasmids.

HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency.

Transcription factor (TF) binds to conservative DNA binding sites in different cellular environments and development stages by physical interaction with interdependent nucleotides. However, systematic computational characterization of the relationship between higher-order nucleotide dependency and TF-DNA binding mechanism in diverse cell types remains challenging. Here, we propose a novel multi-task learning framework HAMPLE to simultaneously predict TF binding sites (TFBS) in distinct cell types by characterizing higher-order nucleotide dependencies. Specifically, HAMPLE first represents a DNA sequence through three higher-order nucleotide dependencies, including k-mer encoding, DNA shape and histone modification. Then, HAMPLE employs the customized gate control and the channel attention convolutional architecture to further capture cell-type-specific and cell-type-shared DNA binding motifs and epigenomic languages. Finally, HAMPLE exploits the joint loss function to optimize the TFBS prediction for different cell types in an end-to-end manner. Extensive experimental results on seven datasets demonstrate that HAMPLE significantly outperforms the state-of-the-art approaches in terms of auROC. In addition, feature importance analysis illustrates that k-mer encoding, DNA shape and histone modification have predictive power for TF-DNA binding in different cellular environments and are complementary to each other. Furthermore, ablation study and interpretable analysis validate the effectiveness of the customized gate control and the channel attention convolutional architecture in characterizing higher-order nucleotide dependencies. The source code is available at https://github.com/ZhangLab312/Hample. Supplementary data are available at Bioinformatics online.

DNA supercoiling-induced shapes alter minicircle hydrodynamic properties

DNA in cells is organized in negatively supercoiled loops. The resulting torsional and bending strain allows DNA to adopt a surprisingly wide variety of 3-D shapes. This interplay between negative supercoiling, looping, and shape influences how DNA is stored, replicated, transcribed, repaired, and likely every other aspect of DNA activity. To understand the consequences of negative supercoiling and curvature on the hydrodynamic properties of DNA, we submitted 336bp and 672bp DNA minicircles to analytical ultracentrifugation (AUC). We found that the diffusion coefficient, sedimentation coefficient, and the DNA hydrodynamic radius strongly depended on circularity, loop length, and degree of negative supercoiling. Because AUC cannot ascertain shape beyond degree of non-globularity, we applied linear elasticity theory to predict DNA shapes, and combined these with hydrodynamic calculations to interpret the AUC data, with reasonable agreement between theory and experiment. These complementary approaches, together with earlier electron cryotomography data, provide a framework for understanding and predicting the effects of supercoiling on the shape and hydrodynamic properties of DNA.

MELD-DNA: Accurate structural prediction of protein-DNA interactions.

Protein-DNA interaction is one of the most important aspects of biology. It is a main step of gene regulation in all organisms and is a potential target for therapeutic and biotech applications. Protein-DNA interactions are distinct from protein-protein interactions due to several factors: 1) DNA sequence and specific interactions (base readout); 2) DNA shape and its deformability (shape readout); 3) Exploration and searching on the DNA for alternative binding states. Such factors dictate affinity and specificity of transcription factors. Traditionally, these interactions were only predicted at the sequence level (e.g. using position weight matrices) and more recently using docking and machine learning models. Here we present MELD-DNA, a too that combines cheap information from experiment (which can be noisy, ambiguous and sparse) with physics-based molecular mechanics force fields using bayesian inference. MELD will accelerate sampling of the binding process by temperature and hamiltonian replica exchanging that is guided by experimental data. We have tested the tool on a diverse set of 15 systems from various TF families and we observed native-like bound structures most of them with room for more improvements. We also compare and benchmark with the state-of-the-art in docking (HADDOCK) and ML (RosettaFold2NA). In this study we discuss several issues in the available methods and techniques that need to be addressed for more accurate predictions. We believe MELD-DNA can be an essential tool in the community from drug design, regulation and fundamental research.

SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength.

Identifying enhancers is a critical task in bioinformatics due to their primary role in regulating gene expression. For this reason, various computational algorithms devoted to enhancer identification have been put forward over the years. More features are extracted from the single DNA sequences to boost the performance. Nevertheless, DNA structural information is neglected, which is an essential factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Here, we propose SENIES, a DNA shape enhanced deep learning predictor, to identify enhancers and their strength. The predictor consists of two layers where the first layer is for enhancer and non-enhancer identification, and the second layer is for predicting the strength of enhancers. Apart from two common sequence-derived features (i.e., one-hot and k-mer), DNA shape is introduced to describe the 3D structures of DNA sequences. Performance comparison with state-of-the-art methods conducted on public datasets demonstrates the effectiveness and robustness of our predictor. The code implementation of SENIES is publicly available at https://github.com/hlju-liye/SENIES.

Perbandingan Pengaruh Agen Plasmid Curing Acriflavine dan Acridine Orange (AO) terhadap Laju Plasmid Curing dengan Kuantifikasi Fluoresensi Ekspresi Protein GFP pada Bakteri Escherichia Coli PRSET-emGFP

Plasmid curing is an activity to remove plasmids in a bacterium. Plasmid itself is a structure that can carry genes that are beneficial for bacteria, such as making the bacteria resistant to antibiotics. Acridine Orange (AO) and Acriflavine are intercalating agents that function to break down the super helical shape of plasmid DNA and then form circular or open linear plasmid DNA which is used to carry out Plasmid Curing in this study and then compare its effect on the rate of plasmid curing by fluorescence quantification of GFP protein expression. on Escherichia coli PRSET-emGFP bacteria. The results obtained from this experiment were the rate of plasmid loss per generation obtained by measuring the activity of the GFP reporter on the recombinant E. coli model given the curing agent Acridine Orange was 9.52% for the AO 50 treatment, 44.2% for the AO 100, 10.4% for the AO 50 treatment. Akriflavina 250, and 0.836% for Acriflavine 125. Therefore, it can be concluded that the Acridine Orange treatment was more effective than the Acriflavine treatment against the pRSET-EmGFP recombinant E. coli curing plasmid.

Brassinosteroid-induced gene repression requires specific and tight promoter binding of BIL1/BZR1 via DNA shape readout.

BRZ-INSENSITIVE-LONG 1 (BIL1)/BRASSINAZOLE-RESISTANT 1 (BZR1) and its homologues are plant-specific transcription factors that convert the signalling of the phytohormones brassinosteroids (BRs) to transcriptional responses, thus controlling various physiological processes in plants. Although BIL1/BZR1 upregulates some BR-responsive genes and downregulates others, the molecular mechanism underlying the dual roles of BIL1/BZR1 is still poorly understood. Here we show that BR-responsive transcriptional repression by BIL1/BZR1 requires the tight binding of BIL1/BZR1 alone to the 10 bp elements of DNA fragments containing the known 6 bp core-binding motifs at the centre. Furthermore, biochemical and structural evidence demonstrates that the selectivity for two nucleobases flanking the core motifs is realized by the DNA shape readout of BIL1/BZR1 without direct recognition of the nucleobases. These results elucidate the molecular and structural basis of transcriptional repression by BIL1/BZR1 and contribute to further understanding of the dual roles of BIL1/BZR1 in BR-responsive gene regulation.

Solid Phase Synthesis of DNA Nanostructures in Heavy Liquid.

Introduction of the solid phase method to synthesize biopolymers has revolutionized the field of biological research by enabling efficient production of peptides and oligonucleotides. One of the advantages of this method is the ease of removal of excess production materials from the desired product, as it is immobilized on solid substrate. The DNA origami method utilizes the nature of nucleotide base-pairing to construct well-defined objects at the nanoscale, and has become a potent tool for manipulating matter in the fields of chemistry, physics, and biology. Here, the development of an approach to synthesize DNA nanostructures directly on magnetic beads, where the reaction is performed in heavy liquid to maintain the beads in suspension is reported. It is demonstrated that the method can achieve high folding yields of up to 90% for various DNA shapes, comparable to standard folding. At the same time, this establishes an easy, fast, and efficient way to further functionalize the DNA origami in one-pot, as well as providing a built-in purification method for easy removal of excess by-products such as non-integrated DNA strands and residual functionalization molecules.

Electrochemical Gene Amplification Signal Detection of Disposable Biochips Using Electrodes.

Real-time Polymerase Chain Reaction (RT-PCR), a molecular diagnostic technology, is spotlighted as one of the quickest and fastest diagnostic methods for the actual coronavirus (SARS-CoV-2). However, the fluorescent label-based technology of the RT-PCR technique requires expensive equipment and a sample pretreatment process for analysis. Therefore, this paper proposes a biochip based on Electrochemical Impedance Spectroscopy (EIS). In this paper, it was possible to see the change according to the concentration by measuring the impedance with a chip made of two electrodes with different shapes of sample DNA.