DNA Sequences In Terms Research Articles

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence.

DNA can stabilize silver nanoclusters (AgN-DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA's vast sequence space challenges the design and discovery of AgN-DNAs with tailored properties. In particular, AgN-DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive AgN-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from AgN-DNA crystal structures, we distill the salient DNA sequence features that determine AgN-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of AgN-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive AgN-DNAs by 12.3 times as compared to training data, nearly doubling the number of known AgN-DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure-property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data.

Small-Angle Scattering and Multifractal Analysis of DNA Sequences.

The arrangement of A, C, G and T nucleotides in large DNA sequences of many prokaryotic and eukaryotic cells exhibit long-range correlations with fractal properties. Chaos game representation (CGR) of such DNA sequences, followed by a multifractal analysis, is a useful way to analyze the corresponding scaling properties. This approach provides a powerful visualization method to characterize their spatial inhomogeneity, and allows discrimination between mono- and multifractal distributions. However, in some cases, two different arbitrary point distributions, may generate indistinguishable multifractal spectra. By using a new model based on multiplicative deterministic cascades, here it is shown that small-angle scattering (SAS) formalism can be used to address such issue, and to extract additional structural information. It is shown that the box-counting dimension given by multifractal spectra can be recovered from the scattering exponent of SAS intensity in the fractal region. This approach is illustrated for point distributions of CGR data corresponding to Escherichia coli, Phospholamban and Mouse mitochondrial DNA, and it is shown that for the latter two cases, SAS allows extraction of the fractal iteration number and the scaling factor corresponding to “ACGT” square, or to recover the number of bases. The results are compared with a model based on multiplicative deterministic cascades, and respectively with one which takes into account the existence of forbidden sequences in DNA. This allows a classification of the DNA sequences in terms of random and deterministic fractals structures emerging in CGR.

Assessment of Genetic Variability of Citrus tristeza virus by SSCP and CE-SSCP.

Single-strand conformation polymorphism (SSCP) is a popular method used to study the genetic heterogeneity and population variability of Citrus tristeza virus (CTV) isolates. It is a simple, low-cost, and highly specific method for mutation detection of specific genes, mostly of the CTV major coat protein gene (p25). The technique is based on a comparison on polyacrylamide gel of electrophoretic profiles of single-stranded (ss) DNA sequences in terms of their spatial conformation. SSCP involves cDNA synthesis and amplification of the target gene, denaturation of single strands, and electrophoresis in non-denaturing conditions. The ssDNAs can be afterward visualized by staining the polyacrylamide gel. Alternatively, using fluorescently labeled primers, the procedure can be performed in automated sequencers equipped with an appropriate capillary (CE-SSCP), which increases the potential of high-throughput analysis, precision, and the reproducibility of results. CE-SSCP can be also directly applied to the virus particles obtained by elution from ELISA plates or tissue-print membranes.

RepDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.

In order to develop powerful computational predictors for identifying the biological features or attributes of DNAs, one of the most challenging problems is to find a suitable approach to effectively represent the DNA sequences. To facilitate the studies of DNAs and nucleotides, we developed a Python package called representations of DNAs (repDNA) for generating the widely used features reflecting the physicochemical properties and sequence-order effects of DNAs and nucleotides. There are three feature groups composed of 15 features. The first group calculates three nucleic acid composition features describing the local sequence information by means of kmers; the second group calculates six autocorrelation features describing the level of correlation between two oligonucleotides along a DNA sequence in terms of their specific physicochemical properties; the third group calculates six pseudo nucleotide composition features, which can be used to represent a DNA sequence with a discrete model or vector yet still keep considerable sequence-order information via the physicochemical properties of its constituent oligonucleotides. In addition, these features can be easily calculated based on both the built-in and user-defined properties via using repDNA. The repDNA Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repDNA/. bliu@insun.hit.edu.cn or kcchou@gordonlifescience.org Supplementary data are available at Bioinformatics online.

Hysteresis in pressure-driven DNA denaturation.

In the past, a great deal of attention has been drawn to thermal driven denaturation processes. In recent years, however, the discovery of stress-induced denaturation, observed at the one-molecule level, has revealed new insights into the complex phenomena involved in the thermo-mechanics of DNA function. Understanding the effect of local pressure variations in DNA stability is thus an appealing topic. Such processes as cellular stress, dehydration, and changes in the ionic strength of the medium could explain local pressure changes that will affect the molecular mechanics of DNA and hence its stability. In this work, a theory that accounts for hysteresis in pressure-driven DNA denaturation is proposed. We here combine an irreversible thermodynamic approach with an equation of state based on the Poisson-Boltzmann cell model. The latter one provides a good description of the osmotic pressure over a wide range of DNA concentrations. The resulting theoretical framework predicts, in general, the process of denaturation and, in particular, hysteresis curves for a DNA sequence in terms of system parameters such as salt concentration, density of DNA molecules and temperature in addition to structural and configurational states of DNA. Furthermore, this formalism can be naturally extended to more complex situations, for example, in cases where the host medium is made up of asymmetric salts or in the description of the (helical-like) charge distribution along the DNA molecule. Moreover, since this study incorporates the effect of pressure through a thermodynamic analysis, much of what is known from temperature-driven experiments will shed light on the pressure-induced melting issue.

The role played by exons in genomic DNA sequence correlations

The codon structure inside exons imposes a strong modulation with period-3 for genomic composition correlations. A new formalism for calculating nucleotide correlations along DNA sequences in terms of an irreducible set of six correlation functions is presented. New procedures to extract the corresponding period-3 modulations are also developed. These modulations are seen to be stronger for the irreducible self-correlation C zz ( k), which accounts only for the binding strength of dinucleotides (z stands for adenine or thymine minus cytosine or guanine concentrations). We investigate and model the relationship between exon distribution and genomic period-3 correlations for the D. melanogaster genome.

High-Resolution Mapping of Sequence-Directed Nucleosome Positioning on Genomic DNA

We have mapped in vitro nucleosome positioning on the sheep β-lactoglobulin gene using high-throughput sequencing to characterise the DNA sequences recovered from reconstituted nucleosomes. This methodology surpasses previous approaches for coverage, accuracy and resolution and, most importantly, offers a simple yet rapid and relatively inexpensive method to characterise genomic DNA sequences in terms of nucleosome positioning capacity. We demonstrate an unambiguous correspondence between in vitro and in vivo nucleosome positioning around the promoter of the gene; identify discrete, sequence-specific nucleosomal structures above the level of the canonical core particle—a feature that has implications for regulatory protein access and higher-order chromatin packing; and reveal new insights into the involvement of periodically organised dinucleotide sequence motifs of the type GG and CC and not AA and TT, as determinants of nucleosome positioning—an observation that supports the idea that the core histone octamer can exploit different patterns of sequence organisation, or structural potential, in the DNA to bring about nucleosome positioning.

Influence of neighbouring base sequences on the mutagenesis induced by 7,8-dihydro-8-oxoguanine in yeast

We have analysed the influence of neighbouring base sequences on the mutagenesis induced by 7,8-dihydro-8-oxoguanine (8-oxoG or G(o)), a typical oxidative lesion of DNA, using the yeast oligonucleotide transformation technique. Two oligonucleotides, oligo-CCG(o) and oligo-CGG(o), each possessing a single 8-oxoG residue and represented by the sequences 5'-CCG(o)-3' and 5'-CGG(o)-3', respectively, were introduced into a chromosome of Saccharomyces cerevisiae and their mutagenic potentials were compared. In a wild-type strain, 8-oxoG showed very weak mutagenic potential in both cases. However, the lesion in 5'-CCG(o)-3' can cause efficient G-to-T transversion in a strain lacking the rad30 gene which encodes yeast DNA polymerase eta (Ypoleta). To explore the properties associated with this translesion synthesis (TLS), the same two oligonucleotides possessing an 8-oxoG were used as templates for a standing-start primer extension assay, and the nucleotide incorporation opposite 8-oxoG was investigated. We found that dATP incorporation opposite 8-oxoG with Ypoleta was low for both sequences. In particular, very low dATP incorporation was observed for the 5'-CCG(o)-3' sequence. These results account for the efficient inhibition of mutagenesis by Ypoleta. TLS plays an important role in one DNA sequence in terms of avoiding mutagenesis induced by 8-oxoG in yeast. In contrast, human yeast DNA polymerase eta showed higher dATP incorporation rates even with the 5'-CCG(o)-3' sequence.

Divergence and Shannon Information in Genomes

Shannon information (SI) and its special case, divergence, are defined for a DNA sequence in terms of probabilities of chemical words in the sequence and are computed for a set of complete genomes highly diverse in length and composition. We find the following: SI (but not divergence) is inversely proportional to sequence length for a random sequence but is length independent for genomes; the genomic SI is always greater and, for shorter words and longer sequences, hundreds to thousands times greater than the SI in a random sequence whose length and composition match those of the genome; genomic SIs appear to have word-length dependent universal values. The universality is inferred to be an evolution footprint of a universal mode for genome growth.

Dynamics of DNA conformations and DNA-protein interactions

AbstractOptical tweezers, atomic force microscopes, patch clamping, or fluorescence techniques make it possible to study both the equilibrium conformations and dynamics of single DNA molecules as well as their interaction with binding proteins. In this paper we address the dynamics of local DNA denaturation (bubble breathing), deriving its dynamic response to external physical parameters and the DNA sequence in terms of the bubble relaxation time spectrum and the autocorrelation function of bubble breathing. The interaction with binding proteins that selectively bind to the DNA single strand exposed in a denaturation bubble are shown to involve an interesting competition of time scales, varying between kinetic blocking of protein binding up to full binding protein-induced denaturation of the DNA. We will also address the potential to use DNA physics for the design of nanosensors. Finally, we report recent findings on the search process of proteins for a specific target on the DNA.

Foreword

Over the past five to ten years we have witnessed a revolution in biological research which is fueled by our increasing capacity to decipher biological information of three types: the digital information embedded in DNA with its four-letter alphabet; the three-dimensional information represented by proteins, the major executors of biological function; and the four-dimensional information of complex biological systems and networks representing the temporal and spatial interaction of multiple components. The analysis of complex systems and networks in their entirety is essential for scientists to understand the molecular basis of fascinating processes such as growth, development, and differentiation, and to extract the biological meaning of emergent properties such as consciousness, memory, and the ability to learn which evolved over billions of years. Not surprisingly, the linear nucleotide sequence in DNA has been the first type of information to be deciphered. Large-scale genetic mapping and DNA sequencing rapidly generate enormous amounts of biological information. In many ways, programs such as the human genome project represent some of the earliest attempts to understand biological complexity. Successful completion of the human genome and similar projects will pose the challenge of interpreting the information contained in billions of nucleotides and of explaining how the interplay of the products of perhaps 100,000 genes results in the myriad of biological phenotypes. Meeting these challenges will require new interdisciplinary strategies which draw from the expertise of scientists from disciplines as different as molecular biology, biochemistry, engineering, chemistry, applied mathematics, and computer science. Protein science is central to such integrated strategies. Separation of proteins from complex mixtures, followed by structural and functional analyses, control of protein function, post-translational processing, and modification and formation of macromolecular complexes of proteins and other biomolecules are but a few examples of topics which are essential for all those scientists who attempt to interpret the linear DNA sequence in terms of the three- and four-dimensional information of complex biological systems and networks. The following chapters detail the necessary protocols for this endeavor, provided by experts in experimental protein science. Two attractive features set this manual apart from other collections of research protocols. First, Current Protocols in Protein Science will be continuously updated and expanded by quarterly additions to the core edition. Secondly, this volume as well as the updates are also available on CD-ROM. Numerous cross-references within the manual by hypertext links, context based searching, and provisions for making individualized notebooks containing frequently used protocols are attractions of the CD-ROM version which are in tune with the increasing dependence on computers in biological research laboratories. Protein science is a diverse, experimentally challenging, and rapidly evolving discipline. This book provides expert guidance in experiment design and execution and promises to remain up-to-date, in contents and in format, for many years to come.

Analysis and prediction of hydrogen bonding in protein-DNA complexes using parallel processors

A number of essential biological functions are controlled by proteins that bind to specific sequences in genomic DNA. In this article we present a simplified model for analyzing DNA-protein interactions mediated exclusively by hydrogen bonds. Based on this model, an optimized algorithm for geometric pattern recognition was developed. The large number of local energy minima are efficiently screened by using a geometric approach to pattern matching based on a square-well potential. The second part of the algorithm represents a closed form solution for minimization based on a quadratic potential. A Monte Carlo method applied to a modified Lennard-Jones potential is used as a third step to rank DNA sequences in terms of pattern matching. Using protein structures derived from four DNA-protein complexes with three-dimensional coordinates established by X-ray diffraction analysis, all possible DNA sequences to which these proteins could bind were ranked in terms of binding energies. The algorithm predicts the correct DNA sequence when at least two hydrogen bonds per base pair are involved in binding to the protein, providing a partial solution to the three-dimensional docking problem. This study lays a framework for future refinements of the algorithm in which the number of assumptions made in the present analysis are reduced. © 1996 by John Wiley & Sons, Inc.

Wavelet based fractal analysis of DNA sequences

Abstract The fractal scaling properties of DNA sequences are analyzed using the wavelet transform. Mapping nucleotide sequences onto a “DNA walk” produces fractal landscapes that can be studied quantitatively by applying the so-called wavelet transform modulus maxima method. This method provides a natural generalization of the classical box-counting techniques to fractal signals, the wavelets playing the role of “generalized oscillating boxes”. From the scaling behavior of partition functions that are defined from the wavelet transform modulus maxima, this method allows us to determine the singularity spectrum of the considered signal and thereby to achieve a complete multifractal analysis. Moreover, by considering analyzing wavelets that make the “wavelet transform microscope” blind to “patches” of different nucleotide composition that are observed in genomic sequences, we demonstrate and quantify the existence of long-range correlations in the noncoding regions. Although the fluctuations in the patchy landscape of the DNA walks reconstructed from both noncoding and (protein) coding regions are found homogeneous with Gaussian statistics, our wavelet-based analysis allows us to discriminate unambiguously between the fluctuations of the former which behave like fractional Brownian motions, from those of the latter which cannot be distinguished from uncorrelated random Brownian walks. We discuss the robustness of these results with respect to various legitimate codings of the DNA sequences. Finally, we comment about the possible understanding of the origin of the observed long-range correlations in noncoding DNA sequences in terms of the nonequilibrium dynamical processes that produce the “isochore structre of the genome”.

The Use of Logistic Models for the Analysis of Codon Frequencies of DNA Sequences in Terms of Explanatory Variables

The development of the regressive logistic model applicable to the analysis of codon frequencies of DNA sequences in terms of explanatory variables is presented. A codon is a triplet of nucleotides that code for an amino acid, and may be considered as a trivariate response (B1, B2, B3), where Bi (i = 1, 2, 3) is a categorical random variable with values A, C, G, T. The linear order of bases in the DNA and possible statistical dependence of the bases in a given codon make the regressive logistic model a suitable tool for the analysis of codon frequencies. A problem of structural zeros arises from the fact that the stopping codons (terminators) do not code for amino acids; this is solved by normalizing the likelihood function. Codon frequencies may also depend on the function of the gene and they are known to differ between genes of the same genome. Differences also occur between synonymous codons for the same amino acid. Thus, the use of covariates that differ between synonymous codons as well as covariates that are constant within codons of the same amino acid may be useful in explaining the frequencies. As an illustration, the method is applied to the human mitochondrial genome using the following as explanatory variables: (1) TSCORE, a measure of the number of single base mutations required for a given codon to become a terminator; (2) AARISK, an indicator of a codon's ability of changing by a single base substitution to triplets coding for amino acids with very different characteristics; (3) AVDIST, a measure of the typicality of the amino acid coded for by the triplets. The results indicate that models that incorporate dependency structure and covariates are to be preferred to either the models comprising covariates alone or dependency structure alone.

Sequence-specific recognition of DNA by zinc-finger peptides derived from the transcription factor Sp1.

We have overexpressed and purified two peptide fragments of Sp1 that contain the three "zinc-finger" domains necessary for specific Sp1 DNA binding. These peptides assume a stable, folded conformation in solution in the presence of Zn2+ as shown by DNA binding assays and NMR spectroscopy. Mobility-shift assays demonstrate that the Sp1 peptides recognize a number of different Sp1 DNA binding sites (GC boxes, with the core sequence GGGCGG). The dissociation constant for a 92-amino acid peptide binding to the GGGGCGGGGC sequence (Kd approximately 10 nM) and the relative affinities for several other DNA sequences definitively demonstrate Sp1-like binding properties. The thermodynamic binding site for Sp1-Zn92 has been mapped using the primer-extension/mobility-shift assay revealing that the 5' portion of the GC box DNA sequence (GGG GCG) contributes more strongly to the total binding energy than the 3' portion (GGGC). These findings are interpreted in the context of the Sp1 amino acid sequence in comparison with the structurally characterized Zif-268/DNA complex. A model is proposed that offers a structural explanation for the ability of Sp1 to recognize a diverse array of DNA sequences in terms of the individual (and different) DNA binding properties of each of the three zinc-finger domains.

Absence of gross change in primary DNA sequence during aging process of mice

The possibility of age-associated changes in DNA sequence in terms of amplification and rearrangement was examined in mouse spleen, liver and brain using the method of Southern transfer and filter hybridization. The DNA regions studied were at and around nine cloned sequences, most of which are known to move or amplify in certain situations. No detectable age-associated change, however, was observed in all DNA regions studied. These results suggest that widespread DNA sequence rearrangements or amplifications do not occur during the ageing process in mice.