Bubble lifetimes in DNA gene promoters and their mutations affecting transcription

Medium viscosity influence on the open states genesis in a DNA molecule

The studies were carried out by the mathematical modeling of DNA mechanical deformations. Numerical calculations done for the interferon alpha 17 gene, which consists of 980 base pairs. It has been established that the genesis and dynamics of open states in the DNA molecule depends on the magnitude of the external influence (torque) and on the viscosity of the environment. In addition, it is shown that the dynamics of open states zones can have a jump-like character with a small change in the magnitude of the torque. When torque is applied to all 980 base pairs of the gene, the following effect is observed: an increase in the viscosity of the medium leads to an increase in the value of the torque necessary for the occurrence of OS and DNA unwinding, i.e. viscosity plays an important stabilizing role in DNA dynamics. Under the influence of a localized torque on different (by the content of A-T and G-C pairs and location) regions of the interferon alpha 17 gene, it was found that the magnitude of the external torque necessary for the occurrence of open states at all calculated values of viscosity depends on the nucleotide composition. The dependence of the torque magnitude required for the open states occurrence on viscosity is observed when the torque is applied to areas close to the gene boundaries. At the same time, the significance of the end effect, which weakens DNA, decreased with increasing viscosity of the medium.Communicated by Ramaswamy H. Sarma.

Examining DNA breathing with pyDNA-EPBD

MOTIVATION

The two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion. This dynamics results in transient openings in the double helix and is referred to as "DNA breathing" or "DNA bubbles." The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding. However, the modeling and computer simulation of these phenomena, have remained a challenge due to the complex interplay of numerous factors, such as, temperature, salt content, DNA sequence, hydrogen bonding, base stacking, and others.

RESULTS

We present pyDNA-EPBD, a parallel software implementation of the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA model that allows us to describe some features of DNA dynamics in detail. The pyDNA-EPBD generates genomic scale profiles of average base-pair openings, base flipping probability, DNA bubble probability, and calculations of the characteristically dynamic length indicating the number of base pairs statistically significantly affected by a single point mutation using the Markov Chain Monte Carlo algorithm.

AVAILABILITY AND IMPLEMENTATION

pyDNA-EPBD is supported across most operating systems and is freely available at https://github.com/lanl/pyDNA_EPBD. Extensive documentation can be found at https://lanl.github.io/pyDNA_EPBD/.

Equilibrium melting probabilities of a DNA molecule with a defect: An exact solution of the Poland-Scheraga model

In this study we derive analytically the equilibrium melting probabilities for basepairs of a DNA molecule with a defect site. We assume that the defect is characterized by a change in the Watson-Crick basepair energy of the defect basepair, and in the associated two stacking energies for the defect, as compared to the remaining parts of the DNA. The defect site could, for instance, occur due to DNA basepair mismatching, cross-linking, or by the chemical modifications when attaching fluorescent labels, such as fluorescent-quencher pairs, to DNA. Our exact solution of the Poland-Scheraga model for DNA melting provides the probability that the labeled basepair, and its neighbors, are open at different temperatures. Our work is of direct importance, for instance, for studies where fluorophore-quencher pairs are used for studying single basepair fluctuations of designed DNA molecules.

Influence of a Single Deuterium Substitution for Protium on the Frequency Generation of Different-Size Bubbles in IFNA17

The influence of a single 2H/1H replacement on the frequency generation of different-size bubbles in the human interferon alpha-17 gene (IFNA17) under various energies was studied by a developed algorithm and mathematical modeling without simplifications or averaging. This new approach showed the efficacy of researching DNA bubbles and open states both when all hydrogen bonds in nitrogenous base pairs are protium and after an 2H-substitution. After a single deuterium substitution under specific energies, it was demonstrated that the non-coding region of IFNA17 had a more significant regulatory role in bubble generation in the whole gene than the promoter had. It was revealed that a single deuterium substitution for protium has an influence on the frequency generation of DNA bubbles, which also depends on their size and is always higher for the smaller bubbles under the largest number of the studied energies. Wherein, compared to the natural condition under the same critical value of energy, the bigger raises of the bubble frequency occurrence (maximums) were found for 11-30 base pair (bp) bubbles (higher by 319%), 2-4 bp bubbles (higher by 300%), and 31 bp and over ones (higher by 220%); whereas the most significant reductions of the indicators (minimums) were observed for 11-30 bp bubbles (lower by 43%) and bubbles size over 30 bp (lower by 82%). In this study, we also analyzed the impact of several circumstances on the AT/GC ratio in the formation of DNA bubbles, both under natural conditions and after a single hydrogen isotope exchange. Moreover, based on the obtained data, substantial positive and inverse correlations were revealed between the AT/GC ratio and some factors (energy values, size of DNA bubbles). So, this modeling and variant of the modified algorithm, adapted for researching DNA bubbles, can be useful to study the regulation of replication and transcription in the genes under different isotopic substitutions in the nucleobases.

Bubble Relaxation Dynamics in Homopolymer DNA Sequences

Understanding the inherent timescales of large bubbles in DNA is critical to a thorough comprehension of its physicochemical characteristics, as well as their potential role on helix opening and biological function. In this work, we employ the coarse-grained Peyrard-Bishop-Dauxois model of DNA to study relaxation dynamics of large bubbles in homopolymer DNA, using simulations up to the microsecond time scale. By studying energy autocorrelation functions of relatively large bubbles inserted into thermalised DNA molecules, we extract characteristic relaxation times from the equilibration process for both adenine-thymine (AT) and guanine-cytosine (GC) homopolymers. Bubbles of different amplitudes and widths are investigated through extensive statistics and appropriate fittings of their relaxation. Characteristic relaxation times increase with bubble amplitude and width. We show that, within the model, relaxation times are two orders of magnitude longer in GC sequences than in AT sequences. Overall, our results confirm that large bubbles leave a lasting impact on the molecule's dynamics, for times between 0.5-500 ns depending on the homopolymer type and bubble shape, thus clearly affecting long-time evolutions of the molecule.

Nonlinear physics opens a new paradigm for accurate transcription start site prediction

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.

Non-linear Hamiltonian models for DNA

Nucleic acids' physical properties have been investigated by theoretical methods based both on fully atomistic representations and on coarse-grained models, e.g., the worm-like-chain, taken from polymer physics. In this review article, I discuss an intermediate (mesoscopic) approach and show how to build a three-dimensional Hamiltonian model which accounts for the main interactions responsible for the stability of the helical molecules. While the 3D mesoscopic model yields a sufficiently detailed description of the helix at the level of the base pair, it also allows one to predict the thermodynamical and structural properties of molecules in solution. Relying on the idea that the base pair fluctuations can be conceived as trajectories, I have built over the past years a computational method based on the time-dependent path integral formalism to derive the partition function. While the main features of the method are presented, I focus here in particular on a newly developed statistical method to set the maximum amplitude of the base pair fluctuations, a key parameter of the theory. Some applications to the calculation of DNA flexibility properties are discussed together with the available experimental data.