Session Y13: DNA and RNA Biophysics

Live

The cytoskeleton plays a key role in governing the intracellular transport of macromolecules and, in turn, controlling biophysical phenomena such as drug delivery, protein function, and transfection. While the impact of crowding by cytoskeleton filaments, including actin and microtubules, has been widely studied, how the non-equilibrium dynamics of cytoskeletal networks driven by motor proteins impact intracellular transport is oft overlooked. Here, we couple single-molecule conformational tracking (SMCT) and differential dynamic microscopy (DDM) to elucidate the transport and conformational dynamics of ring and linear DNA molecules crowded by composite networks of actin and microtubules undergoing active myosin-driven rearrangement. Using DDM, we quantify the ensemble dynamics of the DNA, finding the transport properties (subdiffusive, ballistic, etc.) depend on both DNA size and topology. With SMCT, we measure dynamics that transition between subdiffusive and ballistic, and determine how ring and linear DNA molecules are conformationally affected by interactions with the active cytoskeleton networks.   

Live

Melting of double-stranded nucleic acid such as DNA is a fundamental part of several biological processes. Double-stranded nucleic acid in vivo is subject to small tensile forces induced by thermal fluctuations and active processes. Although these forces in general accelerate bond dissociation, recent experimental and theoretical studies suggest a counterintuitive, stabilizing effect of force on the DNA duplex (roll-over effect). To investigate the effect of ~pN axial force on nucleic acid duplex stability, we developed a novel single-molecule force assay, which utilizes the intrinsic bending rigidity of DNA to exert weak pulling forces down to the entropic force limit. Combining this assay with single-molecule FRET, we measured the melting rates of short (~10 nt) nucleic acid duplexes (DNA-DNA or DNA-RNA) in the presence of weak tension. Interestingly, some duplexes melted more slowly with ~pN tension than without it, indicative of the roll-over effect, while other duplexes showed monotonically faster melting with increasing tension. Our findings indicate that the transition state of duplex melting is structurally diverse, depending on the nucleotide composition and sequence, and suggest that genome stability could be locally modulated in a nontrivial manner due to tension.   

Live

DNA strand displacement is a process by which a DNA duplex is invaded by a single-stranded nucleic acid, annealing to one strand and displacing the other. Strand displacement has many applications in DNA nanoengineering and is fundamental to biological processes such as homologous recombination and R-loop formation. Strand displacement has previously been studied in bulk; however, its kinetics is obscured by a slow, bimolecular toehold formation step. Here, we describe a single-molecule FRET assay dubbed “fission” which allows us to study the first passage time of strand displacement directly. The measured displacement time for a 14-bp domain is on average ~30 ms, with as much as ~10-fold variation among different invader sequences tested. In contrast to DNA duplex stability, the measured displacement time is relatively insensitive to monovalent salt concentration. We also show that extending the length of the invading strand slows down invasion. Substituting DNA invaders with RNA, with identical sequence except for a T->U substitution, also affects invasion times. Finally, we fit a one-dimensional random walk model to our data, providing invasion rates for each of the four DNA bases.   

Live

Chromosomes are positioned non-randomly inside the nucleus to coordinate with their transcriptional activity. The molecular mechanisms that dictate the global genome organization and the nuclear localization of individual chromosomes are not fully understood. We introduce a polymer model to study the organization of the diploid human genome: it is data-driven as all parameters can be derived from Hi-C data; it is also a mechanistic model since the energy function is explicitly written out based on a few biologically motivated hypotheses. These two features distinguish the model from existing approaches and make it useful both for reconstructing genome structures and for exploring the principles of genome organization. We carried out extensive validations to show that simulated genome structures reproduce a wide variety of experimental measurements, including chromosome radial positions and spatial distances between homologous pairs. Detailed mechanistic investigations support the importance of both specific inter-chromosomal interactions and centromere clustering for chromosome positioning. We anticipate the polymer model to be a powerful tool for investigating large scale rearrangements in genome structure upon cell differentiation and tumor progression.   

Live

Density estimation on sequence space is a fundamental problem in machine learning that is of great importance in computational biology. Due to the discrete nature and the high dimensionality of sequence space, how best to estimate such densities from a sample of sequences remains unclear. We present a novel solution to this problem based on Bayesian field theory and spectral graph theory. Our method first identifies a one-parameter family of densities with the empirical frequency at one extreme and a maximum entropy (MaxEnt) estimate at the other. Notably, all densities in this family exactly match the marginal statistics that constrain the MaxEnt distribution. The optimal density within this family is then determined by cross validation. We demonstrate this method in two diverse biological contexts, human 5’ splice sites (4⁹ possible RNA sequences) and karyotypes of human cancer (2²² possible karyotypes). In both cases, our method yields density estimates that have richer structure than the corresponding MaxEnt estimates, better predict held-out test data, and enable visualizations that illuminate underlying biological mechanisms. Our method is thus an effective tool for analyzing biological sequence data, as well as other data types of a discrete combinatorial nature.   

Live

We describe a nanofluidic system in which stochastic resonance (SR) could be observed in the motion of single DNA molecules. SR is a nonlinear dynamical phenomenon occurring in bistable systems where a weak signal that is below a sensor’s threshold for detection can be boosted and made detectable with addition of white noise. We fabricated nanofluidic devices with embedded pits that give rise to a bistable landscape for confined DNA molecules. An applied periodic pressure generates a weak periodic force on the DNA that represents a sub-threshold signal. We also developed a technique to control the noise inside a nano-fluidic device using electrokinetic forces. By computing the correlation between the periodic driving signal and the measured hoping of molecules between adjacent wells, the occurrence of SR can be reveled by a peak in that quantity as a function of noise level.   

Live

Mismatched base pairs of DNA recognized by the DNA mismatch repair system are sharply bent, implicating the role of extreme mechanics of mismatches in their recognition and repair. To investigate the extreme bending mechanics of mismatches, we designed a series of 105-bp dsDNA molecules, each with a single base pair mismatch in the middle, and measured their looping and unlooping rates with single-molecule Fluorescent Resonance Energy Transfer (FRET). The measured looping rates varied up to 15-fold and correlated well with their predicted thermodynamic stabilities. The measured unlooping rates, however, showed little correlation with the looping rates, contrary to what would be expected of a harmonically bending chain. This result suggests that the stiffness parameter of a DNA mismatch is dependent on bending angle, reminiscent of buckling or kinking transition. We propose a simple anharmonic model for DNA mismatch bending, which would better serve to explain not only our data but also the bend-weakening seen in recent molecular dynamic simulations. Our study indicates that different mismatches can differentially affect both bending and unbending steps of the mismatch repair pathway, leading to a nontrivial sequence dependence of mismatch repair efficiency.   

Live

A fascinating concept in physics is that many properties of a system are governed by its Hamiltonian, while an interesting direction rising from this concept is to perturb the energy landscape to modulate and/or bias chemical and biochemical systems and reactions by mechanical means. Here, we present our development of this concept of exploiting mechanical energies/forces to amplify the interactions between DNA and inorganic salts or small organic molecules. Due to the central role of DNA, these interactions are essential in various fundamental cellular processes in living systems and involved in many DNA-damage related diseases. Strategies to improve the sensitivity of the existing techniques for studying DNA interactions with other molecules is always appreciated in situations where the interactions are too weak. We developed a method based on perturbing energy landscapes using mechanical energy stored in the bent DNA molecules. With the bent DNA molecules, these interactions were easily visualized and quantified in gel electrophoresis, which were difficult to measure without bending. In addition, the strength of the interactions of DNA with the various salts/molecules were quantified using the modified Hill equation.   

Live

A cylindrical nanopore device is the simplest experimental setup which can be used to determine the barcodes of a DNA by scanning it multiple times through the nanopore using an alternative voltage bias. We use Brownian dynamics simulation on a model coarse-grained dsDNA to estimate the marker (barcode) locations using the dwell time information. These simulation results provide insights, hard to decipher in an actual experiment. The barcodes introduce non-uniformity in the velocity profile of the monomers. We demonstrate that without accounting for the faster moving DNA segments, the barcode distances will be grossly underestimated if deciphered solely on the basis of the dwell time data for the barcodes. We explain the discrepancies using nonequilibrium tension propagation theory (Sakaue, Phys. E, 1996) and provide a recipe for accurate measurements of the barcodes. Our scheme can readily be generlaized to the multi-nanaopore setups, promising a better degree of reliability in barcodes obtained from the next generation nanopore sequencing devices.   

Live

We use Langevin Dynamics (LD) simulations to investigate the single file diffusion (SFD) of flexible linear polymer molecules in a dilute solution confined to a narrow tube. The transition from SFD, where the mean-square displacement scales like〈x²〉∼ t^1/2, to normal diffusion with〈x²〉∼ t, is studied as a function of the parameters that control polymer confinement and "swapping", such as the diameter of the channel, the polymer concentration, and the polymer contour length and radius-of-gyration. We propose a phase diagram describing different diffusion regimes. We also map this problem onto a modified one-dimensional Lattice Random Walk algorithm where a diffusing particle represents a polymer center of mass. In order to model the polymers entanglement and disentanglement processes, we allow several objects to temporarily share the same lattice site and diffuse together. Extensions of our work to polydisperse polymer solutions, one-dimensional electrophoresis, ring polymers, and DNA mapping are discussed.   

Live

We suggest that Molecular Dynamics (MD) simulations often greatly overestimate the binding free energies between pairs of deformable macromolecules, such as protein-RNA binding energies. This is generally attributed to slow collective modes that do not reach thermal equilibrium on simulation time scales. We present a statistical physics theory of this effect based on the irreversible release of elastic deformation energy. We compare the theory with MD simulations of protein-RNA interactions and present a method to obtain binding free energy by the systematic application of constraints. Conventional soft modes, associated with translation, rotation, and low energy elastic deformations can all be suppressed by the application of constraints. We will discuss that this may not eliminate soft modes associated with irreversible "slipping" between different binding configurations of flexible macromolecules.   

Live

RNA binding proteins are fundamental to many cellular processes. Double stranded RNA binding proteins (dsRBPs) in particular are crucial for RNA interference, mRNA elongation, A-to-I editing, host defense, splicing, and a multitude of other important mechanisms. Since dsRBPs require double stranded RNA to bind, their binding affinity depends on the possible secondary structures of the target RNA molecule. Here, we introduce a quantitative model that allows calculation of the effective affinity of dsRBPs to any RNA given a base affinity and the sequence of the RNA, while fully taking into account the entire secondary structure ensemble of the RNA. We implement this within the Vienna RNA folding package while maintaining its O(n3) time complexity. We find that proteins will bind to random sequences with a ~100-fold change in effective binding affinity, simply based on the structural interactions between their double stranded footprint and the rest of the molecule. We also validate our quantitative model by comparing with experimentally determined binding affinities for transactivation response element RNA-binding protein (TRBP).   

On Demand

The synthesis of DNA and RNA requires high precision to ensure cell survival. The replicative enzymes which synthesize DNA and RNA achieve such high precision by implementing multiple error-correction mechanisms. Some of these error-correction mechanisms are not yet fully understood because of the large number of biochemical steps in DNA and RNA synthesis.
We show here that the fluctuations in the error rate and the speed of a replicative enzyme can reveal details of its error-correction mechanisms. For example, replicative enzymes that discirminate between different monomers in the forward reaction rates tends to yield polymers with less error when thermal fluctuations randomly speed up the synthesis. The opposite occurs for enzymes that discriminate between different monomers only in the rejection rates. Error-speed correlations thus provide a feasible way to identify error-correction mechanisms in biopolymer synthesis.   

On Demand

The centrosome is the primary microtubule-organizing center in animal cells. At the start of cell division, the two centrosomes migrate towards the opposite poles of the nucleus. Their positioning is crucial for proper bipolar spindle assembly and for accurate chromosome segregation and cell division. Previous studies have identified many proteins involved in centrosome separation and centrosome-nucleus association; however, the cellular and biophysical mechanisms driving these processes remain ill-understood. Three alternative models have been proposed: pushing forces between antiparallel overlapping microtubules from the two centrosomes, pulling forces from cortically-bound force generators, and pulling forces from nuclear-bound force generators. Here, we combine quantitative microscopy and mathematical modeling and simulation to determine which of these models (if not all) account for centrosome positioning in early C. elegans embryo. We will present experiments where we used laser ablation to sever different microtubules populations and directly test these models. We will also present large-scale simulations of microtubules and their interactions with force-generators on the cell cortex and the nucleus.   

On Demand

Recently experiments have shown very significant spin activity in biological molecules such
as DNA, proteins, oligopeptides, and aminoacids. Such molecules have in common their
chiral structure, time-reversal symmetry, and the absence of magnetic exchange interactions.
The spin activity is then assumed to be due to either the intrinsic spin-orbit (SO) interaction
or SO coupled to the presence of strong local sources of electric fields. Here
we derive an analytical tight-binding Hamiltonian model for oligopeptides that contemplates
both intrinsic SO and Rashba interaction induced by hydrogen bonding. We use lowest order
perturbation theory band-folding scheme and derive the reciprocal space intrinsic and Rashba
type Hamiltonian terms to evaluate the spin activity of the oligopeptide and its dependence on
molecule uniaxial deformations. SO strengths in the tens of meV are found and explicit spin active
deformation potentials. We find a rich interplay between responses to deformations
both to enhance and diminish SO strength that allows for experimental testing of the orbital model.
Hydrogen bonding as the source of spin activity further enhances, coupled to chirality, the
ubiquity of spin effects in biological molecular structures.