BioSim Lab @ UB

Interplay between divalent cations (Mg2+ and Ca2+) and single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA), as well as stacking interactions, is important in nucleosome stability and phase separation in nucleic acids. Quantitative techniques accounting for ion–DNA interactions are needed to obtain insights into these and related problems. Toward this end, we created a sequence-dependent computational TIS-ION model that explicitly accounts for monovalent and divalent ions. Simulations of the rigid 24 base-pair (bp) dsDNA and flexible ssDNA sequences, dT30 and dA30, with varying amounts of the divalent cations show that the calculated excess number of ions around the dsDNA and ssDNA agree quantitatively with ion-counting experiments. Using an ensemble of all-atom structures generated from coarse-grained simulations, we calculated the small-angle X-ray scattering profiles, which are in excellent agreement with experiments. Although ion-counting experiments mask the differences between Mg2+ and Ca2+, we find that Mg2+ binds to the minor grooves and phosphate groups, whereas Ca2+ binds specifically to the minor groove. Both Mg2+ and Ca2+ exhibit a tendency to bind to the minor groove of DNA as opposed to the major groove. The dA30 conformations are dominated by stacking interactions, resulting in structures with considerable helical order. The near cancellation of the favorable stacking and unfavorable electrostatic interactions leads to dT30 populating an ensemble of heterogeneous conformations. The successful applications of the TIS-ION model are poised to confront many problems in DNA biophysics.

Repeat RNA sequences self-associate to form condensates. Simulations of a coarse-grained single-interaction site model for (CAG)n (n = 30 and 31) show that the salt-dependent free energy gap, ΔGS, between the ground (perfect hairpin) and the excited state (slipped hairpin (SH) with one CAG overhang) of the monomer for (n even) is the primary factor that determines the rates and yield of self-assembly. For odd n, the free energy (GS) of the ground state, which is an SH, is used to predict the self-association kinetics. As the monovalent salt concentration, CS, increases, ΔGS and GS increase, which decreases the rates of dimer formation. In contrast, ΔGS for shuffled sequences, with the same length and sequence composition as (CAG)31, is larger, which suppresses their propensities to aggregate. Although demonstrated explicitly for (CAG) polymers, the finding of inverse correlation between the free energy gap and RNA aggregation is general.

The preponderance of Intrinsically Disordered Proteins (IDPs) in the eukaryotic proteome, and their ability to interact with each other, proteins, RNA, and DNA for functional purposes have made it important to quantitatively characterize their biophysical properties. Towards this end, we developed the transferable Self-Organized Polymer (SOP-IDP) model in order to calculate the properties of a number of IDPs. The calculated and measured radius of gyration values (Rgs) are in excellent agreement, with a correlation coefficient of 0.96. For AP180 and Epsin, the predicted and values obtained using Fluorescence Correlation Spectroscopy for the hydrodynamic radii (Rhs) are also in quantitative agreement. Strikingly, the calculated SAXS profiles for thirty six IDPs also nearly match the experiments. The dependence of Rg, the mean end-to-end distance (Re), and Rh obey the Flory scaling law, Rα ≈ ααN0.59 (α = g, e, and h), suggesting that globally IDPs behave as polymers in a good solvent. The values of αg, αe, and αh are 0.21 nm, 0.53 nm, and 0.16 nm, respectively. Surprisingly, finite size corrections to scaling, expected on theoretical grounds, for all the three quantities are negligible. Sequence dependencies, masked in ensemble properties, emerge through a fine structure analyses of the conformational ensembles using a hierarchical clustering method. Typically, the ensemble of conformations partition into three distinct clusters, with differing extent of population and structural properties. The subpopulations could dictate phase separation tendencies and association with ligands. Without any adjustments to the three parameters in the SOP-IDP model, we obtained excellent agreement with paramagnetic relaxation enhancement (PRE) measurements for α-synuclein. The transferable SOP-IDP model sets the stage for a number of promising applications, including the study of phase separation in IDPs and interactions with nucleic acids.

Low-complexity nucleotide repeat sequences, which are implicated in several neurological disorders, undergo liquid–liquid phase separation (LLPS) provided the number of repeat units, n, exceeds a critical value. Here, we establish a link between the folding landscapes of the monomers of trinucleotide repeats and their propensity to self-associate. Simulations using a coarse-grained Self-Organized Polymer (SOP) model for (CAG)n repeats in monovalent salt solutions reproduce experimentally measured melting temperatures, which are available only for small n. By extending the simulations to large n, we show that the free-energy gap, ΔGS, between the ground state (GS) and slipped hairpin (SH) states is a predictor of aggregation propensity. The GS for even n is a perfect hairpin (PH), whereas it is a SH when n is odd. The value of ΔGS (zero for odd n) is larger for even n than for odd n. As a result, the rate of dimer formation is slower in (CAG)30 relative to (CAG)31, thus linking ΔGS to RNA–RNA association. The yield of the dimer decreases dramatically, compared to the wild type, in mutant sequences in which the population of the SH decreases substantially. Association between RNA chains is preceded by a transition to the SH even if the GS is a PH. The finding that the excitation spectrum—which depends on the exact sequence, n, and ionic conditions—is a predictor of self-association should also hold for other RNAs (mRNA for example) that undergo LLPS.

Although it is known that RNA undergoes liquid–liquid phase separation, the interplay between the molecular driving forces and the emergent features of the condensates, such as their morphologies and dynamic properties, is not well understood. We introduce a coarse-grained model to simulate phase separation of trinucleotide repeat RNAs, which are implicated in neurological disorders. After establishing that the simulations reproduce key experimental findings, we show that once recruited inside the liquid droplets, the monomers transition from hairpin-like structures to extended states. Interactions between the monomers in the condensates result in the formation of an intricate and dense intermolecular network, which severely restrains the fluctuations and mobilities of the RNAs inside large droplets. In the largest densely packed high-viscosity droplets, the mobility of RNA chains is best characterized by reptation, reminiscent of the dynamics in polymer melts. Our work provides a microscopic framework for understanding liquid–liquid phase separation in RNA, which is not easily discernible in current experiments.