Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRNA/peptide binding driven by electrostatics -- Insight from bi-directional pulling simulations
167
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
RNA/protein interactions play crucial roles in controlling gene expression. They are becoming important targets for pharmaceutical applications. Due to RNA flexibility and to the strength of electrostatic interactions, standard docking methods are insufficient. We here present a computational method which allows studying the binding of RNA molecules and charged peptides with atomistic, explicit-solvent molecular dynamics. In our method, a suitable estimate of the electrostatic interaction is used as an order parameter (collective variable) which is then accelerated using bi-directional pulling simulations. Since the electrostatic interaction is only used to enhance the sampling, the approximations used to compute it do not affect the final accuracy. The method is employed to characterize the binding of TAR RNA from HIV-1 and a small cyclic peptide. Our simulation protocol allows blindly predicting the binding pocket and pose as well as the binding affinity. The method is general and could be applied to study other electrostatics-driven binding events.
rate research
Read More
In this paper we consider the problem of RNA folding with pseudoknots. We use a graphical representation in which the secondary structures are described by planar diagrams. Pseudoknots are identified as non-planar diagrams. We analyze the non-planar topologies of RNA structures and propose a classification of RNA pseudoknots according to the minimal genus of the surface on which the RNA structure can be embedded. This classification provides a simple and natural way to tackle the problem of RNA folding prediction in presence of pseudoknots. Based on that approach, we describe a Monte Carlo algorithm for the prediction of pseudoknots in an RNA molecule.
The ongoing effort to detect and characterize physical entanglement in biopolymers has so far established that knots are present in many globular proteins and also abound in viral DNA packaged inside bacteriophages. RNA molecules, on the other hand, have not yet been systematically screened for the occurrence of physical knots. We have accordingly undertaken the systematic profiling of the ~6,000 RNA structures present in the protein data bank. The search identified no more than three deeply-knotted RNA molecules. These are ribosomal RNAs solved by cryo-em and consist of about 3,000 nucleotides. Compared to the case of proteins and viral DNA, the observed incidence of RNA knots is therefore practically negligible. This suggests that either evolutionary selection, or thermodynamic and kinetic folding mechanisms act towards minimizing the entanglement of RNA to an extent that is unparalleled by other types of biomolecules. The properties of the three observed RNA knotting patterns provide valuable clues for designing RNA sequences capable of self-tying in a twist-knot fold.
We enumerate the number of RNA contact structures according to their genus, i.e. the topological character of their pseudoknots. By using a recently proposed matrix model formulation for the RNA folding problem, we obtain exact results for the simple case of an RNA molecule with an infinitely flexible backbone, in which any arbitrary pair of bases is allowed. We analyze the distribution of the genus of pseudoknots as a function of the total number of nucleotides along the phosphate-sugar backbone.
Interaction with divalent cations is of paramount importance for RNA structural stability and function. We here report a detailed molecular dynamics study of all the possible binding sites for Mg$^{2+}$ on a RNA duplex, including both direct (inner sphere) and indirect (outer sphere) binding. In order to tackle sampling issues, we develop a modified version of bias-exchange metadynamics which allows us to simultaneously compute affinities with previously unreported statistical accuracy. Results correctly reproduce trends observed in crystallographic databases. Based on this, we simulate a carefully chosen set of models that allows us to quantify the effects of competition with monovalent cations, RNA flexibility, and RNA hybridization. Our simulations reproduce the decrease and increase of Mg$^{2+}$ affinity due to ion competition and hybridization respectively, and predict that RNA flexibility has a site dependent effect. This suggests a non trivial interplay between RNA conformational entropy and divalent cation binding.
Biophysicists are modeling conformations of interphase chromosomes, often basing the strengths of interactions between segments distant on the genetic map on contact frequencies determined experimentally. Here, instead, we develop a fitting-free, minimal model: bivalent red and green transcription factors bind to cognate sites in runs of beads (chromatin) to form molecular bridges stabilizing loops. In the absence of additional explicit forces, molecular dynamic simulations reveal that bound factors spontaneously cluster -- red with red, green with green, but rarely red with green -- to give structures reminiscent of transcription factories. Binding of just two transcription factors (or proteins) to active and inactive regions of human chromosomes yields rosettes, topological domains, and contact maps much like those seen experimentally. This emergent bridging-induced attraction proves to be a robust, simple, and generic force able to organize interphase chromosomes at all scales.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
