Modeling and design by hierarchical natural moves

Abstract

We develop a unique algorithm implemented in the program MOSAICS (Methodologies for Optimization and Sampling in Computational Studies) that is capable of nanoscale modeling without compromising the resolution of interest. This is achieved by modeling with customizable hierarchical degrees of freedom, thereby circumventing major limitations of conventional molecular modeling. With the emergence of RNA-based nanotechnology, large RNAs in all-atom representation are used here to benchmark our algorithm. Our method locates all favorable structural states of a model RNA of significant complexity while improving sampling accuracy and increasing speed many fold over existing all-atom RNA modeling methods. We also modeled the effects of sequence mutations on the structural building blocks of tRNA-based nanotechnology. With its flexibility in choosing arbitrary degrees of freedom as well as in allowing different all-atom energy functions, MOSAICS is an ideal tool to model and design biomolecules of the nanoscale.hierarchical sampling | junctions | molecular simulation | Monte Carlo | nanostructure C omputational modeling is an important aspect of biology and nanotechnology. In silico design and manipulation of nanostructures often precedes experimental validation (1, 2), while effective computational structure prediction of biomolecules paves the way for biomolecular design (3). Most molecular modeling packages are either general but inefficient in modeling large molecular systems or designed to be effective for modeling only specific types of systems (e.g., coarse-grained modeling of DNA as an elastic rod) and are therefore not general purpose. These limitations are mainly a consequence of two major obstacles to computational modeling: (i) the high dimensionality of the systems studied and (ii) the complexity of the potential energy surface guiding the simulation. When attempting to model nanoscale systems at all-atom resolution, the combination of these two factors often leads to intractable complexity.A wide range of methods has been proposed to remove obstacles presented by high dimensionality (4-6) and complex energy surfaces (7-12), but none of these studies has considered both limitations in the same context. The difficulty of such a unified approach is that limitations arising from both obstacles are related in that reducing dimensionality often results in a more complex energy surface. For example, conformational sampling using dihedral angles, which is a common solution to the high dimensionality problem of macromolecular assemblies, often results in a more complex energy surface with energy barriers that could have been easily avoided in Cartesian space. The algorithm [implemented in MOSAICS (13)] proposed here overcomes the problem of high dimensionality without increasing the complexity of the underlying energy surface. This is achieved by sampling with hierarchical variables: Degrees of freedom that introduce large conformational changes are combined with degrees of freedom that allow for local rearrangeme...

Modeling nucleic acids

“…Recently we applied our chain-closure algorithm to complex RNA systems, exploiting the hierarchy present in RNA structure to define different embedded sets of DOFs [36]. The DOFs used varied in size and complexity: at the lowest level nucleotide planes move as rigid bodies with an all-atom representation; some higher level DOFs involve moving helices (base-paired regions of nucleic acids) independently or as sets of two or more helices (see Figure 2).…”

A nucleotide-level coarse-grained model of RNA

“…Methods that combine fully atomistic representation with hierarchical Monte Carlo sampling were used for conformational sampling of larger structures such as tRNAs. 36,37 Moreover, while the forcefields are improving, they are still under development so that different versions can generate different behavior. [38][39][40] In order to access longer time scales relevant to rare events, such as the breaking of base pairs or the formation of large structures, one needs to use a more coarse-grained description.…”

Graph-based sampling for approximating global helical topologies of RNA

“…Though general automated prediction of RNA tertiary (3D) structure from the primary sequence remains elusive, many effective approaches exist for analyzing and describing 3D RNA structures as well as predicting reasonably 3D aspects of small RNAs, ranging from coarse-grained modeling (1) to various structure assembly (2), energy minimization (3), molecular dynamics (4), and other conformational sampling approaches (5,6).…”