Toward Modular Analysis of Supramolecular Protein Assemblies

A computational model for structural dynamics and reconfiguration of DNA assemblies

Recent advances in constructing a structured DNA assembly whose configuration can be dynamically changed in response to external stimuli have demanded the development of an efficient computational modeling approach to expedite its design process. Here, we present a computational framework capable of analyzing both equilibrium and non-equilibrium dynamics of structured DNA assemblies at the molecular level. The framework employs Langevin dynamics with structural and hydrodynamic finite element models that describe mechanical, electrostatic, base stacking, and hydrodynamic interactions. Equilibrium dynamic analysis for various problems confirms the solution accuracy at a near-atomic resolution, comparable to molecular dynamics simulations and experimental measurements. Furthermore, our model successfully simulates a long-time-scale close-to-open-to-close dynamic reconfiguration of the switch structure in response to changes in ion concentration. We expect that the proposed model will offer a versatile way of designing responsive and reconfigurable DNA machines.

Predicting the Free-Form Shape of Structured DNA Assemblies from Their Lattice-Based Design Blueprint

Structured DNA assemblies have been designed primarily on a three-dimensional lattice because it is easy to arrange and cross-link the helices there. However, when we design free-form structures including wireframes and topologically closed circular objects on a lattice, artificially stretched bonds connecting bases are inevitably and arbitrarily formed. They often lead to nonconvergence or convergence to a wrong configuration in computational analysis to predict the equilibrium shape of the structure when started from its lattice-based configuration, which hinders the design process of free-form structures. Here, we present a computational procedure enabling the shape prediction of free-form structures from their lattice-based design blueprint without any convergence issue. It automatically partitions the structure into substructures and relocates them into a new configuration. When the analysis for calculating the equilibrium shape begins from this configuration, no convergence issue occurs because substructures and stretched bonds connecting them do not overlap and intertwine each other during analysis. Using the proposed approach, we could obtain the free-form shape of a comprehensive set of wireframe and circular structures accurately and quickly. We further demonstrated that it also facilitated a design of wireframe structures with nonstraight edges.

Rapid Computational Analysis of DNA Origami Assemblies at Near-Atomic Resolution

Structural DNA nanotechnology plays an ever-increasing role in advanced biomolecular applications. Here, we present a computational method to analyze structured DNA assemblies rapidly at near-atomic resolution. Both high computational efficiency and molecular-level accuracy are achieved by developing a multiscale analysis framework. The sequence-dependent relative geometry and mechanical properties of DNA motifs are characterized by the all-atom molecular dynamics simulation and incorporated into the structural finite element model successfully without significant loss of atomic information. The proposed method can predict the three-dimensional shape, equilibrium dynamic properties, and mechanical rigidities of monomeric to hierarchically assembled DNA structures at near-atomic resolution without adjusting any model parameters. The calculation takes less than only 15 min for most origami-scale DNA nanostructures consisting of 7000-8000 base-pairs. Hence, it is expected to be highly utilized in an iterative design-analysis-revision process for structured DNA assemblies.

Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities

An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design.

A critical assessment of finite element modeling approach for protein dynamics

Finite element (FE) modeling approach has emerged as an efficient way of calculating the dynamic properties of supramolecular protein structures and their complexes. Its efficiency mainly stems from the fact that the complexity of three-dimensional shape of a molecular surface dominates the computational cost rather than the molecular size or the number of atoms. However, no critical evaluation of the method has been made yet particularly for its sensitivity to the parameters used in model construction. Here, we make a close investigation on the effect of FE model parameters by analyzing 135 representative protein structures whose normal modes calculated using all-atom normal mode analysis are publicly accessible online. Results demonstrate that it is more beneficial to use a contour surface of electron densities as the molecular surface, in general, rather than to employ a solvent excluded surface, and that the solution accuracy is almost insensitive to the model parameters unless we avoid extreme values leading to an inaccurate depiction of the characteristic shapes.