Capsid simulations (Multiscale Modeling)
A viral capsid is a system with many proteins, each with many atoms, Thus, when trying to simulate such a system, we have to face many obstacles, mainly concerned in the resolution of the information and computational time required. Efforts have been focused on simplification of the system. To solve this problem, lately has been proposed an approach to model and simulate such systems, called Multiscale Modeling.
This previous method has been successfully applied to simplify the capsid model, but compromises detail and resolution of its behavior and functionality. It is a great method for preliminary results of structural analysis, but, when concerned of specific and local phenomena, like charge inversion or electrostatic double layers, we could be missing useful information within these simplifications.
This is why we would not rely on this approach, although it could be a great alternative if there is no great impact of local phenomena in our first detailed observation, and is a powerful choice for saving computational effort.
Molecular dynamics (MD) emerged as one of the first simulation methods from the pioneering applications to the dynamics of liquids by Alder and Wainwright and by Rahman in the late 1950s and early 1960s. There are two main methods of approaching MD: the classical and quantum.
In the ‘classical’ mechanics approach to MD simulations molecules are treated as classical objects, resembling very much the ‘ball and stick’ model. Atoms correspond to soft balls and elastic sticks (more accurately seen as springs) correspond to bonds. The laws of classical mechanics (Newton’s differential equations of motion) define the dynamics of the system.
Quantum MD simulations represent an important improvement over the classical approach for small scale systems, and they are used in providing information on a number of biological problems. It mainly is involved in finding the electronic wave function and solving their Schrödinger’s equations. However, they require more computational resources, and often require great oversimplification of the system to balance the computational effort, losing detail of results. At present only the classical MD is practical for simulations of biomolecular systems comprising many thousands of atoms over time scales of nanoseconds without oversimplifying it.
In order to study the role of diffusion, electrostatics and particle dynamics, our idea was to simulate the entire system of capsid, solvent, enzyme and ions, with full detail (full atomistic resolution), in a Molecular Dynamics simulation.
The rate of ion movement across the capsid would give us the diffusion constant of ions, and observing the concentration inside and near the enzyme, we could be able to calculate the concentration near the enzyme that would feed the inputs for further enzyme’s kinetic study. Also, we could observe the ion distribution around the enzyme and capsid walls due to electrostatic interaction.
Because of the time limitation range, we wouldn’t be able of simulating the processes of nucleation and enzyme reaction, but we could be able to simulate several small frames of time, covering “photographs” of the whole phenomena.
Complicated and surprising events may occur, but this method shows the correct dynamical evolution of the system for this potential and these boundary conditions. If an accurate description of the atomic is taken into account, it will be a very accurate representation of the real physical system.
Why is it difficult? Computational Limitations
The time limitation is the most severe problem in MD simulations. Relevant time scales for biologically important processes extend over many orders of magnitude. For example, protein folding may take minutes. While this may become feasible in the near future, the nanosecond time scale for biosystems comprising several tens of thousands of atoms is the current estimated domain of standard MD simulations.
Molecular dynamics is only one of a number of computer methods available to molecular modelers. Global optimization techniques, free energy methods and alternative approaches to conformational analysis enable applications to a much broader range of problems.
In our case, not just these problems were of great relevance, but also the right incorporation and optimization of parameters needed for the simulation, to account for the right interaction of the particles, right charge consideration and refinement of computational parameters. These things take a great deal of time, and require many trials and tests of the system, that, in our case, is too big and implies a greater deal of time. Not being affordable for the current length of the project, this approach was postponed.