A Multiscale Model for Virus Capsid Dynamics

Viruses are infectious agents that can cause epidemics and pandemics. The understanding of virus formation, evolution, stability, and interaction with host cells is of great importance to the scientific community and public health. Typically, a virus complex in association with its aquatic environment poses a fabulous challenge to theoretical description and prediction. In this work, we propose a differential geometry-based multiscale paradigm to model complex biomolecule systems. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum domain of the fluid mechanical description of the aquatic environment from the microscopic discrete domain of the atomistic description of the biomolecule. A multiscale action functional is constructed as a unified framework to derive the governing equations for the dynamics of different scales. We show that the classical Navier-Stokes equation for the fluid dynamics and Newton's equation for the molecular dynamics can be derived from the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows

Addition of flexible linkers to GPU-accelerated coarse-grained simulations of protein-protein docking

Multiprotein complexes are responsible for many vital cellular functions, and understanding their formation has many applications in medical research. Computer simulation has become a valuable tool in the study of biochemical processes, but simulation of large molecular structures such as proteins on a useful scale is computationally expensive. A compromise must be made between the level of detail at which a simulation can be performed, the size of the structures which can be modelled and the time scale of the simulation. Techniques which can be used to reduce the cost of such simulations include the use of coarse-grained models and parallelisation of the code. Parallelisation has recently been made more accessible by the advent of Graphics Processing Units (GPUs), a consumer technology which has become an affordable alternative to more specialised parallel hardware. We extend an existing implementation of a Monte Carlo protein-protein docking simulation using the Kim and Hummer coarse-grained protein model [1] on a heterogeneous GPU-CPU architecture [2]. This implementation has achieved a significant speed-up over previous serial implementations as a result of the efficient parallelisation of its expensive non-bonded potential energy calculation on the GPU. Our contribution is the addition of the optional capability for modelling flexible linkers between rigid domains of a single protein. We implement additional Monte Carlo mutations to allow for movement of residues within linkers, and for movement of domains connected by a linker with respect to each other. We also add potential terms for pseudo-bonds, pseudo-angles and pseudo-torsions between residues to the potential calculation, and include additional residue pairs in the non-bonded potential sum. Our flexible linker code has been tested, validated and benchmarked. We find that the implementation is correct, and that the addition of the linkers does not significantly impact the performance of the simulation. This modification may be used to enable fast simulation of the interaction between component proteins in a multiprotein complex, in configurations which are constrained to preserve particular linkages between the proteins. We demonstrate this utility with a series of simulations of diubiquitin chains, comparing the structure of chains formed through all known linkages between two ubiquitin monomers. We find reasonable agreement between our simulated structures and experimental data on the characteristics of diubiquitin chains in solution

Graphics Processing Unit Accelerated Coarse-Grained Protein-Protein Docking

Graphics processing unit (GPU) architectures are increasingly used for general purpose computing, providing the means to migrate algorithms from the SISD paradigm, synonymous with CPU architectures, to the SIMD paradigm. Generally programmable commodity multi-core hardware can result in significant speed-ups for migrated codes. Because of their computational complexity, molecular simulations in particular stand to benefit from GPU acceleration. Coarse-grained molecular models provide reduced complexity when compared to the traditional, computationally expensive, all-atom models. However, while coarse-grained models are much less computationally expensive than the all-atom approach, the pairwise energy calculations required at each iteration of the algorithm continue to cause a computational bottleneck for a serial implementation. In this work, we describe a GPU implementation of the Kim-Hummer coarse-grained model for protein docking simulations, using a Replica Exchange Monte-Carlo (REMC) method. Our highly parallel implementation vastly increases the size- and time scales accessible to molecular simulation. We describe in detail the complex process of migrating the algorithm to a GPU as well as the effect of various GPU approaches and optimisations on algorithm speed-up. Our benchmarking and profiling shows that the GPU implementation scales very favourably compared to a CPU implementation. Small reference simulations benefit from a modest speedup of between 4 to 10 times. However, large simulations, containing many thousands of residues, benefit from asynchronous GPU acceleration to a far greater degree and exhibit speed-ups of up to 1400 times. We demonstrate the utility of our system on some model problems. We investigate the effects of macromolecular crowding, using a repulsive crowder model, finding our results to agree with those predicted by scaled particle theory. We also perform initial studies into the simulation of viral capsids assembly, demonstrating the crude assembly of capsid pieces into a small fragment. This is the first implementation of REMC docking on a GPU, and the effectuate speed-ups alter the tractability of large scale simulations: simulations that otherwise require months or years can be performed in days or weeks using a GPU