We have been working with the experimental group of Dr. Weisel (University of Pennsylvania, School of Medicine) to resolve the structural details and the mechanism of force-induced unfolding of fibrinogen monomer \(Fb\) and oligomers \((Fb)_n\) (see Figure). Although the physical properties of fibrin fibers, the major structural component of a blood clot, which control their function in hemostasis and wound healing, are have been fully characterized, the underlying mechanism of their force-driven elongation is not understood. We carry out computational studies of the mechanical properties of fibrin protofibrils on GPUs using the SOP-GPU package, in order to characterize the micromechanics of fibrin at the monomer, oligomer, and fiber level. We use GPU-based computations to speedup the molecular simulations. For example, it takes ~15 days to obtain one trajectory of the mechanical unfolding for the oligomer \((Fb)_3\) of three \(Fb\) repeats on a GPU GeForce GTX 480, using the SOP model implemented on a GPU (SOP-GPU). For comparison, it would take ~12 years of the wall-clock time to complete a single simulation run on a single CPU core of comparable level of technology, using the same SOP model.
The results were published in Structure (2010).
Large-size protein systems unfold through the gradual detachment of two or several subdomain and/or through the simultaneous or sequential unraveling of the various secondary structure elements. This allows us to utilize coarse-grained descriptions of biomolecules to describe the global mechano-chemical unfolding reactions. We utilize the SOP-GPU package to carry out Langevin simulations of fibrinogen monomer \(Fb\) (~2,000 residues) and dimer \((Fb)_2\) (~4,000 residues) using the experimental force-ramp conditions, \(f(t)=r_ft\), where \(r_f=\kappa \nu_f\) is the force-loading rate. We use the experimentally relevant values of the pulling speed \(\nu_f=1\) \(\mu m/s\) and the cantilever spring constant \(\kappa=35\) pN/nm. To obtain a single trajectory of unfolding for \(Fb\), the system dynamics should be propagated numerically over as many as \(5 \times 10^9\) iterations (0.2 seconds of real time). To fully utilize the GPU computational resources, we employ the multiple-runs-per-GPU approach, which allows to run many trajectories for the system under the study concurrently on a single GPU. It takes ~17 day to generate 5 trajectories on a GPU GeForce GTX 295 (NVIDIA). For comparison, it would take ~18 months of wall-clock time to complete a single simulation run on a CPU of a similar level of technology. It turns out that the unfolding micromechanics and the corresponding dynamic signatures in the force spectra change dramatically with increased pulling speed (\(\nu_f=2.5\) and \(25\) \(\mu m/s\)).