In total, 960 simulations were generated on Folding@home: 40 for each of the mutants. Simulations employed the same settings as for NpT production of wild-type apo-SETD8. 99.7% of the generated trajectories (957 trajectories) successfully reached 1 μs each (see Appendix 1—figure 24 for length distribution), resulting in the final aggregate dataset of 0.966 ms and 1,931,849 frames. This amount of simulation time corresponds to ~44 GPU years on an NVIDIA GeForce GTX 980 processor. This trajectory dataset without solvent is available via the Open Science Framework at https://osf.io/2h6p4 . The code used for the generation and analysis of the molecular dynamics data is available via a Github repository at https://github.com/choderalab/SETD8-materials .
Geforce gtx 980
Manufactured by NVIDIA
Sourced in United States
The GeForce GTX 980 is a high-performance graphics processing unit (GPU) produced by NVIDIA. It is designed to provide advanced graphics capabilities for a variety of applications, including gaming, video editing, and scientific computing. The GeForce GTX 980 is based on NVIDIA's Maxwell architecture and features 2,048 CUDA cores, a base clock speed of 1,126 MHz, and 4GB of GDDR5 video memory.
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Lab products found in correlation
4 protocols using geforce gtx 980
1
Molecular Dynamics of SETD8 Mutants
2
Membrane-Embedded Ceramide Metadynamics
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We used the phosphorylated form of SMS (SMS1-P and SMS2-P) embedded in a POPC membrane for metadynamics simulations. We employed GPU accelerated Desmond software on an NVIDIA GeForce GTX 980 graphic card, using Langevin chain thermostat and barostat. A combination of two collective variables (CVs) that describes the ceramide (and hydroxylated analogues) movement in the binding site of the phosphorylated protein was defined. For the distance CVs, the Gaussian width was set to 0.05 Å. The starting height of the Gaussian potential was set to 0.03 kcal/mol, and the Gaussians were deposited every 0.09 ps. The simulations were performed at 300 K and 1.013 bar pressure. RESPA integrator was used with a time step of 2.0 fs. For coulombic interactions, short-range cutoff radius was defined at 9 Å. No positional restraints were specified for any of the atoms. The trajectory frames were recorded at an interval of 20 ps for a simulation time of 30 ns. The simulations were visualized in Maestro suite [4 ]. Analysis of the trajectory was performed using Simulation Event Analysis of Maestro and Visual Molecular Dynamics.
3
Stereoscopic Virtual Reality Perception Experiment
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Stimuli were presented in an Oculus Rift CV1 virtual headset. The CV1 has a field of view that extends approximately ±110° diagonally. The screen has a 1,080 x 1,200-pixel resolution per eye and a 90Hz refresh rate. Stimuli were created in Unity (Version 5.5.2f1, Unity Technologies SF, US) on an Alienware Area-51 R2 with an intel i7 core, and a Nvidia GeForce GTX 980 graphics card. The projection was stereoscopic and was actively linked to the position of the participant’s head. Therefore, distance cues were available from stereoscopic cues and motion parallax.
4
Virtual Reality Spatial Exploration Protocol
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For both tasks we used an Oculus Rift DK2 (Oculus, Menlo Park, CA, USA) head-mounted display (HMD), with a nominal static field of view of up to 100°. We extracted head gaze information from the head tracking sensor from the HMD to update the environment (allowing the exploration of the virtual space) and to compute parameters related to space exploration. We used headphones for higher immersion into the virtual environments, and to deliver the sounds in the dual task. The 3D environments were generated using the game engine Unity (Unity Technologies, San Francisco, USA). Raw data were stored in CSV files. We generated statistics from the CSV files to spreadsheets using Python 2.7 scripts. The tasks ran on a PC with Windows 8.1, Intel i-7 core, 8 GB of RAM and a Nvidia Geforce GTX 980. In addition, for the near space task we used MindMaze’s proprietary motion tracking system, based on a stereo camera and markers for detection of upper body movements in the 3D space. The application included a simple graphical user interface that allowed the experimenter to enter subject data (ID number, gender, age, handedness), and to select and launch the level to be performed.
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