AutoDock Vina 1.1.2 15 (link) was used to explore the binding mode between LZY228 and Hsp90 (PDB ID: 1YET ). The PDBQT formats of protein and ligand were prepared by AutoDock Tools 1.5.6 package (http://mgltools.scripps.edu) . The search grid of Hsp90 was identified as center_x: 40.695, center_y: −46.782 and center_z: 65.693 with dimensions size_x: 16, size_y: 16, and size_z: 16. For Vina docking, the default parameters were used if it was not mentioned. Then a MD study was performed to revise the docking result using Amber 12 16–18 (link) on Dell Precision T5500 workstation. Minimization of the complex was first performed (500 steps of each steepest descent and conjugate gradient method), then 500 ps of heating, and 50 ps of density equilibration with weak restraints were done and at last 50 ns of MD simulations were carried out using the GPU (NVIDIA® Tesla K20c, Santa Clara, CA, USA) accelerated PMEMD (Particle Mesh Ewald Molecular Dynamics) module.
Tesla k20c
Manufactured by NVIDIA
Sourced in United States
The Tesla K20c is a high-performance GPU accelerator designed for scientific and technical computing. It features the Kepler GK110 GPU architecture and is equipped with 2,496 CUDA cores, 5 GB of GDDR5 memory, and a memory bandwidth of 208 GB/s. The Tesla K20c is capable of delivering up to 3.52 teraflops of single-precision and 1.17 teraflops of double-precision performance.
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Lab products found in correlation
6 protocols using tesla k20c
1
Docking and MD Simulation of LZY228-Hsp90 Complex
2
Spatiotemporal Excitation Pattern Detection
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Both the model of a coupled array of FHN units and the CML model were programmed with Python 3, and the corresponding simulations were carried out on our cluster with 24 Intel Xeon CPUs. The 3D ventricular cell model was programmed with CUDA C++, and the corresponding simulations were carried out on Nvidia Tesla K20c, K80, and GTX 1080 Ti GPU cards. The detailed algorithms for detecting spatiotemporal excitation patterns in this study are described in the S1 Text and S5 Fig .
3
Multiscale Cardiac Electrophysiology Simulation
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This rabbit ventricular myocyte model contains a 3-dimensional coupled network of CRUs and mitochondria. There are 21504 (64×28×12) CRUs and 5376 (64×14×6) mitochondria (see Song et al. [40 (link)] for the details on the arrangement of these networks.).The membrane potential (V) of the cell is described by
where Cm = 1 μF/cm2 is cell membrane capacitance, and Isti is the stimulus pulse with the current density being -80 μA/cm2 and the duration being 0.5 ms. The formulations of the ionic currents are referred to Song et al. [40 (link)].
The Gilespie method was used to simulate the random transitions of LCCs, RyRs, and mPTPs. The Euler method was used to solve the differential equations, and an adaptive time step method was used to compute the AP upstroke [61 (link)] with a time step 0.001 ms. The time step for computation for the rest of the AP was 0.01 ms. The computer model was programmed in CUDA C++ with double precision on Nvidia Tesla K20c and K80 GPU cards.
where Cm = 1 μF/cm2 is cell membrane capacitance, and Isti is the stimulus pulse with the current density being -80 μA/cm2 and the duration being 0.5 ms. The formulations of the ionic currents are referred to Song et al. [40 (link)].
The Gilespie method was used to simulate the random transitions of LCCs, RyRs, and mPTPs. The Euler method was used to solve the differential equations, and an adaptive time step method was used to compute the AP upstroke [61 (link)] with a time step 0.001 ms. The time step for computation for the rest of the AP was 0.01 ms. The computer model was programmed in CUDA C++ with double precision on Nvidia Tesla K20c and K80 GPU cards.
4
GPU-accelerated On-chip Microscopy Imaging
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To speed up the data processing, the image reconstruction was implemented in CUDA C/C++ and run on a GPU server with an Nvidia Tesla K20c computation accelerator. Software libraries based on CUDA such as CUFFT, CUBLAS, and thrust provided by Nvidia were used. Because operations such as the digital backpropagation of optical waves involve FFT/inverse FFT (IFFT) pairs and these operations are repeated hundreds and even thousands of times, using the FFT/IFFT provided by CUFFT resulted in a significant speedup, which was measured to be ~60-fold faster compared to the MATLAB version of our code based on a central processing unit. Basic operations such as real/complex image arithmetic and downsampling/upsampling were all implemented through our own CUDA kernel functions.
The entire FOV (5.215 mm × 3.940 mm) of our on-chip microscope was digitally divided into 12 square tiles (4 columns and 3 rows), each measuring approximately 1.5 mm × 1.5 mm with some spatial overlap. PSR and multi-height phase recovery steps were done sequentially for each tile, and the reconstructed images were digitally stitched together at the end.
The entire FOV (5.215 mm × 3.940 mm) of our on-chip microscope was digitally divided into 12 square tiles (4 columns and 3 rows), each measuring approximately 1.5 mm × 1.5 mm with some spatial overlap. PSR and multi-height phase recovery steps were done sequentially for each tile, and the reconstructed images were digitally stitched together at the end.
5
High-Performance GPU Simulation Workstation
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All the work was done using windows operating system and the simulation was performed by Dell Workstation with 2x10 core xenon CPU, 32GB SCC RAM(4X8), 2X2 TB HDD, 1x5GB Nvidia Tesla K20C, 1X1 GB Nvidia Quadro K600.
6
Personalized Adaptive Radiotherapy Workflow
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In this work, the navigator echo signal was used to trigger both imaging and the simulated treatment beam. For every pCT instance, an integration time ∆T n is calculated by summing the valid gating intervals as depicted in Figure 4. Together with the machine parameters extraction described in section section 2.5, a relation between momentaneous anatomy and machine state can be built up. Every acquired 3D volume is thus valid for one or more individual segment(s).
Subsequently, using a MC based dose calculation (Hissoiny, Ozell, Bouchard & Després 2011) , dose is reconstructed on the respectively valid pCT n using the individual segment from the logged machine data (Luo et al. 2006) . This produced dose maps D n for all acquired anatomies, which were eventually warped back to the original grid using V n (figure 2c).
For MC calculations, a computer with two 12-core (Intel Xeon E5-2695, Intel, Santa Clara, CA, USA) CPUs and a Tesla K20c (NVIDIA, Santa Clara, CA, USA) GPU was used. One segment took around 15s to calculate with a 5% variance.
Subsequently, using a MC based dose calculation (Hissoiny, Ozell, Bouchard & Després 2011) , dose is reconstructed on the respectively valid pCT n using the individual segment from the logged machine data (Luo et al. 2006) . This produced dose maps D n for all acquired anatomies, which were eventually warped back to the original grid using V n (figure 2c).
For MC calculations, a computer with two 12-core (Intel Xeon E5-2695, Intel, Santa Clara, CA, USA) CPUs and a Tesla K20c (NVIDIA, Santa Clara, CA, USA) GPU was used. One segment took around 15s to calculate with a 5% variance.
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