A customer involved in microbial research inquired about a machine for analyzing the binding of proteins and low-molecular-weight compounds using academic-free tools.We are planning to use it for structure prediction, docking simulation, quantum calculation, and MD calculation, and we are considering research using the following software.
Structure prediction | Alphafold2 |
docking | autodock (GNINA) / ucsf-dock |
quantum computing | GAMESS / firefly |
MD | GROMACS 2022.4 |
As for the specs of the desired machine, it is a condition that AlphaFold2 can be built locally and GROMACS can be operated under general conditions.
The main application is MD calculation and quantum calculation, and in the future you are thinking about developing it for in silico screening.
Based on the contents of the consultation, we proposed the following configuration.
【Main Specifications】
CPU | Xeon Gold 6326 (2.90GHz 16 cores) x2 |
memory | 256GB REG ECC |
Storage 1 | 2TB SSD S-ATA |
Storage 2 | 4TB M.2 SSD |
video | Geforce NVIDIA RTX4080 x2 |
network | On Board (1000Base-T X2) |
Housing + power supply | Tower type housing + 1600W |
OS | None |
It is a 3 CPU configuration equipped with 2rd generation Xeon Scalable.
This is a selection considering that some software you are using recommends the Intel API as a compiler.
Of the software you mentioned, except for GAMESS, it is possible to use the GPU in some way, but there are also some parts that are processed by the CPU, so it is necessary to consider the balance between the CPU and the GPU.
AlphaFold2 is software that requires a GPU and refers to a huge database of about 2.2 TB, so storage performance greatly affects performance. The large-capacity SSD installed as the 2nd storage is intended for AlphaFold2 database storage.
AlphaFold2 and other software use GPGPU differently. AlphaFold2 is Deep Learning based on Tensorflow, but other software is scientific calculation with GPU as the core processor.
In scientific calculations, GPGPU may be required to perform double-precision point arithmetic (fp64), but it is very expensive and requires a dedicated chassis.Since fp64 is not required for any of the software that is assumed to be used, and it is possible to use NVIDIA GPUs that support CUDA, we have selected a video card that also serves as a display output for the installed GPU.
The configuration of this case study is based on the conditions given by the customer.
Please feel free to contact us even if you are considering different conditions from what is posted.
■FAQ・What is AlphaFold2?
・What is GROMACS?
・What is GNINA?
・What is UCSF Dock?
・What is GAMESS?
・What is Firefly?
・What is in silico screening? |
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