alphafold | Open source code for AlphaFold | Machine Learning library
kandi X-RAY | alphafold Summary
kandi X-RAY | alphafold Summary
This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. We also provide an implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update code. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper and, if applicable, the AlphaFold-Multimer paper. Please also refer to the Supplementary Information for a detailed description of the method. You can use a slightly simplified version of AlphaFold with this Colab notebook or community-supported versions (see below).
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Top functions reviewed by kandi - BETA
- Convert atoms to Torsion angles .
- Computes the difference between two residues .
- Makes atom14 positions .
- Entry point for the tool .
- Extract template features from a PDB object .
- Predict a structure .
- Parse a mmCIF file .
- Align a PDB template sequence to a query sequence .
- Wraps a function applying shard_size .
- Process a single hit sequence .
alphafold Key Features
alphafold Examples and Code Snippets
## Creating a working directory for 3DFI:
export RESULTS=~/Results_3DFI
export FSAOUT=$RESULTS/FASTA
mkdir -p $RESULTS
## Running split_Fasta.pl on provided examples:
split_Fasta.pl \
-f $TDFI_HOME/Examples/FASTA/*.fasta \
-o $FSAOUT
## Creat
sudo dnf install aria2
LICENSE=XXXXX ## replace XXXXX by modeller license
MODELLER=modeller-10.1-1.x86_64.rpm
sudo env KEY_MODELLER=$LICENSE rpm -Uvh $MODELLER
export CCP4=/opt/xtal/CCP4/ccp4-7.1/bin/
export PATH=$PATH:$CCP4
sudo dnf install ucsf-
{
"ensembl_cds": "/path/to/Homo_sapiens.GRCh38.cds.all.fa.gz",
"uniprot_pep": "/path/to/UP000005640_9606.fasta.gz",
"gnomad_variants": "/path/to/gnomad_filtered/gnomad_variant_counts_hg38.json",
"uniprot_to_enst": "/path/to/uniprot_to
Community Discussions
Trending Discussions on alphafold
QUESTION
have to use a .sh script to unpack and prep some databases. The code is the following:
...ANSWER
Answered 2021-Nov-18 at 20:51To answer your first question: why is it repeating? Because you are repeating it in your code:
QUESTION
I am trying to run a tensorflow project and I am encountering memory problems on the university HPC cluster. I have to run a prediction job for hundreds of inputs, with differing lengths. We have GPU nodes with different amounts of vmem, so I am trying to set up the scripts in a way that will not crash in any combination of GPU node - input length.
After searching the net for solutions, I played around with TF_FORCE_UNIFIED_MEMORY, XLA_PYTHON_CLIENT_MEM_FRACTION, XLA_PYTHON_CLIENT_PREALLOCATE, and TF_FORCE_GPU_ALLOW_GROWTH, and also with tensorflow's set_memory_growth
. As I understood, with unified memory, I should be able to use more memory than a GPU has in itself.
This was my final solution (only relevant parts)
...ANSWER
Answered 2021-Aug-29 at 18:26Probably this answer will be useful for you. This nvidia_smi python module have some useful tools like checking the gpu total memory. Here I reproduce the code of the answer I mentioned earlier.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install alphafold
Install Docker. Install NVIDIA Container Toolkit for GPU support. Setup running Docker as a non-root user.
Download genetic databases (see below).
Download model parameters (see below).
Check that AlphaFold will be able to use a GPU by running: docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi The output of this command should show a list of your GPUs. If it doesn't, check if you followed all steps correctly when setting up the NVIDIA Container Toolkit or take a look at the following NVIDIA Docker issue.
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