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Merge pull request #13 from amorehead/mamba

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Switch to `mamba` for dependency management, and document other required installation details
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Rohith Krishna 2024-03-11 17:04:19 -07:00 committed by GitHub
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5
.gitignore vendored
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@ -13,3 +13,8 @@ __pycache__/
unit_tests/ unit_tests/
ruff.toml ruff.toml
*/scratch/ */scratch/
csblast-2.2.3/
outputs/
pdb100_2021Mar03/
RFAA_paper_weights.pt
SE3nv-20240131.sif

61
README.md
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@ -20,21 +20,45 @@ RFAA is not accurate for all cases, but produces useful error estimates to allow
<a id="set-up"></a> <a id="set-up"></a>
### Setup/Installation ### Setup/Installation
1. Clone the package 1. Install Mamba
```
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh # accept all terms and install to the default location
rm Mambaforge-$(uname)-$(uname -m).sh # (optionally) remove installer after using it
source ~/.bashrc # alternatively, one can restart their shell session to achieve the same result
```
2. Clone the package
``` ```
git clone https://github.com/baker-laboratory/RoseTTAFold-All-Atom git clone https://github.com/baker-laboratory/RoseTTAFold-All-Atom
cd RoseTTAFold-All-Atom cd RoseTTAFold-All-Atom
``` ```
2. Download the container used to run RFAA. 3. Create Mamba environment
``` ```
wget http://files.ipd.uw.edu/pub/RF-All-Atom/containers/SE3nv-20240131.sif mamba env create -f environment.yaml
conda activate RFAA # NOTE: one still needs to use `conda` to (de)activate environments
cd rf2aa/SE3Transformer/
pip3 install --no-cache-dir -r requirements.txt
python3 setup.py install
cd ../../
``` ```
3. Download the model weights. 4. Configure signalp6 after downloading a licensed copy of it from https://services.healthtech.dtu.dk/services/SignalP-6.0/
```
# NOTE: (current) version 6.0h is used in this example, which was downloaded to the current working directory using `wget`
signalp6-register signalp-6.0h.fast.tar.gz
# NOTE: once registration is complete, one must rename the "distilled" model weights
mv $CONDA_PREFIX/lib/python3.10/site-packages/signalp/model_weights/distilled_model_signalp6.pt $CONDA_PREFIX/lib/python3.10/site-packages/signalp/model_weights/ensemble_model_signalp6.pt
```
5. Install input preparation dependencies
```
bash install_dependencies.sh
```
6. Download the model weights.
``` ```
wget http://files.ipd.uw.edu/pub/RF-All-Atom/weights/RFAA_paper_weights.pt wget http://files.ipd.uw.edu/pub/RF-All-Atom/weights/RFAA_paper_weights.pt
``` ```
4. Download sequence databases for MSA and template generation. 7. Download sequence databases for MSA and template generation.
``` ```
# uniref30 [46G] # uniref30 [46G]
wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gz wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gz
@ -56,11 +80,9 @@ tar xfz pdb100_2021Mar03.tar.gz
We use a library called Hydra to compose config files for predictions. The actual script that runs the model is in `rf2aa/run_inference.py` and default parameters that were used to train the model are in `rf2aa/config/inference/base.yaml`. We highly suggest using the default parameters since those are closest to the training task for RFAA but we have found that increasing loader_params.MAXCYCLE=10 (default set to 4) gives better results for hard cases (as noted in the paper). We use a library called Hydra to compose config files for predictions. The actual script that runs the model is in `rf2aa/run_inference.py` and default parameters that were used to train the model are in `rf2aa/config/inference/base.yaml`. We highly suggest using the default parameters since those are closest to the training task for RFAA but we have found that increasing loader_params.MAXCYCLE=10 (default set to 4) gives better results for hard cases (as noted in the paper).
We use a container system called apptainers which have very simple syntax. Instead of developing a local conda environment, users can use the apptainer to run the model which has all the dependencies already packaged.
The general way to run the model is as follows: The general way to run the model is as follows:
``` ```
SE3nv-20240131.sif -m rf2aa.run_inference --config-name {your inference config} python -m rf2aa.run_inference --config-name {your inference config}
``` ```
The main inputs into the model are split into: The main inputs into the model are split into:
- protein inputs (protein_inputs) - protein inputs (protein_inputs)
@ -90,7 +112,7 @@ When specifying the fasta file for your protein, you might notice that it is nes
Now to predict the sample monomer structure, run: Now to predict the sample monomer structure, run:
``` ```
SE3nv-20240131.sif -m rf2aa.run_inference --config-name protein python -m rf2aa.run_inference --config-name protein
``` ```
<a id="p-na-complex"></a> <a id="p-na-complex"></a>
@ -118,7 +140,7 @@ This repo currently does not support making RNA MSAs or pairing protein MSAs wit
Now, predict the example protein/NA complex. Now, predict the example protein/NA complex.
``` ```
SE3nv-20240131.sif -m rf2aa.run_inference --config-name nucleic_acid python -m rf2aa.run_inference --config-name nucleic_acid
``` ```
<a id="p-sm-complex"></a> <a id="p-sm-complex"></a>
### Predicting Protein Small Molecule Complexes ### Predicting Protein Small Molecule Complexes
@ -127,23 +149,24 @@ Here is an example (from `rf2aa/config/inference/protein_sm.yaml`):
``` ```
defaults: defaults:
- base - base
job_name: "3fap"
job_name: 7qxr
protein_inputs: protein_inputs:
A: A:
fasta_file: examples/protein/7qxr.fasta fasta_file: examples/protein/3fap_A.fasta
B:
fasta_file: examples/protein/3fap_B.fasta
sm_inputs: sm_inputs:
B: C:
input: examples/small_molecule/NSW_ideal.sdf input: examples/small_molecule/ARD_ideal.sdf
input_type: "sdf" input_type: "sdf"
``` ```
Small molecule inputs are provided as sdf files or smiles strings and users are **required** to provide both an input and an input_type field for every small molecule that they want to provide. Metal ions can also be provided as sdf files or smiles strings. Small molecule inputs are provided as sdf files or smiles strings and users are **required** to provide both an input and an input_type field for every small molecule that they want to provide. Metal ions can also be provided as sdf files or smiles strings.
To predict the example: To predict the example:
``` ```
SE3nv-20240131.sif -m rf2aa.run_inference --config-name protein_sm python -m rf2aa.run_inference --config-name protein_sm
``` ```
<a id="higher-order"></a> <a id="higher-order"></a>
### Predicting Higher Order Complexes ### Predicting Higher Order Complexes
@ -172,7 +195,7 @@ sm_inputs:
``` ```
And to run: And to run:
``` ```
SE3nv-20240131.sif -m rf2aa.run_inference --config-name protein_na_sm python -m rf2aa.run_inference --config-name protein_na_sm
``` ```
<a id="covale"></a> <a id="covale"></a>
### Predicting Covalently Modified Proteins ### Predicting Covalently Modified Proteins

329
environment.yaml Normal file
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@ -0,0 +1,329 @@
name: RFAA
channels:
- predector
- pyg
- bioconda
- pytorch
- nvidia
- biocore
- conda-forge
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_kmp_llvm
- absl-py=2.1.0=pyhd8ed1ab_0
- aiohttp=3.9.3=py310h2372a71_0
- aiosignal=1.3.1=pyhd8ed1ab_0
- alsa-lib=1.2.8=h166bdaf_0
- asttokens=2.4.1=pyhd8ed1ab_0
- astunparse=1.6.3=pyhd8ed1ab_0
- async-timeout=4.0.3=pyhd8ed1ab_0
- attr=2.5.1=h166bdaf_1
- attrs=23.2.0=pyh71513ae_0
- blas=2.121=mkl
- blas-devel=3.9.0=21_linux64_mkl
- blast-legacy=2.2.26=2
- blinker=1.7.0=pyhd8ed1ab_0
- brotli=1.1.0=hd590300_1
- brotli-bin=1.1.0=hd590300_1
- brotli-python=1.1.0=py310hc6cd4ac_1
- bzip2=1.0.8=hd590300_5
- c-ares=1.27.0=hd590300_0
- ca-certificates=2024.2.2=hbcca054_0
- cached-property=1.5.2=hd8ed1ab_1
- cached_property=1.5.2=pyha770c72_1
- cachetools=5.3.3=pyhd8ed1ab_0
- cairo=1.16.0=ha61ee94_1014
- certifi=2024.2.2=pyhd8ed1ab_0
- cffi=1.16.0=py310h2fee648_0
- charset-normalizer=3.3.2=pyhd8ed1ab_0
- click=8.1.7=unix_pyh707e725_0
- colorama=0.4.6=pyhd8ed1ab_0
- contourpy=1.2.0=py310hd41b1e2_0
- cryptography=42.0.2=py310hb8475ec_0
- cuda-cudart=11.8.89=0
- cuda-cupti=11.8.87=0
- cuda-libraries=11.8.0=0
- cuda-nvrtc=11.8.89=0
- cuda-nvtx=11.8.86=0
- cuda-runtime=11.8.0=0
- cuda-version=11.8=h70ddcb2_3
- cudatoolkit=11.8.0=h4ba93d1_13
- cudnn=8.8.0.121=hcdd5f01_4
- cycler=0.12.1=pyhd8ed1ab_0
- dbus=1.13.6=h5008d03_3
- deepdiff=6.7.1=pyhd8ed1ab_0
- dgl=1.1.2=cuda112py310hc641c19_2
- executing=2.0.1=pyhd8ed1ab_0
- expat=2.6.1=h59595ed_0
- ffmpeg=4.3=hf484d3e_0
- fftw=3.3.10=nompi_hc118613_108
- filelock=3.13.1=pyhd8ed1ab_0
- flatbuffers=22.12.06=hcb278e6_2
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
- font-ttf-source-code-pro=2.038=h77eed37_0
- font-ttf-ubuntu=0.83=h77eed37_1
- fontconfig=2.14.2=h14ed4e7_0
- fonts-conda-ecosystem=1=0
- fonts-conda-forge=1=0
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- fsspec=2024.2.0=pyhca7485f_0
- gast=0.4.0=pyh9f0ad1d_0
- gettext=0.21.1=h27087fc_0
- giflib=5.2.1=h0b41bf4_3
- glib=2.78.4=hfc55251_4
- glib-tools=2.78.4=hfc55251_4
- gmp=6.3.0=h59595ed_0
- gmpy2=2.1.2=py310h3ec546c_1
- gnutls=3.6.13=h85f3911_1
- google-auth=2.28.2=pyhca7485f_0
- google-auth-oauthlib=0.4.6=pyhd8ed1ab_0
- google-pasta=0.2.0=pyh8c360ce_0
- graphite2=1.3.13=h58526e2_1001
- grpcio=1.51.1=py310h4a5735c_1
- gst-plugins-base=1.22.0=h4243ec0_2
- gstreamer=1.22.0=h25f0c4b_2
- gstreamer-orc=0.4.38=hd590300_0
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- h5py=3.9.0=nompi_py310hcca72df_101
- harfbuzz=6.0.0=h8e241bc_0
- hdf5=1.14.1=nompi_h4f84152_100
- hhsuite=3.3.0=py310pl5321h068649b_10
- icecream=2.1.3=pyhd8ed1ab_0
- icu=70.1=h27087fc_0
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- importlib-metadata=7.0.2=pyha770c72_0
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- jinja2=3.1.3=pyhd8ed1ab_0
- joblib=1.3.2=pyhd8ed1ab_0
- jpeg=9e=h0b41bf4_3
- keras=2.11.0=pyhd8ed1ab_0
- keras-preprocessing=1.1.2=pyhd8ed1ab_0
- keyutils=1.6.1=h166bdaf_0
- kiwisolver=1.4.5=py310hd41b1e2_1
- krb5=1.20.1=h81ceb04_0
- lame=3.100=h166bdaf_1003
- lcms2=2.15=hfd0df8a_0
- ld_impl_linux-64=2.40=h41732ed_0
- lerc=4.0.0=h27087fc_0
- libabseil=20220623.0=cxx17_h05df665_6
- libaec=1.1.2=h59595ed_1
- libblas=3.9.0=21_linux64_mkl
- libbrotlicommon=1.1.0=hd590300_1
- libbrotlidec=1.1.0=hd590300_1
- libbrotlienc=1.1.0=hd590300_1
- libcap=2.67=he9d0100_0
- libcblas=3.9.0=21_linux64_mkl
- libclang=15.0.7=default_hb11cfb5_4
- libclang13=15.0.7=default_ha2b6cf4_4
- libcublas=11.11.3.6=0
- libcufft=10.9.0.58=0
- libcufile=1.9.0.20=0
- libcups=2.3.3=h36d4200_3
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- libcusolver=11.4.1.48=0
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- libdeflate=1.17=h0b41bf4_0
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- libgcc-ng=13.2.0=h807b86a_5
- libgcrypt=1.10.3=hd590300_0
- libgfortran-ng=13.2.0=h69a702a_5
- libgfortran5=13.2.0=ha4646dd_5
- libglib=2.78.4=hf2295e7_4
- libgomp=13.2.0=h807b86a_5
- libgpg-error=1.48=h71f35ed_0
- libgrpc=1.51.1=h4fad500_1
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- libiconv=1.17=hd590300_2
- liblapack=3.9.0=21_linux64_mkl
- liblapacke=3.9.0=21_linux64_mkl
- libllvm15=15.0.7=hadd5161_1
- libnghttp2=1.58.0=h47da74e_0
- libnpp=11.8.0.86=0
- libnsl=2.0.1=hd590300_0
- libnvjpeg=11.9.0.86=0
- libogg=1.3.4=h7f98852_1
- libopus=1.3.1=h7f98852_1
- libpng=1.6.43=h2797004_0
- libpq=15.3=hbcd7760_1
- libprotobuf=3.21.12=hfc55251_2
- libsndfile=1.2.2=hc60ed4a_1
- libsqlite=3.45.1=h2797004_0
- libssh2=1.11.0=h0841786_0
- libstdcxx-ng=13.2.0=h7e041cc_5
- libsystemd0=253=h8c4010b_1
- libtiff=4.5.0=h6adf6a1_2
- libtool=2.4.7=h27087fc_0
- libudev1=253=h0b41bf4_1
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- libuv=1.48.0=hd590300_0
- libvorbis=1.3.7=h9c3ff4c_0
- libwebp-base=1.3.2=hd590300_0
- libxcb=1.13=h7f98852_1004
- libxcrypt=4.4.36=hd590300_1
- libxkbcommon=1.5.0=h79f4944_1
- libxml2=2.10.3=hca2bb57_4
- libzlib=1.2.13=hd590300_5
- llvm-openmp=17.0.6=h4dfa4b3_0
- lz4-c=1.9.4=hcb278e6_0
- markdown=3.5.2=pyhd8ed1ab_0
- markupsafe=2.1.5=py310h2372a71_0
- matplotlib=3.8.3=py310hff52083_0
- matplotlib-base=3.8.3=py310h62c0568_0
- metis=5.1.1=h59595ed_2
- mkl=2024.0.0=ha957f24_49657
- mkl-devel=2024.0.0=ha770c72_49657
- mkl-include=2024.0.0=ha957f24_49657
- mpc=1.3.1=hfe3b2da_0
- mpfr=4.2.1=h9458935_0
- mpg123=1.32.4=h59595ed_0
- mpmath=1.3.0=pyhd8ed1ab_0
- multidict=6.0.5=py310h2372a71_0
- munkres=1.1.4=pyh9f0ad1d_0
- mysql-common=8.0.33=hf1915f5_6
- mysql-libs=8.0.33=hca2cd23_6
- nccl=2.20.5.1=h6103f9b_0
- ncurses=6.4=h59595ed_2
- nettle=3.6=he412f7d_0
- networkx=3.2.1=pyhd8ed1ab_0
- nspr=4.35=h27087fc_0
- nss=3.98=h1d7d5a4_0
- numpy=1.26.4=py310hb13e2d6_0
- oauthlib=3.2.2=pyhd8ed1ab_0
- openbabel=3.1.1=py310heaf86c6_5
- openh264=2.1.1=h780b84a_0
- openjpeg=2.5.0=hfec8fc6_2
- openssl=3.1.5=hd590300_0
- opt_einsum=3.3.0=pyhc1e730c_2
- ordered-set=4.1.0=pyhd8ed1ab_0
- orjson=3.9.15=py310hcb5633a_0
- packaging=23.2=pyhd8ed1ab_0
- pandas=2.2.1=py310hcc13569_0
- pcre2=10.43=hcad00b1_0
- perl=5.32.1=7_hd590300_perl5
- pillow=9.4.0=py310h023d228_1
- pip=24.0=pyhd8ed1ab_0
- pixman=0.43.2=h59595ed_0
- ply=3.11=py_1
- protobuf=4.21.12=py310heca2aa9_0
- psipred=4.01=1
- psutil=5.9.8=py310h2372a71_0
- pthread-stubs=0.4=h36c2ea0_1001
- pulseaudio=16.1=hcb278e6_3
- pulseaudio-client=16.1=h5195f5e_3
- pulseaudio-daemon=16.1=ha8d29e2_3
- pyasn1=0.5.1=pyhd8ed1ab_0
- pyasn1-modules=0.3.0=pyhd8ed1ab_0
- pycparser=2.21=pyhd8ed1ab_0
- pyg=2.5.0=py310_torch_2.0.0_cu118
- pygments=2.17.2=pyhd8ed1ab_0
- pyjwt=2.8.0=pyhd8ed1ab_1
- pyopenssl=24.0.0=pyhd8ed1ab_0
- pyparsing=3.1.2=pyhd8ed1ab_0
- pyqt=5.15.9=py310h04931ad_5
- pyqt5-sip=12.12.2=py310hc6cd4ac_5
- pysocks=1.7.1=pyha2e5f31_6
- python=3.10.13=hd12c33a_0_cpython
- python-dateutil=2.9.0=pyhd8ed1ab_0
- python-flatbuffers=24.3.6=pyh59ac667_0
- python-tzdata=2024.1=pyhd8ed1ab_0
- python_abi=3.10=4_cp310
- pytorch=2.0.1=py3.10_cuda11.8_cudnn8.7.0_0
- pytorch-cuda=11.8=h7e8668a_5
- pytorch-mutex=1.0=cuda
- pytz=2024.1=pyhd8ed1ab_0
- pyu2f=0.1.5=pyhd8ed1ab_0
- qt-main=5.15.8=h5d23da1_6
- re2=2023.02.01=hcb278e6_0
- readline=8.2=h8228510_1
- requests=2.31.0=pyhd8ed1ab_0
- requests-oauthlib=1.3.1=pyhd8ed1ab_0
- rsa=4.9=pyhd8ed1ab_0
- scikit-learn=1.4.1.post1=py310h1fdf081_0
- scipy=1.12.0=py310hb13e2d6_2
- setuptools=69.1.1=pyhd8ed1ab_0
- signalp6=6.0g=1
- sip=6.7.12=py310hc6cd4ac_0
- six=1.16.0=pyh6c4a22f_0
- snappy=1.1.10=h9fff704_0
- sympy=1.12=pypyh9d50eac_103
- tbb=2021.9.0=hf52228f_0
- tensorboard=2.11.2=pyhd8ed1ab_0
- tensorboard-data-server=0.6.1=py310h600f1e7_4
- tensorboard-plugin-wit=1.8.1=pyhd8ed1ab_0
- tensorflow=2.11.0=cuda112py310he87a039_0
- tensorflow-base=2.11.0=cuda112py310h52da4a5_0
- tensorflow-estimator=2.11.0=cuda112py310h37add04_0
- termcolor=2.4.0=pyhd8ed1ab_0
- threadpoolctl=3.3.0=pyhc1e730c_0
- tk=8.6.13=noxft_h4845f30_101
- toml=0.10.2=pyhd8ed1ab_0
- tomli=2.0.1=pyhd8ed1ab_0
- torchaudio=2.0.2=py310_cu118
- torchtriton=2.0.0=py310
- torchvision=0.15.2=py310_cu118
- tornado=6.4=py310h2372a71_0
- tqdm=4.66.2=pyhd8ed1ab_0
- typing-extensions=4.10.0=hd8ed1ab_0
- typing_extensions=4.10.0=pyha770c72_0
- tzdata=2024a=h0c530f3_0
- unicodedata2=15.1.0=py310h2372a71_0
- unzip=6.0=h7f98852_3
- urllib3=2.2.1=pyhd8ed1ab_0
- werkzeug=3.0.1=pyhd8ed1ab_0
- wheel=0.42.0=pyhd8ed1ab_0
- wrapt=1.16.0=py310h2372a71_0
- xcb-util=0.4.0=h516909a_0
- xcb-util-image=0.4.0=h166bdaf_0
- xcb-util-keysyms=0.4.0=h516909a_0
- xcb-util-renderutil=0.3.9=h166bdaf_0
- xcb-util-wm=0.4.1=h516909a_0
- xkeyboard-config=2.38=h0b41bf4_0
- xorg-kbproto=1.0.7=h7f98852_1002
- xorg-libice=1.1.1=hd590300_0
- xorg-libsm=1.2.4=h7391055_0
- xorg-libx11=1.8.4=h0b41bf4_0
- xorg-libxau=1.0.11=hd590300_0
- xorg-libxdmcp=1.1.3=h7f98852_0
- xorg-libxext=1.3.4=h0b41bf4_2
- xorg-libxrender=0.9.10=h7f98852_1003
- xorg-renderproto=0.11.1=h7f98852_1002
- xorg-xextproto=7.3.0=h0b41bf4_1003
- xorg-xproto=7.0.31=h7f98852_1007
- xz=5.2.6=h166bdaf_0
- yarl=1.9.4=py310h2372a71_0
- zip=3.0=hd590300_3
- zipp=3.17.0=pyhd8ed1ab_0
- zlib=1.2.13=hd590300_5
- zstd=1.5.5=hfc55251_0
- pip:
- antlr4-python3-runtime==4.9.3
- assertpy==1.1
- configparser==6.0.1
- git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769
- docker-pycreds==0.4.0
- e3nn==0.3.3
- gitdb==4.0.11
- gitpython==3.1.42
- hydra-core==1.3.2
- omegaconf==2.3.0
- opt-einsum-fx==0.1.4
- pathtools==0.1.2
- promise==2.3
- pynvml==11.0.0
- pyrsistent==0.20.0
- pyyaml==6.0.1
- sentry-sdk==1.41.0
- shortuuid==1.0.12
- smmap==5.0.1
- subprocess32==3.5.4
- wandb==0.12.0

30
input_prep/make_ss.sh Normal file
View file

@ -0,0 +1,30 @@
#!/bin/bash
# From: https://github.com/RosettaCommons/RoseTTAFold
DATADIR="$CONDA_PREFIX/share/psipred_4.01/data"
echo $DATADIR
i_a3m="$1"
o_ss="$2"
ID=$(basename $i_a3m .a3m).tmp
$PIPE_DIR/csblast-2.2.3/bin/csbuild -i $i_a3m -I a3m -D $PIPE_DIR/csblast-2.2.3/data/K4000.crf -o $ID.chk -O chk
head -n 2 $i_a3m > $ID.fasta
echo $ID.chk > $ID.pn
echo $ID.fasta > $ID.sn
makemat -P $ID
psipred $ID.mtx $DATADIR/weights.dat $DATADIR/weights.dat2 $DATADIR/weights.dat3 > $ID.ss
psipass2 $DATADIR/weights_p2.dat 1 1.0 1.0 $i_a3m.csb.hhblits.ss2 $ID.ss > $ID.horiz
(
echo ">ss_pred"
grep "^Pred" $ID.horiz | awk '{print $2}'
echo ">ss_conf"
grep "^Conf" $ID.horiz | awk '{print $2}'
) | awk '{if(substr($1,1,1)==">") {print "\n"$1} else {printf "%s", $1}} END {print ""}' | sed "1d" > $o_ss
rm ${i_a3m}.csb.hhblits.ss2
rm $ID.*

22
install_dependencies.sh Normal file
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@ -0,0 +1,22 @@
#!/bin/bash
# From: https://github.com/RosettaCommons/RoseTTAFold
# install external program not supported by conda installation
case "$(uname -s)" in
Linux*) platform=linux;;
Darwin*) platform=macosx;;
*) echo "unsupported OS type. exiting"; exit 1
esac
echo "Installing dependencies for ${platform}..."
# the cs-blast platform descriptoin includes the width of memory addresses
# we expect a 64-bit operating system
if [[ ${platform} == "linux" ]]; then
platform=${platform}64
fi
# download cs-blast
echo "Downloading cs-blast ..."
wget http://wwwuser.gwdg.de/~compbiol/data/csblast/releases/csblast-2.2.3_${platform}.tar.gz -O csblast-2.2.3.tar.gz
mkdir -p csblast-2.2.3
tar xf csblast-2.2.3.tar.gz -C csblast-2.2.3 --strip-components=1

10
make_msa.sh
View file

@ -8,9 +8,12 @@ out_dir="$2"
CPU="$3" CPU="$3"
MEM="$4" MEM="$4"
# pipe_dir # template database
PIPE_DIR="$5" DB_TEMPL="$5"
DB_TEMPL="$6"
# current script directory (i.e., pipe directory)
SCRIPT=`realpath -s $0`
export PIPE_DIR=`dirname $SCRIPT`
# sequence databases # sequence databases
DB_UR30="$PIPE_DIR/uniclust/UniRef30_2021_06" DB_UR30="$PIPE_DIR/uniclust/UniRef30_2021_06"
@ -109,6 +112,7 @@ then
fi fi
echo "Running PSIPRED" echo "Running PSIPRED"
mkdir -p $out_dir/log
$PIPE_DIR/input_prep/make_ss.sh $out_dir/t000_.msa0.a3m $out_dir/t000_.ss2 > $out_dir/log/make_ss.stdout 2> $out_dir/log/make_ss.stderr $PIPE_DIR/input_prep/make_ss.sh $out_dir/t000_.msa0.a3m $out_dir/t000_.ss2 > $out_dir/log/make_ss.stdout 2> $out_dir/log/make_ss.stderr
if [ ! -s $out_dir/t000_.hhr ] if [ ! -s $out_dir/t000_.hhr ]

2
rf2aa/config/inference/base.yaml
View file

@ -3,7 +3,7 @@ output_path: ""
checkpoint_path: RFAA_paper_weights.pt checkpoint_path: RFAA_paper_weights.pt
database_params: database_params:
sequencedb: "" sequencedb: ""
hhdb: "pdb100_2022Apr19/pdb100_2022Apr19" hhdb: "pdb100_2021Mar03/pdb100_2021Mar03"
command: make_msa.sh command: make_msa.sh
num_cpus: 4 num_cpus: 4
mem: 64 mem: 64