Merge pull request #13 from amorehead/mamba

Switch to `mamba` for dependency management, and document other required installation details
2024-11-14 22:33:58 +00:00 · 2024-03-11 17:04:19 -07:00 · 2024-03-11 17:04:19 -07:00 · 5b5827bbd6
commit 5b5827bbd6
parent 50be55ad7a 1fcd8ec1a9
7 changed files with 436 additions and 23 deletions
--- a/.gitignore
+++ b/.gitignore
@ -13,3 +13,8 @@ __pycache__/
 unit_tests/
 ruff.toml
 */scratch/
+csblast-2.2.3/
+outputs/
+pdb100_2021Mar03/
+RFAA_paper_weights.pt
+SE3nv-20240131.sif
--- a/README.md
+++ b/README.md
@ -20,21 +20,45 @@ RFAA is not accurate for all cases, but produces useful error estimates to allow

 <a id="set-up"></a>
 ### 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
 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
-
 ```
-4. Download sequence databases for MSA and template generation.
+7. Download sequence databases for MSA and template generation.
 ```
 # uniref30 [46G]
 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 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:
 ```
-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:
 - 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:
 ```
-SE3nv-20240131.sif -m rf2aa.run_inference --config-name protein
+python -m rf2aa.run_inference --config-name protein
 ```

 <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. 
 ```
-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>
 ### Predicting Protein Small Molecule Complexes
@ -127,23 +149,24 @@ Here is an example (from `rf2aa/config/inference/protein_sm.yaml`):
 ```
 defaults:
  - base
-
-job_name: 7qxr
+job_name: "3fap"

 protein_inputs:
-  A: 
-    fasta_file: examples/protein/7qxr.fasta
+  A:
+    fasta_file: examples/protein/3fap_A.fasta
+  B: 
+    fasta_file: examples/protein/3fap_B.fasta

 sm_inputs:
-  B:
-    input: examples/small_molecule/NSW_ideal.sdf
+  C:
+    input: examples/small_molecule/ARD_ideal.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. 

 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>
 ### Predicting Higher Order Complexes
@ -172,7 +195,7 @@ sm_inputs:
 ```
 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>
 ### Predicting Covalently Modified Proteins
--- a/environment.yaml
+++ b/environment.yaml
@ -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
+  - fonttools=4.49.0=py310h2372a71_0
+  - freetype=2.12.1=h267a509_2
+  - frozenlist=1.4.1=py310h2372a71_0
+  - 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
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+  - gstreamer=1.22.0=h25f0c4b_2
+  - gstreamer-orc=0.4.38=hd590300_0
+  - gzip=1.13=hd590300_0
+  - 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
+  - idna=3.6=pyhd8ed1ab_0
+  - importlib-metadata=7.0.2=pyha770c72_0
+  - jack=1.9.22=h11f4161_0
+  - 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
+  - libcurand=10.3.5.119=0
+  - libcurl=8.1.2=h409715c_0
+  - libcusolver=11.4.1.48=0
+  - libcusparse=11.7.5.86=0
+  - libdb=6.2.32=h9c3ff4c_0
+  - libdeflate=1.17=h0b41bf4_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=hd590300_2
+  - libevent=2.1.10=h28343ad_4
+  - libexpat=2.6.1=h59595ed_0
+  - libffi=3.4.2=h7f98852_5
+  - libflac=1.4.3=h59595ed_0
+  - 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
+  - libhwloc=2.9.1=hd6dc26d_0
+  - 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
+  - libuuid=2.38.1=h0b41bf4_0
+  - 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
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+  - 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
--- a/input_prep/make_ss.sh
+++ b/input_prep/make_ss.sh
@ -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 $ID.*
--- a/install_dependencies.sh
+++ b/install_dependencies.sh
@ -0,0 +1,22 @@
+#!/bin/bash
+# From: https://github.com/RosettaCommons/RoseTTAFold
+
+# install external program not supported by conda installation
+case "$(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
--- a/make_msa.sh
+++ b/make_msa.sh
@ -8,9 +8,12 @@ out_dir="$2"
 CPU="$3"
 MEM="$4"

-# pipe_dir
-PIPE_DIR="$5"
-DB_TEMPL="$6"
+# template database
+DB_TEMPL="$5"
+
+# current script directory (i.e., pipe directory)
+SCRIPT=`realpath -s $0`
+export PIPE_DIR=`dirname $SCRIPT`

 # sequence databases
 DB_UR30="$PIPE_DIR/uniclust/UniRef30_2021_06"
@ -109,6 +112,7 @@ then
 fi
    
 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

 if [ ! -s $out_dir/t000_.hhr ]
--- a/rf2aa/config/inference/base.yaml
+++ b/rf2aa/config/inference/base.yaml
@ -3,7 +3,7 @@ output_path: ""
 checkpoint_path: RFAA_paper_weights.pt
 database_params:
  sequencedb: ""
-  hhdb: "pdb100_2022Apr19/pdb100_2022Apr19"
+  hhdb: "pdb100_2021Mar03/pdb100_2021Mar03"
  command: make_msa.sh
  num_cpus: 4
  mem: 64