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@@ -32,10 +32,10 @@ Liu, Jun, Hungyu Chen, and Yang Zhang. "A Corporative Language Model for Protein
## Software & Dataset Requirements (for PPLM-Contact)
1. **HH-suite3** for MSA Search: Install [HH-suite3](https://github.com/soedinglab/hh-suite) and set the "hhsuite_dir" parameter in the "pplm_contact/config.py" file.
2. **Uniclust Database**: Download the [Uniclust30 database](http://wwwuser.gwdg.de/~compbiol/uniclust/2021_03/) and set the "UniRef_database" parameter in the "config.py" file.
3. **CCMpred** for DCA: Install [ccmpred](https://github.com/soedinglab/CCMpred), or use the pre-packaged version in the "pplm_contact/external_tools" directory. Set the "ccmpred" parameter in the "config.py" file. You may need to grant permission by running 'chmod +x pplm_contact/external_tools/ccmpred'.
2. **Uniclust Database**: Download the [Uniclust30 database](http://wwwuser.gwdg.de/~compbiol/uniclust/2021_03/) and set the "UniRef_database" parameter in the "pplm_contact/config.py" file.
3. **CCMpred** for DCA: Install [ccmpred](https://github.com/soedinglab/CCMpred), or use the pre-packaged version in the "pplm_contact/external_tools" directory. Set the "ccmpred" parameter in the "pplm_contact/config.py" file. You may need to grant permission by running 'chmod +x pplm_contact/external_tools/ccmpred'.
4. **LoadHHM** for PSSM Calculation: Download [LoadHHM.py](https://github.com/j3xugit/RaptorX-Contact/blob/master/Common/LoadHHM.py) and place the file in the "pplm_contact" directory of the PPLM package, or use the pre-packaged version within the "pplm_contact" directory.
5. **ESM-MSA** for Feature Generation: Install the [ESM package](https://github.com/facebookresearch/esm), or use the pre-packaged version within "pplm_contact/external_tools" directory. Download the pre-trained [ESM-MSA model](https://dl.fbaipublicfiles.com/fair-esm/models/esm_msa1_t12_100M_UR50S.pt) and set the "esm_msa_model" parameter in the "config.py" file.
5. **ESM-MSA** for Feature Generation: Install the [ESM package](https://github.com/facebookresearch/esm), or use the pre-packaged version within "pplm_contact/external_tools" directory. Download the pre-trained [ESM-MSA model](https://dl.fbaipublicfiles.com/fair-esm/models/esm_msa1_t12_100M_UR50S.pt) and set the "esm_msa_model" parameter in the "pplm_contact/config.py" file.
## Download parameters
You can download the pre-trained parameters of PPLM and its downstream models from the following links:<br>