allow searching against BFVD

Author: jgreener64

README.md

@@ -10,7 +10,7 @@ It provides the `progres` Python package that lets you search structures against
 Searching typically takes 1-2 s and is much faster for multiple queries.
 For the AlphaFold database, initial data loading takes around a minute but subsequent searching takes a tenth of a second per query.
 
-Currently [SCOPe](https://scop.berkeley.edu), [CATH](http://cathdb.info), [ECOD](http://prodata.swmed.edu/ecod), the whole [PDB](https://www.rcsb.org), the [AlphaFold structures for 21 model organisms](https://doi.org/10.1093/nar/gkab1061) and the [AlphaFold database TED domains](https://www.biorxiv.org/content/10.1101/2024.03.18.585509) are provided for searching against.
+Currently [SCOPe](https://scop.berkeley.edu), [CATH](http://cathdb.info), [ECOD](http://prodata.swmed.edu/ecod), the whole [PDB](https://www.rcsb.org), [BFVD](https://bfvd.foldseek.com), the [AlphaFold structures for 21 model organisms](https://doi.org/10.1093/nar/gkab1061) and the [AlphaFold database TED domains](https://www.biorxiv.org/content/10.1101/2024.03.18.585509) are provided for searching against.
 Searching is done by domain but [Chainsaw](https://github.com/JudeWells/chainsaw) can be used to automatically split query structures into domains.
 
 A [web server](https://progres.mrc-lmb.cam.ac.uk) is available to run Progres.
@@ -27,7 +27,7 @@ conda install pytorch-scatter pyg -c pyg
 conda install kimlab::stride
 ```
 3. Run `pip install progres`, which will also install [Biopython](https://biopython.org), [mmtf-python](https://github.com/rcsb/mmtf-python), [einops](https://github.com/arogozhnikov/einops) and [pydantic](https://github.com/pydantic/pydantic) if they are not already present.
-4. The first time you search with the software the trained model and pre-embedded databases (~660 MB) will be downloaded to the package directory from [Zenodo](https://zenodo.org/record/7782088), which requires an internet connection. This can take a few minutes. You can set the environmental variable `PROGRES_DATA_DIR` to change where this data is stored, for example if you cannot write to the package directory. Remember to keep it set the next time you run Progres.
+4. The first time you search with the software the trained model and pre-embedded databases (~850 MB) will be downloaded to the package directory from [Zenodo](https://zenodo.org/record/7782088), which requires an internet connection. This can take a few minutes. You can set the environmental variable `PROGRES_DATA_DIR` to change where this data is stored, for example if you cannot write to the package directory. Remember to keep it set the next time you run Progres.
 5. The first time you search against the AlphaFold database TED domains the pre-embedded database (~33 GB) will be downloaded similarly. This can take a while. Make sure you have enough disk space!
 
 Alternatively, a Docker file is available in the `docker` directory.
@@ -93,6 +93,7 @@ The available pre-embedded databases are:
 | `cath40`  | S40 non-redundant domains from [CATH](http://cathdb.info) 23/11/22                                                                                                                         | 31,884            | 1.38 s                     | 2.79 s                     |
 | `ecod70`  | F70 representative domains from [ECOD](http://prodata.swmed.edu/ecod) develop287                                                                                                           | 71,635            | 1.46 s                     | 3.82 s                     |
 | `pdb100`  | All [PDB](https://www.rcsb.org) protein chains as of 02/08/24 split into domains with Chainsaw                                                                                             | 1,177,152         | 2.90 s                     | 27.3 s                     |
+| `bfvd`    | [Big Fantastic Virus Database (BFVD)](https://bfvd.foldseek.com) structures split into domains with Chainsaw                                                                               | 446,655           | 2.66 s                     | 13.3 s                     |
 | `af21org` | [AlphaFold](https://alphafold.ebi.ac.uk) structures for 21 model organisms split into domains by [CATH-Assign](https://doi.org/10.1038/s42003-023-04488-9)                                 | 338,258           | 2.21 s                     | 11.0 s                     |
 | `afted`   | [AlphaFold database](https://alphafold.ebi.ac.uk) structures split into domains by [TED](https://www.biorxiv.org/content/10.1101/2024.03.18.585509) and clustered at 50% sequence identity | 53,344,209        | 67.7 s                     | 73.1 s                     |
 

bin/progres

@@ -27,7 +27,7 @@ parser_search.add_argument("-l", "--querylist",
     help="text file with one query file path per line")
 parser_search.add_argument("-t", "--targetdb", required=True,
     help=("pre-embedded database to search against, either \"scope95\", \"scope40\", "
-          "\"cath40\", \"ecod70\", \"pdb100\", \"af21org\", \"afted\" or a file path"))
+          "\"cath40\", \"ecod70\", \"pdb100\", \"bfvd\", \"af21org\", \"afted\" or a file path"))
 parser_search.add_argument("-f", "--fileformat",
     choices=["guess", "pdb", "mmcif", "mmtf", "coords"], default="guess",
     help="file format of the query structure(s), by default guessed from the file extension")

progres/progres.py

@@ -31,9 +31,9 @@
 dropout_final = 0.0
 default_minsimilarity = 0.8
 default_maxhits = 100
-pre_embedded_dbs = ["scope95", "scope40", "cath40", "ecod70", "pdb100", "af21org"]
+pre_embedded_dbs = ["scope95", "scope40", "cath40", "ecod70", "pdb100", "bfvd", "af21org"]
 pre_embedded_dbs_faiss = ["afted"]
-zenodo_record = "13365312" # This only needs to change when the trained model or databases change
+zenodo_record = "18245422" # This only needs to change when the trained model or databases change
 trained_model_subdir = "v_0_2_0" # This only needs to change when the trained model changes
 database_subdir      = "v_0_2_1" # This only needs to change when the databases change
 progres_dir       = os.path.dirname(os.path.realpath(__file__))
@@ -471,7 +471,7 @@ def download_data_if_required(download_afted=False):
                       sep="", file=sys.stderr)
                 printed = True
             if not printed:
-                print("Downloading data as first time setup (~660 MB) to ", data_dir,
+                print("Downloading data as first time setup (~850 MB) to ", data_dir,
                       ", internet connection required, this can take a few minutes",
                       sep="", file=sys.stderr)
                 printed = True