Describe taxonomy/pairing DB change

Author: milot-mirdita

README.md

@@ -4,6 +4,10 @@ For details of what was changed in v1.5, see [change log](https://github.com/sok
 
 <p align="center"><img src="https://github.com/sokrypton/ColabFold/raw/main/.github/ColabFold_Marv_Logo.png" height="250"/></p>
 
+```diff
++ 04Aug2025: We changed the taxonomy/pairing files for the UniRef100 database. This might affect multimer predictions. Check [the wiki entry](https://github.com/sokrypton/ColabFold/wiki/MSA-Server-Database-History) for details. 
+```
+
 ### Making Protein folding accessible to all via Google Colab!
 
 | Notebooks                                                                                                                                        | monomers | complexes | mmseqs2 | jackhmmer | templates |
@@ -30,10 +34,9 @@ Check the wiki page [old retired notebooks](https://github.com/sokrypton/ColabFo
   - Yes, but be **CAREFUL**, the bfactor column is populated with pLDDT confidence values (higher = better). Phenix.phaser expects a "real" bfactor, where (lower = better). See [post](https://twitter.com/cheshireminima/status/1423929241675120643) from Claudia Millán.
 - What is the maximum length?
   - Limits depends on free GPU provided by Google-Colab `fingers-crossed`
-  - For GPU: `Tesla T4` or `Tesla P100` with ~16G the max length is ~2000
-  - For GPU: `Tesla K80` with ~12G the max length is ~1000
+  - For GPU: `Tesla T4` with ~16G the max length is ~2000
   - To check what GPU you got, open a new code cell and type `!nvidia-smi`
-- Is it okay to use the MMseqs2 MSA server (`cf.run_mmseqs2`) on a local computer?
+- Is it okay to use the MMseqs2 MSA server on a local computer?
   - You can access the server from a local computer if you queries are serial from a single IP. Please do not use multiple computers to query the server.
 - Where can I download the databases used by ColabFold?
   - The databases are available at [colabfold.mmseqs.com](https://colabfold.mmseqs.com)
@@ -80,7 +83,7 @@ colabfold_batch input_sequences.fasta out_dir
 
 First create a directory for the databases on a disk with sufficient storage (940GB (!)). Depending on where you are, this will take a couple of hours:
 
-Note: [MMseqs2 `71dd32ec43e3ac4dabf111bbc4b124f1c66a85f1` (May 28, 2023)](https://github.com/soedinglab/MMseqs2/archive/71dd32ec43e3ac4dabf111bbc4b124f1c66a85f1.zip) is used to create the databases and perform sequece search in the ColabFold MSA server. Please use this version if you want to obtain the same MSAs as the server.
+Note: [MMseqs2 Release 18](https://github.com/soedinglab/MMseqs2/releases/tag/18-8cc5c) is used to create the databases and perform sequece search in the ColabFold MSA server. Please use this version if you want to obtain the same MSAs as the server.
 
 ```shell
 MMSEQS_NO_INDEX=1 ./setup_databases.sh /path/to/db_folder
@@ -192,7 +195,7 @@ Important: Ensure that the `CUDA_VISIBLE_DEVICES` environment variable is set co
 
 Run searches using the GPU server:
 ```
-colabfold_search /path/to/bin/mmseqs input_sequences.fasta /path/to/db_folder msas --gpu 1 --gpu-server 1
+colabfold_search --mmseqs /path/to/bin/mmseqs input_sequences.fasta /path/to/db_folder msas --gpu 1 --gpu-server 1
 ```
 To stop the server(s) when done:
 ```
@@ -234,72 +237,3 @@ For more details, see [GPU-accelerated search](https://github.com/soedinglab/MMs
   Science (2021) doi: [10.1126/science.abj8754](https://doi.org/10.1126/science.abj8754)
 
 [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.5123296.svg)](https://doi.org/10.5281/zenodo.5123296)
-
------------------
-**OLD Updates**
-```diff
-  31Jul2023: 2023/07/31: The ColabFold MSA server is back to normal
-             It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.
-  27Jul2023: ColabFold MSA server issue:
-             We are using the backup server with old databases
-             (UniRef30 2202/PDB70 220313) starting from ~8:30 AM CEST until we resolve the issue.
-             Resolved on 31Jul2023 ~11:10 CEST.
-  12Jun2023: New databases! UniRef30 updated to 2302 and PDB to 230517.
-             We now use PDB100 instead of PDB70 (see notes in the [main](https://colabfold.com) notebook).
-  12Jun2023: We introduced a new default pairing strategy:
-             Previously, for multimer predictions with more than 2 chains,
-             we only pair if all sequences taxonomically match ("complete" pairing).
-             The new default "greedy" strategy pairs any taxonomically matching subsets.
-  30Apr2023: Amber is working again in our ColabFold Notebook
-  29Apr2023: Amber is not working in our Notebook due to Colab update
-  18Feb2023: v1.5.2 - fixing: fixing memory leak for large proteins
-                    - fixing: --use_dropout (random seed was not changing between recycles)
-  06Feb2023: v1.5.1 - fixing: --save-all/--save-recycles
-  04Feb2023: v1.5.0 - ColabFold updated to use AlphaFold v2.3.1!
-  03Jan2023: The MSA server's faulty hardware from 12/26 was replaced.
-             There were intermittent failures on 12/26 and 1/3. Currently,
-             there are no known issues. Let us know if you experience any.
-  10Oct2022: Bugfix: random_seed was not being used for alphafold-multimer.
-             Same structure was returned regardless of defined seed. This
-             has been fixed!
-  13Jul2022: We have set up a new ColabFold MSA server provided by Korean
-             Bioinformation Center. It provides accelerated MSA generation,
-             we updated the UniRef30 to 2022_02 and PDB/PDB70 to 220313.
-  11Mar2022: We use in default AlphaFold-multimer-v2 weights for complex modeling.
-             We also offer the old complex modes "AlphaFold-ptm" or "AlphaFold-multimer-v1"
-  04Mar2022: ColabFold now uses a much more powerful server for MSAs and searches through the ColabFoldDB instead of BFD/MGnify.
-             Please let us know if you observe any issues.
-  26Jan2022: AlphaFold2_mmseqs2, AlphaFold2_batch and colabfold_batch's multimer complexes predictions are
-             now in default reranked by iptmscore*0.8+ptmscore*0.2 instead of ptmscore
-  16Aug2021: WARNING - MMseqs2 API is undergoing upgrade, you may see error messages.
-  17Aug2021: If you see any errors, please report them.
-  17Aug2021: We are still debugging the MSA generation procedure...
-  20Aug2021: WARNING - MMseqs2 API is undergoing upgrade, you may see error messages.
-             To avoid Google Colab from crashing, for large MSA we did -diff 1000 to get
-             1K most diverse sequences. This caused some large MSA to degrade in quality,
-             as sequences close to query were being merged to single representive.
-             We are working on updating the server (today) to fix this, by making sure
-             that both diverse and sequences close to query are included in the final MSA.
-             We'll post update here when update is complete.
-  21Aug2021  The MSA issues should now be resolved! Please report any errors you see.
-             In short, to reduce MSA size we filter (qsc > 0.8, id > 0.95) and take 3K
-             most diverse sequences at different qid (sequence identity to query) intervals
-             and merge them. More specifically 3K sequences at qid at (0→0.2),(0.2→0.4),
-             (0.4→0.6),(0.6→0.8) and (0.8→1). If you submitted your sequence between
-             16Aug2021 and 20Aug2021, we recommend submitting again for best results!
-  21Aug2021  The use_templates option in AlphaFold2_mmseqs2 is not properly working. We are
-             working on fixing this. If you are not using templates, this does not affect the
-             the results. Other notebooks that do not use_templates are unaffected.
-  21Aug2021  The templates issue is resolved!
-  11Nov2021  [AlphaFold2_mmseqs2] now uses Alphafold-multimer for complex (homo/hetero-oligomer) modeling.
-             Use [AlphaFold2_advanced] notebook for the old complex prediction logic.
-  11Nov2021  ColabFold can be installed locally using pip!
-  14Nov2021  Template based predictions works again in the Alphafold2_mmseqs2 notebook.
-  14Nov2021  WARNING "Single-sequence" mode in AlphaFold2_mmseqs2 and AlphaFold2_batch was broken
-             starting 11Nov2021. The MMseqs2 MSA was being used regardless of selection.
-  14Nov2021  "Single-sequence" mode is now fixed.
-  20Nov2021  WARNING "AMBER" mode in AlphaFold2_mmseqs2 and AlphaFold2_batch was broken
-             starting 11Nov2021. Unrelaxed proteins were returned instead.
-  20Nov2021  "AMBER" is fixed thanks to Kevin Pan
-```
------------------