Colabfold_batch connects to mmseqs server despite msa input

[Nuta0]

Expected Behavior

I want to create msas for a batch of heterodimers using colabfold_search and predict the structure for the msas using colabfold_batch without using the mmseqs server for msa generation.

Current Behavior

Colabfold_search is generating msas as expected. At the moment it looks like colabfold_batch is using the server to generate msas again even though I point to the already generate a3m files. I am not sure if it is actually connecting to the server but I do think it is wasting time waiting for something, since it outputs pending.

Steps to Reproduce (for bugs)

Here is the input I use for colabfold_search

module load Miniconda3/22.11.1-1
eval "$(conda shell.bash hook)"
conda activate /data/gpfs/projects/punim1869/shared_bin/localcolabfold/colabfold-conda
module load MMseqs2/15-6f452
colabfold_search input.fasta /data/gpfs/datasets/mmseqs/uniref30_2302 msas

and then colabfold_batch

module load Miniconda3/22.11.1-1
module load CUDA/12.2.0
eval "$(conda shell.bash hook)"
conda activate /data/gpfs/projects/punim1869/shared_bin/localcolabfold/colabfold-conda
colabfold_batch --amber --templates --use-gpu-relax msas predictions

ColabFold Output (for bugs)

Here is are the first few lines of output and log.txt when I run colabfold_batch.

2024-01-26 12:25:04.257622: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-26 12:25:05.750801: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
^M  0%|          | 0/300 [elapsed: 00:00 remaining: ?]^MSUBMIT:   0%|          | 0/300 [elapsed: 00:00 remaining: ?]^MPENDING:   0%|          | 0/300 [elapsed: 00:00 remaining: ?]^M                                                             ^MPENDING:   0%|          | 0/300 [elapsed: 00:00 remaining: ?]^MCOMPLETE:   0%|          | 0/300 [elapsed: 00:09 remaining: ?]^MCOMPLETE: 100%|██████████| 300/300 [elapsed: 00:09 remaining: 00:00]^MCOMPLETE: 100%|██████████| 300/300 [elapsed: 00:14 remaining: 00:00]
^M  0%|          | 0/300 [elapsed: 00:00 remaining: ?]^MSUBMIT:   0%|          | 0/300 [elapsed: 00:00 remaining: ?]^MPENDING:   0%|          | 0/300 [elapsed: 00:01 remaining: ?]

2024-01-26 12:25:00,517 Running colabfold 1.5.5 (941feece178db14c9af1580eefbf4a8fe4e5b5af)
2024-01-26 12:25:05,724 Running on GPU
2024-01-26 12:25:13,220 Found 9 citations for tools or databases
2024-01-26 12:25:13,220 Query 1/100: Heterodimer (length 261)
2024-01-26 12:25:17,176 Sleeping for 8s. Reason: PENDING
2024-01-26 12:26:05,156 Sequence 0 found templates: ['Xxx8']
2024-01-26 12:26:05,156 Sequence 1 found no templates

Context

Providing context helps us come up with a solution and improve our documentation for the future.

Your Environment

[YoshitakaMo]

If you turn on --templates arg, colabfold_batch will try to connect the MSA server to obtain template PDB files. Since the effect of template structure is small for many cases (except limited available MSAs), you can turn off --template.

[Nuta0]

Is there a way of obtaining template PDB files locally? And if not is it feasible to obtain the templates of many predictions (>10000) from the msa server given the resource limitations of the server?

[YoshitakaMo]

Currently, colabfold_search can generate a list of template PDB files together with MSA files. For example,

MMSEQS_PATH="/path/to/your/mmseqs2/for_colabfold"
DATABASE_PATH="/mnt/databases"
INPUTFILE="ras_raf.fasta"
OUTPUTDIR="ras_raf"

colabfold_search \
  --use-env 1 \
  --use-templates 1 \
  --db-load-mode 2 \
  --db2 pdb100_230517 \
  --mmseqs ${MMSEQS_PATH}/bin/mmseqs \
  --threads 4 \
  ${INPUTFILE} \
  ${DATABASE_PATH} \
  ${OUTPUTDIR}

Then, use colabfold_batch with --pdb-hit-file PDBHITFILE, which will be generated by colabfold_search. Note that mmCIF file database (/path/to/pdb_mmcif/mmcif_files) is required in your computer like the original AlphaFold2.

INPUTFILE="RAS_RAF.a3m"
PDBHITFILE="RAS_RAF_pdb100_230517.m8"
LOCALPDBPATH="/path/to/pdb_mmcif/mmcif_files"
RANDOMSEED=0

colabfold_batch \
  --amber \
  --templates \
  --use-gpu-relax \
  --pdb-hit-file ${PDBHITFILE} \
  --local-pdb-path ${LOCALPDBPATH} \
  --random-seed ${RANDOMSEED} \
  ${INPUTFILE} \
  ras_raf

[Nuta0]

That's great. Is there a way of doing this for a batch of proteins with respective .a3m files and .m8 files?

[Nuta0]

I am trying to use a fasta file with multiple complexes as an input for colabfold_search. This is my code:

module load Miniconda3/22.11.1-1
eval "$(conda shell.bash hook)"
conda activate /data/gpfs/projects/punim1869/shared_bin/localcolabfold/colabfold-conda
module load MMseqs2/15-6f452

DATABASE_PATH="/data/gpfs/datasets/mmseqs/uniref30_2302"

colabfold_search \
  --use-env 1 \
  --use-templates 1 \
  --db2 pdb100_230517 \
  --threads 16 \
  input/${input_file} \
  ${DATABASE_PATH} \
  msas

I get this error:

Traceback (most recent call last):
  File "/data/gpfs/projects/punim1869/shared_bin/localcolabfold/colabfold-conda/bin/colabfold_search", line 8, in <module>
    sys.exit(main())
  File "/data/gpfs/projects/punim1869/shared_bin/localcolabfold/colabfold-conda/lib/python3.10/site-packages/colabfold/mmseqs/search.py", line 385, in main
    os.rename(
FileNotFoundError: [Errno 2] No such file or directory: 'msas/pdb100_230517.m8' -> 'msas/Complex_2_pdb100_230517.m8'

Does Colabfold_serach support multiples sequences as an input when generating both .a3m and .m8?

[YoshitakaMo]

Does Colabfold_serach support multiples sequences as an input when generating both .a3m and .m8?

Yes, you can obtain a3m files for multiple input.

Here is my example. I'm using ColabFold 1.5.5 (a00ce1b).

• Input ras_raf.fasta

>RAS_RAF
MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAG
QEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDL
AARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPG
CMSCKCVLS:
PSKTSNTIRVFLPNKQRTVVNVRNGMSLHDCLMKALKVRGLQPECCAVFRLLHEHKGKKARLDWNTDAASLIGEELQVDFL

• command

# I'm using MMseqs2 commit hash 71dd32ec43e3ac4dabf111bbc4b124f1c66a85f1 
colabfold_search \
  --use-env 1 \
  --use-templates 1 \
  --db-load-mode 2 \
  --mmseqs /path/to/your/mmseqs2/bin/mmseqs \ 
  --db2 pdb100_230517 \
  --threads 4 \
  ras_raf.fasta \
  /mnt/databases \
  manual_ras_raf

• /mnt/databases directory

$ ls -lt /mnt/databases

-rw-r--r-- 1 moriwaki staffs   5797891705 May 22  2023 uniref30_2302_db_mapping
-rw-r--r-- 1 moriwaki staffs    667957493 May 22  2023 uniref30_2302_db_taxonomy
-rw-r--r-- 1 moriwaki staffs  64064274015 Jun 13  2023 pdb100_a3m.ffdata
-rw-r--r-- 1 moriwaki staffs      6389810 Jun 13  2023 pdb100_a3m.ffindex
-rw-r--r-- 1 moriwaki staffs  43200163261 Oct  9 17:34 uniref30_2302_db_h
-rw-r--r-- 1 moriwaki staffs   8910693488 Oct  9 17:35 uniref30_2302_db_h.index
-rw-r--r-- 1 moriwaki staffs            4 Oct  9 17:35 uniref30_2302_db_h.dbtype
-rw-r--r-- 1 moriwaki staffs   5787495369 Oct  9 17:36 uniref30_2302_db
-rw-r--r-- 1 moriwaki staffs    879290728 Oct  9 17:36 uniref30_2302_db.index
-rw-r--r-- 1 moriwaki staffs            4 Oct  9 17:36 uniref30_2302_db.dbtype
-rw-r--r-- 1 moriwaki staffs  83036144795 Oct  9 17:57 uniref30_2302_db_seq
-rw-r--r-- 1 moriwaki staffs   8957791292 Oct  9 17:58 uniref30_2302_db_seq.index
-rw-r--r-- 1 moriwaki staffs            4 Oct  9 17:58 uniref30_2302_db_seq.dbtype
-rw-r--r-- 1 moriwaki staffs   8709887243 Oct  9 18:07 uniref30_2302_db_aln
-rw-r--r-- 1 moriwaki staffs    867494002 Oct  9 18:07 uniref30_2302_db_aln.index
-rw-r--r-- 1 moriwaki staffs            4 Oct  9 18:07 uniref30_2302_db_aln.dbtype
lrwxrwxrwx 1 moriwaki staffs           24 Oct  9 18:07 uniref30_2302_db_seq_h.index -> uniref30_2302_db_h.index
lrwxrwxrwx 1 moriwaki staffs           25 Oct  9 18:07 uniref30_2302_db_seq_h.dbtype -> uniref30_2302_db_h.dbtype
lrwxrwxrwx 1 moriwaki staffs           18 Oct  9 18:07 uniref30_2302_db_seq_h -> uniref30_2302_db_h
-rw-r--r-- 1 moriwaki staffs 228709249024 Oct  9 18:20 uniref30_2302_db.idx
-rw-r--r-- 1 moriwaki staffs          506 Oct  9 18:20 uniref30_2302_db.idx.index
-rw-r--r-- 1 moriwaki staffs            4 Oct  9 18:20 uniref30_2302_db.idx.dbtype
lrwxrwxrwx 1 moriwaki staffs           24 Oct  9 18:21 uniref30_2302_db.idx_mapping -> uniref30_2302_db_mapping
lrwxrwxrwx 1 moriwaki staffs           25 Oct  9 18:21 uniref30_2302_db.idx_taxonomy -> uniref30_2302_db_taxonomy
-rw-r--r-- 1 moriwaki staffs            0 Oct  9 18:21 UNIREF30_READY
-rw-r--r-- 1 moriwaki staffs  25108896515 Oct 10 09:07 colabfold_envdb_202108_db_h
-rw-r--r-- 1 moriwaki staffs  18036930897 Oct 10 09:09 colabfold_envdb_202108_db_h.index
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 09:09 colabfold_envdb_202108_db_h.dbtype
-rw-r--r-- 1 moriwaki staffs  26732224605 Oct 10 09:14 colabfold_envdb_202108_db
-rw-r--r-- 1 moriwaki staffs   5260769931 Oct 10 09:15 colabfold_envdb_202108_db.index
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 09:15 colabfold_envdb_202108_db.dbtype
-rw-r--r-- 1 moriwaki staffs  92749953996 Oct 10 09:46 colabfold_envdb_202108_db_seq
-rw-r--r-- 1 moriwaki staffs  18917335740 Oct 10 09:49 colabfold_envdb_202108_db_seq.index
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 09:49 colabfold_envdb_202108_db_seq.dbtype
-rw-r--r-- 1 moriwaki staffs  27929446713 Oct 10 09:57 colabfold_envdb_202108_db_aln
-rw-r--r-- 1 moriwaki staffs   5214433987 Oct 10 09:58 colabfold_envdb_202108_db_aln.index
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 09:58 colabfold_envdb_202108_db_aln.dbtype
-rw-r--r-- 1 moriwaki staffs         1907 Oct 10 11:23 colabfold_envdb_202108_db.idx.index
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 11:23 colabfold_envdb_202108_db.idx.dbtype
lrwxrwxrwx 1 moriwaki staffs           33 Oct 10 13:38 colabfold_envdb_202108_db_seq_h.index -> colabfold_envdb_202108_db_h.index
lrwxrwxrwx 1 moriwaki staffs           34 Oct 10 13:39 colabfold_envdb_202108_db_seq_h.dbtype -> colabfold_envdb_202108_db_h.dbtype
lrwxrwxrwx 1 moriwaki staffs           27 Oct 10 13:39 colabfold_envdb_202108_db_seq_h -> colabfold_envdb_202108_db_h
-rw-r--r-- 1 moriwaki staffs 562358472704 Oct 16 01:28 colabfold_envdb_202108_db.idx
-rw-r--r-- 1 moriwaki staffs            0 Oct 10 13:40 COLABDB_READY
-rw-r--r-- 1 moriwaki staffs           25 Oct 10 13:47 pdb100_230517.source
-rw-r--r-- 1 moriwaki staffs     27989933 Oct 10 13:47 pdb100_230517_h
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 13:47 pdb100_230517_h.dbtype
-rw-r--r-- 1 moriwaki staffs     65092975 Oct 10 13:47 pdb100_230517
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 13:47 pdb100_230517.dbtype
-rw-r--r-- 1 moriwaki staffs      6279753 Oct 10 13:47 pdb100_230517.index
-rw-r--r-- 1 moriwaki staffs      6116273 Oct 10 13:47 pdb100_230517_h.index
-rw-r--r-- 1 moriwaki staffs      5178372 Oct 10 13:47 pdb100_230517.lookup
-rw-r--r-- 1 moriwaki staffs   1443213312 Oct 10 13:47 pdb100_230517.idx
-rw-r--r-- 1 moriwaki staffs          383 Oct 10 13:47 pdb100_230517.idx.index
-rw-r--r-- 1 moriwaki staffs            4 Oct 10 13:47 pdb100_230517.idx.dbtype
-rw-r--r-- 1 moriwaki staffs            0 Oct 10 13:47 PDB_READY
-rw-r--r-- 1 moriwaki staffs            0 Oct 10 13:54 PDB100_READY

Then, I obtained the a3m and m8 files in the manual_ras_raf directory, RAS_RAF_pdb100_230517.m8 contains

101	7kyz_A	0.856	188	26	1	1	188	1	187	3.275E-63	215	167M1I20M
101	2mse_B	0.848	185	28	0	1	185	1	185	1.583E-62	213	185M
101	7tlk_B	0.934	167	11	0	1	167	1	167	4.075E-62	212	167M
101	7t1f_A	0.923	169	13	0	1	169	1	169	4.075E-62	212	169M
...
...
101	6pgo_B	0.804	169	16	2	1	169	1	152	1.592E-46	167	31M7I20M10I101M
101	4m1s_C	0.796	167	15	2	1	167	1	148	2.987E-46	166	28M10I23M9I97M
101	6o62_A	0.299	167	109	3	5	170	8	167	7.677E-46	165	27M6I68M1I15M1D49M
101	4m21_C	0.789	166	15	2	2	167	1	146	6.945E-45	162	27M11I22M9I97M
102	4g3x_B	1.000	77	0	0	5	81	1	77	1.003E-28	110	77M
102	3kud_B	0.986	76	1	0	6	81	1	76	4.892E-28	108	76M
102	3kuc_B	0.973	76	2	0	6	81	1	76	9.221E-28	107	76M
102	1rrb_A	0.986	76	1	0	6	81	1	76	1.266E-27	107	76M
...
...
102	2mse_D	0.578	76	29	1	6	81	1	73	5.603E-22	91	47M3I26M
102	2mse_D	0.578	76	29	1	6	81	1	73	5.603E-22	91	47M3I26M
102	5yxi_A	0.500	74	37	0	5	78	3	76	1.561E-18	81	74M
102	6ntd_B	0.733	75	9	1	5	79	1	64	9.674E-17	76	47M11I17M
102	6ntc_B	0.706	75	10	2	6	80	1	63	9.682E-13	64	17M3I24M9I22M

Note that 101 and 102 represent the first and second sequence in the input fasta file, respectively.

[Nuta0]

Thank you for this example. But how would I use an input file like this?

>complex_a
MFAWVSVSQSYGVIEILKDIMNKVMGIKKKGTNTGITVEDFEQMGEEEVRQHLHDFLRDKKYLVVMDDVWTVDVWRQIHQIFPNVNNGSRILLTTRNMEVARHAEPWIPPHEPHLLNDTHSLELFCRKAFPANQDVPTELEPLSQKLAKR:
MCGGLPLALVVLGGLMSRKDPSYDTWLRVAQSMNWESSGEGQECLGILGLSYNDLPYQLKPCFLYITAFPEDSIIPVSKLARLWIAEGFILEEQRQTMEDTARDWLDELVQRCMIQVVKRSVTRGRVKSIRIHDMLRDFGLLEARKDGFLHVCSTDA
>complex_b
MVVSSHRVAFHDRINEEVAVSSPHLRTLLGSNLILTNAGRFLNGLNLLRVLDLEGARDLKKLPKQMGNMIHLRYLGLRRTGLKRLPSSIGHLLNLQTLDARGTYISWLPKSFWKIRTLRYVYINILAFLSAPIIG:
MDHKNLQALKITWINVDVMDMIRLGGIRFIKNWVTTSDSAEMAYERIFSESFGKSLEKMDSLVSLNMYVKELPKDIFFAHARPLPKLRSLYLGG
>complex_c
MSFQQQQLPDITQFPPNLTKLILISFHLEQDPMPVLEKLPNLRLLELCGAYHGKSMSC:
MSAGGFPRLQHLILEDLYDLEAWRVEVGAMPRLTNLTIRWCGMLKMLPEGLQHVTTVRELKLIDMPREFSDKVRSEDGYKVTHPLHYY

[YoshitakaMo]

I've pushed a fix for this issue, @Nuta0. See #567 .
Please update your (local)ColabFold and try to use CSV format for input.

[Nuta0]

@YoshitakaMo Thank you for fixing this.

I have noticed another related issue, where the templates that are picked at the beginning of the prediction are different when I use colabfold_batch directly compared to when I use colabfold_search and then colabfold_batch.

I use this input:

id,sequence
3kud,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQH:PSKTSNTIRVFLPNKQRTVVNVRNGMSLHDCLMKKLKVRGLQPECCAVFRLLHEHKGKKARLDWNTDAASLIGEELQVDFL
ras,MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPGCMSCKCVLS
1BJP_2,PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR
1BJP_ras,PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPGCMSCKCVLS

When using colabfold_batch --templates --amber --use-gpu-relax input.csv prediction these are the contents of the 1BJP_2_template_domain_names.json file:

{"A": ["3mb2_C", "2fm7_B", "4fdx_A", "3ry0_B", "1bjp_A", "6fps_P", "4faz_C", "7m59_B", "6bgn_C", "1otf_D", "3abf_B", "5clo_C", "6fps_R", "7xuy_A", "5cln_I", "6blm_A", "7puo_F", "2op8_A", "4x1c_F", "6blm_A"], "B": ["3mb2_C", "2fm7_B", "4fdx_A", "3ry0_B", "1bjp_A", "6fps_P", "4faz_C", "7m59_B", "6bgn_C", "1otf_D", "3abf_B", "5clo_C", "6fps_R", "7xuy_A", "5cln_I", "6blm_A", "7puo_F", "2op8_A", "4x1c_F", "6blm_A"]}

However, when doing:

MMSEQS_PATH="/apps/easybuild-2022/easybuild/software/MPI/GCC/11.3.0/OpenMPI/4.1.4/MMseqs2/15-6f452/bin/mmseqs"
DATABASE_PATH="/data/gpfs/datasets/mmseqs/uniref30_2302"
INPUTFILE="input.csv"

DATABASE_PATH=
colabfold_search \
  --use-env 1 \
  --use-templates 1 \
  --db-load-mode 2 \
  --mmseqs ${MMSEQS_PATH} \
  --db2 pdb100_230517 \
  --threads 4 \
  ${INPUTFILE} \
  ${DATABASE_PATH} \
  msas

INPUTFILE="1BJP_2.a3m"
PDBHITFILE="1BJP_2_pdb100_230517.m8"
LOCALPDBPATH="/data/scratch/datasets/alphafold/v2.3.2/pdb_mmcif/mmcif_files"
RANDOMSEED=0

colabfold_batch \
  --amber \
  --templates \
  --use-gpu-relax \
  --pdb-hit-file ${PDBHITFILE} \
  --local-pdb-path ${LOCALPDBPATH} \
  --random-seed ${RANDOMSEED} \
  ${INPUTFILE} \
  prediction

then this is the 1BJP_2_template_domain_names.json file:

{"A": ["4x1c_H", "1bjp_B", "1bjp_A", "1bjp_E", "6fps_N", "6fps_Q", "6fps_P", "7xuy_A", "3ry0_B", "3ry0_A", "2op8_B", "2op8_A", "7puo_F", "7puo_C", "4x1c_G", "7puo_B", "7puo_D", "7puo_E", "7puo_A", "4faz_C"], "B": ["4x1c_H", "1bjp_B", "1bjp_A", "1bjp_E", "6fps_N", "6fps_Q", "6fps_P", "7xuy_A", "3ry0_B", "3ry0_A", "2op8_B", "2op8_A", "7puo_F", "7puo_C", "4x1c_G", "7puo_B", "7puo_D", "7puo_E", "7puo_A", "4faz_C"]}

Clearly the templates are similar between the methods and the resulting predictions are also similar in this instance, but I had cases where the predicted structures were significantly different. Is this intended behaviour?

[Cryptheon]

Hi @YoshitakaMo,

How long does the search usually take? I followed your instructions (https://qiita.com/Ag_smith/items/bfcf94e701f1e6a2aa90) and installed everything on HPC, however, without loading the dataset fully onto RAM. I tested it with a few proteins but the search takes quite a long time (1h+), I assume this is abnormally long.

Do you know if there might be something obvious that could elicit these search times? In my case I ran:

colabfold_search --use-env 1 --use-templates 0 --db-load-mode 2 --mmseqs /projects/0/prjs0859/ml/algorithms/colabfold/mmseqs/bin/mmseqs --threads 8 /projects/0/prjs0859/ml/inputs/alphafold/fastas/7XTB_5.fasta /projects/2/managed_datasets/AlphaFold_mmseqs2/ /projects/0/prjs0859/ml/outputs/msa/

Any input would be greatly apprecited, thanks!

[milot-mirdita]

Long search times are expected for single queries. colabfold_search is intended for larger scale runs with hundreds or thousands of queries.

It still works for single queries but doesn’t scale down well.

[crisdarbellay]

Hello,
I followed the instruction of https://qiita.com/Ag_smith/items/bfcf94e701f1e6a2aa90, but I still find myself with long search time (~1h). I have a huge ram (~750GB), I should be able to reproduce around same speed as the colabfold server right? I have around 5'000 predictions to make. How could I optimize the run and search time?
Thank you for your work!