| ## MSA data pipeline | |
| If you download our released wwPDB dataset as in [training.md](./training.md), the mmcif_msa [450G] dir has the following directory structure. | |
| ```bash | |
| ├── seq_to_pdb_index.json [45M] # sequence to integers mapping file | |
| ├── mmcif_msa [450G] # msa files | |
| ├── 0 | |
| ├── uniref100_hits.a3m | |
| ├── mmseqs_other_hits.a3m | |
| ├── 1 | |
| ├── uniref100_hits.a3m | |
| ├── mmseqs_other_hits.a3m | |
| ├── 2 | |
| ├── uniref100_hits.a3m | |
| ├── mmseqs_other_hits.a3m | |
| ... | |
| ├── 157201 | |
| ├── uniref100_hits.a3m | |
| ├── mmseqs_other_hits.a3m | |
| ``` | |
| Each integer in the first-level directory under mmcif_msa (for example, 0, 1, 2, and 157201) represents a unique protein sequence. The key of `seq_to_pdb_index.json` is the unique protein sequence, and the value is the integer corresponding to the first-level subdirectory of mmcif_msa mentioned above. | |
| This document is used to provide the steps to convert the MSA obtained from colabfold into the Protenix training format. | |
| ### Steps to get your own MSA data for training | |
| #### Step1: get input protein sequence | |
| Run the following command: | |
| ```python | |
| python3 scripts/msa/step1-get_prot_seq.py | |
| ``` | |
| you will get outputs in `scripts/msa/data/pdb_seqs` dir. The result dir is as follows, | |
| ```bash | |
| ├── pdb_index_to_seq.json # mapping integers to sequences | |
| ├── seq_to_pdb_index.json # mapping sequences to integers identifiers when saving MSA, This file is required in training for finding local MSA path from sequence | |
| ├── pdb_seq.fasta # Input of MSA | |
| ├── pdb_seq.csv # Intermediate Files | |
| ├── seq_to_pdb_id_entity_id.json # Intermediate Files | |
| ``` | |
| #### Step2: run msa search | |
| We give detailed environment configuration and search commands in | |
| ```python | |
| scripts/msa/step2-get_msa.ipynb | |
| ``` | |
| The searched MSA is in `scripts/msa/data/mmcif_msa_initial`, The result dir is as follows, | |
| ```bash | |
| ├── 0.a3m | |
| ├── 1.a3m | |
| ├── 2.a3m | |
| ├── 3.a3m | |
| ├── pdb70_220313_db.m8 | |
| ├── uniref_tax.m8 # record Taxonomy ID which is used by MSA Pairing | |
| ``` | |
| #### Steps3: MSA Post-Processing | |
| The overall solution is to search the MSA containing taxonomy information only once for the unique sequence, and pair it according to the species information of each MSA. | |
| For MSA Post-Processing, Taxonomy ID from UniRef30 DB is added to MSAs and MSAs is split into `uniref100_hits.a3m` and `mmseqs_other_hits.a3m`, which correspond to `pairing.a3m` and `non_pairing.a3m` in inference stage respectively. | |
| You can run: | |
| ```python | |
| python3 scripts/msa/step3-uniref_add_taxid.py | |
| python3 scripts/msa/step4-split_msa_to_uniref_and_others.py | |
| ``` | |
| The final pairing and non_pairing MSAs in `scripts/msa/data/mmcif_msa` is as follows: | |
| ``` | |
| >query | |
| GPTHRFVQKVEEMVQNHMTYSLQDVGGDANWQLVVEEGEMKVYRREVEENGIVLDPLKATHAVKGVTGHEVCNYFWNVDVRNDWETTIENFHVVETLADNAIIIYQTHKRVWPASQRDVLYLSVIRKIPALTENDPETWIVCNFSVDHDSAPLNNRCVRAKINVAMICQTLVSPPEGNQEISRDNILCKITYVANVNPGGWAPASVLRAVAKREYPKFLKRFTSYVQEKTAGKPILF | |
| >UniRef100_A0A0S7JZT1_188132/ 246 0.897 6.614E-70 2 236 237 97 331 332 | |
| --THRFADKVEEMVQNHMTYSLQDVGGDANWQLVIEEGEMKVYRREVEENGIVLDPLKATHAVKGVTGHEVCHYFWDTDVRNDWETTIDNFNVVETLSDNAIIVYQTHKRVWPASQRDILFLSAIRKILAKNENDPDTWLVCNFSVDHDKAPPTNRCVRAKINVAMICQTLVSPPEGDKEISRDNILCKITYVANVNPGGWAPASVLRAVAKREYPKFLKRFTSYVQEKTAGNPILF | |
| >UniRef100_A0A4W6GBN4_8187/ 246 0.893 9.059E-70 2 236 237 373 607 608 | |
| --THRFANKVEEMVQNHMTYSLQDVGGDANWQLVIEEGEMKVYRREVEENGIVLDPLKATHSVKGVTGHEVCHYFWDTDVRMDWETTIENFNVVEKLSENAIIVYQTHKRVWPASQRDVLYLSAIRKIMATNENDPDTWLVCNFSVDHNNAPPTNRCVRAKINVAMICQTLVSPPEGDKEISRDNILCKITYVANVNPGGWAPASVLRAVAKREYPKFLKRFTSYVQEKTAGKPILF | |
| ``` | |
| ``` | |
| >query | |
| MAEVIRSSAFWRSFPIFEEFDSETLCELSGIASYRKWSAGTVIFQRGDQGDYMIVVVSGRIKLSLFTPQGRELMLRQHEAGALFGEMALLDGQPRSADATAVTAAEGYVIGKKDFLALITQRPKTAEAVIRFLCAQLRDTTDRLETIALYDLNARVARFFLATLRQIHGSEMPQSANLRLTLSQTDIASILGASRPKVNRAILSLEESGAIKRADGIICCNVGRLLSIADPEEDLEHHHHHHHH | |
| >MGYP001165762451 218 0.325 1.019E-59 5 230 244 3 228 230 | |
| -----DKVEFLKGVPLFSELPEAHLQSLGELLIERSYRRGATIFFEGDPGDALYIVRSGIVKISRVAEDGREKTLAFLGKGEPFGEMALIDGGPRSAIAQALEATSLYALHRADFLAALTENPALSLGVIKVLSARLQQANAQLMDLVFRDVRGRVAQALLDLARR-HGVPLTNGRMISVKLTHQEIANLVGTARETVSRTFAELQDSGIIRIeGRNIVLLDAAQLEGYAAG------------- | |
| >A0A160T8V6 218 0.285 1.019E-59 0 227 244 0 229 237 | |
| MPTTRDsnAVQALQVVPFFANLPEDHVAALAKALVPRRFSPGQVIFHLGDPGGLLYLISRGKIKISHTTSDGQEVVLAILGPGDFFGEMALIDDAPRSATAITLEPSETWTLHREEFIQYLTDNPEFALHVLKTLARHIRRLNTQLADIFFLDLPGRLARTLLNLADQ-YGRRAADGTIIDLSLTQTDLAEMTGATRVSINKALGRFRRAGWIQvTGRQVTVLDRAALEAL---------------- | |
| >AP58_3_1055460.scaffolds.fasta_scaffold1119545_2 216 0.304 3.581E-59 10 225 244 5 221 226 | |
| ----------LSRVPLFAELPPERIHELAQSVRRRTYHRGETIFHKGDPGNGLYIIAAGQVKIVLPSEMGEEAMLAVLEGGEFFGELALFDGLPRSATVVAVQNAEVLVLHRDDFMSFVGRNPEVVSALFAALSRRLRDADEMIEDAIFLDVPGRLAKRLLDLAEKHGRAEEKGGVAIDLKLTQQDLAAMVGATRESVNKHLGWMRDHGLIQLDRqRIVILKPDDLR------------------ | |
| ``` | |
| ### Format of MSA | |
| In `uniref100_hits.a3m`(training stage) or `pairing.a3m`(inference stage), the header must starts with the following format, which we use for pairing: | |
| ``` | |
| >UniRef100_{hitname}_{taxonomyid}/ | |
| ``` | |
| we also provide a pipeline of local Colabfold_search to Generate Protenix-Compatible MSAs in [colabfold_compatible_msa.md](./colabfold_compatible_msa.md). | |