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OneScience
Life Sciences
Protein Language Model
Protein Structure Prediction
Variant Effect Prediction
ESM
Model card Files
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  1. LICENSE +21 -0
  2. data/fasta/few_proteins.fasta +6 -0
  3. data/fasta/some_proteins.fasta +30 -0
  4. data/inverse_folding/5YH2.cif +0 -0
  5. data/inverse_folding/5YH2.pdb +0 -0
  6. data/inverse_folding/5YH2_mutated_seqs.fasta +8 -0
  7. data/inverse_folding/example.json +0 -0
  8. data/variant_prediction/BLAT_ECOLX_Ranganathan2015.csv +0 -0
  9. data/variant_prediction/aggregated_rho.csv +17 -0
  10. data/variant_prediction/aggregated_rho_round3.csv +17 -0
  11. data/variant_prediction/rho_pp.csv +575 -0
  12. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.40_.50_plddt_.40_.50.txt +2 -0
  13. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.40_.50_plddt_.50_.60.txt +2 -0
  14. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.50_.60_plddt_.50_.60.txt +3 -0
  15. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.50_.60_plddt_.70_.80.txt +2 -0
  16. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.50_.60_plddt_0_.40.txt +2 -0
  17. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.60_.70_plddt_.50_.60.txt +2 -0
  18. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.60_.70_plddt_.70_.80.txt +2 -0
  19. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.60_.70_plddt_.80_.90.txt +2 -0
  20. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.70_.80_plddt_.60_.70.txt +2 -0
  21. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.70_.80_plddt_.90_1.txt +2 -0
  22. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_.80_.90_plddt_.60_.70.txt +2 -0
  23. weight/esm-main/scripts/atlas/v2023_02/full/esm2_embeddings/tm_0_.40_plddt_0_.40.txt +29 -0
  24. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.40_.50_plddt_0_.40.txt +2 -0
  25. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.50_.60_plddt_.40_.50.txt +2 -0
  26. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.50_.60_plddt_.50_.60.txt +3 -0
  27. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.60_.70_plddt_.70_.80.txt +2 -0
  28. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.60_.70_plddt_0_.40.txt +1 -0
  29. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_.40_.50.txt +1 -0
  30. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_.50_.60.txt +2 -0
  31. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_.60_.70.txt +2 -0
  32. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_.70_.80.txt +13 -0
  33. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_.80_.90.txt +2 -0
  34. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_.90_1.txt +2 -0
  35. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.70_.80_plddt_0_.40.txt +1 -0
  36. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.80_.90_plddt_.60_.70.txt +2 -0
  37. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.80_.90_plddt_.70_.80.txt +2 -0
  38. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.80_.90_plddt_.80_.90.txt +25 -0
  39. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.80_.90_plddt_.90_1.txt +4 -0
  40. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.90_1_plddt_.70_.80.txt +2 -0
  41. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_.90_1_plddt_.90_1.txt +17 -0
  42. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_0_.40_plddt_.40_.50.txt +2 -0
  43. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_0_.40_plddt_.50_.60.txt +2 -0
  44. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_0_.40_plddt_.60_.70.txt +1 -0
  45. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_0_.40_plddt_.70_.80.txt +1 -0
  46. weight/esm-main/scripts/atlas/v2023_02/full/tarballs/tm_0_.40_plddt_0_.40.txt +29 -0
  47. weight/esm-main/tests/test_inverse_folding.py +71 -0
  48. weight/esm-main/tests/test_load_all.py +56 -0
  49. weight/esm-main/tests/test_notebooks.py +64 -0
  50. weight/esm-main/tests/test_readme.py +184 -0
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ MIT License
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+
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+ Copyright (c) Meta Platforms, Inc. and affiliates.
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ copies of the Software, and to permit persons to whom the Software is
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+ furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all
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+ copies or substantial portions of the Software.
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+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ SOFTWARE.
data/fasta/few_proteins.fasta ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ >UniRef50_UPI0003108055
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+ MPADAREYLESKHATRRFDRPAEVAGVVAFLLSDDTSFVIGAGYLVDGGYTALRAARGRLGPRRQAAPAVKLLPNTDSPR
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+ >UniRef50_A0A223SCH7
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+ MGPPRWWKGITGLAAVVHRADPEDKADLYAKMGLYLEYHPETRIVEARIKPRLHDVCESKVSEGGLEPPCP
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+ >UniRef50_A0A090SUK6
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+ MKTPEDRVFRATDEYSDFVMACRYKGNEREFVIASHDKLNEAQVETLTSYLSGEWFKKTYITGIMNDSDGVLSQHEEYGDEVFCQPLDELRVDRYIMMV
data/fasta/some_proteins.fasta ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ >UniRef50_A0A1E3NP16
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+ AVIYYRFRSQKPDHIATIKFDGTGLTVFELKRDIILANNLLHSTDVDIVLYSTEDIQDTKSWGYQNGGSSSAGERELDDDNEVVPRSTTVLVRRTMTPKKNKGNVQRYVAGKPRLQVSGTNSVNKSISLGNNVGGTMNFGDAATNGDEDDMIKKMFSVQDEQWSQQQDVMATATRVDNFRTNVNEPVPEYYICYKCGEKGKHHIKNCPKNNDPNWEGVRVRKTTGIPKSHLKAIENPEDTIRDSNSSGNTTYMVNDEGKYVVAVADTKAWEKYQKTKKGESGGYLNGDVDVDDGELKDPETGKLWKSPVRIPCCNKIFSRKIIEDKLIDSDFTCPSCGKEQIYLDTLVADEELQAKVDEYVKNLSENKNNDGNSPKRRQVNPAGATANTSQLPQIPMMPMPPINMQMPPMNIGMPPFMPFMPMPGMNP
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+ >UniRef50_UPI000836A30F
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+ MTLRTLLALSILALAAAATVQARPGAPPCSPLGLKYQPGACEKWKREHPDVNPDGVVQTVTIVNNSSSVLGGYVTFWHANNEHTDVDLPGVKPGETWTANGSWTVGQAPYYLLYSSFQDSYGDPVTFYAVPISKYPALAKKLPPEPDNCQSNHFRMVFGDGPQYVYEQHSGAVVTGGQTDKNTLCQILGCPTGGSTAGGLNTQNRTTSRSAPQPVYFRSCDP
5
+ >UniRef50_A0A2G8L8Y3
6
+ MAQKRYEENLSPYAELFRSKLIEDFHLLESFDEHGKSDAPKYYSKDFEDPARQDKMMLENPHGLVKFQVYSRKEPGEHFMLVLILSNSIALGLQAEVSESDDPKFAGLKLALDIFDYCSLFLFMVEIILKWIDNFWSFWSDNWNIFDFAVTVGSFVPEIINFFAGDIGGSMVRVIVRNLRVFRILRSLKMVSRFRQVRLIALAIGKAFSAITFIMLLLFTFLYIFAITGIIFFDTYTRSERQDLKYKDSFRSLPRAMITLFQLFTLDQWYKLLNDMWKVMDSMIPLGYIILWICIGSFIFRNVFVGIMVNNFQSIRNDLFEEVKEQEAARQIIQDTEKFNEELSRQEKKLNANRRGTLYQSPTVQPPKPNQPQPSQLAGLDNSETDEQSVSQEDESNTDGQTSLSGTDSYDLLGESSDSLFRRSSDGMIDKDKLSTNWEKTVHDNLTLLTSTPSETLWPRDTLFRYFQLMESLMENLQERQDLQDLAYHSLLQIFDSFDTSA
7
+ >UniRef50_A0A1D5ZRM3
8
+ MPCVAHECHPRLPAANHCRSLSCLGTPAAGWSSGDDDREEDELDTKQVILNEMRNREMRKRSSRCSVDSPTLSGAFAWSFTPLHPRSSIEKVSCTEEEKEAASDSDNESEAFFSVKSFFTRSTSRAATVASSTDMDPPATWEGLRGCEGWPFGLCP
9
+ >UniRef50_UPI0003108055
10
+ MPADAREYLESKHATRRFDRPAEVAGVVAFLLSDDTSFVIGAGYLVDGGYTALRAARGRLGPRRQAAPAVKLLPNTDSPR
11
+ >UniRef50_A0A223SCH7
12
+ MGPPRWWKGITGLAAVVHRADPEDKADLYAKMGLYLEYHPETRIVEARIKPRLHDVCESKVSEGGLEPPCP
13
+ >UniRef50_A0A090SUK6
14
+ MKTPEDRVFRATDEYSDFVMACRYKGNEREFVIASHDKLNEAQVETLTSYLSGEWFKKTYITGIMNDSDGVLSQHEEYGDEVFCQPLDELRVDRYIMMV
15
+ >UniRef50_V4AGU2
16
+ MHLGSIYLMVVLLIYFAYTDDRKERENVDVINPEENLVQDDEQYVGDSTENIEKSGSEEEEEDKEAIEEEEDEEEELNYRYIPEPAQDIVDNGKKYIQVHCSFKQDESLIRHPSNCSRYFVCSYGVVEEMPVCDDGEVFSIQVSECVKKGSENDDCDKLPFDSPPEITRGTQPSLIWHPRHKSPRSQFRQPTTLQMKVPHIELESFTCSATGKVLSHHSENCAWYYNCSAHPDAVMQTFYSGFIMECPYPQLFSTETKQCEDFEDVKCGDRYEPKSPCDYRANHCHETSHCIPCWVRYASCLELPDGLNPWSELEWKPFFVECYKERTVFQGVCDKSAVFSPLTRACETPYSIPRQHGGWRPVCDGRRDGIYADEYGRCDIYYVCKGYIFTGFFRCEKGEMFNPVISICQKPEAVPYPCGDLEMPNICESSLNGYHLDMFGRCTHYFECKDQQLEGISMCPSGIFNPELQICESSRDQPKPCGNLTNLCTHKNDGFHSDENDCTKVFQCERGLTMTSYDCSGSVRTECDVCNTPTECNDKPNGLYPNLKEGVGYYYDCVRSQIQNHYKCDKEKGGPIFNPVKQRCFYPEDLCKEVFSLKIAW
17
+ >UniRef50_R5PKX9
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+ MQGGEQDVFEHRQVREKVIALKNHADAAAQGAAEFERLSFEQDVAALDGFETDQAAQKRGFAAARRPENHRDFFVVKREVDAVENHSVAELLYETARFQNNVIGHFYAFHFFSRALAASDTGQHARK
19
+ >UniRef50_UPI0003674933
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+ MKKKEDILNLEHTLLPWMGRTMKVLDYFIGDFLNLKGIELTKVQWILLKKLNEQNGQPQQNLAFLTNRDKASLARLITTMEKKKLVERIPSKIDGRINHIFITKHGCEILQKSAPVIEKVVGCIQEGISPEEIETVIKVMQKVNNNINRASN
21
+ >UniRef50_E9K9Y4
22
+ MADNVLMAYHIVHDPDERAKHVLNTKKLYKWRITEKTKGTPVVGNVALVQTQFAKRTPVMIYATKEVANDLSDLQPVKVFTNNRDQETVNQTFDDLMR
23
+ >UniRef50_A0A2C9LWN7
24
+ MGKITGLLFFLFTSVRVTPSMKNTVNDIYRVRKDVLTKYRNTEEYDLGQRKGPLKQMDTRETRTPYGHFNSDTAFISPKNILKATRPLAFHLGNLKNQTSPCSTWNLTVDCSYKSLDHIESSWFPSNTTVLLLNNNKLVTLHNETFAQLTNLTRLDLSSNDIRRIDAGAFQGLHNLQELNLHMHCCNFTDHYSLESVFAPLRNLRILNAMHNSDVGVLTYSYTFLTRLPLLQSLSIDFDLDTLYCGPEFNDLKNLTFLQFSGQVMYIDDRSFQNVAQLKNLSMDHLSNINNISHNAFKPLSNLKVLTMYHVLLYVQEILSLLEPFQGRNMTEITLDTTTRTLTQVNPTRNGILTNHDTKYLMNICLESFTLIDNRIFYIKPDAVQNIYTWKKCLMHLYIASNPIQGNNFALIRLFTLDNLKSFTFINMFRACHEFQPFPQSSPPATRNVASSSISSQNNHYQKDTTSNQQQMIRHSPFSPYLDMIDENPYQLNIPNYIFISPSLQYMNFQRLVMSQSFEYHFILVGAQNVTSLDISDSGFYRFNGLMEGVSAIKTLIISGNDVSVLSVSFFDTFVSLENFAISSCKLDRDFISLNSRRIFQNHTRLQELDISSNSLNYLSQNTFSYNNRLMWLNMSGNQFKDIPFDLTNTPELQFLDIRFNSLTTIDETTAQQMDHLVTKSGKLEILLEGNVLSCSCSDLSFMRWMRMTLVTFDQNGNFTCMNTDGERKYTLDYSNLDSLWRECWGSFFLYFALIMLCLYCIGVFAVFITMRNKNFIVSFFLQLFGGFKLHSRRDYPVGVYIGYSDKDYQFPCKELRSFIESSLKLKTFLIDRDLIASVDKASGIIEALNASWRILLVCSKSFLKEDDWSMFTMRSAIYTQTPANPARVVVLVHKDCLPLLPPALLSSVNDENICAVSEWAMNYEMMQMLTTRLH
25
+ >UniRef50_Q9REE6
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+ MSLRGRELLTSEERLELVRIPEDISEQELGRNFTLSNFDLELIKNRRRDYNRLGFAVQLCVLRFPGWSLNDAEPIPKKVLQHLARQLHVDPDCFSLYSSREA
27
+ >UniRef50_A0A226D4M8
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+ MQKVINFPIWRRYYFSECGNCNDFRPDGVKKVHPPQEKSRTMDDEILAAPEVTIPFEPSDPSEVVVNLISSEEEDDDDVIQIVEEKSVDKAERQRRRQKKKDLWAARKLQRNKGQNVPLQTAWQRGPRPQETSSFPTPPQQSGGAQQKPLSPILISTAGSPNTSGAPTPANVSQQPTPTPSFVHTTASTSTHPEISLNINSDLALLIRLGPDGRPILTRVENVEQNNTSTSTPTTTRKLPPAPPPPKISFDTQTGESLLNGELITRPIIDITTDSPPSITHAATVSPQTSRTSGPPTLSPISPPPRTTQSNPAHPPPRYEPRKSRHPPRDPLASSSSSSSSSPSPPPTSRARHTSSANIPPPLEPLFLNTTQLIHLIKTCRKCEKSFPTRCDGVIHQKKEHNRKHCPVCFLTLSRHGNTYKDHLNMYHALEGDKEMVVCPFCAVEHSFDGLYNHIGRSHLIPVESKGESEHEVIFSVAPSPQSNGHQTRSVTQGNTPPKNDTNSPPNLEKRRPGPASKTRKTTNDPVPSTSRTGLICHKYVPDVETPPSKAGRNFESRAGRNFESRNVYPPATNRLNPPRNKSPPRNKNLPRNKSPPRNKSLPRNKTPPPSSSRSSSSRSVSNNLRRKNPTPPPPTQPPPKKVAKPDEAGINEKIQAAIKAVNARVHIERSVHHPNNRSDREIPSTSRTVTSRHKVPTSKTSGNTSVRKDPSPPPPLQTPPKTTNLELSKVQKARIVEDVRSIVRDVRITRVLDDGEVPSTSRAVEEEKKKEEKKNETRARLPRSSRVGERSSGYFQMAEGIDFSPENPTPRSKDLKAIHISKLNLIYQQLKCYPDTSVIANVAKECGVEIPVVAKWFTKKHMEYCQKTQQRKRKRKPPELR
29
+ >UniRef50_UPI000B82D8F0
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+ MGGLHLIELRNVNIEFDKKLIEDGTIKIYDGKITAIIGESGSGKTSLLYLLGLISSNHRYLYSFDDVTLDLSNDFEMSRIRKQKIGYIFQDNNLVENLTIFENIRLSATIAGINITDKEIKSYLEFVELGYIDSNHYPRKLSGGERQRVAIACALAKQPELILADEPTSALDTVNSEIIMGIFKKIAHKDKKKIVIATHNDRIYNEADVIYEIKNNKIQLVKGESSNESSKKEPEDYDNNVKLTPRFYFDYAIKTSRKGRFAKNLMIVLSAIAIAFSSVMYNFGDTFVKEQEKLMDAISDKEIFVVNMTAPLNTILDIDENLSIQDKDAELLRNISYVDTIYPYFEFRSIGYPLINETEASEGYIVVSKGQKEEKYTFAESKDNPYDKYVIIPYYPEQNLERRLKEKLSDESSDKVYISSQLAQLLGIENLKESVSLRVLTYVPIAQHETQMTVRPEGIVYEIDIDLSKVVELDLKIEGILDESVRNRYSNSGNNAIYVPYHKMQMILTQTQNSATIDTNLEYIEWRPSAFVVFAKSYNDVGLVIERVSSINPNFRAVSEYQDIESMNAIVKNTREIGLVIVIVILIIIFLLMSIIHMNHILDRKYEISLLKANGLTKIELTKLVSVESLRHVFLVSLISSVISLVVTKVMNLLFEEIA
data/inverse_folding/5YH2.cif ADDED
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data/inverse_folding/5YH2.pdb ADDED
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data/inverse_folding/5YH2_mutated_seqs.fasta ADDED
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+ >mut_1
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+ SVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
3
+ >mut_2
4
+ DVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
5
+ >mut_3
6
+ EVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
7
+ >mut_4
8
+ VVLQSLFEHPLYRTVLPDLTEEDTLFNLNAEIRLYPKAASESYPNWLRFHIGINRYELYSRHNPVIAALLRDLLSQKISSVGMKSGGTQLKLIMSFQNYGQALFKPMKQTREQETPPDFFYFSDFERHNAEIAAFHLDRILDFRRVPPVAGRLVNMTREIRDVTRDKKLWRTFFVSPANNICFYGECSYYCSTEHALCGKPDQIEGSLAAFLPDLALAKRKTWRNPWRRSYHKRKKAEWEVDPDYCDEVKQTPPYDRGTRLLDIMDMTIFDFLMGNMDRHHYETFEKFGNDTFIIHLDNGRGFGKHSHDEMSILVPLTQCCRVKRSTYLRLQLLAKEEYKLSSLMEESLLQDRLVPVLIKPHLEALDRRLRLVLKVLSDCVEKDGFSAVVENDLD
data/inverse_folding/example.json ADDED
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data/variant_prediction/BLAT_ECOLX_Ranganathan2015.csv ADDED
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data/variant_prediction/aggregated_rho.csv ADDED
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+ ,valid,valid,valid,full,full,full,test,test,test
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+ ,rho,rho_boot_mean,rho_boot_std,rho,rho_boot_mean,rho_boot_std,rho,rho_boot_mean,rho_boot_std
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+ prediction_name,,,,,,,,,
4
+ DeepSequence (published),0.5602602238441781,0.5612750863332869,0.011985038405770958,0.5136039187155506,0.512778392574446,0.017143498537203965,0.4985534977063157,0.4971342978135294,0.01880751793444042
5
+ DeepSequence (published) - single,0.5430017493589612,0.5443701641109099,0.012423838414787655,0.5020580499120493,0.5011407877592315,0.017404933590781577,0.4888504049291744,0.4871958276457871,0.01901173848626349
6
+ DeepSequence (replicated),0.5635921424785667,0.5646995390005713,0.01208513622243054,0.5201346211408833,0.5192886065212002,0.01673076687983822,0.5061160658706628,0.5046399186246291,0.018229357414485857
7
+ ESM-1b,0.5666981361139574,0.5676141841913277,0.012025131590764206,0.4585307340241866,0.45722552072809847,0.019420008294609183,0.42363802367264763,0.42161627444963734,0.021805452392623693
8
+ ESM-1v (+further training),0.5979754992703865,0.5985949225433556,0.011700006848349927,0.5382829634571142,0.5368211143294155,0.01685153619316537,0.5190273067431552,0.516894079421693,0.018513319852783258
9
+ ESM-1v (zero shot),0.593067646900253,0.5941859676949833,0.012216511625608212,0.5089550689778174,0.5077512066859666,0.01778501791633543,0.48182197932541876,0.47986902571531576,0.019581310268182918
10
+ EVMutation (published),0.5490624796568302,0.5497402994229479,0.012972877244416833,0.508419480578392,0.5068193077204962,0.017724141740786323,0.49530883571437995,0.4929738265261569,0.01925680770735712
11
+ EVMutation (replicated),0.5512073418060958,0.5516069925840847,0.012682710408380227,0.5112223872556211,0.509417510409684,0.01815069563293862,0.49832401481998395,0.4958080000308451,0.019914561834409074
12
+ MSA Transformer,0.5987868160929384,0.5996093743584658,0.011619082338376548,0.5422875260142875,0.5414621273787668,0.016736474302820666,0.5240619485695612,0.5227049509337023,0.01838724590425425
13
+ PSSM,0.4633509799617207,0.46414678680632565,0.013608999290250463,0.4604575410937561,0.45896520887271747,0.018669177520973506,0.45952417371699333,0.4572937321199404,0.020301493079271263
14
+ ProtBERT-BFD,0.5166978551105863,0.5173010932587435,0.0135134115364477,0.42751993235504165,0.4278749478751994,0.020253227983813266,0.39875286049841424,0.39902780420308837,0.022427362321673118
15
+ TAPE,0.16039305189488504,0.16335386054336812,0.015534139865906533,0.17144437052454356,0.1748040435344602,0.023020918331052527,0.17500931201798176,0.1784976509509415,0.025436008158518972
16
+ UniRep,0.17009503694214162,0.1687392944334104,0.018385140300946963,0.15554362973563243,0.15406970922460442,0.022971233340804673,0.15084962741095204,0.14933758496369925,0.024450618192371674
17
+ gt,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0
data/variant_prediction/aggregated_rho_round3.csv ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,valid,valid,valid,full,full,full,test,test,test
2
+ ,rho,rho_boot_mean,rho_boot_std,rho,rho_boot_mean,rho_boot_std,rho,rho_boot_mean,rho_boot_std
3
+ prediction_name,,,,,,,,,
4
+ DeepSequence (published),0.56,0.561,0.012,0.514,0.513,0.017,0.499,0.497,0.019
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+ DeepSequence (published) - single,0.543,0.544,0.012,0.502,0.501,0.017,0.489,0.487,0.019
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+ DeepSequence (replicated),0.564,0.565,0.012,0.52,0.519,0.017,0.506,0.505,0.018
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+ ESM-1b,0.567,0.568,0.012,0.459,0.457,0.019,0.424,0.422,0.022
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+ ESM-1v (+further training),0.598,0.599,0.012,0.538,0.537,0.017,0.519,0.517,0.019
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+ ESM-1v (zero shot),0.593,0.594,0.012,0.509,0.508,0.018,0.482,0.48,0.02
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+ EVMutation (published),0.549,0.55,0.013,0.508,0.507,0.018,0.495,0.493,0.019
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+ EVMutation (replicated),0.551,0.552,0.013,0.511,0.509,0.018,0.498,0.496,0.02
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+ MSA Transformer,0.599,0.6,0.012,0.542,0.541,0.017,0.524,0.523,0.018
13
+ PSSM,0.463,0.464,0.014,0.46,0.459,0.019,0.46,0.457,0.02
14
+ ProtBERT-BFD,0.517,0.517,0.014,0.428,0.428,0.02,0.399,0.399,0.022
15
+ TAPE,0.16,0.163,0.016,0.171,0.175,0.023,0.175,0.178,0.025
16
+ UniRep,0.17,0.169,0.018,0.156,0.154,0.023,0.151,0.149,0.024
17
+ gt,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0
data/variant_prediction/rho_pp.csv ADDED
@@ -0,0 +1,575 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,protein_name,prediction_name,rho,rho_boot_mean,rho_boot_std
2
+ 112,BLAT_ECOLX_Ranganathan2015,gt,1.0,1.0,0.0
3
+ 113,BLAT_ECOLX_Ranganathan2015,MSA Transformer,0.7734667485457659,0.7738027379896001,0.005810229642357195
4
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5
+ 115,BLAT_ECOLX_Ranganathan2015,EVMutation (published),0.7176707275279989,0.7184384489293669,0.005172838583114061
6
+ 116,BLAT_ECOLX_Ranganathan2015,DeepSequence (published) - single,0.755008978954388,0.7561275888133361,0.006397359248039358
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8
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+ 120,BLAT_ECOLX_Ranganathan2015,DeepSequence (replicated),0.7907734920246668,0.7896796440415632,0.004950863463811736
11
+ 121,BLAT_ECOLX_Ranganathan2015,EVMutation (replicated),0.7409416788010579,0.7414034481390834,0.004511986638702662
12
+ 122,BLAT_ECOLX_Ranganathan2015,ProtBERT-BFD,0.5066624675266317,0.5086811085713664,0.008432655997093638
13
+ 123,BLAT_ECOLX_Ranganathan2015,TAPE,0.1437575573041793,0.14718890398398213,0.00854664176422607
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+ 124,BLAT_ECOLX_Ranganathan2015,UniRep,0.03205236543975054,0.03109080054090812,0.01290097463153492
15
+ 125,BLAT_ECOLX_Ranganathan2015,ESM-1b,0.7244733058050135,0.7244715086369378,0.0054093172279529814
16
+ 322,PABP_YEAST_Fields2013-doubles,gt,1.0,1.0,0.0
17
+ 323,PABP_YEAST_Fields2013-doubles,MSA Transformer,0.7092749798819215,0.7083666356138891,0.0028152329308467395
18
+ 324,PABP_YEAST_Fields2013-doubles,PSSM,0.5022876340276876,0.5009430131340434,0.003914000389196363
19
+ 325,PABP_YEAST_Fields2013-doubles,EVMutation (published),0.6334326213147348,0.6319953143501386,0.0031257311261953294
20
+ 326,PABP_YEAST_Fields2013-doubles,DeepSequence (published) - single,0.697950719553739,0.6969873459722575,0.002722620907521595
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+ 327,PABP_YEAST_Fields2013-doubles,DeepSequence (published),0.7135955622146342,0.7126803646172054,0.0026005801571960326
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+ 328,PABP_YEAST_Fields2013-doubles,ESM-1v (zero shot),0.6808954491918657,0.6802766119879265,0.003337373999951867
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+ 329,PABP_YEAST_Fields2013-doubles,ESM-1v (+further training),0.6724484972163407,0.6715887930967597,0.003131790856684628
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+ 330,PABP_YEAST_Fields2013-doubles,DeepSequence (replicated),0.6690002200834452,0.6682783362133506,0.002924928420789724
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+ 331,PABP_YEAST_Fields2013-doubles,EVMutation (replicated),0.6370081500864611,0.6365136199984236,0.0033115051939607903
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+ 332,PABP_YEAST_Fields2013-doubles,ProtBERT-BFD,0.7018606830370693,0.7013726492421438,0.0029126542395483735
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+ 333,PABP_YEAST_Fields2013-doubles,TAPE,0.08509310570891564,0.0848841242307348,0.0055652064474624435
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+ 334,PABP_YEAST_Fields2013-doubles,UniRep,0.25423830212504706,0.2528601717205464,0.005044020233400653
29
+ 335,PABP_YEAST_Fields2013-doubles,ESM-1b,0.6989229474973527,0.6986292605564589,0.0033935909980897867
30
+ 0,AMIE_PSEAE_Whitehead,gt,1.0,1.0,0.0
31
+ 1,AMIE_PSEAE_Whitehead,MSA Transformer,0.6078201305487886,0.6074995010322104,0.006655174664199262
32
+ 2,AMIE_PSEAE_Whitehead,PSSM,0.3851814980540226,0.3857114471207458,0.01419582618409421
33
+ 3,AMIE_PSEAE_Whitehead,EVMutation (published),0.5482672326862741,0.5494300599441264,0.009296902066198478
34
+ 4,AMIE_PSEAE_Whitehead,DeepSequence (published) - single,0.6192004998182683,0.6235042563787871,0.009786304100603813
35
+ 5,AMIE_PSEAE_Whitehead,DeepSequence (published),0.64337902253515,0.6466208891535095,0.008118407538893896
36
+ 6,AMIE_PSEAE_Whitehead,ESM-1v (zero shot),0.6666021525370552,0.6679182566255217,0.008613681679123195
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+ 7,AMIE_PSEAE_Whitehead,ESM-1v (+further training),0.671863659978349,0.6738279065439714,0.008391215501243852
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+ 8,AMIE_PSEAE_Whitehead,DeepSequence (replicated),0.6459861864504847,0.6488672280630794,0.007018822987447003
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+ 9,AMIE_PSEAE_Whitehead,EVMutation (replicated),0.5609325769621947,0.561649692444916,0.008422893320380829
40
+ 10,AMIE_PSEAE_Whitehead,ProtBERT-BFD,0.6338505619537509,0.6354477176484321,0.009554450012056636
41
+ 11,AMIE_PSEAE_Whitehead,TAPE,0.10859786063312615,0.11076584881696006,0.015015379880043802
42
+ 12,AMIE_PSEAE_Whitehead,UniRep,0.07739084999605612,0.073724376050283,0.01240093912413262
43
+ 13,AMIE_PSEAE_Whitehead,ESM-1b,0.5853972837117107,0.5836827247268739,0.009689198977483172
44
+ 476,TIM_SULSO_b0,gt,1.0,1.0,0.0
45
+ 477,TIM_SULSO_b0,MSA Transformer,0.652792451949853,0.6557516749540702,0.01600526744858049
46
+ 478,TIM_SULSO_b0,PSSM,0.5593694164359105,0.566935653048683,0.01690630663394768
47
+ 479,TIM_SULSO_b0,EVMutation (published),0.586140631338893,0.5919185710638765,0.01641131140995354
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+ 480,TIM_SULSO_b0,DeepSequence (published) - single,0.5440966818223795,0.5487337742437185,0.015747346163591917
49
+ 481,TIM_SULSO_b0,DeepSequence (published),0.5505948722659953,0.5565402351008099,0.014555638377778407
50
+ 482,TIM_SULSO_b0,ESM-1v (zero shot),0.6424600791408842,0.647621332390191,0.016028029413886244
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+ 483,TIM_SULSO_b0,ESM-1v (+further training),0.6528241665901272,0.6555645155980578,0.015379852940306115
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+ 484,TIM_SULSO_b0,DeepSequence (replicated),0.58320310154665,0.5889306881826475,0.015779505439458753
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+ 485,TIM_SULSO_b0,EVMutation (replicated),0.5887928613337577,0.5940831887831108,0.016569303240529226
54
+ 486,TIM_SULSO_b0,ProtBERT-BFD,0.6317983649729998,0.6345885687258263,0.01602936969330677
55
+ 487,TIM_SULSO_b0,TAPE,0.5532247068671533,0.5587646824504885,0.019898766536311598
56
+ 488,TIM_SULSO_b0,UniRep,0.16614520326579557,0.17309558518510257,0.03137269514815842
57
+ 489,TIM_SULSO_b0,ESM-1b,0.6237553534772002,0.6315291160092146,0.015666927222771784
58
+ 266,KKA2_KLEPN_Mikkelsen2014,gt,1.0,1.0,0.0
59
+ 267,KKA2_KLEPN_Mikkelsen2014,MSA Transformer,0.6483744764229661,0.6501362354726463,0.008392887010519141
60
+ 268,KKA2_KLEPN_Mikkelsen2014,PSSM,0.33148417825671017,0.3321644636508253,0.00954461359833698
61
+ 269,KKA2_KLEPN_Mikkelsen2014,EVMutation (published),0.6040252893227205,0.6046576561680694,0.007611417821309178
62
+ 270,KKA2_KLEPN_Mikkelsen2014,DeepSequence (published) - single,0.6428015918045534,0.646183322009227,0.006466715772852643
63
+ 271,KKA2_KLEPN_Mikkelsen2014,DeepSequence (published),0.6622233077856657,0.6647874075249056,0.007006878882782913
64
+ 272,KKA2_KLEPN_Mikkelsen2014,ESM-1v (zero shot),0.627837107524989,0.6303585274093482,0.008597860670353218
65
+ 273,KKA2_KLEPN_Mikkelsen2014,ESM-1v (+further training),0.6540013975518588,0.6568513820339432,0.008249320892729436
66
+ 274,KKA2_KLEPN_Mikkelsen2014,DeepSequence (replicated),0.6708322568508106,0.6736802365189244,0.007635987315614941
67
+ 275,KKA2_KLEPN_Mikkelsen2014,EVMutation (replicated),0.6078971023630376,0.6084473779526628,0.009849549243896777
68
+ 276,KKA2_KLEPN_Mikkelsen2014,ProtBERT-BFD,0.5441498820889966,0.5461574522717653,0.011666264975033773
69
+ 277,KKA2_KLEPN_Mikkelsen2014,TAPE,0.19644765859472593,0.19660401466689767,0.013538150129702716
70
+ 278,KKA2_KLEPN_Mikkelsen2014,UniRep,0.24229449050660715,0.2442377698228683,0.014857522651962994
71
+ 279,KKA2_KLEPN_Mikkelsen2014,ESM-1b,0.5796639591499797,0.5828984127122095,0.009462666946528803
72
+ 182,DLG4_RAT_Ranganathan2012,gt,1.0,1.0,0.0
73
+ 183,DLG4_RAT_Ranganathan2012,MSA Transformer,0.5106330149144778,0.5128948633920939,0.020360553500470727
74
+ 184,DLG4_RAT_Ranganathan2012,PSSM,0.4603978833849677,0.4625263978112141,0.023120281422982173
75
+ 185,DLG4_RAT_Ranganathan2012,EVMutation (published),0.5352653453343902,0.539345546690929,0.02299279306281621
76
+ 186,DLG4_RAT_Ranganathan2012,DeepSequence (published) - single,0.5918364209116883,0.5957477584911891,0.01995659395689766
77
+ 187,DLG4_RAT_Ranganathan2012,DeepSequence (published),0.6036625648098791,0.6070883056488765,0.020478051115311577
78
+ 188,DLG4_RAT_Ranganathan2012,ESM-1v (zero shot),0.5939968225123472,0.596847197984075,0.017630058333348964
79
+ 189,DLG4_RAT_Ranganathan2012,ESM-1v (+further training),0.5682242782581113,0.5694370162367897,0.018958485435275826
80
+ 190,DLG4_RAT_Ranganathan2012,DeepSequence (replicated),0.5765783481552431,0.5780455229510647,0.020809463738300338
81
+ 191,DLG4_RAT_Ranganathan2012,EVMutation (replicated),0.5241180720329418,0.5261248836538202,0.02186178951891566
82
+ 192,DLG4_RAT_Ranganathan2012,ProtBERT-BFD,0.5595215663542064,0.5614010576594576,0.01854665905518769
83
+ 193,DLG4_RAT_Ranganathan2012,TAPE,0.013351052100670802,0.023584538183512672,0.014457178947554198
84
+ 194,DLG4_RAT_Ranganathan2012,UniRep,0.3016230099651694,0.29971140046709394,0.027613438646412317
85
+ 195,DLG4_RAT_Ranganathan2012,ESM-1b,0.5275305911412679,0.5302881928240022,0.01742943283364256
86
+ 532,UBC9_HUMAN_Roth2017,gt,1.0,1.0,0.0
87
+ 533,UBC9_HUMAN_Roth2017,MSA Transformer,0.5257424624114496,0.5264407612968436,0.016782723059713636
88
+ 534,UBC9_HUMAN_Roth2017,PSSM,0.4252037519097337,0.42647455982971144,0.01848680680775899
89
+ 535,UBC9_HUMAN_Roth2017,EVMutation (published),0.509842177492847,0.5077129136778502,0.015575205557209867
90
+ 536,UBC9_HUMAN_Roth2017,DeepSequence (published) - single,0.5301569663083602,0.5321597182510774,0.017474789730172
91
+ 537,UBC9_HUMAN_Roth2017,DeepSequence (published),0.5451981164854786,0.5458299698225401,0.01779569747351641
92
+ 538,UBC9_HUMAN_Roth2017,ESM-1v (zero shot),0.5278002974052306,0.5289273564517684,0.0174313365381068
93
+ 539,UBC9_HUMAN_Roth2017,ESM-1v (+further training),0.530438889646823,0.5308418048769536,0.016194450129423676
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+ 540,UBC9_HUMAN_Roth2017,DeepSequence (replicated),0.564757773228155,0.566254233293921,0.01584492457358362
95
+ 541,UBC9_HUMAN_Roth2017,EVMutation (replicated),0.5197625999541963,0.5169223464706466,0.015797268774811208
96
+ 542,UBC9_HUMAN_Roth2017,ProtBERT-BFD,0.4566745629330005,0.4597664455496374,0.01695356254093292
97
+ 543,UBC9_HUMAN_Roth2017,TAPE,0.2663201366958923,0.2655109350197859,0.020940445702932447
98
+ 544,UBC9_HUMAN_Roth2017,UniRep,0.027526620911384754,0.032226022213806015,0.0170540626567071
99
+ 545,UBC9_HUMAN_Roth2017,ESM-1b,0.42880952407240114,0.4257117461019339,0.016052256587648167
100
+ 56,BG505_env_Bloom2018,gt,1.0,1.0,0.0
101
+ 57,BG505_env_Bloom2018,MSA Transformer,0.5171538292833168,0.5171496622518762,0.006910262108734762
102
+ 58,BG505_env_Bloom2018,PSSM,0.4805043665958063,0.47934729964627765,0.006403736305762083
103
+ 59,BG505_env_Bloom2018,EVMutation (published),0.4088382668800448,0.4081773036708733,0.005327774682093342
104
+ 60,BG505_env_Bloom2018,DeepSequence (published) - single,0.17427055696697,0.17391089639228693,0.007096603606133151
105
+ 61,BG505_env_Bloom2018,DeepSequence (published),0.17901824140808725,0.17897151771830583,0.007361446332203004
106
+ 62,BG505_env_Bloom2018,ESM-1v (zero shot),0.5263381402665136,0.5258084748455352,0.007358753329943184
107
+ 63,BG505_env_Bloom2018,ESM-1v (+further training),0.5320285787368926,0.5326341655547056,0.006038819273548771
108
+ 64,BG505_env_Bloom2018,DeepSequence (replicated),0.12964060496065777,0.12952804062160664,0.009474191082563171
109
+ 65,BG505_env_Bloom2018,EVMutation (replicated),0.4183486299186111,0.41871016990435506,0.006336285035457084
110
+ 66,BG505_env_Bloom2018,ProtBERT-BFD,0.325305964247549,0.32240107597741696,0.007706083919970625
111
+ 67,BG505_env_Bloom2018,TAPE,0.028456890112386356,0.03179954870869962,0.006177994317554592
112
+ 68,BG505_env_Bloom2018,UniRep,0.20075668930719107,0.20277486226220803,0.008814544928765732
113
+ 69,BG505_env_Bloom2018,ESM-1b,0.46213634322466174,0.4615123969304536,0.007864462687152629
114
+ 462,SUMO1_HUMAN_Roth2017,gt,1.0,1.0,0.0
115
+ 463,SUMO1_HUMAN_Roth2017,MSA Transformer,0.5159144148964154,0.5204033887826773,0.016144534346014247
116
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117
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118
+ 466,SUMO1_HUMAN_Roth2017,DeepSequence (published) - single,0.4546171147375774,0.4563868817701918,0.018789136591575727
119
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120
+ 468,SUMO1_HUMAN_Roth2017,ESM-1v (zero shot),0.4891908408067041,0.4902530587936492,0.02001929801145162
121
+ 469,SUMO1_HUMAN_Roth2017,ESM-1v (+further training),0.4870961371636435,0.48754122676040595,0.017288489612331478
122
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123
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124
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125
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126
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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139
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140
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141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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167
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168
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169
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170
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171
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172
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173
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174
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175
+ 299,MTH3_HAEAESTABILIZED_Tawfik2015,DeepSequence (published),0.7267412245284101,0.7233050341503924,0.011532195128181771
176
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177
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178
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179
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180
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182
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183
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184
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185
+ 337,PABP_YEAST_Fields2013-singles,MSA Transformer,0.6686627777483488,0.6635711675242917,0.016177764258807882
186
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187
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188
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189
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190
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191
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193
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197
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199
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200
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201
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202
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203
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204
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205
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207
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212
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213
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214
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215
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216
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217
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218
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219
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220
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224
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226
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227
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228
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229
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231
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232
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233
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234
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240
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241
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+ 241,HSP82_YEAST_Bolon2016,EVMutation (published),0.49012823437511943,0.4936437806547088,0.009824172602805919
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248
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254
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255
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257
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+ 527,TPMT_HUMAN_Fowler2018,EVMutation (replicated),0.5415452371370989,0.5367554768824575,0.014285595575941081
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420
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421
+ 573,YAP1_HUMAN_Fields2012-singles,ESM-1b,0.49074132050160213,0.47904274853673057,0.033063480562130175
422
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423
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424
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425
+ 143,BRCA1_HUMAN_BRCT,EVMutation (published),0.575430631741363,0.5736725403805653,0.02241134787829384
426
+ 144,BRCA1_HUMAN_BRCT,DeepSequence (published) - single,0.56817101100728,0.5677360233280984,0.023388317280203356
427
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428
+ 146,BRCA1_HUMAN_BRCT,ESM-1v (zero shot),0.43099744949022667,0.43820237951190066,0.026042969300316396
429
+ 147,BRCA1_HUMAN_BRCT,ESM-1v (+further training),0.5902839735003296,0.5924129041526023,0.022139044593609002
430
+ 148,BRCA1_HUMAN_BRCT,DeepSequence (replicated),0.6048855640279864,0.6073185827311438,0.022386535818213062
431
+ 149,BRCA1_HUMAN_BRCT,EVMutation (replicated),0.5943999321364778,0.5914745443226089,0.021113952368322746
432
+ 150,BRCA1_HUMAN_BRCT,ProtBERT-BFD,0.3807653003587669,0.38891615029062426,0.025074152619186402
433
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434
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435
+ 153,BRCA1_HUMAN_BRCT,ESM-1b,0.5015315617812091,0.5064634555923619,0.023953542534915467
436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
+ 405,PTEN_HUMAN_Fowler2018,ESM-1b,0.4531834407900847,0.4504022413646627,0.019849910120253392
450
+ 406,RASH_HUMAN_Kuriyan,gt,1.0,1.0,0.0
451
+ 407,RASH_HUMAN_Kuriyan,MSA Transformer,0.4803453272496495,0.47796060028078263,0.017296002593559925
452
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453
+ 409,RASH_HUMAN_Kuriyan,EVMutation (published),0.426341747437809,0.4229653964620471,0.01796419071897713
454
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455
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456
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457
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458
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459
+ 415,RASH_HUMAN_Kuriyan,EVMutation (replicated),0.41905311471869217,0.4152533339275954,0.0186456211620091
460
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461
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462
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463
+ 419,RASH_HUMAN_Kuriyan,ESM-1b,0.34359820576597994,0.3393198546702645,0.02233342505800977
464
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465
+ 421,RL401_YEAST_Bolon2013,MSA Transformer,0.5393318004129806,0.539097206955352,0.021107284547232343
466
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467
+ 423,RL401_YEAST_Bolon2013,EVMutation (published),0.45052851516991577,0.44375165687449697,0.020730159359455567
468
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469
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470
+ 426,RL401_YEAST_Bolon2013,ESM-1v (zero shot),0.3780305382545389,0.3758572950839938,0.024438101597142162
471
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472
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473
+ 429,RL401_YEAST_Bolon2013,EVMutation (replicated),0.4649054471564614,0.45886647149033805,0.023302945044134975
474
+ 430,RL401_YEAST_Bolon2013,ProtBERT-BFD,0.39888508524198757,0.39548316549478596,0.024888924809883948
475
+ 431,RL401_YEAST_Bolon2013,TAPE,0.14136362276356793,0.1330418322501083,0.03239530659402373
476
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477
+ 433,RL401_YEAST_Bolon2013,ESM-1b,0.24192958338903614,0.24085320243486236,0.026555927080409752
478
+ 504,TPK1_HUMAN_Roth2017,gt,1.0,1.0,0.0
479
+ 505,TPK1_HUMAN_Roth2017,MSA Transformer,0.3415408511396077,0.33848473630002246,0.021212292675018753
480
+ 506,TPK1_HUMAN_Roth2017,PSSM,0.22072657020579714,0.2217644870830699,0.02369863818079656
481
+ 507,TPK1_HUMAN_Roth2017,EVMutation (published),0.2469522178641623,0.24730841435573284,0.01871959166700927
482
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483
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484
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485
+ 511,TPK1_HUMAN_Roth2017,ESM-1v (+further training),0.3317814621900089,0.3260704249321161,0.020979034647608977
486
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487
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488
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489
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490
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491
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492
+ 448,RL401_YEAST_Fraser2016,gt,1.0,1.0,0.0
493
+ 449,RL401_YEAST_Fraser2016,MSA Transformer,0.47482528061086265,0.4755124342582121,0.022569966034399314
494
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495
+ 451,RL401_YEAST_Fraser2016,EVMutation (published),0.40924774479083326,0.414388594923377,0.023024753763437884
496
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497
+ 453,RL401_YEAST_Fraser2016,DeepSequence (published),0.4461365210878178,0.44899827665090486,0.019914780607228476
498
+ 454,RL401_YEAST_Fraser2016,ESM-1v (zero shot),0.32760495384990906,0.3291822704728004,0.02640133685107885
499
+ 455,RL401_YEAST_Fraser2016,ESM-1v (+further training),0.32990765588855403,0.3301471135676151,0.027322775648250797
500
+ 456,RL401_YEAST_Fraser2016,DeepSequence (replicated),0.4347770704966268,0.44167537164330406,0.017646832557385868
501
+ 457,RL401_YEAST_Fraser2016,EVMutation (replicated),0.4137359503211986,0.42342848446020753,0.023604416626258205
502
+ 458,RL401_YEAST_Fraser2016,ProtBERT-BFD,0.352256182908315,0.35488121411289225,0.026464030488060585
503
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504
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505
+ 461,RL401_YEAST_Fraser2016,ESM-1b,0.20980461592629226,0.21406696973008782,0.028067024964270798
506
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507
+ 169,CALM1_HUMAN_Roth2017,MSA Transformer,0.23711821254687918,0.23943485564742276,0.03113662255833698
508
+ 170,CALM1_HUMAN_Roth2017,PSSM,0.1940025702015548,0.19913438415655305,0.02630867989950736
509
+ 171,CALM1_HUMAN_Roth2017,EVMutation (published),0.23244288732891077,0.23757839644300488,0.02612586314740137
510
+ 172,CALM1_HUMAN_Roth2017,DeepSequence (published) - single,0.22492618953594645,0.22906576202846649,0.030735041631348013
511
+ 173,CALM1_HUMAN_Roth2017,DeepSequence (published),0.22789905079105036,0.22969671581656934,0.029946806999461846
512
+ 174,CALM1_HUMAN_Roth2017,ESM-1v (zero shot),0.2617141623664426,0.2652270689648074,0.025228781176764935
513
+ 175,CALM1_HUMAN_Roth2017,ESM-1v (+further training),0.2721918164887156,0.2732936184582442,0.027420080243011954
514
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515
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516
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517
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518
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519
+ 181,CALM1_HUMAN_Roth2017,ESM-1b,0.24496865240057522,0.2451996389337371,0.02464309631183189
520
+ 434,RL401_YEAST_Bolon2014,gt,1.0,1.0,0.0
521
+ 435,RL401_YEAST_Bolon2014,MSA Transformer,0.473016586760016,0.4787538735227928,0.01635240282545988
522
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523
+ 437,RL401_YEAST_Bolon2014,EVMutation (published),0.39726026462632547,0.40239056557397346,0.0228080866519743
524
+ 438,RL401_YEAST_Bolon2014,DeepSequence (published) - single,0.4295635873940029,0.4359091171923386,0.020125832508895207
525
+ 439,RL401_YEAST_Bolon2014,DeepSequence (published),0.4226902253289641,0.4296887661396075,0.02042130768745852
526
+ 440,RL401_YEAST_Bolon2014,ESM-1v (zero shot),0.2555902509515176,0.26273723347289263,0.023433435512064758
527
+ 441,RL401_YEAST_Bolon2014,ESM-1v (+further training),0.30819485583382966,0.3149369605358795,0.022303272256952115
528
+ 442,RL401_YEAST_Bolon2014,DeepSequence (replicated),0.4208601802656798,0.427057708121087,0.022928982939476122
529
+ 443,RL401_YEAST_Bolon2014,EVMutation (replicated),0.3795164863252923,0.38353041172591634,0.022737389271498636
530
+ 444,RL401_YEAST_Bolon2014,ProtBERT-BFD,0.3305475177656725,0.33333306438483723,0.024066951141161263
531
+ 445,RL401_YEAST_Bolon2014,TAPE,0.27031483697506303,0.2748860791185031,0.023958009305355937
532
+ 446,RL401_YEAST_Bolon2014,UniRep,0.16611039235432376,0.16134631003484823,0.029548951247265007
533
+ 447,RL401_YEAST_Bolon2014,ESM-1b,0.18258453677698241,0.18424186541936532,0.02190939840845001
534
+ 28,B3VI55_LIPST_Whitehead2015,gt,1.0,1.0,0.0
535
+ 29,B3VI55_LIPST_Whitehead2015,MSA Transformer,0.25670663558953755,0.2568287164790948,0.014303617378529112
536
+ 30,B3VI55_LIPST_Whitehead2015,PSSM,0.17356487805783094,0.17229178651491212,0.014476649812023814
537
+ 31,B3VI55_LIPST_Whitehead2015,EVMutation (published),0.22415909454397737,0.22201195664151258,0.015151263735652506
538
+ 32,B3VI55_LIPST_Whitehead2015,DeepSequence (published) - single,0.2314762900256378,0.23010900979180499,0.015350637015825532
539
+ 33,B3VI55_LIPST_Whitehead2015,DeepSequence (published),0.2314408826509719,0.23018066970458023,0.015030815308284325
540
+ 34,B3VI55_LIPST_Whitehead2015,ESM-1v (zero shot),0.2361417829414803,0.235599751959246,0.01380734986895981
541
+ 35,B3VI55_LIPST_Whitehead2015,ESM-1v (+further training),0.2424504346933426,0.24107272979662708,0.013833009304666605
542
+ 36,B3VI55_LIPST_Whitehead2015,DeepSequence (replicated),0.24479443697061962,0.2433108240939475,0.015917153531149808
543
+ 37,B3VI55_LIPST_Whitehead2015,EVMutation (replicated),0.2251732484740546,0.2236926065862732,0.015150897486335867
544
+ 38,B3VI55_LIPST_Whitehead2015,ProtBERT-BFD,0.22029752268681757,0.22189350964142437,0.012296008345577645
545
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546
+ 40,B3VI55_LIPST_Whitehead2015,UniRep,0.052410727620420754,0.051318677282283995,0.012063912744717856
547
+ 41,B3VI55_LIPST_Whitehead2015,ESM-1b,0.21414102345131894,0.21411325444647447,0.01376144270479094
548
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549
+ 281,MK01_HUMAN_Johannessen,MSA Transformer,0.21600719562316348,0.21324362844208555,0.010869477909858859
550
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551
+ 283,MK01_HUMAN_Johannessen,EVMutation (published),0.2411806504637287,0.23555029839403527,0.014919109500043859
552
+ 284,MK01_HUMAN_Johannessen,DeepSequence (published) - single,0.21622583201496393,0.2118857875656635,0.01330985778159382
553
+ 285,MK01_HUMAN_Johannessen,DeepSequence (published),0.2262833888426422,0.2222242751773241,0.013845563303298817
554
+ 286,MK01_HUMAN_Johannessen,ESM-1v (zero shot),0.18610638097061274,0.1807887324699657,0.011421581908870595
555
+ 287,MK01_HUMAN_Johannessen,ESM-1v (+further training),0.1711612951379414,0.1656998628430867,0.011097399419126037
556
+ 288,MK01_HUMAN_Johannessen,DeepSequence (replicated),0.2262833888426422,0.2222242751773241,0.013845563303298817
557
+ 289,MK01_HUMAN_Johannessen,EVMutation (replicated),0.2411806504637287,0.23555029839403527,0.014919109500043859
558
+ 290,MK01_HUMAN_Johannessen,ProtBERT-BFD,0.03761357865712906,0.032009850323277314,0.013532258402167076
559
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560
+ 292,MK01_HUMAN_Johannessen,UniRep,0.16466166691457634,0.15865978272069822,0.012603347408739064
561
+ 293,MK01_HUMAN_Johannessen,ESM-1b,0.020968477238634434,0.014843912213619878,0.011595884022987897
562
+ 350,PA_FLU_Sun2015,gt,1.0,1.0,0.0
563
+ 351,PA_FLU_Sun2015,MSA Transformer,0.40499549570010246,0.4071708283839041,0.020127599355418007
564
+ 352,PA_FLU_Sun2015,PSSM,0.4870284991956425,0.48503558622411713,0.017254863079323307
565
+ 353,PA_FLU_Sun2015,EVMutation (published),0.4799931222249091,0.47963201439188896,0.017318908492685997
566
+ 354,PA_FLU_Sun2015,DeepSequence (published) - single,0.47395691140007384,0.47172219190050396,0.017516562617318692
567
+ 355,PA_FLU_Sun2015,DeepSequence (published),0.4782770520849889,0.4776150037730753,0.01692698104707
568
+ 356,PA_FLU_Sun2015,ESM-1v (zero shot),0.10354167707274801,0.10113956150869738,0.014997098538970815
569
+ 357,PA_FLU_Sun2015,ESM-1v (+further training),0.5736052915722709,0.5754278729569327,0.011947354137920797
570
+ 358,PA_FLU_Sun2015,DeepSequence (replicated),0.49574925840401,0.49600755923376527,0.014212454786638254
571
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weight/esm-main/tests/test_inverse_folding.py ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ #
3
+ # This source code is licensed under the MIT license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ def test_esm_if1():
7
+
8
+ import json
9
+ import numpy as np
10
+ from pathlib import Path
11
+ from scipy.stats import special_ortho_group
12
+ from tqdm import tqdm
13
+ import torch
14
+
15
+ import esm
16
+ import esm.inverse_folding
17
+
18
+ example_file = Path(__file__).absolute().parent / "inverse_folding_test_example.json"
19
+ with open(example_file) as f:
20
+ examples = json.load(f)
21
+
22
+ model, alphabet = esm.pretrained.esm_if1_gvp4_t16_142M_UR50()
23
+ model = model.eval()
24
+ batch_converter = esm.inverse_folding.util.CoordBatchConverter(alphabet)
25
+
26
+ with torch.no_grad():
27
+ print('Testing batch inference on 3 examples...')
28
+ # Test batch with multiple examples
29
+ batch = [(e["coords"], None, e["seq"]) for e in examples[:3]]
30
+ coords, confidence, strs, tokens, padding_mask = (
31
+ batch_converter(batch)
32
+ )
33
+ prev_output_tokens = tokens[:, :-1]
34
+ target = tokens[:, 1:]
35
+ logits, _ = model.forward(coords, padding_mask, confidence,
36
+ prev_output_tokens)
37
+ loss = torch.nn.functional.cross_entropy(logits, target, reduction='none')
38
+ coord_mask = torch.all(torch.all(torch.isfinite(coords), dim=-1), dim=-1)
39
+ coord_mask = coord_mask[:, 1:-1]
40
+ avgloss = torch.sum(loss * coord_mask) / torch.sum(coord_mask)
41
+ expected_ppl = 4.40
42
+ np.testing.assert_allclose(
43
+ expected_ppl,
44
+ torch.exp(avgloss).item(),
45
+ atol=1e-02,
46
+ )
47
+
48
+ print('Testing on 10 examples from validation set...')
49
+ # Test batch with single example
50
+ for example in tqdm(examples):
51
+ batch = [(example["coords"], None, example["seq"])]
52
+ coords, confidence, strs, tokens, padding_mask = (
53
+ batch_converter(batch)
54
+ )
55
+ prev_output_tokens = tokens[:, :-1]
56
+ target = tokens[:, 1:]
57
+ logits, _ = model.forward(coords, padding_mask, confidence,
58
+ prev_output_tokens)
59
+ assert torch.any(torch.isnan(logits)) == False
60
+
61
+ # Test equivariance
62
+ R = special_ortho_group.rvs(3)
63
+ R = torch.tensor(R, dtype=torch.float32)
64
+ coords = torch.matmul(coords, R)
65
+ logits_rotated, _ = model.forward(coords, padding_mask,
66
+ confidence, prev_output_tokens)
67
+ np.testing.assert_allclose(
68
+ logits.detach().numpy(),
69
+ logits_rotated.detach().numpy(),
70
+ atol=1e-01
71
+ )
weight/esm-main/tests/test_load_all.py ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ #
3
+ # This source code is licensed under the MIT license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ import pytest
7
+ from pathlib import Path
8
+ import torch
9
+ import esm
10
+
11
+ # Directly from hubconf.py
12
+ model_names = """
13
+ esm1_t6_43M_UR50S,
14
+ esm1_t12_85M_UR50S,
15
+ esm1_t34_670M_UR50S,
16
+ esm1_t34_670M_UR50D,
17
+ esm1_t34_670M_UR100,
18
+ esm1b_t33_650M_UR50S,
19
+ esm_msa1_t12_100M_UR50S,
20
+ esm_msa1b_t12_100M_UR50S,
21
+ esm1v_t33_650M_UR90S,
22
+ esm1v_t33_650M_UR90S_1,
23
+ esm1v_t33_650M_UR90S_2,
24
+ esm1v_t33_650M_UR90S_3,
25
+ esm1v_t33_650M_UR90S_4,
26
+ esm1v_t33_650M_UR90S_5,
27
+ esm_if1_gvp4_t16_142M_UR50,
28
+ esm2_t6_8M_UR50D,
29
+ esm2_t12_35M_UR50D,
30
+ esm2_t30_150M_UR50D,
31
+ esm2_t33_650M_UR50D,
32
+ esm2_t36_3B_UR50D,
33
+ esm2_t48_15B_UR50D
34
+ """
35
+ model_names = [mn.strip() for mn in model_names.strip(" ,\n").split(",")]
36
+
37
+
38
+ @pytest.mark.parametrize("model_name", model_names)
39
+ def test_load_hub_fwd_model(model_name: str) -> None:
40
+ model, alphabet = getattr(esm.pretrained, model_name)()
41
+ # batch_size = 2, seq_len = 3, tokens within vocab
42
+ dummy_inp = torch.tensor([[0, 1, 2], [3, 4, 5]])
43
+ if "esm_msa" in model_name:
44
+ dummy_inp = dummy_inp.unsqueeze(0)
45
+ output = model(dummy_inp) # dict
46
+ logits = output["logits"].squeeze(0)
47
+ assert logits.shape == (2, 3, len(alphabet))
48
+
49
+
50
+ @pytest.mark.parametrize("model_name", model_names)
51
+ def test_load_local(model_name: str) -> None:
52
+ # Assumes everything has already been loaded & cached.
53
+ local_path = Path.home() / ".cache/torch/hub/checkpoints" / (model_name + ".pt")
54
+ if model_name.endswith("esm1v_t33_650M_UR90S"):
55
+ return # skip; needs to get rerouted to specific instance
56
+ model, alphabet = esm.pretrained.load_model_and_alphabet_local(local_path)
weight/esm-main/tests/test_notebooks.py ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ #
3
+ # This source code is licensed under the MIT license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ import os
7
+ import subprocess
8
+ import pytest
9
+ from pathlib import Path
10
+
11
+ notebook_dir = Path(__file__).parents[1] / "examples"
12
+ notebook_fns = notebook_dir.glob("*.ipynb")
13
+
14
+
15
+ def convert_notebook_to_py(nb_fn: Path, py_fn: Path) -> None:
16
+ """
17
+ From https://stackoverflow.com/questions/17077494/how-do-i-convert-a-ipython-notebook-into-a-python-file-via-commandline
18
+ """
19
+ import nbformat
20
+ from nbconvert import PythonExporter
21
+
22
+ with open(nb_fn) as fh:
23
+ nb = nbformat.reads(fh.read(), nbformat.NO_CONVERT)
24
+
25
+ exporter = PythonExporter()
26
+ source, meta = exporter.from_notebook_node(nb)
27
+
28
+ # Skip the magic, which gets converted to `get_ipython()`
29
+ source.replace("get_ipython", "# get_ipython")
30
+
31
+ with open(py_fn, "w+") as fh:
32
+ fh.writelines(source)
33
+
34
+
35
+ def run_multiple(cmds):
36
+ for cmd in cmds.strip().split("\n"):
37
+ print(cmd)
38
+ subprocess.run(cmd.strip(), shell=True, check=True)
39
+
40
+
41
+ def do_setup(nb_name):
42
+ """ Do any setup work; see intro of the notebook """
43
+ if nb_name == "sup_variant_prediction":
44
+ cmds = """
45
+ curl -O https://dl.fbaipublicfiles.com/fair-esm/examples/P62593_reprs.tar.gz
46
+ tar -xzf P62593_reprs.tar.gz
47
+ curl -O https://dl.fbaipublicfiles.com/fair-esm/examples/P62593.fasta
48
+ """
49
+ run_multiple(cmds)
50
+ else:
51
+ print(f"No setup work for {nb_name}")
52
+
53
+
54
+ @pytest.mark.parametrize("nb_fn", list(notebook_fns))
55
+ def test_run_notebook(nb_fn: Path, tmp_path: Path):
56
+ """ Simply make sure the notebooks run from a-z """
57
+ py_fn = tmp_path / (nb_fn.stem + ".py")
58
+ print(py_fn)
59
+ convert_notebook_to_py(nb_fn, py_fn)
60
+ os.chdir(notebook_dir)
61
+ do_setup(nb_fn.stem)
62
+ _globals = {}
63
+ exec(py_fn.read_text(), _globals)
64
+ # No asserts, just running is enough for now
weight/esm-main/tests/test_readme.py ADDED
@@ -0,0 +1,184 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ #
3
+ # This source code is licensed under the MIT license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ import sys
7
+ import subprocess
8
+ import tempfile
9
+ import requests
10
+ import shutil
11
+ from pathlib import Path
12
+ import torch
13
+ import esm
14
+
15
+
16
+ def test_readme_1():
17
+ import torch
18
+
19
+ model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm2_t33_650M_UR50D")
20
+
21
+
22
+ def test_readme_2():
23
+ import torch
24
+ import esm
25
+
26
+ # Load ESM-2 model
27
+ model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
28
+ batch_converter = alphabet.get_batch_converter()
29
+ model.eval() # disables dropout for deterministic results
30
+
31
+ # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
32
+ data = [
33
+ ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
34
+ ("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
35
+ ("protein2 with mask","KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
36
+ ("protein3", "K A <mask> I S Q"),
37
+ ]
38
+ batch_labels, batch_strs, batch_tokens = batch_converter(data)
39
+ batch_lens = (batch_tokens != alphabet.padding_idx).sum(1)
40
+
41
+ # Extract per-residue representations (on CPU)
42
+ with torch.no_grad():
43
+ results = model(batch_tokens, repr_layers=[33], return_contacts=True)
44
+ token_representations = results["representations"][33]
45
+
46
+ # Generate per-sequence representations via averaging
47
+ # NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.
48
+ sequence_representations = []
49
+ for i, tokens_len in enumerate(batch_lens):
50
+ sequence_representations.append(token_representations[i, 1 : tokens_len - 1].mean(0))
51
+
52
+ # Look at the unsupervised self-attention map contact predictions
53
+ try:
54
+ import matplotlib.pyplot as plt
55
+ for (_, seq), tokens_len, attention_contacts in zip(data, batch_lens, results["contacts"]):
56
+ plt.matshow(attention_contacts[: tokens_len, : tokens_len])
57
+ plt.title(seq)
58
+ plt.show()
59
+ except ImportError:
60
+ pass # dont need mpl to run test
61
+
62
+
63
+ def _run_py_cmd(cmd, **kwargs):
64
+ this_python = sys.executable
65
+ cmd.replace("python", this_python)
66
+ subprocess.run(cmd, shell=True, check=True, **kwargs)
67
+
68
+
69
+ def test_readme_esmfold():
70
+ import torch
71
+ import esm
72
+
73
+ model = esm.pretrained.esmfold_v1()
74
+ model = model.eval().cuda()
75
+
76
+ sequence = "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"
77
+ # Multimer prediction can be done with chains separated by ':'
78
+
79
+ with torch.no_grad():
80
+ output = model.infer_pdb(sequence)
81
+
82
+ with open("result.pdb", "w") as f:
83
+ f.write(output)
84
+
85
+ #import biotite.structure.io as bsio
86
+ #struct = bsio.load_structure("result.pdb", extra_fields=["b_factor"])
87
+ #print(struct.b_factor.mean()) # this will be the pLDDT
88
+ with open("result.pdb") as f:
89
+ lines = [line for line in f.readlines() if line.startswith('ATOM')]
90
+ bfactors = [float(line[60:66]) for line in lines]
91
+ assert torch.allclose(torch.Tensor(bfactors).mean(), torch.Tensor([88.3]), atol=1e-1)
92
+
93
+
94
+ def test_readme_3():
95
+ # NOTE modification on copy paste from README for speed:
96
+ # * some_proteins -> few_proteins (subset)
97
+ # * I computed reference values a while ago for: esm1b -> esm1 and layers 33 -> 34
98
+ cmd = """
99
+ python scripts/extract.py esm1_t34_670M_UR50S examples/data/few_proteins.fasta examples/data/few_proteins_emb_esm1/ \
100
+ --repr_layers 0 33 34 --include mean per_tok
101
+ """
102
+ _run_py_cmd(cmd)
103
+ confirm_all_tensors_equal(
104
+ "examples/few_proteins_emb_esm1/",
105
+ "https://dl.fbaipublicfiles.com/fair-esm/tests/some_proteins_emb_esm1_t34_670M_UR50S_ref",
106
+ )
107
+
108
+
109
+ def assert_pt_file_equal(f, fref):
110
+ a = torch.load(f)
111
+ b = torch.load(fref)
112
+ # set intersection of dict keys:
113
+ which_layers = a["representations"].keys() & b["representations"].keys()
114
+ assert which_layers, "Expected at least one layer appearing in both dumps"
115
+ for layer in which_layers:
116
+ assert torch.allclose(a["representations"][layer], b["representations"][layer], atol=1e-3)
117
+
118
+
119
+ def confirm_all_tensors_equal(local_dir: str, ref_dir: str) -> None:
120
+ # TODO use pytest built-in fixtures for tmp_path https://docs.pytest.org/en/6.2.x/fixture.html#fixtures
121
+ for fn in Path(local_dir).glob("*.pt"):
122
+ with tempfile.NamedTemporaryFile(mode="w+b", prefix=fn.name) as f:
123
+ ref_url = f"{ref_dir}/{fn.name}"
124
+ with requests.get(ref_url, stream=True) as r:
125
+ shutil.copyfileobj(r.raw, f)
126
+ f.seek(0)
127
+ assert_pt_file_equal(fn, f)
128
+
129
+
130
+ def test_msa_transformers():
131
+ _test_msa_transformer(*esm.pretrained.esm_msa1_t12_100M_UR50S())
132
+ _test_msa_transformer(*esm.pretrained.esm_msa1b_t12_100M_UR50S())
133
+
134
+
135
+ def _test_msa_transformer(model, alphabet):
136
+ batch_converter = alphabet.get_batch_converter()
137
+ # Make an "MSA" of size 3
138
+ data = [
139
+ ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
140
+ ("protein2", "MHTVRQSRLKSIVRILEMSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
141
+ ("protein3", "MHTVRQSRLKSIVRILEMSKEPVSGAQL---LSVSRQVIVQDIAYLRSLGYNIVAT----VLAGG"),
142
+ ]
143
+ batch_labels, batch_strs, batch_tokens = batch_converter(data)
144
+
145
+ with torch.no_grad():
146
+ results = model(batch_tokens, repr_layers=[12], return_contacts=True)
147
+ token_representations = results["representations"][12]
148
+ assert token_representations.shape == (1, 3, 66, 768)
149
+
150
+
151
+ def test_variant_readme_1():
152
+ cmd = """
153
+ python predict.py \
154
+ --model-location esm1v_t33_650M_UR90S_1 esm1v_t33_650M_UR90S_2 esm1v_t33_650M_UR90S_3 esm1v_t33_650M_UR90S_4 esm1v_t33_650M_UR90S_5 \
155
+ --sequence HPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW \
156
+ --dms-input ./data/BLAT_ECOLX_Ranganathan2015.csv \
157
+ --mutation-col mutant \
158
+ --dms-output ./data/BLAT_ECOLX_Ranganathan2015_labeled.csv \
159
+ --offset-idx 24 \
160
+ --scoring-strategy wt-marginals
161
+ """
162
+ _run_py_cmd(cmd, cwd="examples/variant-prediction/")
163
+
164
+
165
+ def test_variant_readme_2():
166
+ cmd = """
167
+ python predict.py \
168
+ --model-location esm_msa1b_t12_100M_UR50S \
169
+ --sequence HPETLVKVKDAEDQLGARVGYIELDLNSGKILESFRPEERFPMMSTFKVLLCGAVLSRVDAGQEQLGRRIHYSQNDLVEYSPVTEKHLTDGMTVRELCSAAITMSDNTAANLLLTTIGGPKELTAFLHNMGDHVTRLDRWEPELNEAIPNDERDTTMPAAMATTLRKLLTGELLTLASRQQLIDWMEADKVAGPLLRSALPAGWFIADKSGAGERGSRGIIAALGPDGKPSRIVVIYTTGSQATMDERNRQIAEIGASLIKHW \
170
+ --dms-input ./data/BLAT_ECOLX_Ranganathan2015.csv \
171
+ --mutation-col mutant \
172
+ --dms-output ./data/BLAT_ECOLX_Ranganathan2015_labeled.csv \
173
+ --offset-idx 24 \
174
+ --scoring-strategy masked-marginals \
175
+ --msa-path ./data/BLAT_ECOLX_1_b0.5.a3m
176
+ """
177
+ _run_py_cmd(cmd, cwd="examples/variant-prediction/")
178
+
179
+
180
+ if __name__ == "__main__":
181
+ confirm_all_tensors_equal(
182
+ "examples/few_proteins_emb_esm1/",
183
+ "https://dl.fbaipublicfiles.com/fair-esm/tests/some_proteins_emb_esm1_t34_670M_UR50S_ref/",
184
+ )