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Co-authored-by: Jessica Lin <jessicajxlin@users.noreply.huggingface.co>

.gitattributes

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 CatPred-DB_Cleaned/kcat_max_wt_singleSeqs_wpdbs_cleaned.csv filter=lfs diff=lfs merge=lfs -text
 CatPred-DB_Cleaned/ki_mean_wt_singleSeqs_wpdbs_cleaned.csv filter=lfs diff=lfs merge=lfs -text
 CatPred-DB_Cleaned/km_mean_wt_singleSeqs_wpdbs_cleaned.csv filter=lfs diff=lfs merge=lfs -text
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+188,10821,10981,MVAAPCARRLARRSHSALLAALTVLLLQTLVVWNFSSLDSGAGERRGGAAVGGGEQPPPAPAPRRERRDLPAEPAAARGGGGGGGGGGGGRGPQARARGGGPGEPRGQQPASRGALPARALDPHPSPLITLETQDGYFSHRPKEKVRTDSNNENSVPKDFENVDNSNFAPRTQKQKHQPELAKKPPSRQKELLKRKLEQQEKGKGHTFPGKGPGEVLPPGDRAAANSSHGKDVSRPPHARKTGGSSPETKYDQPPKCDISGKEAISALSRAKSKHCRQEIGETYCRHKLGLLMPEKVTRFCPLEGKANKNVQWDEDSVEYMPANPVRIAFVLVVHGRASRQLQRMFKAIYHKDHFYYIHVDKRSNYLHRQVLQVSRQYSNVRVTPWRMATIWGGASLLSTYLQSMRDLLEMTDWPWDFFINLSAADYPIRTNDQLVAFLSRYRDMNFLKSHGRDNARFIRKQGLDRLFLECDAHMWRLGDRRIPEGIAVDGGSDWFLLNRRFVEYVTFSTDDLVTKMKQFYSYTLLPAESFFHTVLENSPHCDTMVDNNLRITNWNRKLGCKCQYKHIVDWCGCSPNDFKPQDFHRFQQTARPTFFARKFEAVVNQEIIGQLDYYLYGNYPAGTPGLRSYWENVYDEPDGIHSLSDVTLTLYHSFARLGLRRAETSLHTDGENSCRYYPMGHPASVHLYFLADRFQGFLIKHHATNLAVSKLETLETWVMPKKVFKIASPPSDFGRLQFSEVGTDWDAKERLFRNFGGLLGPMDEPVGMQKWGKGPNVTVTVIWVDPVNVIAATYDILIESTAEFTHYKPPLNLPLRPGVWTVKILHHWVPVAETKFLVAPLTFSNRQPIKPEEALKLHNGPLRNAYMEQSFQSLNPVLSLPINPAQVEQARRNAASTGTALEGWLDSLVGGMWTAMDICATGPTACPVMQTCSQTAWSSFSPDPKSELGAVKPDGRLR,brenda,Q86Y38,O=c1nc(O)ccn1[C@@H]1O[C@H](COP(=O)(O)OP(=O)(O)O)[C@@H](O)[C@H]1O,0.0108,37.0,6.5,2.4.2.26,9606.0,-1.9665762445130504,-1.9665762445130504,O=c1nc(O)ccn1[C@@H]1O[C@H](COP(=O)(O)OP(=O)(O)O)[C@@H](O)[C@H]1O__MVAAPCARRLARRSHSALLAALTVLLLQTLVVWNFSSLDSGAGERRGGAAVGGGEQPPPAPAPRRERRDLPAEPAAARGGGGGGGGGGGGRGPQARARGGGPGEPRGQQPASRGALPARALDPHPSPLITLETQDGYFSHRPKEKVRTDSNNENSVPKDFENVDNSNFAPRTQKQKHQPELAKKPPSRQKELLKRKLEQQEKGKGHTFPGKGPGEVLPPGDRAAANSSHGKDVSRPPHARKTGGSSPETKYDQPPKCDISGKEAISALSRAKSKHCRQEIGETYCRHKLGLLMPEKVTRFCPLEGKANKNVQWDEDSVEYMPANPVRIAFVLVVHGRASRQLQRMFKAIYHKDHFYYIHVDKRSNYLHRQVLQVSRQYSNVRVTPWRMATIWGGASLLSTYLQSMRDLLEMTDWPWDFFINLSAADYPIRTNDQLVAFLSRYRDMNFLKSHGRDNARFIRKQGLDRLFLECDAHMWRLGDRRIPEGIAVDGGSDWFLLNRRFVEYVTFSTDDLVTKMKQFYSYTLLPAESFFHTVLENSPHCDTMVDNNLRITNWNRKLGCKCQYKHIVDWCGCSPNDFKPQDFHRFQQTARPTFFARKFEAVVNQEIIGQLDYYLYGNYPAGTPGLRSYWENVYDEPDGIHSLSDVTLTLYHSFARLGLRRAETSLHTDGENSCRYYPMGHPASVHLYFLADRFQGFLIKHHATNLAVSKLETLETWVMPKKVFKIASPPSDFGRLQFSEVGTDWDAKERLFRNFGGLLGPMDEPVGMQKWGKGPNVTVTVIWVDPVNVIAATYDILIESTAEFTHYKPPLNLPLRPGVWTVKILHHWVPVAETKFLVAPLTFSNRQPIKPEEALKLHNGPLRNAYMEQSFQSLNPVLSLPINPAQVEQARRNAASTGTALEGWLDSLVGGMWTAMDICATGPTACPVMQTCSQTAWSSFSPDPKSELGAVKPDGRLR,./data/raw/alphafold//AF-Q86Y38-F1-model_v4.pdb,1551,32,96,1472
+192,6886,7002,MAMLKNYGACLKDSTLQFPRPNCGDINKRRNAKWKSAQAAVIPNFHLPMRSFEVKNRTSVDDIKSLRLITAIKTPYLPDGRFDLEAYDALVNMQIVDGAEGVIVGGTTGEGQLMSWDEHIMLIGHTVNCFGGSIKVIGNTGSNSTREAIHATEQGFAVGMHAALHINPYYGKTSLEGLVSHFESVLPMGPTVIYNVPSRTGQDIPPGVIHTVAQSANLAGVKECVGNDRIKQYTDNRIVVWSGNDDQCHDAKWDYGATGVISVTSNLIPGLMRQLLFKGKNPSLNAKIMPLVNWLFEEPNPIGLNTALAQLGVVRPVFRLPYVPLPLAKRVEFVNIVKEIGRENFVGEKDVKVLDDDDFILVGRY,brenda,D7U7T8,NCCCC[C@H](N)C(=O)O,0.049,30.0,8.0,4.3.3.7,29760.0,-1.3098039199714864,-1.3098039199714864,NCCCC[C@H](N)C(=O)O__MAMLKNYGACLKDSTLQFPRPNCGDINKRRNAKWKSAQAAVIPNFHLPMRSFEVKNRTSVDDIKSLRLITAIKTPYLPDGRFDLEAYDALVNMQIVDGAEGVIVGGTTGEGQLMSWDEHIMLIGHTVNCFGGSIKVIGNTGSNSTREAIHATEQGFAVGMHAALHINPYYGKTSLEGLVSHFESVLPMGPTVIYNVPSRTGQDIPPGVIHTVAQSANLAGVKECVGNDRIKQYTDNRIVVWSGNDDQCHDAKWDYGATGVISVTSNLIPGLMRQLLFKGKNPSLNAKIMPLVNWLFEEPNPIGLNTALAQLGVVRPVFRLPYVPLPLAKRVEFVNIVKEIGRENFVGEKDVKVLDDDDFILVGRY,./data/raw/alphafold//AF-D7U7T8-F1-model_v4.pdb,1068,631,2008,736
+196,8376,8511,MVRAIITNAPNGGVKIENVNINEPEHYEVKLRPVYTGLCGTDRGEVLGNLSFAYNEPGYNYLVLGHEAICQVIEASENPYKIKPGDYVVPVVRRPGKCVNCRIGREDDCSDGDKHEAGITGLHGFMRDYFYDEAKNLVKINDKNMVKVAVLTEPTKNVMKAFEVFDTVSKRSIFQGDDSTNLTKNCLIIGTGSEAFLYAFMAREYRFNVFMTNRHPVGEEKLSIISRINADFYDYTREDPLKGIDLLIDTSGDPGTIFRFVRKMNYNGVVILFGTNGRAPATSIDGEDIDYIIERNISLVGSVDGAKRHYLRAVEYLEKWNYSEGSVINRLITGVFEPEDVSIFTKKPENEIKSVIKWS,brenda,Q6L047,Nc1ncnc2c1ncn2[C@@H]1O[C@H](COP(=O)(O)OP(=O)(O)OP(=O)(O)O)[C@@H](O)[C@H]1O,5.9,55.0,6.5,1.1.1.360,46632.0,0.7708520116421442,0.7708520116421442,Nc1ncnc2c1ncn2[C@@H]1O[C@H](COP(=O)(O)OP(=O)(O)OP(=O)(O)O)[C@@H](O)[C@H]1O__MVRAIITNAPNGGVKIENVNINEPEHYEVKLRPVYTGLCGTDRGEVLGNLSFAYNEPGYNYLVLGHEAICQVIEASENPYKIKPGDYVVPVVRRPGKCVNCRIGREDDCSDGDKHEAGITGLHGFMRDYFYDEAKNLVKINDKNMVKVAVLTEPTKNVMKAFEVFDTVSKRSIFQGDDSTNLTKNCLIIGTGSEAFLYAFMAREYRFNVFMTNRHPVGEEKLSIISRINADFYDYTREDPLKGIDLLIDTSGDPGTIFRFVRKMNYNGVVILFGTNGRAPATSIDGEDIDYIIERNISLVGSVDGAKRHYLRAVEYLEKWNYSEGSVINRLITGVFEPEDVSIFTKKPENEIKSVIKWS,./data/raw/alphafold//AF-Q6L047-F1-model_v4.pdb,1271,889,2195,1038
+200,7095,7215,MTATPESLFPTGDELDSSQLQMESDEVDTLKEGEDPADRMHPFLAIYELQSLKVHPLVFAPGVPVTAQVVGTERYTSGSKVGTCTLYSVRLTHGDFSWTTKKKYRHFQELHRDLLRHKVLMSLLPLARFAVAYSPARDAGNREMPSLPRAGPEGSTRHAASKQKYLENYLNRLLTMSFYRNYHAMTEFLEVSQLSFIPDLGRKGLEGMIRKRSGGHRVPGLTCCGRDQVCYRWSKRWLVVKDSFLLYMCLETGAISFVQLFDPGFEVQVGKRSTEARHGVRIDTSHRSLILKCSSYRQARWWAQEITELAQGPGRDFLQLHRHDSYAPPRPGTLARWFVNGAGYFAAVADAILRAQEEIFITDWWLSPEVYLKRPAHSDDWRLDIMLKRKAEEGVRVSILLFKEVELALGINSGYSKRALMLLHPNIKVMRHPDQVTLWAHHEKLLVVDQVVAFLGGLDLAYGRWDDLHYRLTDLGDSSESAASQPPTPRPDSPATPDLSHNQFFWLGKDYSNLITKDWVQLDRPFEDFIDRETTPRMPWRDVGVVVHGLPARDLARHFIQRWNFTKTTKAKYKTPTYPYLLPKSTSTANQLPFTLPGGQCTTVQVLRSVDRWSAGTLENSILNAYLHTIRESQHFLYIENQFFISCSDGRTVLNKVGDEIVDRILKAHKQGWCYRVYVLLPLLPGFEGDISTGGGNSIQAILHFTYRTLCRGEYSILHRLKAAMGTAWRDYISICGLRTHGELGGHPVSELIYIHSKVLIADDRTVIIGSANINDRSLLGKRDSELAVLIEDTETEPSLMNGAEYQAGRFALSLRKHCFGVILGANTRPDLDLRDPICDDFFQLWQDMAESNANIYEQIFRCLPSNATRSLRTLREYVAVEPLATVSPPLARSELTQVQGHLVHFPLKFLEDESLLPPLGSKEGMIPLEVWT,brenda,O14939,NS(=O)(=O)Cc1noc2ccccc12,0.011,22.0,7.5,4.2.1.1,9606.0,-1.958607314841775,-1.958607314841775,NS(=O)(=O)Cc1noc2ccccc12__MTATPESLFPTGDELDSSQLQMESDEVDTLKEGEDPADRMHPFLAIYELQSLKVHPLVFAPGVPVTAQVVGTERYTSGSKVGTCTLYSVRLTHGDFSWTTKKKYRHFQELHRDLLRHKVLMSLLPLARFAVAYSPARDAGNREMPSLPRAGPEGSTRHAASKQKYLENYLNRLLTMSFYRNYHAMTEFLEVSQLSFIPDLGRKGLEGMIRKRSGGHRVPGLTCCGRDQVCYRWSKRWLVVKDSFLLYMCLETGAISFVQLFDPGFEVQVGKRSTEARHGVRIDTSHRSLILKCSSYRQARWWAQEITELAQGPGRDFLQLHRHDSYAPPRPGTLARWFVNGAGYFAAVADAILRAQEEIFITDWWLSPEVYLKRPAHSDDWRLDIMLKRKAEEGVRVSILLFKEVELALGINSGYSKRALMLLHPNIKVMRHPDQVTLWAHHEKLLVVDQVVAFLGGLDLAYGRWDDLHYRLTDLGDSSESAASQPPTPRPDSPATPDLSHNQFFWLGKDYSNLITKDWVQLDRPFEDFIDRETTPRMPWRDVGVVVHGLPARDLARHFIQRWNFTKTTKAKYKTPTYPYLLPKSTSTANQLPFTLPGGQCTTVQVLRSVDRWSAGTLENSILNAYLHTIRESQHFLYIENQFFISCSDGRTVLNKVGDEIVDRILKAHKQGWCYRVYVLLPLLPGFEGDISTGGGNSIQAILHFTYRTLCRGEYSILHRLKAAMGTAWRDYISICGLRTHGELGGHPVSELIYIHSKVLIADDRTVIIGSANINDRSLLGKRDSELAVLIEDTETEPSLMNGAEYQAGRFALSLRKHCFGVILGANTRPDLDLRDPICDDFFQLWQDMAESNANIYEQIFRCLPSNATRSLRTLREYVAVEPLATVSPPLARSELTQVQGHLVHFPLKFLEDESLLPPLGSKEGMIPLEVWT,./data/raw/alphafold//AF-O14939-F1-model_v4.pdb,1090,661,2043,774
+201,5780,5879,MKPKLMYQELKVPAEEPASELPMNEIEAWKAAEKKARWVLLVLILAVVGFGALMTQLFLWEYGDLHLFGPNQRPAPCYDPCEAVLVESIPEGLDFPNASTSNPSTSQAWLGLLAGAHSSLDIASFYWTLTNNDTHTQEASAQQGEEVLRQLQTLAPRGVKVRIAVSKPNGPQPQADLQALLQSGAQVRMVDMQKLTHGVLHTKFWVVDQTHFYLGSANMDWRSLTQVKELGVVMYNCSCLARDLTKIFEAYWFLGQAGSSIPSTWPRPYDTRYNQETPMEICLNGTPALAYLASAPPPLCPSGRTPDLKALLNVVDNARSFIYIAVMNYLPIMEFSHPRRFWPAIDDGLRRAAYERGVKVRLLISCWGHSDPSMRAFLLSLAALRDNHTHSDIQVKLFVVPADDAQARIPYARVNHNKYMVTERATYIGTSNWSGSYFTETAGTSLLVTQNGRGGLRSQLEAVFLRDWDSPYSHDLDAAADSVGNACRLL,uniprot_search,Q2KJJ8,N=C(N)O,2.0,37.0,6.2375,3.1.16.1,9913.0,0.3010299956639812,0.3010299956639812,N=C(N)O__MKPKLMYQELKVPAEEPASELPMNEIEAWKAAEKKARWVLLVLILAVVGFGALMTQLFLWEYGDLHLFGPNQRPAPCYDPCEAVLVESIPEGLDFPNASTSNPSTSQAWLGLLAGAHSSLDIASFYWTLTNNDTHTQEASAQQGEEVLRQLQTLAPRGVKVRIAVSKPNGPQPQADLQALLQSGAQVRMVDMQKLTHGVLHTKFWVVDQTHFYLGSANMDWRSLTQVKELGVVMYNCSCLARDLTKIFEAYWFLGQAGSSIPSTWPRPYDTRYNQETPMEICLNGTPALAYLASAPPPLCPSGRTPDLKALLNVVDNARSFIYIAVMNYLPIMEFSHPRRFWPAIDDGLRRAAYERGVKVRLLISCWGHSDPSMRAFLLSLAALRDNHTHSDIQVKLFVVPADDAQARIPYARVNHNKYMVTERATYIGTSNWSGSYFTETAGTSLLVTQNGRGGLRSQLEAVFLRDWDSPYSHDLDAAADSVGNACRLL,./data/raw/alphafold//AF-Q2KJJ8-F1-model_v4.pdb,895,420,1778,489
+203,10807,10966,MADAEPTGGGGKGAEDIGGAAEAHSRDSPASAALPTAPSTSSSSADNGNLHRSSTMPGVIKDAEIITETTGPSNFERSKTERRRQNNDPAKQLLDDKISVRKKLKMLNRIATVRDDGTVVVDVPSSLDLAPLDVGGEDGYGDVTVEESLDGADIPSIPPMQIVILIVGTRGDVQPFVAIAKRLQDYGHRVRLATHANYKEFVLTAGLEFFPLGGDPKLLAEYMVKNKGFLPSGPSEIPIQRKQMKEIIFSLLPACKDPDPDTGIPFKVDAIIANPPAYGHTHVAEALKVPIHIFFTMPWTPTSEFPHPLSRVKTSAGYRLSYQIVDSMIWLGIRDMINEFRKKKLKLRPVTYLSGSQGSGSDIPHGYIWSPHLVPKPKDWGPKIDVVGFCFLDLASDYEPPEELVKWLEAGDKPIYVGFGSLPVQDPTKMTETIIQALEMTGQRGIINKGWGGLGTLAEPKDSIYVLDNCPHDWLFLQCKAVVHHGGAGTTAAGLKAACPTTIVPFFGDQQFWGDRVHARGVGPVPIPVEQFNLQKLVDAMKFMLEPEVKEKAVELAKAMESEDGVTGAVRAFLKHLPSSKEDENSPPPTPHGFLEFLGPVSKCLGCS,uniprot_search,O22678,O=c1nc(O)ccn1[C@@H]1O[C@H](COP(=O)(O)OP(=O)(O)O)[C@@H](O)[C@H]1O,0.047,38.0,7.5,2.4.1.173,4498.0,-1.3279021420642825,-1.3279021420642825,O=c1nc(O)ccn1[C@@H]1O[C@H](COP(=O)(O)OP(=O)(O)O)[C@@H](O)[C@H]1O__MADAEPTGGGGKGAEDIGGAAEAHSRDSPASAALPTAPSTSSSSADNGNLHRSSTMPGVIKDAEIITETTGPSNFERSKTERRRQNNDPAKQLLDDKISVRKKLKMLNRIATVRDDGTVVVDVPSSLDLAPLDVGGEDGYGDVTVEESLDGADIPSIPPMQIVILIVGTRGDVQPFVAIAKRLQDYGHRVRLATHANYKEFVLTAGLEFFPLGGDPKLLAEYMVKNKGFLPSGPSEIPIQRKQMKEIIFSLLPACKDPDPDTGIPFKVDAIIANPPAYGHTHVAEALKVPIHIFFTMPWTPTSEFPHPLSRVKTSAGYRLSYQIVDSMIWLGIRDMINEFRKKKLKLRPVTYLSGSQGSGSDIPHGYIWSPHLVPKPKDWGPKIDVVGFCFLDLASDYEPPEELVKWLEAGDKPIYVGFGSLPVQDPTKMTETIIQALEMTGQRGIINKGWGGLGTLAEPKDSIYVLDNCPHDWLFLQCKAVVHHGGAGTTAAGLKAACPTTIVPFFGDQQFWGDRVHARGVGPVPIPVEQFNLQKLVDAMKFMLEPEVKEKAVELAKAMESEDGVTGAVRAFLKHLPSSKEDENSPPPTPHGFLEFLGPVSKCLGCS,./data/raw/alphafold//AF-O22678-F1-model_v4.pdb,1547,28,90,1464
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+255,4488,4574,MCGIWALFGSDDCLSVQCLSAMKIAHRGPDAFRFENVNGYTNCCFGFHRLAVVDPLFGMQPIRVKKYPYLWLCYNGEIYNHKKMQQHFEFEYQTKVDGEIILHLYDKGGIEQTICMLDGVFAFVLLDTANKKVFLGRDTYGVRPLFKAMTEDGFLAVCSEAKGLVTLKHSATPFLKVEPFLPGHYEVLDLKPNGKVASVEMVKYHHCRDVPLHALYDNVEKLFPGFEIETVKNNLRILFNNAVKKRLMTDRRIGCLLSGGLDSSLVAATLLKQLKEAQVQYPLQTFAIGMEDSPDLLAARKVADHIGSEHYEVLFNSEEGIQALDEVIFSLETYDITTVRASVGMYLISKYIRKNTDSVVIFSGEGSDELTQGYIYFHKAPSPEKAEEESERLLRELYLFDVLRADRTTAAHGLELRVPFLDHRFSSYYLSLPPEMRIPKNGIEKHLLRETFEDSNLIPKEILWRPKEAFSDGITSVKNSWFKILQEYVEHQVDDAMMANAAQKFPFNTPKTKEGYYYRQVFERHYPGRADWLSHYWMPKWINATDPSARTLTHYKSAVKA,uniprot_search,P08243,CS(=O)(C[C@H](N)C(=O)O)=NP(=O)(O)OC[C@H]1O[C@@H](n2cnc3c(N)ncnc32)[C@@H](O)C1O,0.000285,30.0,7.416666666666667,6.3.5.4,9606.0,-3.54515513999149,-3.54515513999149,CS(=O)(C[C@H](N)C(=O)O)=NP(=O)(O)OC[C@H]1O[C@@H](n2cnc3c(N)ncnc32)[C@@H](O)C1O__MCGIWALFGSDDCLSVQCLSAMKIAHRGPDAFRFENVNGYTNCCFGFHRLAVVDPLFGMQPIRVKKYPYLWLCYNGEIYNHKKMQQHFEFEYQTKVDGEIILHLYDKGGIEQTICMLDGVFAFVLLDTANKKVFLGRDTYGVRPLFKAMTEDGFLAVCSEAKGLVTLKHSATPFLKVEPFLPGHYEVLDLKPNGKVASVEMVKYHHCRDVPLHALYDNVEKLFPGFEIETVKNNLRILFNNAVKKRLMTDRRIGCLLSGGLDSSLVAATLLKQLKEAQVQYPLQTFAIGMEDSPDLLAARKVADHIGSEHYEVLFNSEEGIQALDEVIFSLETYDITTVRASVGMYLISKYIRKNTDSVVIFSGEGSDELTQGYIYFHKAPSPEKAEEESERLLRELYLFDVLRADRTTAAHGLELRVPFLDHRFSSYYLSLPPEMRIPKNGIEKHLLRETFEDSNLIPKEILWRPKEAFSDGITSVKNSWFKILQEYVEHQVDDAMMANAAQKFPFNTPKTKEGYYYRQVFERHYPGRADWLSHYWMPKWINATDPSARTLTHYKSAVKA,./data/raw/alphafold//AF-P08243-F1-model_v4.pdb,738,225,1655,254
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+304,1106,1129,MALRGVSVRLLSRGPGLHVLRTWVSSAAQTEKGGRTQSQLAKSSRPEFDWQDPLVLEEQLTTDEILIRDTFRTYCQERLMPRILLANRNEVFHREIISEMGELGVLGPTIKGYGCAGVSSVAYGLLARELERVDSGYRSAMSVQSSLVMHPIYAYGSEEQRQKYLPQLAKGELLGCFGLTEPNSGSDPSSMETRAHYNSSNKSYTLNGTKTWITNSPMADLFVVWARCEDGCIRGFLLEKGMRGLSAPRIQGKFSLRASATGMIIMDGVEVPEENVLPGASSLGGPFGCLNNARYGIAWGVLGASEFCLHTARQYALDRMQFGVPLARNQLIQKKLADMLTEITLGLHACLQLGRLKDQDKAAPEMVSLLKRNNCGKALDIARQARDMLGGNGISDEYHVIRHAMNLEAVNTYEGTHDIHALILGRAITGIQAFTASK,brenda,Q92947,CC(C)(COP(=O)(O)OP(=O)(O)OC[C@H]1O[C@@H](n2cnc3c(N)ncnc32)[C@H](O)[C@@H]1OP(=O)(O)O)[C@@H](O)C(O)=NCCC(O)=NCCSOC(=O)SCCC(=O)O,0.0069,25.0,7.6,1.3.8.6,9606.0,-2.161150909262745,-1.9016080311311456,CC(C)(COP(=O)(O)OP(=O)(O)OC[C@H]1O[C@@H](n2cnc3c(N)ncnc32)[C@H](O)[C@@H]1OP(=O)(O)O)[C@@H](O)C(O)=NCCC(O)=NCCSOC(=O)SCCC(=O)O__MALRGVSVRLLSRGPGLHVLRTWVSSAAQTEKGGRTQSQLAKSSRPEFDWQDPLVLEEQLTTDEILIRDTFRTYCQERLMPRILLANRNEVFHREIISEMGELGVLGPTIKGYGCAGVSSVAYGLLARELERVDSGYRSAMSVQSSLVMHPIYAYGSEEQRQKYLPQLAKGELLGCFGLTEPNSGSDPSSMETRAHYNSSNKSYTLNGTKTWITNSPMADLFVVWARCEDGCIRGFLLEKGMRGLSAPRIQGKFSLRASATGMIIMDGVEVPEENVLPGASSLGGPFGCLNNARYGIAWGVLGASEFCLHTARQYALDRMQFGVPLARNQLIQKKLADMLTEITLGLHACLQLGRLKDQDKAAPEMVSLLKRNNCGKALDIARQARDMLGGNGISDEYHVIRHAMNLEAVNTYEGTHDIHALILGRAITGIQAFTASK,./data/raw/alphafold//AF-Q92947-F1-model_v4.pdb,304,1683,1031,2431
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+623,7404,7526,MAIKLGIISDPISGFNIKKDTGFAMMMQAQARGYEIYYMEMDDLSLRQGQAYATAAKAQVFDDPNNWYQLEQKTTIALGDLDVILMRKDPPFDTEYIYATYILERAEQAGTLIVNKPQSLRDANEKLFTAWFSEYTPDTLVTRSQKQIREFLAQHKDIILKPLDGMGGASIFRVKADDANIGVICETLTEHGSRFAMVQNYVPEITQGDKRVLVVDGEVIPYCLARIPQNGETRGNLAVGGRGEARPLSESDLKIAQAVAPTLKEKGLIFVGLDIIGDKLTEINVTAPTCVKEIEAAYDISITGILFDAIEKRLAK,brenda,Q3IFA2,N[C@@H](CCC(O)=N[C@@H](CSSC[C@H](N=C(O)CC[C@H](N)C(=O)O)C(O)=NCC(=O)O)C(O)=NCC(=O)O)C(=O)O,10.7,30.0,7.8,6.3.2.3,228.0,1.0293837776852095,1.0293837776852095,N[C@@H](CCC(O)=N[C@@H](CSSC[C@H](N=C(O)CC[C@H](N)C(=O)O)C(O)=NCC(=O)O)C(O)=NCC(=O)O)C(=O)O__MAIKLGIISDPISGFNIKKDTGFAMMMQAQARGYEIYYMEMDDLSLRQGQAYATAAKAQVFDDPNNWYQLEQKTTIALGDLDVILMRKDPPFDTEYIYATYILERAEQAGTLIVNKPQSLRDANEKLFTAWFSEYTPDTLVTRSQKQIREFLAQHKDIILKPLDGMGGASIFRVKADDANIGVICETLTEHGSRFAMVQNYVPEITQGDKRVLVVDGEVIPYCLARIPQNGETRGNLAVGGRGEARPLSESDLKIAQAVAPTLKEKGLIFVGLDIIGDKLTEINVTAPTCVKEIEAAYDISITGILFDAIEKRLAK,./data/raw/alphafold//AF-Q3IFA2-F1-model_v4.pdb,1103,677,109,794
+676,7376,7497,MGLLTTGGQPLQWGTEENNRAKEYVSAHGIQQFLWVYNKQKELPDFPFLWGDEIEHQLVRLESRKVKLSLNAADVIKRLSQSSGESTAEWRPEYGSFMVESLPGKPYSSNVDSLCSVEVNMRRRYHMLDAAAGDNTFAVTLVTFPLMGVGGFTTSTETESPCSQSLFVPDACINDSHPRFKALTNNIRLRRGRKVCIQVPMFIDRYTMERTVDPRVNIDLHPRNVEIVCTFSGEKTSSKGKKFSCDTITPKRVPLENEAITNMTHLYTPVTHYYYAQYFQNLQAERVKQRYQACPCPVPSVNHPCIYMDCMAFGMGCNCLQLTMQLPNEAQARHIYDQLGILCPLFLALSSATPFQKGILCESDVRWLTITASVDDRKYEEVPHIIKSRYDSISVFVSSLTPNLEEFNDEVLRINDSYYNLLTREGVDSRLATHIAHLFIRDPLVIYDQMIDIDDHTHVDHFENIQSTNWQTVRLKLPVLDSTLGWRVEFRVMDVMPTPFENAAYSVFVVLLTRAIMRFGAVFYTKLSIVDENMGRAHNINPCQQHYIMRRDIFASKVTTDPSENCELTVGEVINGKPGEYYGLIPLVRRYLEEENIQSDVVEGYLNFISKRACGEIPTAAQYLRNFVKKHPDYREDSRLTEQIAHDVVNHVHQLACGGNASESMIGAYTLGSKRQREG,brenda,Q26820,N[C@@H](CCC(O)=N[C@@H](CS)C(O)=NCC(=O)O)C(=O)O,1.1,37.0,8.0,6.3.2.2,5691.0,0.041392685158225,0.041392685158225,N[C@@H](CCC(O)=N[C@@H](CS)C(O)=NCC(=O)O)C(=O)O__MGLLTTGGQPLQWGTEENNRAKEYVSAHGIQQFLWVYNKQKELPDFPFLWGDEIEHQLVRLESRKVKLSLNAADVIKRLSQSSGESTAEWRPEYGSFMVESLPGKPYSSNVDSLCSVEVNMRRRYHMLDAAAGDNTFAVTLVTFPLMGVGGFTTSTETESPCSQSLFVPDACINDSHPRFKALTNNIRLRRGRKVCIQVPMFIDRYTMERTVDPRVNIDLHPRNVEIVCTFSGEKTSSKGKKFSCDTITPKRVPLENEAITNMTHLYTPVTHYYYAQYFQNLQAERVKQRYQACPCPVPSVNHPCIYMDCMAFGMGCNCLQLTMQLPNEAQARHIYDQLGILCPLFLALSSATPFQKGILCESDVRWLTITASVDDRKYEEVPHIIKSRYDSISVFVSSLTPNLEEFNDEVLRINDSYYNLLTREGVDSRLATHIAHLFIRDPLVIYDQMIDIDDHTHVDHFENIQSTNWQTVRLKLPVLDSTLGWRVEFRVMDVMPTPFENAAYSVFVVLLTRAIMRFGAVFYTKLSIVDENMGRAHNINPCQQHYIMRRDIFASKVTTDPSENCELTVGEVINGKPGEYYGLIPLVRRYLEEENIQSDVVEGYLNFISKRACGEIPTAAQYLRNFVKKHPDYREDSRLTEQIAHDVVNHVHQLACGGNASESMIGAYTLGSKRQREG,./data/raw/alphafold//AF-Q26820-F1-model_v4.pdb,1098,672,102,786
+687,8400,8535,MLPFATIPATERNSAAQHQDPSPMSQPFDLPALASSLADKSPQDILKAAFEHFGDELWISFSGAEDVVLVDMAWKLNRNVKVFSLDTGRLHPETYRFIDQVREHYGIAIDVLSPDPRLLEPLVKEKGLFSFYRDGHGECCGIRKIEPLKRKLAGVRAWATGQRRDQSPGTRSQVAVLEIDGAFSTPEKPLYKFNPLSSMTSEEVWGYIRMLELPYNSLHERGYISIGCEPCTRPVLPNQHEREGRWWWEEATHKECGLHAGNLISKA,brenda,O05927,Nc1ncnc2c1ncn2[C@@H]1O[C@H](COP(=O)(O)OS(=O)(=O)O)[C@@H](O)[C@H]1O,1.0,20.0,8.5,1.8.4.10,287.0,0.0,0.0,Nc1ncnc2c1ncn2[C@@H]1O[C@H](COP(=O)(O)OS(=O)(=O)O)[C@@H](O)[C@H]1O__MLPFATIPATERNSAAQHQDPSPMSQPFDLPALASSLADKSPQDILKAAFEHFGDELWISFSGAEDVVLVDMAWKLNRNVKVFSLDTGRLHPETYRFIDQVREHYGIAIDVLSPDPRLLEPLVKEKGLFSFYRDGHGECCGIRKIEPLKRKLAGVRAWATGQRRDQSPGTRSQVAVLEIDGAFSTPEKPLYKFNPLSSMTSEEVWGYIRMLELPYNSLHERGYISIGCEPCTRPVLPNQHEREGRWWWEEATHKECGLHAGNLISKA,./data/raw/alphafold//AF-O05927-F1-model_v4.pdb,1276,900,2206,1049
+701,6446,6556,MRPGHLMVLALLAPTLIAPPVFAQSVLTRDNGAKVGDNQNSQTAGATGPTLLQDVQLIQKLQRFDRERIPERVVHARGTGVKGEFTATADLSSLTKAKVFSAGEKTPVFVRFSSVVHGNHSPETLRDPHGFATKFYTSEGNWDLVGNNFPTFFIRDAIKFPDMVHAFKPDPRTNLDDDSRRFDFFSHVPEATRTLTLLYSNEGTPAGYRFMDGNGVHAYKLVNAQGEVHYVKFHWKTLQGIKNLDPKQVAAVQAKDYSHLTNDLVGAIKKGDYPKWDLYVQVLKPEDLAKFDFDPLDATKIWPDVPEQKVGQMVLNKNVDNFFQETEQVAMAPANLVPGIEPSEDRLLQGRIFSYADTQLYRIGANGLSLPVNRPRVAVNNGNQDGQGNIGQTTSGVNYEPSRLEPRPQDTAARYSELPLSGTTQQAKITREQNFKQAGELYRSYSKKDRADLVQSFGESLATTDTESKHLMLSFLYKADPEYGTGVTRVAKGDLARVKQLASSLQD,brenda,Q8L2Z7,N=c1nc[nH][nH]1,2.0,37.0,7.0,1.11.1.6,339.0,0.3010299956639812,0.3010299956639812,N=c1nc[nH][nH]1__MRPGHLMVLALLAPTLIAPPVFAQSVLTRDNGAKVGDNQNSQTAGATGPTLLQDVQLIQKLQRFDRERIPERVVHARGTGVKGEFTATADLSSLTKAKVFSAGEKTPVFVRFSSVVHGNHSPETLRDPHGFATKFYTSEGNWDLVGNNFPTFFIRDAIKFPDMVHAFKPDPRTNLDDDSRRFDFFSHVPEATRTLTLLYSNEGTPAGYRFMDGNGVHAYKLVNAQGEVHYVKFHWKTLQGIKNLDPKQVAAVQAKDYSHLTNDLVGAIKKGDYPKWDLYVQVLKPEDLAKFDFDPLDATKIWPDVPEQKVGQMVLNKNVDNFFQETEQVAMAPANLVPGIEPSEDRLLQGRIFSYADTQLYRIGANGLSLPVNRPRVAVNNGNQDGQGNIGQTTSGVNYEPSRLEPRPQDTAARYSELPLSGTTQQAKITREQNFKQAGELYRSYSKKDRADLVQSFGESLATTDTESKHLMLSFLYKADPEYGTGVTRVAKGDLARVKQLASSLQD,./data/raw/alphafold//AF-Q8L2Z7-F1-model_v4.pdb,993,539,1904,623
+706,4647,4734,MESYKPMWLKGAIILAINLMERGYKPVAVGLGERDFYIDIKSDTSITLDEVKKAISKNVLANVPIENNQVVYKGNKVSIVEDKVSIPTNLNPKYFEILNISTHHPNPNEQYVRIRGVAFETEEQLKDYLTWLEKAEETDHRLIGEKLDLFSFHEEAGSGLVLFHPKGQTLRNELITFMREINDSMGYQEVYTSHVLKTDIWKISGHYTLYRDKLIVFNMEGDEYGVKPMNCPAHILIYKSKPRTYRDLPIRFSEFGHVYRWEKKGELYGLLRVRGFVQDDGHIFLKEDQLKEEIKMLISKTVEVWHKFGFKDDDIKPYLSTRPDESIGSDELWEKATNALISALQESGLKFGIKEKEGAFYGPKIDFEIRDSLGRWWQLSTIQVDFNLPERFKLEYIDKDGTKKRPVMVHRAIYGSIDRFVAILLEHFKGKLPTWLSPVQVKVLPITDEVNEYAEKVLNDMRKRRIRAEIDYAGETLSRRIKNAYDQGVPYILIVGKKEASQGTVTVRARGNIEVRNVKFEKFLDLMITEITQRDVEQTTAKALK,uniprot_search,A0A0E3JZT2,C[C@@H]1C[C@H](C)C[C@H](C)[C@@H](O)CC(=O)O[C@H]([C@@H]2CCC[C@H]2C(=O)O)C/C=C/C=C(/C#N)[C@H](O)[C@@H](C)C1,4.5e-06,55.0,7.5,6.1.1.3,2287.0,-5.346787486224656,-5.346787486224656,C[C@@H]1C[C@H](C)C[C@H](C)[C@@H](O)CC(=O)O[C@H]([C@@H]2CCC[C@H]2C(=O)O)C/C=C/C=C(/C#N)[C@H](O)[C@@H](C)C1__MESYKPMWLKGAIILAINLMERGYKPVAVGLGERDFYIDIKSDTSITLDEVKKAISKNVLANVPIENNQVVYKGNKVSIVEDKVSIPTNLNPKYFEILNISTHHPNPNEQYVRIRGVAFETEEQLKDYLTWLEKAEETDHRLIGEKLDLFSFHEEAGSGLVLFHPKGQTLRNELITFMREINDSMGYQEVYTSHVLKTDIWKISGHYTLYRDKLIVFNMEGDEYGVKPMNCPAHILIYKSKPRTYRDLPIRFSEFGHVYRWEKKGELYGLLRVRGFVQDDGHIFLKEDQLKEEIKMLISKTVEVWHKFGFKDDDIKPYLSTRPDESIGSDELWEKATNALISALQESGLKFGIKEKEGAFYGPKIDFEIRDSLGRWWQLSTIQVDFNLPERFKLEYIDKDGTKKRPVMVHRAIYGSIDRFVAILLEHFKGKLPTWLSPVQVKVLPITDEVNEYAEKVLNDMRKRRIRAEIDYAGETLSRRIKNAYDQGVPYILIVGKKEASQGTVTVRARGNIEVRNVKFEKFLDLMITEITQRDVEQTTAKALK,./data/raw/alphafold//AF-A0A0E3JZT2-F1-model_v4.pdb,756,250,1682,284
+748,9180,9325,MACLPDDSGPHVGHSTPPRLDQEPCTLSSQKTVTVTPPNFPLTGKVAPPGSKSITNRALLLAALAKGTSRLSGALKSDDTRHMSVALRQMGVTIDEPDDTTFVVTSQGSLQLPAQPLFLGNAGTAMRFLTAAVATVQGTVVLDGDEYMQKRPIGPLLATLGQNGIQVDSPTGCPPVTVHGMGKVQAKRFEIDGGLSSQYVSALLMLAACGEAPIEVALTGKDIGARGYVDLTLDCMRAFGAQVDAVDDTTWRVAPTGYTAHDYLIEPDASAATYLWAAEVLTGGRIDIGVAAQDFTQPDAKAQAVIAQFPNMQATVVGSQMQDAIPTLAVLAAFNNTPVRFTELANLRVKECDRVQALHDGLNEIRPGLATIEGDDLLVASDPALAGTACTALIDTHADHRIAMCFALAGLKVSGVRIQDPDCVAKTYPDYWKAWPSLGVHLND,brenda,A1Z0H6,O=C(O)CNCP(=O)(O)O,0.035,28.0,7.0,2.5.1.19,294.0,-1.4559319556497243,-1.380663378358078,O=C(O)CNCP(=O)(O)O__MACLPDDSGPHVGHSTPPRLDQEPCTLSSQKTVTVTPPNFPLTGKVAPPGSKSITNRALLLAALAKGTSRLSGALKSDDTRHMSVALRQMGVTIDEPDDTTFVVTSQGSLQLPAQPLFLGNAGTAMRFLTAAVATVQGTVVLDGDEYMQKRPIGPLLATLGQNGIQVDSPTGCPPVTVHGMGKVQAKRFEIDGGLSSQYVSALLMLAACGEAPIEVALTGKDIGARGYVDLTLDCMRAFGAQVDAVDDTTWRVAPTGYTAHDYLIEPDASAATYLWAAEVLTGGRIDIGVAAQDFTQPDAKAQAVIAQFPNMQATVVGSQMQDAIPTLAVLAAFNNTPVRFTELANLRVKECDRVQALHDGLNEIRPGLATIEGDDLLVASDPALAGTACTALIDTHADHRIAMCFALAGLKVSGVRIQDPDCVAKTYPDYWKAWPSLGVHLND,./data/raw/alphafold//AF-A1Z0H6-F1-model_v4.pdb,1365,1009,2332,1189
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datasets/splits/km-random_train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dbf56824bbdc3a25c6750799a48c4cf6a00ed47455ca2bfe2618c8f7da32a84e
+size 38841357

datasets/splits/km-random_trainval.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:671b2dbe52cb44291bf50dd265dcbd8c8d3b7dc854680e8d48219c4e201c71fa
+size 43167143

datasets/splits/km-random_trainvaltest.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a567c1f3e26e72cf5167cd5ed82df49e016181a8f7c4b886c6faaf7cef831e42
+size 47925400

scripts/add_pdb_paths.py

@@ -0,0 +1,53 @@
+import requests
+from joblib import Parallel, delayed
+import os
+import argparse
+import sys
+import json
+import pandas as pd
+from tqdm import tqdm
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_file", help="data file", type=str, required=True)
+    parser.add_argument("--alphafold_dir", help="directory where alphafold models are stroed", type=str, required=True)
+    parser.add_argument("--esm_dir", help="directory where to store esm models", type=str, required=True)
+    parser.add_argument("--out_file", help="out file", type=str, required=True)
+    
+    args, unparsed = parser.parse_known_args()
+    parser = argparse.ArgumentParser()
+
+    return args
+
+args = parse_args()
+
+def _is_empty_model(modelpath):
+    try:
+        structure = esm.inverse_folding.util.load_structure(modelpath, 'A')
+        return False
+    except:
+        return True
+
+import ipdb
+ipdb.set_trace()
+alphafold_models = os.listdir(args.alphafold_dir)
+esm_models = os.listdir(args.esm_dir)
+
+af_model_path = lambda uni: args.alphafold_dir + f'/AF-{uni}-F1-model_v4.pdb'
+esm_model_path = lambda uni: args.esm_dir + f'/ESMFold-{uni}-v1.pdb'
+
+df = pd.read_csv(args.data_file)
+
+modelpaths = []
+for uni in tqdm(df.uniprot):
+    modelpath = af_model_path(uni)
+    modelpath2 = esm_model_path(uni)
+    if not _is_empty_model(modelpath): modelpaths.append(modelpath)
+    elif not _is_empty_model(modelpath2): modelpaths.append(modelpath2)
+    else: modelpaths.append(None)
+df['pdbpath'] = modelpaths
+
+df.info()
+df.dropna(subset=['pdbpath'],inplace=True)
+df.reset_index(inplace=True,drop=True)
+df.to_csv(args.out_file)

scripts/count_natural.py

@@ -0,0 +1,36 @@
+import json
+import pandas as pd
+from tqdm import tqdm
+from rdkit import Chem
+
+natural_list = set(json.load(open('./all_natural_metabolite_names.json')))
+metabolite_inchi_smiles_dic = pd.read_csv('../datasets/metabolite_inchi_smiles_brenda_pubchem.tsv',sep='\t',index_col='metabolite')
+name_to_smiles = {}
+for ind, row in tqdm(metabolite_inchi_smiles_dic.iterrows()):
+    try:
+        name_to_smiles[row.name] = Chem.MolToSmiles(Chem.MolFromSmiles(row.smiles))
+    except:
+        continue
+    
+natural_smi_list = [name_to_smiles[name] for name in natural_list if name in name_to_smiles]
+
+for parameter in ['kcat','km','ki']:
+    print(parameter)
+    datafile = f'./data/processed/splits_wpdbs/{parameter}-random_trainvaltest.csv'
+
+    if parameter=='kcat':
+        subcol = 'reactant_smiles'
+    else:
+        subcol = 'substrate_smiles'
+        
+    df = pd.read_csv(datafile)
+    count = 0
+    for ind, row in tqdm(df.iterrows()):
+        natural = True
+        substrates = row[subcol].split('.')
+        for sub in substrates:
+            if not sub in natural_smi_list:
+                natural = False
+        if natural: count+=1
+    
+    print(count)

scripts/create_pdbrecords.py

@@ -0,0 +1,35 @@
+import pandas as pd
+import argparse
+import json
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate json records for test file with only sequences"
+    )
+    parser.add_argument(
+        "--in_file",
+        help="Path to csv file",
+        required=True,
+    )
+    
+    parser.add_argument("--out_file", help="output file for json records")
+
+    args = parser.parse_args()
+    return args
+
+args = parse_args()
+df = pd.read_csv(args.data_file)
+assert('pdbpath' in df.columns)
+assert('sequence' in df.columns)
+
+import json
+dic_full = {}
+for ind, row in df.iterrows():
+    dic = {}
+    dic['name'] = row.pdbpath
+    dic['seq'] = row.sequence
+    dic_full[row.pdbpath] = dic
+
+f = open(args.out_file,'w')
+f.write(json.dumps(dic_full))
+f.close()

scripts/make_splits.py

@@ -0,0 +1,149 @@
+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import PandasTools, Draw
+from rdkit import DataStructs
+from rdkit.ML.Cluster import Butina
+from rdkit.Chem import rdMolDescriptors as rdmd
+from rdkit.Chem import Descriptors, rdChemReactions
+import seaborn as sns
+from tqdm import tqdm
+import os
+from sklearn.model_selection import GroupKFold, train_test_split
+
+def assign_seq_clusters(df, id_cutoff):
+    seqcol = 'sequence'
+    import tempfile
+    import uuid
+
+    # Create a temporary directory and get a unique filename within that directory
+    # with tempfile.TemporaryDirectory() as unique_filedir:
+    unique_filedir = str(uuid.uuid4())
+    # Using UUID for generating a unique filename
+    unique_filename = unique_filedir + '_temp_seqs.fasta'
+
+    print('Clustering seqs..')
+    seqs_written = set()
+    seqind_to_seq = {}
+    f = open(unique_filename,'w')
+    for ind,seq in enumerate(df[seqcol]):
+        if pd.isna(seq): continue
+        if not seq in seqs_written: 
+            f.write(f'>{ind}\n{seq}\n')
+            seqs_written.add(seq)
+            seqind_to_seq[ind] = seq
+        else: continue
+    f.close()
+
+    cmd = f'mmseqs easy-cluster {unique_filename} {unique_filedir}_clusterRes /tmp/ --min-seq-id {id_cutoff} -v 0'
+    status = os.system(cmd)
+    print(status, cmd)
+    cluster_df = pd.read_csv(f'{unique_filedir}_clusterRes_cluster.tsv',sep='\t',names=['query_ind','target_ind'])
+    os.system(f'rm -r {unique_filedir}*')
+        
+    q_to_tinds = {}
+    for qind, tind in zip(cluster_df.query_ind,cluster_df.target_ind):
+        if not qind in q_to_tinds:
+            q_to_tinds[qind] = [tind]
+        else: 
+            q_to_tinds[qind].append(tind)
+    c = 0
+    ind_to_cluster = {}
+    for q, ts in q_to_tinds.items():
+        ind_to_cluster[q] = c
+        for t in ts: ind_to_cluster[t] = c
+        c+=1
+    seq_to_cluster = {seqind_to_seq[ind]:c for ind, c in ind_to_cluster.items()}
+    
+    cluster_col = []
+    for seq in df[seqcol]:
+        if seq in seq_to_cluster:
+            cluster_col.append(seq_to_cluster[seq])
+        else:
+            # print('no cluster found for ', seq)
+            cluster_col.append(-1)
+    df[f'{seqcol}_{int(100*id_cutoff)}cluster'] = cluster_col
+    return df
+
+def make_splits(df, frac = 0.1):    
+    # ipdb.set_trace()
+    df.reset_index(drop=True, inplace=True)
+    train_df, test_df = train_test_split(df, test_size = frac, random_state=0)
+    train_df.reset_index(drop=True, inplace=True)
+    train_df, val_df = train_test_split(train_df, test_size = frac, random_state=0)
+    train_df.reset_index(drop=True, inplace=True)
+    val_df.reset_index(drop=True, inplace=True)
+    test_df.reset_index(drop=True, inplace=True)
+    
+    print(f'Train: {len(train_df)*100/len(df)}')
+    print(f'Val: {len(val_df)*100/len(df)}')
+    print(f'Test: {len(test_df)*100/len(df)}')
+    return train_df, val_df, test_df
+
+def print_stats(train, test, parameter, dire, sim_list = [0.4, 0.6, 0.8, 0.99]):
+    sequence_column = 'sequence'
+    smiles_column = 'smiles'
+    if parameter=='kcat': smiles_column='reactant_'+smiles_column
+    else: smiles_column='substrate_'+smiles_column
+    sim_list.reverse()
+    print('-'*20)
+    print('**Sequence stats**')
+    print('-'*20)
+    for sim in sim_list:
+        simperc = int(sim*100)
+        colname = sequence_column+f'_{simperc}cluster'
+        print('At sim-cutoff:', sim,)
+        now = test[~test[colname].isin(train[colname])]
+        now.to_csv(f'{dire}/{parameter}-seq_test_{colname}.csv')
+        perc = len(now)/len(test)
+        print('Test perc.:', perc)
+        print('Test number:', int(perc*len(test)))
+        print('\n')
+    print('-'*20)
+        
+def assign_all_clusters(df, is_kcat, sim_list = [0.4, 0.6, 0.8, 0.99]):
+    for sim in sim_list:
+        print(f'Assigning clusters at sim-cutoff = {sim}')
+        df = assign_seq_clusters(df, id_cutoff=sim)
+    return df
+
+def make_and_savesplits(df, param, dire):
+    if param=='kcat': is_kcat = True
+    else: is_kcat = False
+    frac = 0.1
+    train, val, test = make_splits(df, frac)
+    seqpre = 'random'
+    if not os.path.exists(dire):
+        os.mkdir(dire)
+    train.to_csv(dire+param+f'-{seqpre}_train.csv')
+    val.to_csv(dire+param+f'-{seqpre}_val.csv')
+    test.to_csv(dire+param+f'-{seqpre}_test.csv')
+    trainval = pd.concat([train,val]).reset_index(drop=True)
+    trainval.to_csv(dire+param+f'-{seqpre}_trainval.csv')
+    trainvaltest = pd.concat([trainval,test]).reset_index(drop=True)
+    trainvaltest.to_csv(dire+param+f'-{seqpre}_trainvaltest.csv')
+    print_stats(trainval, test, param, dire)
+    return train, val, test
+    
+def main(args):
+    datafile, parameter, outdir = args.input_file, args.param, args.save_dir
+    df = pd.read_csv(datafile)
+    df = assign_all_clusters(df, is_kcat=(parameter=='kcat'))#, 
+    df_train, df_val, df_test = make_and_savesplits(df, parameter, dire=outdir)
+    
+import argparse
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--param", help="parameter name", 
+                        type=str, required=True)
+    parser.add_argument("--input_file", help="input_file", 
+                        type=str, required=True)
+    parser.add_argument("--save_dir", help="dir to save splits", 
+                        type=str, required=True)
+
+    args, unparsed = parser.parse_known_args()
+    
+    return args
+
+args = parse_args()
+main(args)

scripts/pdb/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scripts/pdb/Untitled.ipynb

@@ -0,0 +1,524 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63b89129-eb15-489c-acae-c6255df4d9f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
+      "/home/ubuntu/.local/lib/python3.8/site-packages/Bio/pairwise2.py:278: BiopythonDeprecationWarning: Bio.pairwise2 has been deprecated, and we intend to remove it in a future release of Biopython. As an alternative, please consider using Bio.Align.PairwiseAligner as a replacement, and contact the Biopython developers if you still need the Bio.pairwise2 module.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
+    "# SPDX-License-Identifier: MIT-0\n",
+    "\n",
+    "\"\"\"\n",
+    "Parse PDB files to extract the coordinates of the 4 key atoms from AAs to\n",
+    "generate json records compatible to the LM-GVP model.\n",
+    "\n",
+    "This script is intended for Fluorescence and Protease datasets from TAPE.\n",
+    "\"\"\"\n",
+    "\n",
+    "import json\n",
+    "import os\n",
+    "import argparse\n",
+    "from collections import defaultdict\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "from joblib import Parallel, delayed\n",
+    "from Bio.PDB import PDBParser\n",
+    "from Bio.PDB.Polypeptide import three_to_one\n",
+    "\n",
+    "import xpdb\n",
+    "from contact_map_utils import parse_pdb_structure\n",
+    "\n",
+    "\n",
+    "def parse_args():\n",
+    "    \"\"\"Prepare argument parser.\n",
+    "\n",
+    "    Args:\n",
+    "\n",
+    "    Return:\n",
+    "\n",
+    "    \"\"\"\n",
+    "    parser = argparse.ArgumentParser(\n",
+    "        description=\"Generate GVP ProteinGraph datasets representing protein\"\n",
+    "        + \"structures.\"\n",
+    "    )\n",
+    "    parser.add_argument(\n",
+    "        \"--data-file\",\n",
+    "        help=\"Path to protein data frame, including sequences, paths to\"\n",
+    "        + \" structure files and labels\",\n",
+    "        required=True,\n",
+    "    )\n",
+    "    parser.add_argument(\n",
+    "        \"-t\",\n",
+    "        \"--target-variable\",\n",
+    "        help=\"target variable in the protein data frame\",\n",
+    "        required=True,\n",
+    "    )\n",
+    "    parser.add_argument(\"-o\", \"--output\", help=\"output dir for graphs\")\n",
+    "\n",
+    "    args = parser.parse_args()\n",
+    "    return args\n",
+    "\n",
+    "\n",
+    "def get_atom_coords(residue, target_atoms=[\"N\", \"CA\", \"C\", \"O\"]):\n",
+    "    \"\"\"Extract the coordinates of the target_atoms from an AA residue.\n",
+    "\n",
+    "    Args:\n",
+    "        residue: a Bio.PDB.Residue object representing the residue.\n",
+    "        target_atoms: Target atoms which residues will be returned.\n",
+    "\n",
+    "    Retruns:\n",
+    "        Array of residue's target atoms (in the same order as target atoms).\n",
+    "    \"\"\"\n",
+    "    return np.asarray([residue[atom].coord for atom in target_atoms])\n",
+    "\n",
+    "\n",
+    "def structure_to_coords(struct, target_atoms=[\"N\", \"CA\", \"C\", \"O\"], name=\"\"):\n",
+    "    \"\"\"Convert a PDB structure in to coordinates of target atoms from all AAs\n",
+    "\n",
+    "    Args:\n",
+    "        struct: a Bio.PDB.Structure object representing the protein structure\n",
+    "        target_atoms: Target atoms which residues will be returned.\n",
+    "        name: String. Name of the structure\n",
+    "\n",
+    "    Return:\n",
+    "        Dictionary with the pdb sequence, atom 3D coordinates and name.\n",
+    "    \"\"\"\n",
+    "    output = {}\n",
+    "    # get AA sequence in the pdb structure\n",
+    "    pdb_seq = \"\".join(\n",
+    "        [three_to_one(res.get_resname()) for res in struct.get_residues()]\n",
+    "    )\n",
+    "    output[\"seq\"] = pdb_seq\n",
+    "    # get the atom coords\n",
+    "    coords = np.asarray(\n",
+    "        [\n",
+    "            get_atom_coords(res, target_atoms=target_atoms)\n",
+    "            for res in struct.get_residues()\n",
+    "        ]\n",
+    "    )\n",
+    "    output[\"coords\"] = coords.tolist()\n",
+    "    output[\"name\"] = name\n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "def parse_pdb_gz_to_json_record(parser, sequence, pdb_file_path, name=\"\"):\n",
+    "    \"\"\"\n",
+    "    Reads and reformats a pdb strcuture into a dictionary.\n",
+    "\n",
+    "    Args:\n",
+    "        parser: a Bio.PDB.PDBParser or Bio.PDB.MMCIFParser instance.\n",
+    "        sequence: String. Sequence of the structure.\n",
+    "        pdb_file_path: String. Path to the pdb file.\n",
+    "        name: String. Name of the protein.\n",
+    "\n",
+    "    Return:\n",
+    "        Dictionary with the pdb sequence, atom 3D coordinates and name.\n",
+    "    \"\"\"\n",
+    "    struct = parse_pdb_structure(parser, sequence, pdb_file_path)\n",
+    "    record = structure_to_coords(struct, name=name)\n",
+    "    return record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "402ad072-edd1-4c04-a4c0-93390862770b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "72469cb0-878a-478b-849d-ee88774f3708",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('../../../data/processed/merged/kcat_max_wt_singleSeqs_wpdbs.csv')\n",
+    "\n",
+    "# PDB parser\n",
+    "sloppyparser = PDBParser(\n",
+    "    QUIET=True,\n",
+    "    PERMISSIVE=True,\n",
+    "    structure_builder=xpdb.SloppyStructureBuilder(),\n",
+    ")\n",
+    "\n",
+    "additional_path_pdb = '../../../'\n",
+    "# 2. Parallel parsing structures and converting to protein records\n",
+    "records = Parallel(n_jobs=-1)(\n",
+    "    delayed(parse_pdb_gz_to_json_record)(\n",
+    "        sloppyparser,\n",
+    "        df.iloc[i][\"sequence\"],\n",
+    "        additional_path_pdb+df.iloc[i][\"pdbpath\"],\n",
+    "        df.iloc[i][\"pdbpath\"].split(\"/\")[-1],\n",
+    "    )\n",
+    "    for i in tqdm(range(df.shape[0]))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0add270e-bf61-4513-acb6-3ace461fdaa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_col = 'log10kcat_max'\n",
+    "smiles_col = 'reactant_smiles'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "50e0b9ac-dcfd-4a43-88b4-d7ae36338644",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outprefix = '../../../data/processed/merged/kcat_max_wt_singleSeqs_wpdbs'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bb37b182-aa17-41d5-a7ba-5066248804ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of records: 23197\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3. Segregate records by splits\n",
+    "for i, rec in enumerate(records):\n",
+    "    row = df.iloc[i]\n",
+    "    target = row[target_col]\n",
+    "    smiles = row[smiles_col]\n",
+    "    rec[\"target\"] = target\n",
+    "    rec[\"ec\"] = row[\"ec\"]\n",
+    "    rec[\"taxonomy_id\"] = row[\"taxonomy_id\"]\n",
+    "    \n",
+    "# 4. write to disk\n",
+    "print(\"number of records:\", len(records))\n",
+    "outfile = os.path.join(f\"{outprefix}_pdbrecords.json\")\n",
+    "json.dump(records, open(outfile, \"w\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "62b95f57-9214-4649-879b-c63f60e72726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['seq', 'coords', 'name', 'target', 'ec', 'taxonomy_id'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records[0].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5cf2060a-b444-4e7b-88d5-5987d9553507",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'AF-U3KRF2-F1-model_v4.pdb'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records[0]['name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4857a4ed-7ae7-4603-8baf-df113f2b7fa4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scripts/pdb/contact_map_utils.py

@@ -0,0 +1,186 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+
+"""
+Helpers for parsing protein structure files and generating contact maps.
+"""
+
+import gzip
+import numpy as np
+import pandas as pd
+from io import StringIO
+from sklearn.metrics import pairwise_distances
+from Bio import pairwise2
+from Bio.Seq import Seq
+from Bio.PDB.Polypeptide import three_to_one, is_aa
+
+
+def idx_align(pdb_seq: str, og_seq: str):
+    """Maps indices of original and PDB sequences to mutual alignment
+    Args:
+        pdb_seq: Sequence of amino acids as extracted from PDB file
+        og_seq: Original sequence of amino acids
+    Returns:
+        Tuple where the first element is a list of integers containing the indices in
+        the PDB sequence for which aligned information is available and the second element
+        is a list of integers containing the indices in the original sequence
+        for which aligned information is available
+    """
+    alignment = pairwise2.align.globalxx(Seq(og_seq), Seq(pdb_seq))[0]
+    og_seq_align = alignment.seqA
+    og_idxs = []
+    og_offset = 0
+
+    pdb_seq_align = alignment.seqB
+    pdb_idxs = []
+    pdb_offset = 0
+    for i in range(len(pdb_seq_align)):
+        valid = True
+        pdb_res = pdb_seq_align[i]
+        og_res = og_seq_align[i]
+        if pdb_res == "-":
+            pdb_offset += 1
+            valid = False
+        if og_res == "-":
+            og_offset += 1
+            valid = False
+        if valid and (pdb_res == og_res):
+            pdb_idxs.append(i - pdb_offset)
+            og_idxs.append(i - og_offset)
+
+    return pdb_idxs, og_idxs
+
+
+def calc_adj_matrix(structure, dist_thresh=10.0):
+    """Returns an adjacency matrix for a PDB structure
+    Args:
+        structure : BioPython PDB Structure object representing a protein containing N residues
+        dist_thresh : float, optional. Value specifying threshold for classifying contacts between residues
+
+    Returns:
+        Tuple where the first element is a np.ndarray adjacency matrix with shape NxN, where N is the number of residues in `structure` and the second element is np.ndarray NxN matrix specifying distances between residues.
+    """
+    residues = list(structure.get_residues())
+    coords = np.asarray([residue["CA"].coord for residue in residues])
+    dist_mat = pairwise_distances(coords, metric="euclidean")
+    return 1 * (dist_mat < dist_thresh), dist_mat
+
+
+def gunzip_to_ram(gzip_file_path):
+    """
+    gunzip a gzip file and decode it to a io.StringIO object.
+
+    Args:
+        gzip_file_path: String. Gunzip filepath.
+
+    Returns:
+        io.StringIO object.
+    """
+    content = []
+    with gzip.open(gzip_file_path, "rb") as f:
+        for line in f:
+            content.append(line.decode("utf-8"))
+
+    temp_fp = StringIO("".join(content))
+    return temp_fp
+
+
+def _parse_structure(parser, name, file_path):
+    """Parse a .pdb or .cif file into a structure object.
+    The file can be gzipped.
+
+    Args:
+        parser: a Bio.PDB.PDBParser or Bio.PDB.MMCIFParser instance.
+        name: String. name of protein
+        file_path: String. Filpath of the pdb or cif file to be read.
+
+    Retruns:
+        a Bio.PDB.Structure object representing the protein structure.
+
+    """
+    if pd.isnull(file_path):
+        return None
+    if file_path.endswith(".gz"):
+        structure = parser.get_structure(name, gunzip_to_ram(file_path))
+    else:  # not gzipped
+        structure = parser.get_structure(name, file_path)
+    return structure
+
+
+parse_pdb_structure = _parse_structure  # for backward compatiblity
+
+
+def parse_structure(pdb_parser, cif_parser, name, file_path):
+    """Parse a .pdb file or .cif file into a structure object.
+    The file can be gzipped.
+
+    Args:
+        pdb_parser: a Bio.PDB.PDBParser instance
+        cif_parser: Bio.PDB.MMCIFParser instance
+        name: String. name of protein
+        file_path: String. Filpath of the pdb or cif file to be read.
+
+    Return:
+        a Bio.PDB.Structure object representing the protein structure.
+    """
+    if file_path.rstrip(".gz").endswith("pdb"):
+        return _parse_structure(pdb_parser, name, file_path)
+    else:
+        return _parse_structure(cif_parser, name, file_path)
+
+
+def get_energy(pdb_file_path):
+    """Get total pose energy from a PDB file.
+
+    Args:
+        pdb_file_path: String. Path to the pdb file.
+
+    Return:
+        Energy score.
+
+    """
+    if pdb_file_path.endswith(".pdb.gz"):
+        pdb_file = gunzip_to_ram(pdb_file_path)
+    else:
+        pdb_file = open(pdb_file_path, "r")
+
+    parse = False
+    score = None
+    for line in pdb_file:
+        if line.startswith("#BEGIN_POSE_ENERGIES_TABLE"):
+            parse = True
+        if parse and line.startswith("pose"):
+            score = float(line.strip().split()[-1])
+    return score
+
+
+def three_to_one_standard(res):
+    """Encode non-standard AA to X.
+
+    Args:
+        res: a Bio.PDB.Residue object representing the residue.
+
+    Return:
+        String. One letter code of the residue.
+    """
+    if not is_aa(res, standard=True):
+        return "X"
+    return three_to_one(res)
+
+
+def is_aa_by_target_atoms(res):
+    """Tell if a Residue object is AA
+
+    Args:
+        res: a Bio.PDB.Residue object representing the residue.
+
+    Return:
+       Bool. Wheather or not the residue is AA.
+    """
+    target_atoms = ["N", "CA", "C", "O"]
+    for atom in target_atoms:
+        try:
+            res[atom]
+        except KeyError:
+            return False
+    return True

scripts/pdb/prepare_data.py

@@ -0,0 +1,156 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+
+"""
+Parse PDB files to extract the coordinates of the 4 key atoms from AAs to
+generate json records compatible to the LM-GVP model.
+
+This script is intended for Fluorescence and Protease datasets from TAPE.
+"""
+
+import json
+import os
+import argparse
+from collections import defaultdict
+import pandas as pd
+import numpy as np
+
+from tqdm import tqdm
+from joblib import Parallel, delayed
+from Bio.PDB import PDBParser
+from Bio.PDB.Polypeptide import three_to_one
+
+import xpdb
+from contact_map_utils import parse_pdb_structure
+
+
+def parse_args():
+    """Prepare argument parser.
+
+    Args:
+
+    Return:
+
+    """
+    parser = argparse.ArgumentParser(
+        description="Generate json records for GVP from pdb files"
+    )
+    parser.add_argument(
+        "--data_file",
+        help="Path to csv file containing pdbpaths",
+        required=True,
+    )
+    parser.add_argument(
+        "--target_col",
+        help="target column in data",
+        required=True,
+    )
+    parser.add_argument(
+        "--smiles_col",
+        help="smiles column in data",
+        required=True,
+    )
+    parser.add_argument("--out_prefix", help="output prefix for json records")
+
+    args = parser.parse_args()
+    return args
+
+
+def get_atom_coords(residue, target_atoms=["N", "CA", "C", "O"]):
+    """Extract the coordinates of the target_atoms from an AA residue.
+
+    Args:
+        residue: a Bio.PDB.Residue object representing the residue.
+        target_atoms: Target atoms which residues will be returned.
+
+    Retruns:
+        Array of residue's target atoms (in the same order as target atoms).
+    """
+    return np.asarray([residue[atom].coord for atom in target_atoms])
+
+
+def structure_to_coords(struct, target_atoms=["N", "CA", "C", "O"], name=""):
+    """Convert a PDB structure in to coordinates of target atoms from all AAs
+
+    Args:
+        struct: a Bio.PDB.Structure object representing the protein structure
+        target_atoms: Target atoms which residues will be returned.
+        name: String. Name of the structure
+
+    Return:
+        Dictionary with the pdb sequence, atom 3D coordinates and name.
+    """
+    output = {}
+    # get AA sequence in the pdb structure
+    pdb_seq = "".join(
+        [three_to_one(res.get_resname()) for res in struct.get_residues()]
+    )
+    output["seq"] = pdb_seq
+    # get the atom coords
+    coords = np.asarray(
+        [
+            get_atom_coords(res, target_atoms=target_atoms)
+            for res in struct.get_residues()
+        ]
+    )
+    output["coords"] = coords.tolist()
+    output["name"] = name
+    return output
+
+
+def parse_pdb_gz_to_json_record(parser, sequence, pdb_file_path, name=""):
+    """
+    Reads and reformats a pdb strcuture into a dictionary.
+
+    Args:
+        parser: a Bio.PDB.PDBParser or Bio.PDB.MMCIFParser instance.
+        sequence: String. Sequence of the structure.
+        pdb_file_path: String. Path to the pdb file.
+        name: String. Name of the protein.
+
+    Return:
+        Dictionary with the pdb sequence, atom 3D coordinates and name.
+    """
+    struct = parse_pdb_structure(parser, sequence, pdb_file_path)
+    record = structure_to_coords(struct, name=name)
+    return record
+
+if __name__=='__main__':
+    args = parse_args()
+    df = pd.read_csv(args.data_file)
+
+    # PDB parser
+    sloppyparser = PDBParser(
+        QUIET=True,
+        PERMISSIVE=True,
+        structure_builder=xpdb.SloppyStructureBuilder(),
+    )
+
+    # 2. Parallel parsing structures and converting to protein records
+    records = Parallel(n_jobs=-1)(
+        delayed(parse_pdb_gz_to_json_record)(
+            sloppyparser,
+            df.iloc[i]["sequence"],
+            df.iloc[i]["pdbpath"],
+            df.iloc[i]["pdbpath"].split("/")[-1],
+        )
+        for i in tqdm(range(df.shape[0]))
+    )
+    
+    target_col = args.target_col
+    smiles_col = args.smiles_col
+    
+    for i, rec in enumerate(records):
+        row = df.iloc[i]
+        target = row[target_col]
+        smiles = row[smiles_col]
+        rec["target"] = target
+        rec["ec"] = row["ec"]
+        rec["taxonomy_id"] = row["taxonomy_id"]
+
+    outprefix = args.out_prefix
+    # 4. write to disk
+    print("number of records:", len(records))
+    outfile = os.path.join(f"{outprefix}_pdbrecords.json")
+    json.dump(records, open(outfile, "w"))
+    print('Success!')

scripts/pdb/xpdb.py

@@ -0,0 +1,151 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+
+"""
+PDB parsers for large files with Biopython.
+Modified from https://biopython.org/wiki/Reading_large_PDB_files
+"""
+
+import sys
+import Bio.PDB
+import Bio.PDB.StructureBuilder
+from Bio.PDB.Residue import Residue
+
+
+class SloppyStructureBuilder(Bio.PDB.StructureBuilder.StructureBuilder):
+    """Cope with resSeq < 10,000 limitation by just incrementing internally."""
+
+    def __init__(self, verbose=False):
+        Bio.PDB.StructureBuilder.StructureBuilder.__init__(self)
+        self.max_resseq = -1
+        self.verbose = verbose
+
+    def init_residue(self, resname, field, resseq, icode):
+        """Initiate a new Residue object.
+
+        Arguments:
+            resname: string, e.g. "ASN"
+            field: hetero flag, "W" for waters, "H" for hetero residues, otherwise blanc.
+            resseq: int, sequence identifier
+            icode: string, insertion code
+
+        Return:
+            None
+        """
+        if field != " ":
+            if field == "H":
+                # The hetero field consists of
+                # H_ + the residue name (e.g. H_FUC)
+                field = "H_" + resname
+        res_id = (field, resseq, icode)
+
+        if resseq > self.max_resseq:
+            self.max_resseq = resseq
+
+        if field == " ":
+            fudged_resseq = False
+            while self.chain.has_id(res_id) or resseq == 0:
+                # There already is a residue with the id (field, resseq, icode)
+                # resseq == 0 catches already wrapped residue numbers which
+                # do not trigger the has_id() test.
+                #
+                # Be sloppy and just increment...
+                # (This code will not leave gaps in resids... I think)
+                #
+                # XXX: shouldn't we also do this for hetero atoms and water??
+                self.max_resseq += 1
+                resseq = self.max_resseq
+                res_id = (field, resseq, icode)  # use max_resseq!
+                fudged_resseq = True
+
+            if fudged_resseq and self.verbose:
+                sys.stderr.write(
+                    "Residues are wrapping (Residue "
+                    + "('%s', %i, '%s') at line %i)."
+                    % (field, resseq, icode, self.line_counter)
+                    + ".... assigning new resid %d.\n" % self.max_resseq
+                )
+        residue = Residue(res_id, resname, self.segid)
+        self.chain.add(residue)
+        self.residue = residue
+        return None
+
+
+class SloppyPDBIO(Bio.PDB.PDBIO):
+    """PDBIO class that can deal with large pdb files as used in MD simulations
+
+    - resSeq simply wrap and are printed modulo 10,000.
+    - atom numbers wrap at 99,999 and are printed modulo 100,000
+
+    """
+
+    # The format string is derived from the PDB format as used in PDBIO.py
+    # (has to be copied to the class because of the package layout it is not
+    # externally accessible)
+    _ATOM_FORMAT_STRING = (
+        "%s%5i %-4s%c%3s %c%4i%c   "
+        + "%8.3f%8.3f%8.3f%6.2f%6.2f      %4s%2s%2s\n"
+    )
+
+    def _get_atom_line(
+        self,
+        atom,
+        hetfield,
+        segid,
+        atom_number,
+        resname,
+        resseq,
+        icode,
+        chain_id,
+        element="  ",
+        charge="  ",
+    ):
+        """Returns an ATOM string that is guaranteed to fit the ATOM format.
+
+        - Resid (resseq) is wrapped (modulo 10,000) to fit into %4i (4I) format
+        - Atom number (atom_number) is wrapped (modulo 100,000) to fit into
+          %5i (5I) format
+
+        Args: #TODO
+            atom:
+            hetfield:
+            segid:
+            atom_number:
+            resname:
+            resseq:
+            icode:
+            chain_id:
+            element:
+            charge:
+
+        Returns:
+            #TODO
+        """
+        if hetfield != " ":
+            record_type = "HETATM"
+        else:
+            record_type = "ATOM  "
+        name = atom.get_fullname()
+        altloc = atom.get_altloc()
+        x, y, z = atom.get_coord()
+        bfactor = atom.get_bfactor()
+        occupancy = atom.get_occupancy()
+        args = (
+            record_type,
+            atom_number % 100000,
+            name,
+            altloc,
+            resname,
+            chain_id,
+            resseq % 10000,
+            icode,
+            x,
+            y,
+            z,
+            occupancy,
+            bfactor,
+            segid,
+            element,
+            charge,
+        )
+        return self._ATOM_FORMAT_STRING % args

scripts/processing/README.md

@@ -0,0 +1,11 @@
+
+# Obtaining raw data
+1. BRENDA raw data has been obtained from their website. Recently, they made .json format download for the database available. In this work, 'brenda_2022_2.json' is used. Also, raw data for compounds was obtained from https://www.brenda-enzymes.org/search_result.php?a=13 and placing a blank query. Thanks to samgoldman97 for the trick. Even with this some brenda compounds didn't have a known SMILES/InChi string. So, we also used the Pubchem id exchange service to map names (synonyms) to SMILES. All these are saved in ./data/raw/brenda/
+2. SABIO-rk raw data has been obtained using sbml exports from their website. Because only a 100 entries are shown in their website at a time, we manually exported a lot of sbml files from their website. Also, we scraped individual entry html files. These form the raw data. Saved in ./data/raw/sabio/
+
+# Processing raw data
+1. Processing BRENDA is relatively straightforward because everything is in the json file except SMILES strings of substrates and Sequences of proteins. See scripts/data/processing/ for details.
+2. SABIO sbml files are a bit more tricky to process. We used an sbml parser from libsbml. Also, BeautifulSoup to parse html files of individual entries. See scripts/data/processing/ for details.
+
+# Merging processed data
+1. Since data comes from two different sources, they need to be merged to make sure there are no unexpected duplicates. Also, each 'sequence,smiles' pair can appear in several entries (possibly with different parameter values). So, these have to be handled appropriately. See scripts/data/merging/ for details.