A bacterial phyla dataset for protein function prediction.

Protein function prediction has been the most worked upon and the most challenging problem for computational biologists. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions. New un-annotated sequences are being added to the public protein databases (e.g. UniprotKB) at an enormous pace [1]. Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s). Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins [2]. For proteins with well characterised close relatives, it is trivial to infer function. Orphan proteins without discernible sequence relatives present a greater challenge [3]. Here the task of experimental characterisation is blind and becomes unwieldy. It is highly unlikely that all known proteins will ever be completely experimentally characterised [4]. Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. The Dataset was generated by calculating the sequence, sub-sequence, physiochemical, annotation-based features for each 171,212 reviewed proteins using method in [10]. These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. The Dataset contains the Entry and Entry name of each sequence corresponding to UniprotKB Database. This dataset being huge in size (171,212 samples X 9890 features, 1739 classes with multiple values) and equipped with enough number of positive and negative samples of each 1739 class, is good for testing efficiency of any upcoming deep learning models [5]. We divided the full dataset of 171,212 reviewed proteins in the ratio 3:1 to form Train/Test dataset 1; train dataset with 128,409 samples and test dataset with 42,803 samples to facilitate training of a deep learning model. The train and test datasets are stratified to contain good proportion of each 1739 classes. We then prepared a dataset 2 of pathogenic unreviewed proteins of the 9 bacterial phyla each with 9890 features same as train/train dataset of reviewed proteins but without target labels in order to predict their functions using deep learning model proposed in [5].

Researchers trying to design new <span class="Disease">deep learning models can use this dataset for testing performance of their model.

Data

The 171,212 extracted reviewed protein samples belong to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Each Phyla has a Train and Test.csv (comma separated values) files, where Train file contains the 75% of data and Test file contains 25% of the data from each Phyla. A Test dataset 2 was constructed for pathogenic unreviewed protein sequences belonging to 9 bacterial Phyla. These entries in UniProtKB have yet not received any annotation [[1], [2], [3], [4]] towards Gene Ontology and therefore can be used for prediction.

Each data file contains the following columns given below in points 1 to 8.

Entry

Entry is the unique ID given to each protein entry available on UniProtKB.

Entry name

Entry name is a mnemonic identifier for the unique ID provided to each protein entry.

Sequence

Amino acid sequence for the cor<span class="Chemical">responding protein entry.

Sequence based Featu<span class="Chemical">res

These are the attributes guided by the primary structure of protein.

Physicochemical Featu<span class="Chemical">res

These are the attributes based on the physical and chemical properties of the monomeric unit of a protein i.e. an amino acid.

Annotation based features

These are the attributes based on already present annotations regarding subcellular localisation, binding preference of proteins and presence of transmembrane regions.

Subsequence based featu<span class="Chemical">res

These are the attributes corresponding to the local similarities within a given protein sequ<span class="Gene">ence.

Gene Ontology (Molecular Function domain only) terms

The following are the names of supplementary data files along with their short description:

Dataset 1 (FASTA files of Dataset 1): Fasta Sequences of 171,212 proteins of 9 bacterial phyla in 2 parts with names “Dataset1 non-proteo.fasta” (containing fasta sequences of all proteins of phylum other than proteobacterium) and “Dataset1 proteo.fasta” (containing fasta sequences of all proteins of phylum proteobacterium).

These two fasta files are zipped together (fasta seq of dataset.zip).

Dataset 2 (Train Dataset 1): with feature vectors extracted from reviewed proteins (75% of 171,212 reviewed proteins) of 9 Bacterial phyla. A total of 18 excel sheets all zipped, also available on project's GitHub repository.

Dataset 3 (Test Dataset 1): with feature vectors extracted from reviewed proteins (25% of 171,212 reviewed proteins) 9 Bacterial phyla. A total of 12 excel sheets all zipped, also available on project's GitHub repository.

Dataset 4 (Test Dataset 2): with feature vectors extracted from unreviewed and hypothetical Proteins of 9 Bacterial phyla from pathogenic bacterial species (9 excel sheets all zipped).

Dataset 5: Predictions on Test Dataset 2 (9 excel sheets all zipped).

Experiment design, materials, and methods

Using web-scraping libraries in Python [7], reviewed proteins of 9 bacterial phyla were extracted from UniprotKB. These samples were filtered based on the relevant 1739 Gene Ontology (belonging to molecular function domain only) terms. Further, for each sample, Motifs were extracted from Prosite server [9] using Python. These Motifs were analysed to remove redundancy and added as feature in dataset. Finally, for each sample, Sequence-based, sub-sequence-based [8], annotation-based and Physiochemical features were calculated along with Gene Ontology (Molecular Function) as a target label (If a sample consist a GO term, it had 1 in the corresponding column, else 0). All the features are generated using method in [10] utilising the following packages: Biopython [7], and I-feature [6]. The dataset acquired is then randomly split into two parts: Train (75%) and Test (25%) for each phylum, each of which is stratified to contain good proportion of each 1739 classes.

Every Train/Test dataset which is part of this bacterial phyla dataset for protein function prediction is having 9890 features and 1739 GO terms stored in excel (CSV) sheet format. Test dataset 2 is not having any target label associated with its entries as this dataset is used for predictions and belongs to hypothetical and unreviewed category.

A complete list of these 9890 features extracted for training/testing is shown below and summarised in Table 1 [5]: (Note: Entry, Entry name and Sequence may not be used for training but they are present in each csv file).

Table 1

Summary of different feature groups and their descriptors [5].

S. No	Feature Group	Feature Name	Python Package used	Number of descriptor values
1.	Sequence-based	Protein Length	Biopython	1
2.	Sequence-based	Amino acid composition	ifeature	20
3.	Sequence-based	Dipeptide composition	Ifeature	400
4.	Sequence-based	Tripeptide composition	ifeature	8000
5.	Sequence-based	Pseudo amino acid composition	ifeature	49
6.	Subsequence-based	Motif count	Biopython	541
7.	Physicochemical-based	Molecular weight	Biopython	1
8.	Physicochemical-based	Instability index	Biopython	1
9.	Physicochemical-based	Isoelectric point	Biopython	1
10.	Physicochemical-based	GRAVY	Biopython	1
11.	Physicochemical-based	Extinction Coefficient	Biopython	2
12.	Physicochemical-based	Secondary structure fraction	Biopython	3
13.	Physicochemical-based	Grouped amino acid composition	ifeature	5
14.	Physicochemical-based	Moran autocorrelation	ifeature	232
15.	Physicochemical-based	Composition, Transition and Distribution	ifeature	273
16.	Physicochemical-based	Conjoint Triad	ifeature	343
17.	Annotation-based	Annotation based features (subcellular localisation, binding preference and presence of transmembrane region)	urllib (web-scrapping)	17
			TOTAL	9890

Sequence based features (Count of Amino acid compositions, Dipeptide composition, Tripeptide composition) of protein sequences with a minimum length of 30 amino acids.

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VYM,VYN,VYP,VYQ,VYR,VYS,VYT,VYV,VYW,VYY,WAA,WAC,WAD,WAE,WAF,WAG,WAH,WAI,WAK,WAL,WAM,WAN,WAP,WAQ,WAR,WAS,WAT,WAV,WAW,WAY,WCA,WCC,WCD,WCE,WCF,WCG,WCH,WCI,WCK,WCL,WCM,WCN,WCP,WCQ,WCR,WCS,WCT,WCV,WCW,WCY,WDA,WDC,WDD,WDE,WDF,WDG,WDH,WDI,WDK,WDL,WDM,WDN,WDP,WDQ,WDR,WDS,WDT,WDV,WDW,WDY,WEA,WEC,WED,WEE,WEF,WEG,WEH,WEI,WEK,WEL,WEM,WEN,WEP,WEQ,WER,WES,WET,WEV,WEW,WEY,WFA,WFC,WFD,WFE,WFF,WFG,WFH,WFI,WFK,WFL,WFM,WFN,WFP,WFQ,WFR,WFS,WFT,WFV,WFW,WFY,WGA,WGC,WGD,WGE,WGF,WGG,WGH,WGI,WGK,WGL,WGM,WGN,WGP,WGQ,WGR,WGS,WGT,WGV,WGW,WGY,WHA,WHC,WHD,WHE,WHF,WHG,WHH,WHI,WHK,WHL,WHM,WHN,WHP,WHQ,WHR,WHS,WHT,WHV,WHW,WHY,WIA,WIC,WID,WIE,WIF,WIG,WIH,WII,WIK,WIL,WIM,WIN,WIP,WIQ,WIR,WIS,WIT,WIV,WIW,WIY,WKA,WKC,WKD,WKE,WKF,WKG,WKH,WKI,WKK,WKL,WKM,WKN,WKP,WKQ,WKR,WKS,WKT,WKV,WKW,WKY,WLA,WLC,WLD,WLE,WLF,WLG,WLH,WLI,WLK,WLL,WLM,WLN,WLP,WLQ,WLR,WLS,WLT,WLV,WLW,WLY,WMA,WMC,WMD,WME,WMF,WMG,WMH,WMI,WMK,WML,WMM,WMN,WMP,WMQ,WMR,WMS,WMT,WMV,WMW,WMY,WNA,WNC,WND,WNE,WNF,WNG,WNH,WNI,WNK,WNL,WNM,WNN,WNP,WNQ,WNR,WNS,WNT,WNV,WNW,WNY,WPA,WPC,WPD,WPE,WPF,WPG,WPH,WPI,WPK,WPL,WPM,WPN,WPP,WPQ,WPR,WPS,WPT,WPV,WPW,WPY,WQA,WQC,WQD,WQE,WQF,WQG,WQH,WQI,WQK,WQL,WQM,WQN,WQP,WQQ,WQR,WQS,WQT,WQV,WQW,WQY,WRA,WRC,WRD,WRE,WRF,WRG,WRH,WRI,WRK,WRL,WRM,WRN,WRP,WRQ,WRR,WRS,WRT,WRV,WRW,WRY,WSA,WSC,WSD,WSE,WSF,WSG,WSH,WSI,WSK,WSL,WSM,WSN,WSP,WSQ,WSR,WSS,WST,WSV,WSW,WSY,WTA,WTC,WTD,WTE,WTF,WTG,WTH,WTI,WTK,WTL,WTM,WTN,WTP,WTQ,WTR,WTS,WTT,WTV,WTW,WTY,WVA,WVC,WVD,WVE,WVF,WVG,WVH,WVI,WVK,WVL,WVM,WVN,WVP,WVQ,WVR,WVS,WVT,WVV,WVW,WVY,WWA,WWC,WWD,WWE,WWF,WWG,WWH,WWI,WWK,WWL,WWM,WWN,WWP,WWQ,WWR,WWS,WWT,WWV,WWW,WWY,WYA,WYC,WYD,WYE,WYF,WYG,WYH,WYI,WYK,WYL,WYM,WYN,WYP,WYQ,WYR,WYS,WYT,WYV,WYW,WYY,YAA,YAC,YAD,YAE,YAF,YAG,YAH,YAI,YAK,YAL,YAM,YAN,YAP,YAQ,YAR,YAS,YAT,YAV,YAW,YAY,YCA,YCC,YCD,YCE,YCF,YCG,YCH,YCI,YCK,YCL,YCM,YCN,YCP,YCQ,YCR,YCS,YCT,YCV,YCW,YCY,YDA,YDC,YDD,YDE,YDF,YDG,YDH,YDI,YDK,YDL,YDM,YDN,YDP,YDQ,YDR,YDS,YDT,YDV,YDW,YDY,YEA,YEC,YED,YEE,YEF,YEG,YEH,YEI,YEK,YEL,YEM,YEN,YEP,YEQ,YER,YES,YET,YEV,YEW,YEY,YFA,YFC,YFD,YFE,YFF,YFG,YFH,YFI,YFK,YFL,YFM,YFN,YFP,YFQ,YFR,YFS,YFT,YFV,YFW,YFY,YGA,YGC,YGD,YGE,YGF,YGG,YGH,YGI,YGK,YGL,YGM,YGN,YGP,YGQ,YGR,YGS,YGT,YGV,YGW,YGY,YHA,YHC,YHD,YHE,YHF,YHG,YHH,YHI,YHK,YHL,YHM,YHN,YHP,YHQ,YHR,YHS,YHT,YHV,YHW,YHY,YIA,YIC,YID,YIE,YIF,YIG,YIH,YII,YIK,YIL,YIM,YIN,YIP,YIQ,YIR,YIS,YIT,YIV,YIW,YIY,YKA,YKC,YKD,YKE,YKF,YKG,YKH,YKI,YKK,YKL,YKM,YKN,YKP,YKQ,YKR,YKS,YKT,YKV,YKW,YKY,YLA,YLC,YLD,YLE,YLF,YLG,YLH,YLI,YLK,YLL,YLM,YLN,YLP,YLQ,YLR,YLS,YLT,YLV,YLW,YLY,YMA,YMC,YMD,YME,YMF,YMG,YMH,YMI,YMK,YML,YMM,YMN,YMP,YMQ,YMR,YMS,YMT,YMV,YMW,YMY,YNA,YNC,YND,YNE,YNF,YNG,YNH,YNI,YNK,YNL,YNM,YNN,YNP,YNQ,YNR,YNS,YNT,YNV,YNW,YNY,YPA,YPC,YPD,YPE,YPF,YPG,YPH,YPI,YPK,YPL,YPM,YPN,YPP,YPQ,YPR,YPS,YPT,YPV,YPW,YPY,YQA,YQC,YQD,YQE,YQF,YQG,YQH,YQI,YQK,YQL,YQM,YQN,YQP,YQQ,YQR,YQS,YQT,YQV,YQW,YQY,YRA,YRC,YRD,YRE,YRF,YRG,YRH,YRI,YRK,YRL,YRM,YRN,YRP,YRQ,YRR,YRS,YRT,YRV,YRW,YRY,YSA,YSC,YSD,YSE,YSF,YSG,YSH,YSI,YSK,YSL,YSM,YSN,YSP,YSQ,YSR,YSS,YST,YSV,YSW,YSY,YTA,YTC,YTD,YTE,YTF,YTG,YTH,YTI,YTK,YTL,YTM,YTN,YTP,YTQ,YTR,YTS,YTT,YTV,YTW,YTY,YVA,YVC,YVD,YVE,YVF,YVG,YVH,YVI,YVK,YVL,YVM,YVN,YVP,YVQ,YVR,YVS,YVT,YVV,YVW,YVY,YWA,YWC,YWD,YWE,YWF,YWG,YWH,YWI,YWK,YWL,YWM,YWN,YWP,YWQ,YWR,YWS,YWT,YWV,YWW,YWY,YYA,YYC,YYD,YYE,YYF,YYG,YYH,YYI,YYK,YYL,YYM,YYN,YYP,YYQ,YYR,YYS,YYT,YYV,YYW,YYY,Xc1.A,Xc1.C,Xc1.D,Xc1.E,Xc1.F,Xc1.G,Xc1.H,Xc1.I,Xc1.K,Xc1.L,Xc1.M,Xc1.N,Xc1.P,Xc1.Q,Xc1.R,Xc1.S,Xc1.T,Xc1.V,Xc1.W,Xc1.Y,Xc2.lambda1,Xc2.lambda2,Xc2.lambda3,Xc2.lambda4,Xc2.lambda5,xXc2.lambda6,Xc2.lambda7,Xc2.lambda8,Xc2.lambda9,Xc2.lambda10,Xc2.lambda11,Xc2.lambda12,Xc2.lambda13,Xc2.lambda14,Xc2.lambda15,Xc2.lambda16,Xc2.lambda17,Xc2.lambda18,Xc2.lambda19,Xc2.lambda20,Xc2.lambda21,Xc2.lambda22,Xc2.lambda23,Xc2.lambda24,Xc2.lambda25,Xc2.lambda26,Xc2.lambda27,Xc2.lambda28,Xc2.lambda29

Physicochemical based featu<span class="Chemical">res:

aliphatic,aromatic,postivecharge,negativecharge,uncharge. 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gravy, instability_index, helix, sheet, turn, cysteines, cystines, Length

Annotation based features:

Transmembrane, DNA Binding, Metal Binding, Nucleotide Binding, cytoplasm, membrane, cell wall, secreted, periplasm, cell surface, cell envelope, chlorosome, cellular thylakoid membrane, cellular cromatopore membrane, single-pass membrane protein, multi-pass membrane protein, peripheral membrane protein.

Subsequence based features (Motifs): Dataset contains count of these motifs [8] for corresponding protein.

G-{A}-{KGR}-x(2)-[LIVMFTAP]-{R}-x-[AGC]-C-[STA](2)-[STAG]-x(2)-{LI}-[LIVMF],G-[AV]-F-[STA]-x-R-[SA]-x(2)-R-P-N,[IVT]-[L<span class="Chemical">IVMC]-[IVT]-[HS]-D-[SGAV]-[AV]-R,[LIVMF]-[LIVMSTA]-x-[LIVMFYC]-[FYWSTHE]-x(2)-[FYWGTN]-C-[GATPLVE]-[PHYWSTA]-C-{I}-x-{A}-x(3)-[LIVMFYWT],[LIVTMS]-[LIVP]-[LIV]-[KQ]-x-[ND]-Q-[INV]-[GA]-[ST]-[LIVM]-[STL]-[DERKAQG]-[STA],"[IL]-[GA]-x(2)-[LIVMF]-[SGADENK]-x(0,1)-[KR]-x-H-[STPA]-[STAV]-[LIVM]-x(2)-[SGAMN]-x(3)-[LIVM]",[LIV]-[LIVFY]-[FY]-x-[ST]-{V}-x-[AGC]-x-T-{P}-x(2)-A-{L}-x-[LIV],"[HQ]-[IVT]-x-[LIVFY]-x-[IV]-x(4)-{E}-[STA]-x(2)-F-[YM]-x(2,3)-[LMF]-G-[LMF]","[LIVMA]-[AG]-[IVT]-[LIVMFY]-[AG]-x-G-[NHKRQGSAC]-[LIV]-G-x(13,14)-[LIVMFT]-{A}-x-[FYWCTH]-[DNSTK]",Y-[CSAM]-x(2)-[VSG]-A-[GSA]-[LIVAT]-[IV]-G-x(2)-[LMSC]-x(2)-[LIV],"[GSTAI]-[SANQCVIT]-D-x-K-[GSACN]-x(1,2)-[LIVMA]-x(2)-[LIVMFY]-x(12,17)-[LIVM]-x-[LIVMF]-[LIVMSTAGC]-[LIVMFA]-x(2)-[DNGM]-E-E-x(0,1)-[GSTNE]","C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-H″,[GW]-x-[DNIE]-x-H-H-x(2)-E−[STAGC]-x-[VMFYHS]-K,"[FWV]-x(0,1)-[LIVM]-D-P-[LIVM]-D-[SG]-[ST]-x(2)-[FYA]-x(0,1)-[HKRNSTY]","[LIVM]-x(2)-[GSACIVM]-x-[LIV]-[GTIV]-[STP]-C-x(0,1)-T-N-[GSTANI]-x(4)-[LIVMA]",[YA]-[GLIVMSTAC]-D-T-D-[SG]-[LIVMFTC]-{LA}-[LIVMSTAC],[LYGSTANEQ]-x(3)-[GSTAENQ]-x-[PGE]-R-x-[LIVFYWA]-x-[LIVMFTA]-[STAGNQ]-[LIVMFYGTA]-x-[LIVMFYWGTADQ]-x-F>,E-x(5)-[GND]-x-[SAG]-x(2)-[IV]-x-[DE]-[LIV]-x(2)-[ST]-G-x-T-[LMI],[RH]-G-x(2)-P-x-G(3)-x-[LIV],[GDC]-x(2)-[NSTAVY]-x(2)-[IV]-[GSTA]-x(2)-[LIVMFYWCT]-x-[LIVMFYWCR]-x(3)-[NST]-[LIVM]-x(2)-{T}-x(2)-[NRHSA]-[LIVMSTA]-x(2)-[KR],P-[LIVM]-x-[FYL]-[LIVMAT]-[GS]-{Q}-[GS]-[EQ]-x-{K}-x(2)-[LIVMF],"F-[GSADEI]-x-[LVAQ]-A-x(3)-[ST]-x(3,4)-[STQ]-x(3,5)-[GER]-G-x-[LIVM]-[GS]",[LIVMFYC]-{A}-[HY]-x-D-[LIVMFY]-[RSTAC]-{D}-{PF}-N-[LIVMFYC](3),W-x(2)-[LIVM]-D-[VFY]-[LIVM](3)-D-x-P-P-G-T-[GS]-D,"K–P-[LIVMFYA]-x(3,5)-[NPAT]-[GA]-[GSTAN]-[GA]-x-H-x(3)-S″,[FYLVA]-x-{GVEP}-{DILV}-G-[QE]-{LPYG}-C-[LIVMGSTANC]-[AGCN]-{HE}-[GSTADNEKR],[LIVMF]-x-[LIVMFAG]-{T}-x-[STAGI]-H-D-[STANQ]-{V}-[LIVM]-x(2)-[LIVMFY]-x(2)-[STA],D-R-G-H-[QLIM]-x(3)-[AG],[LIVM]-E-x-E−[LIVM]-G-x(2)-[GM]-[GSTA]-x-E,R-G-x(2)-E-N-x-N-G-[LIVM](2)-R-[QE]-[LIVMFY](2)-P-K,"[PALF]-x(2,3)-[LIV]-x(3)-[LIVM]-[STAC]-[STV]-x-[GANK]-G-x-T-x(2)-[AG]-[LIV]-x(2)-[LMF]-[DENQK]",D-P-x-F-[LIVMFYW]-x(2)-H-x(3)-D,"[KR]-[LIVM](2)-[GASL]-x-[GT]-x-[LIVMA]-x(2,5)-[LIVMF]-x-[LIVMF]-x(3,4)-[LIVMFCA]-[ST]-x(2)-A-x(3)-[LIVM]-x(3)-G″,[LIVM]-x(2)-P-x(2)-[FY]-x(4)-C-x-G-x-C,[LIVMFTAR]-[LIVMF]-x-D-x-K-x(2)-D-[IV]-[ADGP]-x-T-[CLIVMNTA],P-[LIVM]-x-[LIVM]-x(2)-[LIVM]-A-x(2)-[LIVMFT]-x(2)-[HS]-x-S-T-[LIVM]-S-R,"[FYKH]-G-[FL]-[IL]-x(6,7)-[DER]-[LIVM]-[FQ]-x-H-x-[STKR]-x-[LIVMFYC]",H–Y-x-[GT]-D-[LIVMAF]-[DNSH]-x-P-x-H-[PA]-x-N,[ST]-[LIVMFY]-D-[LIVM]-D-x(3)-[PAQ]-x(3)-P-[GSA]-x(7)-G,[EQ]-{LNYH}-x-[ATV]-[FY]-{LDAM}-{T}-W-{PG}-N,"[PS]-x-[SAC]-x-[LIVMFY](2)-[QN]-x(2)-N-P-x(4)-[TA]-x(9,11)-[KRD]-x-[LIV]-[GN]-x-C″,R-R-T-[IV]-[ATN]-K-Y-R,[LIVF]-x(2)-D-x-[NH]-x(7)-[ACL]-x(6)-[LIVMF]-x(7)-[LIVM]-E−[DENQ]-P,[LIV]-[GALMY]-[LIVMF]-{Q}-[GSA]-H-x-D-[TV]-[STAV],"[YWG]-[LIVFYWTA](2)-[VGS]-H-[LNP]-x-V-x(44,47)-H-H″,H-x(3)-[GA]-[LIVMT]-R-[HF]-[LIVMF]-x-[FYWM]-D-x-[GVA],[FY]-{L}-C-{PGAD}-[VA]-{LC}-H,G-x-[LIVM](2)-x-R-Q-R-G-x(5)-G,"K-[STNV]-{F}-x-[GSAM]-[SAILV]-x-[KRA]-R-[IVFY]-x(14,16)-[GSANQKR]-H″,"[LIVMFY]-[LIVMC]-x-E−[LIVMFYC]-K-[KRSPQV]-[STAHKRYC]-S-P-[STRK]-x(3,7)-[LIVMFYST]",[DENKS]-x-[FLIV]-x(2)-[GSTC]-x-P-C-x-{V}-[FYWLIM]-S,[GS]-x-[LIVMFA]-x(2)-[AS]-[DNEQASH]-[GNEKT]-G-[STIM]-[LIVMFY](3)-[DE]-[EK]-[LIVM],"C-x(2)-C-x(3,5)-[STACD]-x(4)-C-x-[LIVFQ]-C-x(4)-[RD]-[NQDS]",[LIV]-{LA}-[EDQ]-[FYWKR]-V-{VF}-[LIVF]-G-[LF]-[ST],G-I-[GR]-P-x-Y-x(2)-K-x(2)-R,[GSTEQKRV]-Q-[LIVT]-[VAF]-[SAGQ]-G-{DG}-[LIVMNK]-{TK}-x-[LIVMFY]-{S}-[LIVMFYA]-[DENQKRHSIV],D-[LIVMA]-P-G-[LIVM](2)-[DEYPKQV]-[GN]-A-x(2)-G-x-G,[LIVM]-x(2)-[LIVM]-[STAVC]-[GE]-[QV]-x(2)-[LIVMA]-x-[STC]-x-[STAG]-[KRH]-x-[STA],[IL]-x-[STV]-[GT]-x(2)-[KR]-x-[KRAF]-x(6)-[DE]-x-[LIMV]-[LIVMT]-[TE]-x-[STAG]-[KR],"[LIVM]-G-x(3)-Q-x(2,3)-[ND]-[IFL]-x-[RE]-D-[LIVMFY]-x(2)-[DE]-x(4,7)-R-x-[FY]-x-P″,"[LIVMFY]-x-[LIVM]-[STAG]-G-T-[NK]-G-K-x-[STG]-x(4)-{A}-x-{EAD}-[LIVM](2)-x(3,4)-[GSKQT]",F-[GSTV]-P-R-L-[G>],R-C-[LIVM]-x-C-x-R-C-[LIVMT]-x-[LMFY],G-[GAV]-S-[GS](2)-G-x-[GSAE]-[GSAVYCT]-x-[LIVMT]-[GSA]-x(6)-[GSAT]-x-[GA]-x-[DE]-x-[GA]-x-S-[LIVM]-R-x-P-[GSACTL],[GR]-C-[IV]-G-R-[ILS]-x-W,[LIV]-x(3)-C-[NDP]-[LIVMF]-[DNQRS]-C-x-[FYM]-C,"[LIVMSTAG]-[LIVMFSAG]-{SH}-{RDE}-[LIVMSA]-[DE]-{TD}-[LIVMFYWA]-G-R-[RK]-x(4,6)-[GSTA]",[SAV]-[IVW]-[LVA]-[LIV]-G-[PNS]-G-L-[GP]-x-[DENQT],D-G-[PD]-S-A-[GS]-[LIVMCA]-[TA]-[LIVM],R-P-C-x(11)-C-V-S,[LIV]-[LIVMFYWGA](2)-[DNEQG]-[LIVMGST]-{SENR}-N-E−[PV]-[RHDNSTLIVFY],[FY]-{GL}-x-[LIVMA]-{IP}-x(2)-[FYWHNT]-[DENQSA]-x-L-x-[DN]-x(3)-[KR]-{F}-{P}-[FYI],K-[LIVM]-x-R-D-x(3)-R-G-x-[ST]-x-E,[LIVM]-[ST]-A-[STAG]-H-C,R-x(2)-[GSAV]-K-x(3)-[LIVMFY]-[AGQ]-x(2)-Y-x(2)-[GS]-x(3)-[LIVMA],D-x-[LI]-x(4)-G-x-D-x-[LI]-x-G-G-x(3)-D,Q-[LV]-[NT]-[FY]-[ST]-x(2)-W,P-x(3)-[LIVM](2)-x-G-x-C-[LIVMF](2)-K,F-x-[EK]-x-S-[GT]-R-T,"W-x(2)-[LIVF]-x(6,7)-G-[LIVM]-[FYRA]-[NH]-x(3)-[STAQLIVM]-[ASC]-x(2)-[PA]","C–P-x(0,1)-[ST]-N-[ILV]-G-T″,H-[GSA]-x-[LVCYT]-H-[LAI]-[LIMSANQVF]-G-[FYWMH]-x-[HD],[GSDNA]-W-T-[LIVM]-x-[FY]-W-x-W-W,"[RP]-x(0,1)-C-x(11,12)-[LIVMF]-{L}-[LIVMF]-[SC]-[RG]-x-{D}-{PK}-[RN]",[KNQS]-[PSTLNH]-{D}-{F}-[LIVMFA]-[KRGSADN]-x-[LIVYSTA]-[KR]-[KRHQS]-[DESTANQRL]-[LIV]-A-[KRCQVT]-[LIVMA],"[IVRLP]-[DYN]-[YLF]-x(2,3)-[LIVMTPFS]-x(2)-[LIVM]-x(2)-[FYTS]-[LIVMT]-[STNQG]-[DERPN]-x(1,2)-[GYAH]-[KCR]-[LIVM]-x(3)-[RHG]-[LIVMASR]","L-R-x(2)-[TS]-[GSDNQ]-x-[GSA]-[LIVMF]-x(0,1)-[DENKAC]-x-K-[KRNEQS]-[AV]-L″,"[CA]-[DE]-[LIVM](2)-[NQV]-[GTA]-D-[GA]-[SG]-x(2,3)-[TAVLC]-[AT]",[STAGN]-{E}-[STAG]-[LIVMF]-R-L-{LP}-[SAGV]-N-[LIVMT],D-[SGDN]-D-[PE]-[LIVMF]-D-[LIVMGAC],[GSTALIVN]-{PCHR}-{KND}-H-E−[LIVMFYW]-{DEHRKP}-H-{EKPC}-[LIVMFYWGSPQ],[LI]-[IVCAP]-D-x-K-[LIFY]-E−[FI]-G,[KRG]-[KR]-x-[GSAC]-[KRQVA]-[LIVMK]-[WY]-[LIVM]-[KRN]-[LIVM]-[LFY]-[APK],[LIVMN]-[KR]-G-N-H-E,[STVN]-G-C-x(3)-C-x(6)-[DE]-[LIVMF]-[GAT]-[LIVMF],[KRQSEAT]-[GS]-x-R-H-x(2)-[GSNHKLCD]-x(2)-[LIVMCT]-[RNH]-G-Q,[LIVMA]-G-[EQ]-H-G-[DN]-[ST],H-G-[STM]-x-[VIC]-[STAGC]-[GS]-x-[LIVMA]-[STAGCLV]-[SAGM],"[DENG]-{A}-[DENQGSTARK]-x(0,2)-[DENQARK]-[LIVFY]-{CP}-G-{C}-W-[FYWLRH]-{D}-[LIVMTA]",[LIVMACST]-H-P-[LIVM]-x-[KRQV]-[LIVMF](2)-x-[AP]-H,"[KR]-x(2)-[ST]-G-[GAR]-x(5,6)-[KRHSA]-x-[KRT]-x-[KR]-x-[EA]-[LIMPA]-G″,[PK]-x-[LIVMFY]-x-[LIVMFY]-x(2)-{E}-x-H-[STAG]-x-E-x-[LIVM]-[STAG]-{L}-x(5)-[LIVMFYTA],[DKG]-x(2)-[FLV]-[STKD]-x(5)-C-[LMNQ]-[GA]-x-C-x(2)-[GA]-P,K-[LIVM]-x(5)-[LIVMA]-D-[RK]-[DN]-[LI]-Y,[DE]-[LIVMF](2)-[HEQS]-x-G-x-[LIVMFA]-G-L-[LIVMFYE]-x-[GSAM]-[LIVMAP],T-[LIVMFYW]-[STAG]-K-[SAG]-[LIVMFYWR]-[SAG]-{ENKR}-{TNDR}-[SAG],[LIVM]-[STAG]-x-[LIVM]-[DENQRHSTA]-G-x(3)-[AG](3)-x(4)-[LIVMST]-x-[CSTA]-[DQHP]-[LIVMFYA],[GS]-{PR}-S-M-{RS}-[PS]-[AT]-[LF],"E-x(2)-[ERK]-E-x-C-x(6)-[EDR]-x(10,11)-[FYA]-[YW]",A-[AS]-{L}-[DEQ]-E−{A}-{Q}-{R}-x-G-G-[GA],G-x(2)-[GNF]-x(4)-[VAI]-x(2)-G-[FY]-x(2)-[NH]-[FYWL]-L-x(5)-[GA]-x(3)-[STNG],[ASL]-[FY]-S-G-G-[LV]-D-T-[ST],"G-[YV]-x-[ST]-x(2)-[IVAS]-G-K-x(0,1)-[FYWMK]-[HL]",Y-R-N-x-W-[NS]-E−[LIVM]-R-T-L-H-F-x-G,E−[ST]-C-G-x-C-x-P-C-R-x-G,[GN]-[AS]-G-D-Q-G-x(3)-G-[FYHG],T-G-x-P-[LIVM](2)-D-A-x-M-[RA]-x-[LIVM],"[GDEN]-D-x-[IV]-x-[IV]-[LIVMA]-x-G-x(2)-[KRA]-[GNQK]-x(2,3)-[GA]-x-[IV]",G-[LIVMFYKRSAQT]-[LIVMAGPF]-[QAM]-x-[LIVMFYCA]-x-D-[AGIM]-[LIVMFTA]-[KS]-[LVMYSTI]-[LIVMFYGA]-x-[KRE]-[EQG],"[KRQ]-[LIVMA]-x(2)-[GSTALIV]-{FYWPGDN}-x(2)-[LIVMSA]-x(4,9)-[LIVMF]-x-{PLH}-[LIVMSTA]-[GSTACIL]-{GPK}-{F}-x-[GANQRF]-[LIVMFY]-x(4,5)-[LFY]-x(3)-[FYIVA]-{FYWHCM}-{PGVI}-x(2)-[GSADENQKR]-x-[NSTAPKL]-[PARL]",D-x(3)-G-[LIVMF]-x(6)-[STAV]-[LIVMFYW]-[PT]-x-[STAV]-x(2)-[QR]-x-C-x(2)-H,"[FYWL]-D-G-S-S-x(6,8)-[DENQSTAK]-[SA]-[DE]-x(2)-[LIVMFY]",[DGH]-[IVSAC]-T-[ST]-N-P-[STA]-[LIVMF](2),[LIVMFY]-{G}-[LIVMFYAC]-[DNQ]-[RKHQS]-[PST]-F-[LIVMFY]-[LIVMFYC]-x-[LIVMFAH],[RKQN]-x(2)-{G}-x-[RH]-[GAS]-x-G-[KRQS]-x(8)-{L}-[HDN]-[LIVM]-{A}-[LIVMS]-x-[LIVM],[GSTA]-R-[NQ]-P-x(5)-{A}-x-{F}-x(2)-[LIVMFYW](2)-x(3)-[LIVMFYW]-x-[DE],[ASV]-S-C-[NT]-T-{S}-x-[LIM],"W-x(9,11)-[VFY]-[FYW]-x(6,7)-[GSTNE]-[GSTQCR]-[FYW]-{R}-{SA}-P″,H-[GN]-x(2)-[GC]-E−[DNT]-G-x-[LIVMAFT]-[QSAPH]-[GSA],[MFYGS]-x-[PST]-x(2)-K-[LIVMFYW]-{G}-W-[LIVMF]-{E}-[DENQTKR]-[ENQH],C-x(3)-[KRSN]-P-[KRAGL]-C-x(2)-C-x(5)-C,[FYW]-P-[GS]-N-[LIVM]-R-[EQ]-L-x-[NHAT],[GA]-x(2)-Y-x-[FY]-[LIVMKHRT],"[LIVM](2)-x-D-D-x(2,4)-D-x(4)-R-R-[GH]",G-[GA]-G-[ASC]-[FY]-S-x-K-[DE],C-[STAGM]-G-[HFYL]-C-x-[ST],"G-[KRQEA]-x(3)-[FYVIM]-x-[ACVTI]-x(2)-[LIVMA]-[LIVMAT]-[AG]-[DN]-x(2,3)-G-x-[LIVMA]-[GS]-x-[SAG]-x(5,6)-[DEQGHS]-[LIVMARFY]-x(2,3)-[AS]-[LIVMFRY]",[FY]-[PA]-x-K-[SACV]-[NHCLFW]-x(4)-[LIVMF]-[LIVMTA]-x(2)-[LIVMA]-x(3)-[GTE],[LIVMF]-[DN]-x-F-P-[QHYWM]-[ST]-x-[HR]-[LIVMFYT]-E,[GSW]-x-[LIVTSACD]-[GH]-x(2)-[GSAE]-[GSHYQ]-x-[LIVTP]-[GAST]-[GAS]-x(3)-[LIVMT]-x-[HNS]-[GA]-x-[GTAC],H-x-C-G-G-N-V-G-D,"P–F-D-[LIVMFYQN]-[STAGPVMI]-E−[GACS]-E-x(0,2)-[EQLN]-[LIVMS]-x(1,2)-G″,[LIVMF]-x(2)-E−[AG]-[YWG]-[QRFGS]-[SG]-[STAN]-G-x-[SAF],G-x-[KRC]-[DENQRH]-L-[SA]-Y-x-I-[KRNSA],[LIVMFYSNAD]-x(2)-A-x(2)-R-[NH]-[KRQLYAT]-[LIVMFSA]-[KRA]-R-x-[LIVMTA]-[KR],[LIVM]-x(2)-H-[LIVMFY]-x(3)-{S}-x-D-x(2)-[STAGN]-x(3)-[LF]-x(2)-{A}-x(6)-[LIVM]-x(2)-[FY],[LIVFYAN]-[LIVMFA]-x(2)-D-[LIVMF]-[ND]-G-T-[LIV]-[LVY]-[STANLM],[IF]-x-[RH]-x(4)-[EQ]-R-x(2)-H-x(2)-[GAS]-[GASTFY]-[GAST],[GSTNP]-x(6)-[FYVHR]-[IVN]-[KEP]-x-G-[STIVKRQ]-Y-[DNQKRMV]-[EP]-x(3)-[LIMVA],G-x-[FYW]-x-[LIVMFYW]-x-[CST]-x-{PR}-{K}-x(2)-{S}-x-{LFH}-G-[LM]-x(3)-[LIVMFYW],[LIVMFA]-x-{GPRV}-[LIVMFYC](2)-{LPC}-[STAC]-[GSTANQEKR]-[STALV]-[HY]-[LIVMF]-G,"[STNAQ]-[LIAMV]-x(0,1)-[RNGSYKE]-x(4,5)-[LM]-[EIVLA]-x(2)-[GESD]-[LFYWHA]-[LIVC]-x(7)-[DNS]-[RKQG]-[RK]-x(6)-[TS]-x(2)-[GAS]","[GA]-[LIVM]-[PKV]-x(0,1)-E-x(3)-[NG]-E-x(1,3)-R-[VT]-[AG]-x-[ST]-P-x-[GSTVN]-[VA]-x(2)-[LI]-x-[KRHNGSED]-x-G″,G-[GNHD]-[SGA]-[GR]-x-R-x-[SGAWRV]-C-x(2)-[IV],A-L-[KR]-[IF]-[FY]-[STA]-[STAD]-[LIVMQ]-R,[LIVMFGAC]-[LIVMTADN]-[LIVFSA]-D-[ST]-G-[STAV]-[STAPDENQ]-{GQ}-[LIVMFSTNC]-{EGK}-[LIVMFGTA],[IV]-{K}-[TACI]-Y-[RKH]-{E}-[LM]-L-[DE],[LIVMF]-[LIMN]-E−[LIVMCA]-N-[PATLIVM]-[KR]-[LIVMSTAC],[ST]-x(3)-G-[DY]-G-[KR]-[IV]-[FW]-[LIVM]-x(2)-[LIVM],[RKH]-x-{Y}-{I}-x-{I}-{L}-D-x-M-G-x-N-x-[LIVMA],"[KFQ]-[RGMP]-[TN]-[FYWL]-[EQSG]-x(5)-[KRHS]-x(4,5)-G-F-x(2)-R″,"[DN]-P-[PAS]-R-x-G-x(14,19)-[LIVMAF]-[LIVMCAF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STAPG]-x(2)-[RGANQS],[NS]-[TS]-D-A-E-G-R-[LVMI],[KNQ]-x(2)-{K}-x(3)-{A}-{L}-x(9)-[LIVMFY]-x(2)-[DENHR]-x(2)-[GS]-[LIVMF]-[STDNQC]-[VTA]-x-[DENQKHPSA]-[LIVMSAD]-x(2)-[LIMF]-[KR],[APV]-[GS]-M-G-[LIVMN]-Y-[IVC]-[LIVMFY]-x(2)-[DENPHKRQS],"[EQ]-x-L-Y-[DEQSTLM]-x(3,12)-[LIVST]-[ST]-Y-x-R-[ST]-[DEQSN]",G-[FYIL]-[DE]-[LIVMT]-[DE]-[LIVMF]-{PS}-{YG}-x-[LIVMA]-[VAGC]-{TPRG}-{GL}-[LIVMAGN],[LIVMFY]-G-x(2)-[FYL]-Q-[LIVM]-x-D-D-[LIVMFY]-x-[DNG],"[GA]-[LIV](3)-x(9,10)-[DNS]-G-x(4)-[FY]-x(2)-[NT]-x(2)-V-[LIV]",[GE]-x(2)-[LIV](2)-[STY]-[ST]-{A}-x-G-[LIVM](2)-x(4)-[AG]-[KRHAYIL],[EQ]-x(4)-[HGQ]-x(5)-[GSTA]-x(3)-[FYV]-x(3)-[AG]-x(2)-[AV]-H-x(7)-P,[LIVMY]-[DE]-x-H-H-x(2)-E-x(2)-[GCA]-[LIVM]-[STAVCL]-[LIVMF],[STA]-x(5)-G-x-[QKRN]-x(2)-[LIVMQ]-[KRQT]-x(2)-[KR]-x-[GS]-x(2)-[KQ]-x-[LIVM](3),[LIVMFY](2)-[EK]-x-G-[LIVM]-[GA]-G-x(2)-D-x-[GST]-x-[LIVM](2),"[GSA]-x-[LIVMFA]-[ASM]-x(2)-[STACLIV]-[GSDENQR]-[LIVC]-[STANHK]-x(3)-[LIVM]-[RHF]-x-[YW]-[DEQ]-x(2,3)-[GHDNQ]-[LIVMF](2)",R-M-G-x-[GR]-K-G-x(4)-[FWKR],"[GSK]-F-x(2)-[LIVMF]-x(4)-[RKEQA]-x(2)-[RST]-x(1,2)-[GA]-x-[KN]-P-x-[TN]","R-{G}-x(2)-[LIVM]-x(3)-[LIVM]-x(16,17)-[STA]-x(2)-T-[LIVMA]-[RH]-[KRNAQ]-D-[LIVMF]",[DE]-x-A-[LIY]-[KR]-[RA]-[FL]-K-[KR]-x(3)-[KR],L-R-[DE]-G-x-Q-x(4)-{L}-x(5)-K,[LIV]-{KG}-[LIVFY]-[LIVMST]-G-[HYWV]-S-{YAG}-G-[GSTAC],S–V-A-G-L-G-G-C-P-Y,"H–F-x(2)-[EQ]-[ENQ]-x(2)-[LMF]-x(4,7)-[FY]-x(5,6)-H-x(3)-[HR]","G-x(8,9)-G-x-[STA]-H-[LIVMFY]-[LIVMC]-[DERN]-[HRKL]-[LMFAT]-x-[LFSTH]-x-[GSTAN]-[GST]",[LIV]-[GAED]-x(2)-[STAV]-x-[LIV]-x(3)-[LIVAC]-x-[LIV]-[GAED]-x(2)-[STAVR]-x-[LIV]-[GAED]-x(2)-[STAV]-x-[LIV]-x(3)-[LIV],[RK]-x(4)-[GAS]-H-x-[QL]-[QR]-[GS]-[GF]-x(5)-[DE]-[RL],[GA]-[IMFAT]-H-[LIVF]-H-{S}-x-[GP]-[SDG]-x-[STAGDE],"A-x(3)-[GDTN]-[IF]-x-[DNQTKEH]-x-[DEAQ]-x-[LIVM]-x-[LIVMC]-x-[NS]-x(2)-[GS]-x(4,5)-[AV]-x-[LIVMEF]-[STY]",K–F-G-x-G-D-G,[APF]-D-[LIVMF](2)-{T}-[LIVM]-Q-E−{G}-K,[LIVM]-[LIVMF]-G-[GAV]-G-x-[AV]-[GA]-x(2)-[SA]-x(3)-[GA]-x-[SGR]-[LIVM]-[GN]-A-x-V-x(3)-[DE],"[GS]-[STG]-[LIVM]-[STG]-[SAC]-S-G-[DH]-L-x-[PN]-L-[SA]-x(2,3)-[SAGVTL]",C–K-x(2)-N-T-F,R-P-[LIVMT]-x(3)-[LIVM]-x(6)-[LIVMWPK]-x(4)-S-x(2)-H-R-x-[ST],[LIVM]-x-[AG]-[LIVMF](2)-N-x-T-x-[DN]-S-[FLMI]-x-D-x-[SG],"[FYVMT]-x(1,3)-[LIVMH]-[APNT]-[LIVM]-x(1,2)-[LIVM]-H-x-D-H-[GACH]",[SAG]-G-G-T-G-[SA]-G,"[LIVM]-[DNG]-[LIVMF]-N-x-G-C-[PS]-x(3,4)-[LIVMASQ]-x(5,6)-G-[SACY]",G-A-K-R-H,T-x(2)-[LIVMF]-G-x-A-[SAC]-S-[MSA]-[PAG]-[STA],[LIV]-[LIFYMV]-x-[LIVM]-D-[DEA]-[LIVF]-x(2)-[EHCGK]-L-D-x(2)-[KRH]-x(3)-[LIVF],G-S-x(2)-M-x-{RS}-K-x-N,[IV]-G-[KR]-[ST]-G-x-[LIVM]-[STNK]-x-[VTLYF]-x(2)-[LVMF]-x-[PS]-[IV],[LIVMFYW](2)-{NLPA}-{T}-G-D-[NH]-{PIEW}-x(2)-[SND]-x(2)-[SG],R-I-A-R-N-[TQ]-x(2)-[LIVMFY](2)-x-[EQH]-E-x(4)-[KRN]-x(2)-D-P-x-[GSA]-G-S,L-[IV]-A-H-[STACH]-Y-[STV]-[RT]-Y-[LIVM]-G,[GA]-G-x-G-D-[TV]-[LT]-[STA]-G-x-[LIVM],[RKQGTF]-x(2)-G-N-[SA]-[LIVF]-x-[VIP]-x-[LVMT]-x(3)-[LIVM]-x(3)-[LIVM],[LIVMA]-{R}-E-G-[DN]-S-A-{F}-[STAG],"[AG]-[RK]-[LI]-x(1,2)-[LIV]-[FY]-E-x(2)-P-[LIVM]-[GSA]",C-x-{P}-C-{C}-x-C-{CP}-x-{C}-C-[PEG],E-x-[LIVM]-N-[ST]-[SA]-[LIV]-E-x(2)-V-D,"[GS]-x(4)-[LIVMT]-x(4)-[LIVMF]-x(2)-[CSAM]-[LMFY]-x(6)-[STC]-x(4,5)-[PAC]-x-[LIVMF]-x-[LIVMF]-x(8)-C-x(1,2)-[CH]",[LIVMFYC]-x-[HY]-x-D-[LIVMFY]-K-x(2)-N-[LIVMFYCT](3),[IV]-x-D-S-[GAS]-[GASC]-[GAST]-[GA]-T,[IV]-x-[IV]-[SA]-T-[NQ]-M-A-G-R-G-x-D-I-x-L,H-[STAG]-{ADNV}-{VGFI}-{YAR}-[LIVME]-{SDEP}-x-[LIVMFYW]-P-[FYW],[AG]-G-G-x-G-[STKA]-x-L-x(2)-L-[TA]-x(3)-[AST]-x-P-[AS]-[LV],D-[KRSTGANQFYW]-x(3)-E−[KRAQ]-x-[RKQD]-[GC]-[IVMK]-[ST]-[IV]-x(2)-[GSTACKRNQ],G-[AM]-G-[AR]-Y-[LIVM]-C-G-[DE](2)-[STA](2)-[LIM](2)-[END]-S,[LIVMFYC]-[SA]-[SAPGLVFYKQH]-G-[DENQMW]-[KRQASPCLIMFW]-[KRNQSTAVM]-[KRACLVM]-[LIVMFYPAN]-{PHY}-[LIVMFW]-[SAGCLIVP]-{FYWHP}-{KRHP}-[LIVMFYWSTA],[SA]-x-[RK]-x-Q-[LIVMT]-Q-E−[RNAK]-[LIM]-[TSNV],F-[IVFY]-G-[LM]-M-[G>],[LIVMFW]-x(2)-H-x-H-[DN]-D-x-G-x-[GAS]-x-[GASLI],[KRHGTCVN]-[VT]-[LIVMF]-[LIVMC]-R-x-D-x-N-[SACV]-P,"[YW]-x-[STKV]-x-[KR]-[NSKQ]-x(3,4)-[PATQS]-x(1,2)-[LIVMF]-[EAQVSIT]-x(2)-K-[FYH]-[CSD]","[LIVAMSFT]-x(3)-[GAHDVSI]-x-[GSAIVCT]-R-[LIVMCAFST]-[DE]-[LIVMFAYGT]-[LIVMFAR]-x(7,12)-[LIVWCAF]-x-[EK]-[LIVAPMT]-N-[STPA]-x-P-[GA]",[ST]-[DM]-H-[LIC]-x(2)-[FA]-[LIY]-[EQK]-R-x(2)-[QNKA],[DET]-[LIVMTA]-{NSYL}-{RPFC}-[LIVM]-[LIVMSTAG]-[SAG]-[LIVMSTAG]-H-[STA]-[LIVMFY],G-x(5)-E-x(4)-[TAGCV]-[LIVMACF]-x-R-[EL]-[LIVMFGSTA]-x-[EA]-E-x-[GNDTHR],[LIVM]-x-K-[FY]-G-G-[ST]-[SC]-[LIVM],[LIVMSTAC]-[LIVMFYWSTAGC]-[LIMSTAG]-[LIVMSTAGC]-x(2)-[DN]-x-{P}-[LIVMWSTAC]-{DP}-[LIVMFSTAG]-W-[DEN]-[LIVMFSTAGCN],"[EKH]-[LHVI]-x(9,10)-[IVNLR]-x(3)-[LIV]-x(6)-G-D-x(2)-E-N-[GSA]-x-Y″,D-{W}-[DNS]-{ILVFYW}-[DENSTG]-[DNQGHRK]-{GP}-[LIVMC]-[DENQSTAGC]-x(2)-[DE]-[LIVMFYW],"D-[ST]-[FY]-[RP]-[KHQ]-x(7,8)-[FYWD]-[ST]-[FYW](2)","[LIFAT]-{IL}-x(2)-W-x(2,3)-[PE]-x-{VF}-[LIVMFY]-[DENQS]-[STA]-[AV]-[LIVMFY]",E-x-G-G-P-x(2)-[GA]-x-G-C-[AG]-G,[FYIV]-{ND}-[FYVG]-[LIVM]-D-[LIVMF]-x-[STA]-K-x-{K}-[FY],[LIVF]-{LV}-x-[GANQK]-[NLG]-[SA]-[GA]-[TAI]-[STAGV]-{N}-R-x-[LIVMFYAT]-x-[GSTAP],C-x(2)-D-[LIVM]-x(6)-[ST]-x(4)-S-[HYR]-[HQ],[LIVMFGA]-E−[LIMSTAC]-[GS]-G-[KNLM]-[SADN]-[TAPFV],"H-x(4,5)-F-[LIVMFTP]-x-[FW]-H-R-x(2)-[LVMT]-x(3)-E″,"[LIVMFR]-x-[GSTACQI]-[LIVMF]-x(1,2)-[GSTALVM]-x(0,1)-[GSN]-[LIVMFY]-x-[LIVM]-x(4)-[DEN]-x-[TS]-[PS]-x-[PA]-[STCHF]-[DN]",[GDN]-[DEQTR]-x-[LIVMFY]-x(2)-[LIVM]-x-[AIV]-M-K-[LVMAT]-x(3)-[LIVM]-x-[SAV],"G-[LIVMFY]-x(2)-[LIVMFY]-x-[LIVM]-D-[DF]-x(1,2)-W-x(3,7)-[RV]-[DNSF]",[LIVMFY](3)-x-G-[DEQ]-[STE]-G-[STAV]-G-K-x(2)-[LIVMFY],[LIVM]-x-D-{EK}-[EDNTY]-[DG]-[RKHDENQ]-x-[LIVM]-x-{E}-{Q}-x(2)-Y-x-[LIVM],[PSA]-[LQ]-x-E−[YF]-Y-[LIVM](2)-[DE]-x-[FYWHN],D-x-L-G-D-V-V-C-G-G-F-[AGSP]-x-P,W-[QKR]-[NSD]-[SA]-[LIV]-R-H,"C-x(1,4)-C-[GSANHK]-x(1,2)-[IVML]-x(7,11)-R-[GSANPVLMT]-x(2)-[FYWIL]-C-x(2)-C-Q″,[LIVMT]-[RK]-[LIVM]-G-[LIVM]-G-x-G-[SRK]-[LIVMAT]-C-x-T,"[GSTA]-[KR]-x(6)-G-x-[LIVMT]-x(2)-[NQSCH]-x(1,3)-[LIVFCA]-x(3)-[LIV]-[DENQ]-x(7)-[LMT]-x(2)-G-x(2)-[GS]",G-[IVT]-[LVAC](2)-[IVT]-D-[DE]-[FL]-[DNST],[LIVM]-[PAIV]-[KR]-[ST]-{EPQG}-{RFI}-x(2)-R-{SVAF}-x-[GSTAEQK]-[NSL]-x-{LVRI}-[LIVMFA],[PG]-x-[GS]-C-[GA]-E−[EQ]-x-[LIVM],[FY]-P-S-[AGMS]-C-G-K-T-[NS],"[STAIV]-[PQDEL]-[DE]-[LIV]-[LIVTA]-Q-x-[STAV]-[LIVMFYC]-[LIVMAK]-x-[GSTAIV]-[LIMFYWQ]-x(12,14)-[STAP]-[FYW]-[LIF]-x(2)-[IV]",L-I-G-D-D-E-H-x-W-x-[DEPKVNA]-x-[GVS]-[IV]-x-N,G-x-[DN]-F-x-K-x-D-E,S-E−[HN]-x-[LIVM]-x(4)-[FYH]-x(2)-E−[LIVMGA]-H-[LIVMFA](2),[LIVMFY]-x(2)-G-x(2)-Y-x-F-x-K-x(2)-[SN]-[STAV]-[LIVMFYW]-V,[IVLC]-M-[LIVM]-G-Y-S-D-S-x-K-[DF]-[STAG]-G,[GSAIVK]-{FE}-[FYW]-x-[LIVMF]-x(2)-{K}-x-[NHG]-[FY]-[DE]-x-[LIVMFY]-[LIVM]-{N}-{G}-[LIVMAKR],[LIFV]-x(6)-[LIF]-[LIVF]-x-[GSDE]-[GSTADNPE]-[PASG]-x(2)-R-R-x-[FYW]-[LIVMF]-[DN],C-x-C-x(2)-{V}-x(2)-G-{C}-x-C,G-R-x-N-[LIV]-I-G-[DE]-H-x-D-Y,[DEQGSTALMKRH]-[LIVMFYSTAC]-[GNQ]-[LIVMFYAG]-[DNEKHS]-S-[LIVMST]-{PCFY}-[STAGCPQLIVMF]-[LIVMATN]-[DENQGTAKRHLM]-[LIVMWSTA]-[LIVGSTACR]-{LPIY}-{VY}-[LIVMFA],"[GSA]-x(2,6)-[LIVMSCP]-x-{N}-[LIVMF]-[DNS]-[LIVMCA]-G-G-G-[LIVMFY]-[GSTPCEQ]",[LIVMFC]-G-D-[GSANQ]-x-N-D-x(3)-[LIMFY]-x(2)-[AV]-x(2)-[GSCP]-x(2)-[LMP]-x(2)-[GAS],[LIVM]-x-[LIVMT]-x(2)-G-C-x(3)-C-[STAN]-[FY]-C-x-[LIVMT]-x(4)-G,C–P-x-C-[DE]-x-[GS](2)-x-C-x-L-Q,[STAGDN]-Y-x-Y-E−{AV}-{L}-[DE]-[KR]-[STAGCI],"[LIVMF]-[QKRHSA]-{E}-x-[LIVMAC]-x(5,6)-[LIVMW]-[RKAYF]-x-[STACIVMF]-[PV]-{LG}-[LIVMF]-x-[FYI]-x(2)-D″,V-[ASV]-[TS]-[IVLA]-[RQ]-[AGS]-[LIM]-[KER]-x-[HN]-[GAS]-[GLKD],[SGATV]-{D}-x(2)-[LIVMA]-R-[LIVMA]-x-[FW]-H-{V}-[SAC],[NQH]-x(4)-P-x-H-x(2)-[SAG]-x(11)-[SAGC]-x-H-[SAG](2),S-[DN]-[GA]-D-[LIVAP]-[LIVAG]-x-H-[STAC]-x(2)-[DNT]-[SAG]-x(2)-[SGA],[GSTADE]-[KREQSTIV]-x-{EPRK}-{VPGL}-x-[KRDN]-S-[LIVMF](2)-{EVPL}-[LIVM]-{EATN}-x-[LIVM]-[GADE],G-x(2)-[LIVWPQT]-x(3)-[GACST]-C-[GSTAM]-[LIMPTA]-C-[LIMV]-[GA],[DNSK]-[PSTV]-x-[SAG](2)-[GD]-D-x(3)-[SAGV]-[AG]-[LIVMFYA]-[LIVMSTAP],[STN]-x(2)-[DENQ]-[LIVMT]-[GAS]-x(4)-[LIVMF]-[PSTG]-x(3)-[LIVMA]-x-[NQR]-[LIVMA]-[EQH]-x(3)-[LIVMFWK]-x(2)-[LIVM],[DEQSKN]-x-[LIVMF]-[SA]-[LIVMF]-G-[ST]-N-D-[LIVM]-x-Q-[LIVMFYGT]-[STALIV]-[LIVMFY]-[GAS]-x(2)-R,Y-[KR]-G-[AS]-[AE]-Y,"[GSTENA]-x-[LIVMF]-P-x(5)-[LIVMW]-x(2,3)-[LI]-[PAS]-G-[IV]-[GA]-x(3)-[GAC]-x(2,3)-[LIVMA]-x(1,2)-[GSALVI]-[LIVMFYW]-[GANKD]","P-x(0,2)-[GSTAN]-[DENQGAPK]-x-[LIVMFP]-[HT]-[LIVMYAC]-G-[HNTG]-[LIVMFYSTAGPC]",L-x(3)-[GRS]-[LIVY]-x(2)-[STA]-x(2)-G-x(2)-G-G-[FYIV]-x-[LIF],[GSDN]-[DEQHKM]-x(2)-L-x(3)-[SAG](2)-G-G-x-G-x(4)-Q-x(2)-[KRS],[LIVMF]-[LIVMFY]-[DN]-[LIVMFS]-G-[GSH]-[GS]-[AST]-x(3)-[ST]-[LIVM]-[LIVMFC],G-G-x-C-[LIVA]-x(2)-G-C-[LIVM]-P,[QDE]-x-{P}-G-[GS]-x-G-[LIVMFY]-x(2)-[DEN]-x(4)-[KR]-x(3)-[DEN],[LIVMCA]-[LIVM](2)-[LITF]-[LITN]-G-G-T-G-x(4)-D,"[LIVNS]-x-{L}-[LIVMFA]-x-C-x-[STAGCDNH]-C-x(3)-[LIVFG]-{LV}-x(2)-[LIV]-x(9,11)-[IVA]-x-[LVFYS]",[LIVMFYWCA]-[LIVMFYW](2)-D-G-[FYI]-P-R-x(3)-[NQ],[QGF]-[WLCF]-G-D-E−[GA]-K-[GA],D-K-T-G-T-[LIVM]-[TI],R-[LF]-G-D-P-E-x-[EQIM],[DENSK]-x-[LIVMDET]-x(3)-[LIVMFTA](2)-x(6)-G-K-[KR]-x(5)-[LIVMF]-[LIVMFC]-x(2)-[STAC],[LVSAT]-[LIVA]-x(2)-[LIVMT]-[PSD]-x(3)-[LI]-[LIVMT]-[LIVMST]-E-T-D-x-P,Y-[LIVAC]-R-[VA]-S-[ST]-x(2)-Q,G-[DE]-x(2)-[LIVM]-{E}-x-{V}-[LIVM]-[DT]-R-[LIVM]-[GSA],[FY]-x(2)-[STCNLVA]-x-[FV]-H-[RH]-[LIVMNS]-[LIVM]-x(2)-F-[LIVM]-x-Q-[AGFT]-G,W-x(4)-[YF]-D-x(3)-[DN]-[LIVMFYT]-[LIVMFY](3)-x(2)-G-x(2)-[STAG]-[PVT],"[LIVMA]-x-[LIVM]-M-[ST]-[VS]-x-P-x(3)-[GN]-Q-x(0,1)-[FMK]-x(6)-[NKR]-[LIVMC]",[LIV]-[LIVMGSTC]-[DET]-[RH]-[FYHCS]-x(2)-S-[GSTNP]-x-[AVC]-[FY]-[STANQ],E−[ST]-[EA]-R-E-A-[RK]-x-[LI],[EQ]-[YF]-A-[LIVM]-x(2)-[LIVM]-x(4)-[LIVMF](3)-x-G-H-x(2)-C-G,[RK]-x-[STA]-x(2)-S-x-C-Y-[SL],[GSTAP]-x(2)-[DNEQA]-[LIVM]-[GSA]-x(2)-[LIVMFYT]-[GAN]-[LIVMST]-[ST]-x(6)-R-[LIVT]-x(2)-[LIVM]-x(3)-G,[LIVMFY](2)-D-[STA]-H-x-H-[LIVMFP]-[DN],"[GA]-x(1,2)-[DE]-x-Y-x-[STAPV]-x-C-[NKR]-x-[CH]-[LIVMFYWH]",K-x-[AV]-x(4)-G-x(2)-[LIVT]-x-V-P-x(2)-[LIVC]-x(2)-[GD],"[MFY]-x-G-H-G-[LIVMC]-[GSHN]-x(3)-H-x(4)-[LIVM]-x(1,2)-[HN]-[YWVHF]",P-x(2)-R-G-[STAIV](2)-x-N-[APK]-x-[DE]

A complete list of 1739 GO terms (of molecular function domain only) assigned as target labels for forming Train/Test datasets is shown below:

GO:0047529,GO:0008565,GO:0015254,GO:0017040,GO:0005534,GO:0003906,GO:0048307,GO:0008926,GO:0047462,GO:0004396,GO:0050421,GO:0002020,GO:0052856,GO:0008800,GO:0003989,GO:0001217,GO:0003690,GO:0033752,GO:0005355,GO:0016677,GO:0016208,GO:0043199,GO:0047985,GO:0004802,GO:0003896,GO:0016830,GO:0031071,GO:0052621,GO:0047943,GO:0004639,GO:0004747,GO:0046428,GO:0043142,GO:0015094,GO:0018457,GO:0008673,GO:0016855,GO:0032267,GO:0004130,GO:0008774,GO:0046526,GO:0004721,GO:0018506,GO:0008885,GO:0036094,GO:0102009,GO:0008705,GO:0106099,GO:0015423,GO:0036355,GO:0004055,GO:0032131,GO:0008745,GO:0050660,GO:0004672,GO:0015112,GO:0005516,GO:0008904,GO:0008138,GO:0015344,GO:0000310,GO:0000210,GO:0004109,GO:0008974,GO:0015426,GO:0047806,GO:0044183,GO:0036220,GO:0052858,GO:0061798,GO:0046564,GO:0004615,GO:0102162,GO:0016403,GO:0004854,GO:0010333,GO:0047595,GO:0005506,GO:0030337,GO:0015595,GO:0047429,GO:0047761,GO:0004477,GO:0046857,GO:0061503,GO:0042578,GO:0098519,GO:0102568,GO:0018455,GO:0070968,GO:0008907,GO:0043822,GO:0004823,GO:0048365,GO:0015288,GO:0008795,GO:0015623,GO:0016168,GO:0015293,GO:0003746,GO:0004411,GO:0016774,GO:0047862,GO:0004180,GO:0009384,GO:0004072,GO:0005215,GO:0042289,GO:0008980,GO:0004832,GO:0004156,GO:0047702,GO:0051537,GO:0070026,GO:0043140,GO:0004473,GO:0008710,GO:0004640,GO:0004739,GO:0003862,GO:1904047,GO:0003910,GO:0019003,GO:0016799,GO:0016651,GO:0030272,GO:0015420,GO:0008802,GO:0008902,GO:0015099,GO:0018467,GO:0043792,GO:0030983,GO:0140078,GO:0017136,GO:0016868,GO:0004129,GO:0047324,GO:0050020,GO:0052912,GO:0004359,GO:0016615,GO:0031420,GO:0008137,GO:0043022,GO:0004619,GO:0050262,GO:0015095,GO:0016410,GO:0009389,GO:0047330,GO:0004113,GO:0004497,GO:0019146,GO:0016491,GO:0004812,GO:0003908,GO:0047112,GO:0008869,GO:0008713,GO:0047348,GO:0031177,GO:0009486,GO:0016298,GO:0016841,GO:0004160,GO:0004328,GO:1901973,GO:0004821,GO:0034700,GO:0008725,GO:0050001,GO:0016620,GO:0070569,GO:0102921,GO:0005085,GO:0036054,GO:0097367,GO:0050301,GO:0015232,GO:0003842,GO:0004222,GO:0016436,GO:0016775,GO:0008690,GO:0008237,GO:0050319,GO:0018452,GO:0004065,GO:0103046,GO:0008833,GO:0043873,GO:0008198,GO:0008853,GO:0008409,GO:0102008,GO:0017091,GO:0008964,GO:0004066,GO:0019826,GO:0035439,GO:0036222,GO:0015643,GO:0052821,GO:0004590,GO:0045145,GO:0031419,GO:0005345,GO:0043682,GO:0005384,GO:0004750,GO:0008324,GO:0043885,GO:0051745,GO:0003935,GO:0008073,GO:0050279,GO:0036200,GO:0017137,GO:0008758,GO:0016880,GO:0008413,GO:0052908,GO:0003874,GO:0033848,GO:0008134,GO:0016810,GO:0032448,GO:0070181,GO:0050256,GO:0000155,GO:0008047,GO:0005507,GO:0016852,GO:0004594,GO:0050163,GO:0016165,GO:0015616,GO:0004386,GO:0052618,GO:0008968,GO:0015413,GO:0004842,GO:0033958,GO:0030248,GO:0008556,GO:0034335,GO:0030170,GO:0043904,GO:0008121,GO:0008872,GO:0008772,GO:0004674,GO:0047389,GO:0102252,GO:0019825,GO:0018799,GO:0106026,GO:0050223,GO:0000287,GO:0004644,GO:0003697,GO:0034025,GO:0004748,GO:0009009,GO:0047974,GO:0008483,GO:0043365,GO:0016655,GO:0009028,GO:0051287,GO:0070566,GO:0016758,GO:0047067,GO:0008720,GO:0047150,GO:0002134,GO:0009012,GO:0004340,GO:0004503,GO:0018805,GO:0004399,GO:0046820,GO:0005344,GO:0015609,GO:0019150,GO:0017151,GO:0018597,GO:0008879,GO:0032549,GO:0097351,GO:0047917,GO:0008478,GO:0015184,GO:0047121,GO:0004779,GO:0018710,GO:0050897,GO:0103039,GO:0009046,GO:0035888,GO:0016433,GO:0004310,GO:0047900,GO:0070905,GO:0061602,GO:0042626,GO:0044212,GO:0008768,GO:0050385,GO:0045148,GO:0015436,GO:0016882,GO:0000179,GO:0032575,GO:0008970,GO:0008835,GO:0035539,GO:0047840,GO:0022857,GO:0004829,GO:0004452,GO:0047113,GO:0008888,GO:0102299,GO:0004526,GO:0003848,GO:0008177,GO:0034611,GO:0008080,GO:0008097,GO:0052739,GO:0001108,GO:0061599,GO:0008819,GO:0033613,GO:0018818,GO:0015462,GO:0047652,GO:0004131,GO:0003868,GO:0003863,GO:0033748,GO:0016740,GO:0047972,GO:0008876,GO:0008792,GO:0033677,GO:0005354,GO:0008934,GO:0033204,GO:0050484,GO:0004176,GO:0008375,GO:0018456,GO:0030507,GO:0018802,GO:0035731,GO:0004040,GO:0050515,GO:0008787,GO:0050292,GO:0008878,GO:0015438,GO:0004788,GO:0047545,GO:0061693,GO:0004377,GO:0016778,GO:0016798,GO:0050140,GO:0000822,GO:0016207,GO:0016795,GO:0015021,GO:0047553,GO:0047154,GO:0004089,GO:0043752,GO:0015562,GO:0004325,GO:0016836,GO:0051575,GO:0102391,GO:0000175,GO:0016989,GO:0008716,GO:0047693,GO:0003856,GO:0047844,GO:0008553,GO:0008779,GO:0004824,GO:0004475,GO:0047929,GO:0035438,GO:0015667,GO:0090729,GO:0008296,GO:0060698,GO:0008863,GO:0051063,GO:0008986,GO:0016879,GO:0015572,GO:0036422,GO:0004749,GO:0102908,GO:0051213,GO:0004527,GO:0008854,GO:0004532,GO:0036311,GO:0009045,GO:0016614,GO:0070406,GO:0052764,GO:0031072,GO:0004137,GO:0097100,GO:0016878,GO:0003922,GO:0034005,GO:0019899,GO:0015441,GO:0070538,GO:0004024,GO:0052693,GO:0050519,GO:0052680,GO:0044603,GO:0004482,GO:0004512,GO:0008263,GO:0004635,GO:0015078,GO:0000049,GO:0003977,GO:0004813,GO:0008234,GO:0047991,GO:0000309,GO:0102229,GO:0003779,GO:0008976,GO:0047075,GO:0008756,GO:0004614,GO:0018753,GO:0047584,GO:0050084,GO:0000150,GO:0009041,GO:0000166,GO:0043273,GO:0047609,GO:0030975,GO:0003729,GO:0047527,GO:0009030,GO:0004148,GO:0003917,GO:0043884,GO:0005518,GO:0050083,GO:0004494,GO:0018859,GO:0016979,GO:0051499,GO:0008320,GO:0009975,GO:0050695,GO:0016823,GO:0050470,GO:0016763,GO:0016627,GO:0008940,GO:0050518,GO:0043798,GO:0045300,GO:0008922,GO:0005319,GO:0030246,GO:0035312,GO:0051212,GO:0004149,GO:0004311,GO:0050560,GO:0052619,GO:0048502,GO:0047471,GO:0030145,GO:0052692,GO:0018861,GO:0047817,GO:0090612,GO:0004736,GO:0004806,GO:0018459,GO:0048039,GO:0034040,GO:0018800,GO:0003678,GO:0004081,GO:0017108,GO:0047465,GO:0004576,GO:0033739,GO:0047954,GO:0004412,GO:0009381,GO:0033999,GO:0008727,GO:0004400,GO:0004743,GO:0003904,GO:0030613,GO:0036356,GO:0008966,GO:0003755,GO:0003905,GO:0032357,GO:0004401,GO:0030234,GO:0046812,GO:0008108,GO:0008783,GO:0051907,GO:0009881,GO:0043726,GO:0001000,GO:0018648,GO:0047721,GO:0043750,GO:0004138,GO:0034459,GO:0004332,GO:0008738,GO:0008477,GO:0004556,GO:0035375,GO:0009007,GO:0003920,GO:0016817,GO:0061712,GO:0048038,GO:0050225,GO:0016154,GO:0017110,GO:0009038,GO:0061501,GO:0102264,GO:0043751,GO:0008463,GO:0004540,GO:0009017,GO:0043878,GO:0051787,GO:0102710,GO:0016301,GO:0018492,GO:0004765,GO:0016887,GO:0050192,GO:0004844,GO:0003924,GO:0071111,GO:0019158,GO:0015419,GO:0002094,GO:0019104,GO:0004015,GO:0047700,GO:0017113,GO:0001968,GO:0004136,GO:0004630,GO:0016767,GO:0036361,GO:0009024,GO:0009020,GO:0031176,GO:0008706,GO:0008695,GO:0051392,GO:0004715,GO:0070403,GO:0017168,GO:0003824,GO:0015208,GO:0047869,GO:0047334,GO:0035595,GO:0004825,GO:0016743,GO:0008757,GO:0008817,GO:0046983,GO:0001073,GO:0008763,GO:0003714,GO:0050572,GO:0008820,GO:0032843,GO:0050066,GO:0015093,GO:0004044,GO:0000900,GO:0052911,GO:0043880,GO:0004035,GO:0018706,GO:0016833,GO:0008685,GO:0032556,GO:0003826,GO:0047540,GO:0043906,GO:0047441,GO:0102040,GO:0015035,GO:0008199,GO:0004427,GO:0004001,GO:0050278,GO:0004856,GO:0005536,GO:0016209,GO:0016831,GO:0034480,GO:0004450,GO:0061594,GO:0015291,GO:0034618,GO:0008784,GO:0004071,GO:0004735,GO:0004316,GO:0004416,GO:0050531,GO:0005524,GO:0016625,GO:0008276,GO:0050510,GO:0000286,GO:0032450,GO:0070041,GO:0008171,GO:0018788,GO:0030570,GO:0000985,GO:0016805,GO:0018697,GO:0043023,GO:0017116,GO:0008852,GO:0050532,GO:0005525,GO:0046917,GO:0008796,GO:0008094,GO:0004592,GO:0004155,GO:0008659,GO:0008694,GO:0001406,GO:0070404,GO:0004525,GO:0051500,GO:0061598,GO:0004425,GO:0044374,GO:0047695,GO:0008127,GO:0047727,GO:0004520,GO:0034701,GO:0030697,GO:1990404,GO:0047419,GO:0004818,GO:0004106,GO:0008807,GO:0010855,GO:0034200,GO:0003984,GO:0000908,GO:0008235,GO:0008868,GO:0005471,GO:0004008,GO:0047611,GO:0031418,GO:0003996,GO:0050565,GO:0070011,GO:0008270,GO:0004781,GO:0004017,GO:0002953,GO:0046992,GO:0051015,GO:0008650,GO:0008815,GO:0009025,GO:0043907,GO:0004252,GO:0008740,GO:0052636,GO:0050194,GO:0050096,GO:0043531,GO:0008973,GO:0008428,GO:0016840,GO:0009029,GO:0050311,GO:0009037,GO:0043775,GO:0016779,GO:0008448,GO:0102007,GO:0030340,GO:0009883,GO:0004462,GO:0016675,GO:0004019,GO:0050242,GO:0008906,GO:0043856,GO:0102023,GO:0004817,GO:0004828,GO:0015169,GO:0015612,GO:0042380,GO:0008936,GO:0090586,GO:0008831,GO:0008765,GO:0019200,GO:0044600,GO:0008301,GO:<span 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Specifications Table

Subject	Biochemistry, Genetics and Molecular Biology (General)
Specific subject area	Deep learning task for protein function prediction of 9 bacterial phyla into multi-valued and multi-class labels
Type of data	Tables (excel sheets) and Fasta files
How data were acquired	Web-Scraping and Feature Generation through Python libraries
Data format	Raw:
	- Fasta Sequences of 171,212 proteins of 9 bacterial phyla
	Analysed and Filtered:
	- Train/Test Dataset 1 with 9890 extracted features and 1739 GO terms in the form of Training vectors for 171,212 proteins of 9 bacterial phyla
	- Test Dataset 2 with 9890 extracted features for unreviewed protein of the 9 phyla extracted from UniProtKB for predictions using deep neural network based protein function prediction model [5].
Parameters for data collection	Both Reviewed and Unreviewed protein sequences were collected from UniprotKB belonging to 9 bacterial Phyla. Reviewed Proteins were used to generate Dataset for Training and Testing (Train/Test Dataset 1).Unreviewed Proteins (with annotation score 1 or 2 out of 5, and proteins with evidence of existence level either predicted or uncertain) from UniprotKB belonging to 9 bacterial Phyla were used to generate Dataset for predictions only (Test Dataset 2).
Description of data collection	Data was collected using Python Web-Scraping library from UniprotKB and Prosite Servers. The 323,719 reviewed protein Sequences were downloaded from UniprotKB and their Motifs were extracted from the Prosite Server. The Sequences were then filtered using relevant 1739 Gene Ontology (Molecular Function domain). The sequence, subsequence (motif count), annotation, and physiochemical features for filtered 171,212 protein sequences were generated using method in [10]. The final Dataset contains Entry, Entry name, Sequences, 9890 generated features and 1739 GO terms for each sample.
Data source location	https://www.uniprot.org/(for downloading reviewed and unreviewed protein sequences of 9 bacterial Phyla)https://prosite.expasy.org/(for acquiring motifs of protein sequences of 9 bacterial phyla)
Data accessibility	With the articleAs well as in a public repository:Repository name: GitHubURL: https://github.com/sarry2905/Protein-Function-Prediction
Related research article	Author's name: Sarthak Mishra, Yash Pratap Rastogi, Suraiya Jabin, Punit Kaur, Mohammad Amir, Shabanam KhatoonTitle: A deep neural network based model for function prediction of hypothetical proteins from pathogenic bacterial species [5]Journal: Computational Biology and Chemistry (under review)DOI: (under progress)

Value of the DataThis dataset can be used for training a machine learning based model for probable function prediction of proteins belonging to the considered bacterial phyla without functional annotation i.e. under category unreviewed (TrEMBL)-computationally analysed on UniProtKB.• This dataset contributes important step towards the protein function prediction problem for bacterial species. • Researchers trying to design new deep learning models can use this dataset for testing performance of their model. • We provide 1739 molecular function domain GO terms as target label in the dataset for designing a supervised learning model but these 1739 GO terms can be used as features as well for some other kind of study such as clustering of bacterial proteins into functional groups etc. • This dataset being huge in size, can be used to test and design GPU based parallelized deep learning algorithms for multi-class labelling.