dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank all study participants, their families, and friends for their support and participation, and our study coordinators. The co-investigators and contributors from PDBP,  HBS, DIGPD, PreCEPT/PostCEPT, PICNICS, CamPaIGN, PROPARK , as well as acknowledgements for PPMI and DATATOP are listed in the appendix. Funding. National Institutes of Health, U. S. Department of Defense  AEL has served as an advisor for Abbvie,  Acorda ,  Avanir Pharmaceuticals, Bristol Myers Squibb, Cipla, Intekrin , and  Merck ; received honoraria from  Medtronic ,  Teva ,  UCB ,  AbbVie ; received grants from  Brain Canada ,  Canadian Institutes of Health Research, Edmond J Safra Philanthropic Foundation ,  Michael J. Fox Foundation , the  Ontario Brain Institute ,  National Parkinson Foundation ,  Parkinson Society Canada, Physicians Services Incorporated (PSI) ,  W.
Funding. This work was supported in part by  NIH  grants  U01 NS082157 ,  U01NS095736  (to CRS),  U. S. Department of Defense  grants  W81XWH-1-0007  (BR) and  W81XWH-15-10007  (to CRS);  M. E. M. O. Hoffman Foundation  (to CRS);  Brigham & Women's Hospital Departmental Funds  (to BB). The  Harvard Biomarkers Study  is supported by the  Harvard NeuroDiscovery Center (HNDC) , the  Parkinson's Disease Biomarkers Program (PDBP)   U01 NS082157 ,  U01NS100603  of the  NINDS , and the  Massachusetts Alzheimer's Disease Research Center (ADRC)   P50 AG005134  grant of the  National Institute on Aging ,  Harvard Aging Brain Study  grant  P01 AG036694. The PreCEPT/PostCEPT cohort was funded by  Cephalon Inc and Lundbeck  for the parent PRECEPT clinical trial and follow-up PostCEPT cohort, and the  Department of Defense Neurotoxin Exposure Treatment Parkinson's Research Program  ( W23RRYX7022N606 ),  NINDS PD-DOC  ( NS050095 ), the  Parkinson's Disease Foundation (New York, NY).
Additional funding information for the PreCEPT/PostCEPT cohort and corresponding investigators is listed in Ref 9. The CamPaIGN and PICNICS studies received funding support from the  Wellcome Trust ,  MRC ,  Parkinson's UK, Cure-PD , the  Patrick Berthoud Trust , the  Van Geest Foundation and NIHR funding of a Biomedical Research Centre  at the  University of Cambridge/Addenbrooke's Hospital. DIGPD cohort was promoted by the  Assistance Publique Hôpitaux de Paris , and founded by the  French clinical research hospital program -PHRC (code  AOR08010 ). The research leading to these results was has received funding from the program " Investissements d'Avenir "  ANR-10-IAIHU-06. DATATOP was supported by  NIH  grant  NS24778. The PROPARK study was funded by the  Prinses Beatrix Fonds  (project no.
The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information.
We investigate two fusion approaches (in the feature level and decision level) and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC) code (six main classes) on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97. 8% and 95. 5% (based on Hamming-loss) of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85. 4% of the multi-labeled enzymes when the number of reactions is unknown.
INTRODUCTION The ever-growing PDB database contains more than 110,000 proteins that are characterized by different properties including their structure, biological function, chemical composition, or solubility in solvents. Protein classification is important since it allows estimating the properties of novel proteins according to the group to which they are predicted to belong. Enzymes are a type of proteins that are classified according to the chemical reactions they catalyze into six primary classes, oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases. The classes are denoted by the enzyme commission (EC) number  (NC-IUBMB, 1992)  and have been determined based on experimental evidence. Systematic annotation, reliability, and reproducibility of protein functions are discussed in  Valencia (2005).
Classification of enzymes is a central issue because it helps understanding enzymatic behavior during chemical reactions. While the vast majority of enzymes have been found to perform particular reactions, a non-negligible number of enzymes can perform different reactions and can hence be directly associated with multiple enzymatic functions  (Guyon et al. , 2006). During the last decade, various machine learning techniques have been proposed for both single-label and multi-label enzyme classification on different datasets. Among single-label classification studies, some  (Dobson & Doig, 2005)  used only structural information and achieved an accuracy of 35% for top-ranked prediction using support vector machine (SVM) with a one-against-one voting scheme on 498 enzymes from the PDB database.
Applying SVM on sequence features has been done by  Mohammed & Guda (2015)  and achieved an accuracy of 98. 39% after training on 150,000+ enzymes with 10-fold cross-validation. Osman & Choong-Yeun Liong (2010)  extracted only gene or amino acid sequence information and applied neural networks obtaining an accuracy of 72. 94% after training the networks on 1,200 enzymes from the PDB database and testing on 2,000 others. Volpato, Adelfio & Pollastri (2013)  achieved 96% accuracy with a 10-fold cross-validation scheme on 6,081 entries of the ENZYME database. Sequence structure and amino acid information were also used by des Jardins et al. (1997) ,  Kumar & Choudhary (2012)  and  Lee et al. (2007) , who obtained testing accuracies ranging from 74% to 88. 2% using the Swiss-Prot database. Combination of sequence, structure, and chemical properties of enzymes was also explored by  Borgwardt et al.
(2005)  using kernel methods and SVM on the BRENDA database and achieved an accuracy of 93% with six-fold cross-validation on information extracted through protein graph models. Multi-label classification using different methods such as RAkEL-RF and MLKNN  (Wang et al. , 2014)  or MULAN  (Zou et al. , 2013)  was performed on single-and multi-labeled enzymes. In particular, the latter was assessed on enzymes from the Swiss-Prot database based on their amino acid composition and their physico-chemical properties and involved the use of position-specific scoring matrices. In the best scenario, a macro-averaged precision of 99. 31% was obtained on a set of 2,840 multi-functional enzymes after 10-fold cross-validation. A summary of other alignment-free methods used to predict enzyme classes is presented in Table  1.
Single-label classification The performance of single-label classification has been assessed based on the confusion matrix whose elements C(x, y) with x, y ∈ v1, 6b, indicate the number of enzymes that belong to class x and are predicted as belonging to class y. Two metrics are based on this definition: the overall accuracy that evaluates the proportion of correctly classified enzymes among the total number of enzymes and the balanced accuracy that avoids inflated performance estimates on imbalanced datasets. They are defined by:.
Multi-label classification As described in the methods' section, the optimal fusion parameter a was empirically determined for each dataset (single-or multi-functional) and fusion scheme. The optimal values are shown in Fig. 3  from which it can be seen that the values of a for the decision-level fusion in multi-label classification (a = 0. 69, 0. 73, 0. 76, 0. 80, respectively for the SVM-NN, SVM-SVM, NN-NN, and NN-SVM methods) are approximately 20% smaller compared to the values obtained in single-label classification (a = 0. 95, 0. 99, respectively for the  SVM and NN methods). This shows that structural information plays  Note: The total number of enzymes with 2, 3 and 4 labels each are 765, 14, 4, respectively. a more significant role in differentiating enzymatic activity in the case of multi-labeled enzymes than in single-label classification (which is mostly based on amino-acid sequence information).
4%, which is at least 1. 3% ahead of the performance achieved considering only one type of information. One of the most impressive rises in performance stems from the NN-SVM method, which proves to outperform SI and  AA methods by +4. 1%, +2. 6%, and +4. 6% in terms of precision, M-precision and m-precision, respectively. Not only does it show that the relevance of class predictions is improved overall, but also and more importantly that small-populated classes benefit from this progression as well. The code was written in Matlab and Python languages and is freely and publicly available at https://figshare. com/s/a63e0bafa9b71fc7cbd7. Running on a single Intel Xeon X5650 processor, the average prediction time of the enzymatic function(s) of a new enzyme was less than 3 s. Computations were achieved using high performance computing (HPC) resources from the "mesocentre" computing center of Ecole Centrale de Paris (http://www.
The single-and multi-label classification models have been trained and tested on enzymes assumed to perform single or multiple reactions, correspondingly. However, the single-label enzymes might be associated with other reactions not detected yet and in fact be multi-label. In order to assess the method in a more general scenario, we mixed both single-and multi-label information during training phase and observed a slight improvement in prediction accuracy. Specifically, we chose to examine the NN-NN fusion scheme because of its small computation time, and merged SI and AA probabilities obtained by both datasets I and II. This model achieved 89. 2% subset accuracy and 95. 8% accuracy for the multi-label dataset (by cross-validation) indicating an increase of 4. 5% and 2. 1% in respect to the results obtained with the NN-NN scheme trained only on multi-labeled data (shown in Figs.
3  and 5 ). This also corresponds to an increase of 3. 8% and 0. 3%, respectively, from the best fusion schemes. Moreover, since it is unknown for new (testing) enzymes if they perform unique reactions, they have to be treated as multi-label. In order to estimate the performance of the single-label model in the case of unknown enzymes, we tested the best single-label classifier (i. e. , the NN on the decision level) on the multi-label dataset. For 93. 0% of the enzymes the model predicted correctly one of their actual labels, whereas the prediction of all actual labels (by selecting the classes with the highest probability scores) was correct in 44. 8% of the enzymes. Furthermore, we investigated techniques dealing with imbalanced classes but did not observe any conclusive outcome. In particular, ADASYN improved overall accuracy on the single-label dataset by 0.
1% but reduced balanced accuracy by 1. 1%. In conclusion, computational models calculated from experimentally acquired annotations of large datasets provide the means for fast, automated, and reproducible prediction of functional activity of newly discovered enzymes and thus can guide scientists in deciphering metabolic pathways and in developing potent molecular agents. Future work includes the representation of the whole 3D geometry using additional structural attributes and the incorporation of deep learning architectures that have proven to be powerful tools in supervised learning. The main advantage of deep learning techniques is the automatic exploitation of features and tuning of performance in a seamless fashion, that optimizes conventional analysis frameworks.
Validation of Predictions. Cross-omics correlations in Nephroseq database. To place our predictions in the context of existing literature in the field and compile relevant evidence, as a first step, kidney transcriptomics data from DN patients and healthy controls were retrieved from the Nephroseq database (www. nephroseq. org) and the mRNA level of expression of the 17 predicted proteases in DN was evaluated. In total, 5 profiling datasets comparing DN vs. healthy control subjects were found in the database, from which protease transcripts (p val < 0. 05 in DN vs. controls) were extracted. Those datasets originated from 3 scientific publications related to DN i. e. Ju et al. 17  , Schmid et al. 18  and Woroniecka et al. 19  (summarized in Supplementary Table  4 ). We found a total of 12 protease transcripts overlapping with the 17 predicted proteases (Table  3  and Supplementary Table  5 ).
The predicted protease activity trend was in agreement with the respective mRNA expression trend for 6 proteases, namely CTSD, CTSK, MMP-1, MMP-7, MMP-8 and MMP-13. For 4 proteases (PCSK6, MMP-2, MMP-3, MMP-26), opposing expression trends at the mRNA level could be observed in comparison to the predicted activity changes, whereas in the cases of PCSK5 and PCSK7 the transcriptomics data were inconclusive with respect to providing clear expression trends for the respective mRNAs in DN (Table  3 ). Literature datasets. We further correlated our predictions with protein expression data on proteases of urinary extracellular vesicles (UEVs) in type 1 DN patients, as reported by Musante et al. 4. In this study, authors investigated changes in the expression of 34 proteases and 32 protease inhibitors through analysis of urine samples of diabetic patients divided in normo-, micro-and macroalbuminuric groups  20.
We compared the relative protein expression change of the reported protease levels between normo-and macroalbuminuric patients with the deregulated proteases predicted in our analysis (Table  3 ). Out of the 4 proteases found to be significantly changing in disease in both datasets (i. e. our study and the study of Musante et al. 4  ), the predicted deregulation of 3 (CTSD, MMP-7, MMP-9) was in agreement with the reported protein expression trend and 1 (MMP-3) presented opposite trend (Table  3 ). Collectively, these comparisons to the existing relevant literature did not allow reaching conclusive statements with respect to our predictions, in part due to differences in study design (different cases and controls in each case), which further enhanced the need for experimental verification of our findings.
Methods Patient Population and Data Collection. The urinary CE-MS peptide profiling datasets as well as the clinical data were retrieved from the Human Urinary Proteome database  56, 57. The study was approved by the ethics committee from Hannover Medical School, Germany (ID: 3116-2016), fulfilling all the requirements on the protection of the individuals participating. Details regarding samples processing, capillary electrophoresis-mass spectrometry (CE-MS) analysis, data processing and storage can be found in Supplementary Methods. In brief, urine samples were collected from 10 different centres: Steno Diabetes Center (Gentofte, Denmark; n = 63); Austin Health and Northern Health, University of Melbourne, (Melbourne, Australia; n = 44); Diabetes Centre of the Isala Clinics, (Zwolle, The Netherlands; n = 33); Hannover Medical School (Hannover, Germany; n = 33); Human Nutrition and Metabolism Research and Training Center, Karl Franzens University of Graz (Graz, Austria; n = 19); Barbara Davis Center for Childhood Diabetes, University of Colorado Denver (Denver, USA; n = 15); Charles University (Prague, Czech Republic; n = 10); University Medical Center (Groningen, The Netherlands; n = 9); RD-Néphrologie, Néphrologie Dialyse St.
Statistical Analysis. After testing for normal distribution, continuous data were compared by the Mann-Whitney test, as this test has proven to be of superior statistical power in peptidomics datasets  59. In order to control for the false discovery rate at 0. 05, the p-values were adjusted by the Benjamini and Hochberg method  60  implemented in the Bioconductor package multtest  61. Differential expression was based on a p-value of <0. 05 after correction for multiple testing and a fold change >1. 5 for upregulated or <0. 66 for downregulated peptides. Protease Prediction and Protease Activity Prediction. In order to link urinary peptides to the proteases potentially involved in their generation, the open-source tool for protease prediction -Proteasix (www. proteasix. org)  15 was used for the analysis.
Briefly, Proteasix uses information about naturally occurring peptides i. e. corresponding protein UniProt ID and start and stop amino acid position to predict potential cleaving proteases. Proteasix retrieves information about cleavage sites (CS) from protease databases (MEROPS, BRENDA) considering also cleavage site restrictions (from ENZYME database). As a result, a list of predicted proteases is generated. Proteases labelled as "Observed" or "Observed in different substrate" (in case given cleavage site was identified using different substrate) come from the literature-curated collection of experimentally observed cleavage site associations, whereas high/medium/low probability predictions are based on calculation of the probability of cleavage based on MEROPS protease specificity matrices.
In our study, in order to increase reliability of predictions, analysis was based only on proteases with observed cleavage sites (Supplementary Table  2 ). As an input for our analysis, the list of the 302 sequenced, differentially expressed peptides (p val < 0. 05 and fold change >1. 5) obtained from the CE-MS analysis was used (Supplementary Table  1 ). List of predicted proteases is available in Supplementary Table  2. For the protease activity prediction the following metrics were calculated:   3 ). Prediction Investigation Through Transcriptomics Data Analysis. Nephroseq (www. nephroseq. org, University of Michigan, Ann Arbor, MI) is a kidney transcriptomics data repository equipped with tools for data analysis and visualization. The list of 17 predicted proteases was uploaded in Nephroseq in the form of EntrezGene IDs and corresponding gene expression was checked in the available diabetic nephropathy datasets.
DN datasets were selected through Primary Filters > Group > Diabetic nephropathy. There were five DN datasets on comparison of DN vs. Healthy Living Donor groups (Supplementary Table  4 ). Significantly deregulated genes (p val < 0. 05) appearing in those five sets were extracted and differential expression results were compared with the predicted proteolytic activity Mice. Ins2 Akita mice were used as a model of type I diabetes and were initially obtained from the Jackson Laboratory (US) and further bred in the local animal facility (Toulouse) under SPF conditions. Kidney samples (~1/6th of the kidney cortex) from 4 months old mice with diabetes (Hz/wt) and wild-type (wt/wt) mice were snap frozen after sacrifice. Db/db mice were used as a model of type 2 diabetes. Db/Dm (control) and Db/Db (diabetic) mice (Jackson Laboratory (US)) were subjected to right side uninephrectomy (UNx) under anesthesia at 8 weeks of age to accelerate development of DN as previously described  62.
The concentration of urinary albumin was measured by ELISA using the AlbuWell kit (Exocell). Zymography. Sample preparation. Frozen kidney tissue (7 to 47 mg) was resuspended in 1x PBS (Gibco), pH 7. 5 and homogenized with stainless steel beads (Next Advance) in the Bullet Blender Homogenizer (Next Advance). Samples were centrifuged (16,000 × g, 10 min, RT) to remove beads and debris. Protein concentration was determined by Bradford assay (Bio Rad) in the supernatant which was stored at -80 °C until further use. Zymography protocol. Zymography was performed in 8% polyacrylamide gels containing 0. 2% (w/v) gelatin (Porcine gelatin, Sigma Aldrich). Five independent experiments were performed analysing 4 biological replicates per group and the obtained data were combined in order to increase the statistical power.
The protein amount of samples loaded on each well was 50 μg. Reported data include the relative fold change of protease activity among each diabetic group compared to the corresponding control, from each independent experiment. Electrophoresis was performed in TGS buffer (Bio Rad) (Tris 25 mM, Glycine 192 mM, 0. 1% (w/v) SDS, pH 8. 3) at 5 mA per gel, at room temperature for about 4-5 hours in non-reducing conditions. After electrophoresis, the gels were incubated with the equilibration buffer (25 mM Tris -HCl pH 7. 8, Bio Rad, 2. 5% (v/v) Triton-X 100, AppliChem) for 1. 5 hours at 4 °C (protein renaturation). Gels were then incubated with the activation buffer (25 mM Tris -HCl pH 7. 8, Bio Rad, 5 mM CaCl 2 , Sigma Aldrich) for 24 h at 37 °C followed by a wash with water and fixed with a solution of 30% methanol (Fischer), 10% acetic acid (Carlo Erba) for half an hour.
Staining was performed for 30 minutes with Coomassie Brilliant Blue R-250 (Fluka) at room temperature. Finally, the gels were destained with water and scanned at a GS-800 imaging densitometer (Bio Rad) in transmission mode. The images were analysed with the Quantity One software (Bio Rad). Data Availability. All data generated or analysed during this study are included in this published article (and its Supplementary Information files). As described above, all methods were carried out in accordance with relevant guidelines and regulations.
Acknowledgements This work was supported in part by 'Clinical and system -omics for the identification of the  Molecular Determinants of established Chronic Kidney Disease ' (iMODE-CKD;  PEOPLE-ITN-GA-2013-608332 ). P. M. received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie  grant agreement No. 642937  (RENALTRACT;  MSCA-ITN-2014-642937 ). We would like to thank  Marion Gillet ,  Dimitri Marsal  and  Guylène Feuillet  for their excellence assistance in the animal experiments and animal care-taking (Toulouse). Part of the work for J. K. , J. L. B. and J. P. S. was supported by the  French National Agency of Research (ANR)  under grant number  ANR-12-BSV1-0035-01.
Isolation and identification of bacteria Pseudomonas mendocina strain S5. 2 was isolated from a vineyard soil in Riquewihr, in the Alsace region of France. Isolation of this strain was performed using KG minimal medium as previously described  (Chong et al. 2016). Routine maintenance of the culture was performed on Luria-Bertani (LB) (Merck, NJ, USA) medium at 28 °C. Identification of the strain was conducted via 16S rRNA gene sequencing followed by phylogenetic analysis and pairwise similarity analysis using EzBioCloud database (http://www. ezbiocloud. net/identify) (Kim et al. 2012). Phylogenetic analysis was performed using molecular evolutionary genetic analysis (MEGA) version 6. 06  (Tamura et al. 2013)  with the list of hits from EzBio-Cloud 16S rRNA database. Scanning electron microscopy (SEM) observation of strain S5.