dataesr/scientific-paragraphs-categorization huggingface dataset:
Circulating ICE clones are phylogenetically congruent with their P. aeruginosa host To assess the phylogenetic relationship among the 85 representative ICEs and between their hosts, we compared maximum-likelihood phylogenetic trees of both. The ICE-tree was constructed from a multiple sequence alignment on the complete sequence of each element and bootstrapped (n=1000) to find majority branches (Fig. 2a ). As outgroups, we added ICEs from two P. aeruginosa strains selected from the study of Botelho et al. 38. These ICEclc-like elements encode carbapenemases (blaDIM-1 in IOMTU133, GenBank accession number GCA_001548335; and blaNDM-1 and blaPME-1 in SP4371, GenBank accession number GCA_003950255). The tree topology suggested there are 8 major circulating clones (labeled a-h in Fig. 2a ), plus several more distinct ICEs, with as low as 60% similarity (in terms of percentage of nucleotide identity, Fig.
This strongly suggests that in addition to clonal divergence, there is horizontal transfer of the ICEs in the hospital environment. We next correlated the ICE phylogenetic signal with metadata such as the date and geographical location of sampling, isolation source, and MLST of the host (Fig. 3 ). Some of the ICEs found in strains isolated in the 00's are still found in more recent strains. For example, ICE_28832 isolated in 2008 is closely related to ICE_STH1 which is present in a P. aeruginosa strain isolated in 2017. Some others, like ICE_13524, isolated in 2003, are absent in more recent isolates from our dataset. Interestingly, a few closely related ICEs are found in strains isolated in different geographical regions (e. g. , ICE_26193, from a strain isolated in 2012 in Lausanne, is closely related to ICE_37295, present in a P.
Strain isolations and sequencing Strains from the CHUV were isolated and typed during ongoing infection control surveillance and investigations; some of which have been described elsewhere  [65] [66] [67]. Strains from CHU Besançon originated from a study on the evaluation of a new typing system  68. Strains from HUG were isolated during an outbreak of VIM-producing P. aeruginosa  69. Strains from the CHU Montpellier were isolated in the course of a research protocol (ClinicalTrials. gov ID: NCT02751658) that studied the dynamics of hospital environment contamination by waterborne pathogens associated to healthcare infections. All strains were sequenced using Illumina MiSeq technology.
Quality check and de novo assembly The quality of the Illumina raw reads was assessed with FastQC (v0. 11. 9)  70. Reads were trimmed with trimmomatic (v0. 39)  71  to remove short and low-quality reads and adapters. Genomes were de novo assembled with SPAdes (v3. 15. 2)  72. The resulting contigs were filtered to keep only those longer than 500 bp and with a kmer coverage higher than 10. Single chromosomes were generated with RagTag (v2. 1. 0)  73  from the filtered contigs of every genome, by scaffolding and patching the filtered contigs against a reference complete genome of P. aeruginosa strain H26027 (NCBI accession number: CP033684), also isolated from the Lausanne University Hospital. The genome scaffolds have been deposited with the European Nucleotide Archives (ENA) under the accession study number PRJEB61470.
Sequence comparison maps (Fig. 1b , 4b and S2) were generated using genoplotR  80. The presence of a tRNA-encoding gene at the site of insertion was detected with tRNAscan-SE (v2. 0)  81. Relaxase families were determined using MOBscan 82. Pair-wise average nucleotide identities were calculated using fastANI (v1. 33)  83  for the genomes and Bio3d (v2. 4-3)  84  for the aligned ICEs and integrases. Percentages of GC-content were determined employing a sliding window method (https://github. com/DamienFr/GC_content_in_sliding_window) by using default parameters for the genomes, a window size of 100 bp and a step size of 10 for the ICEs. Estimations of the total number and variety of ICEs and IMEs were made by using ICEfinder (https://bioinfomml. sjtu. edu. cn/ICEfinder/ICEfinder. html).
To this set, we included ICEs from two external P. aeruginosa strains (NCBI accession number: NZ_AP017302 and NZ_CP034369) from the study of Botelho et al. (2020)   38  that were found to encode carbapenem resistance genes. To build the species tree, with help of OrthoFinder (v2. 3. 8)  87  we identified all single-copy orthologous genes among the 85 P. aeruginosa genomes and extracted all the corresponding nucleotide sequences using a custom python script; yielding a total of 4260 ortholog genes. Next, the gene sequences were aligned with MAFFT (v7. 475)  88  and concatenated, from which a maximum-likelihood phylogenetic tree was built with IQ-TREE 2 (v2. 0. 6)  89. The general time reversible model, with unequal rates and unequal base frequencies, was used to compute the species tree  90.
2. 2. 3 Testing across the evolutionary tree on model and non-model species Furthermore, we asked whether SENSE-PPI behavior is preserved across the phylogenetic tree and, in particular, for non-model species. For this, we constructed SENSE-PPI datasets for the four non-model and six model species (see Methods). A coherent behavior of SENSE-PPI across species and time evolution would support the use of SENSE-PPI on species at a fixed evolutionary distance from the training one for which little is known about protein interactions. Consistent with our expectations, the behavior of SENSE-PPI is illustrated in Figure  1  (see also Table  SI 4). The evaluation of SENSE-PPI on 10 testing datasets across species provides valuable insights, although it does not o↵er a comprehensive assessment of performance.
pisum after training SENSE-PPI on a dataset made of species that are evolutionary closer to aphids than to humans. This dataset is a merge of two separate datasets for D. melanogaster and C. elegans (see Methods), sharing a common ancestor with the aphid at 362My and 572My respectively compared to the common ancestor shared with H. sapiens at 708Mys. This merge was necessary due to the absence of su ciently many high-confidence interactions in D. melanogaster alone. Hence, a relatively close model species was chosen, C. elegans, so that the size of the resulting testing set could be comparable to the human D-SCRIPT dataset used in Table  1. In line with our expectations, PPI predictions on non-model organisms are improved when closer model organisms are used to train SENSE-PPI. Test scores for A.
pisum improved from 0. 660 to 0. 701 in F1-score, and from 0. 626 to 0. 689 in MCC. Moreover, by combining the training set from the two species with the SENSE-PPI human dataset, we can further improve predictions, achieving an F1-score of 0. 742 and an MCC of 0. 724. This observation should be taken as a general recommendation, even though the improvement is not exceptionally high due to SENSE-PPI general high performance over a large interval of mean pair sequence identity values as illustrated by the plateau of the curve in Figure  1A.
Larger protein language models improve performance PLMs significantly contribute to enhanced performance. To see this, we consider two sizes of ESM2 embedding modules and analyze how MCC scores change at di↵erent values of the mean pair sequence identity for the four D-SCRIPT datasets of M. musculus, D. melanogaster, C. elegans, and S. cerevisiae (Figure  1EF ). Figure  1E  shows the SENSE-PPI performance obtained with the smaller version of ESM2, based on 35M parameters (with 1M more parameters used in the rest of the SENSE-PPI architecture), and Figure  1F  demonstrates the performance of the SENSE-PPI regular version, based on 3B parameters (with 2M more parameters for the rest of the SENSE-PPI architecture). The ESM2 model with 3B parameters highly improves the predictions on pairs of proteins with low values of mean pair sequence identity compared to the smaller version of ESM2.
For instance, for a pair sequence identity < 0. 2, predictions on the fly display an MCC of 0. 2 (Figure  1E ) versus 0. 4 (Figure  1F ). Moreover, for entries with high values of mean pair sequence identity, the performance based on both embeddings remains comparable. This remains true for all 4 datasets and shows that ESM2 based on a higher number of parameters has better generalization capability.
Evaluation on the human intrinsically disordered protein dataset Approximately 33% of eukaryotic proteins have significant disordered regions, with an increasing occurrence of disorder in higher organisms  [38] , and predicting interactions between proteins that are possibly disordered becomes an urgent demand  [32, 63, 50, 51]. Deep learning approaches based on sequences are expected to open the way to this challenge. SENSE-PPI was additionally compared to the IDPpi model  [39]  designed to predict the interaction between proteins with stable structure and intrinsically disordered proteins (IDPs). Table  SI  2 shows the results for both models. The models were tested on the same five test sets used in  [39]  but were trained di↵erently. Whereas the IDPpi training set included the IDPs in the test data, SENSE-PPI was trained on the D-SCRIPT human dataset, which contained no information on IDPs.
Human-virus PPIs Predicting and understanding virus-host PPIs is important for developing new therapeutic interventions, but knowledge of virus-host interactions covered by current databases, such as VirHostNet  [11] , is limited. We therefore tested the ability of SENSE-PPI to predict interactions between a known human protein and an unknown viral protein. We trained SENSE-PPI on a human-virus PPI dataset  [61]  that contains interactions 6 between human and viral proteins, i. e. only cross-species interactions are included. Data were split between training and testing, so that test data only included interactions for Epstein-Barr and influenza viruses. The model was trained on the set with a large variety of viral species interacting with human proteins (for dataset details see Methods).
We required that all human proteins involved in the test have been seen during training (this dataset corresponds to class "C2" in  [13] ), a condition that is reasonable in terms of potential applications. The results of this test are presented in Table  SI  3. The overall high scores may be explained by the fact that the model is already familiar with all the human proteins present in the data  [13]. However, it should be noted that the viral proteins from training and testing are drastically di↵erent: by searching the closest matches for every test viral protein in the training set (using MMseqs2, see Methods), we computed a mean sequence identity of 0. 21 for the Epstein-Barr virus and of 0 for the Influenza viruses (no matches were found even with sensitivity parameter values in the range of  7-10 [54] ).
PPI network reconstruction In order to verify the performance of SENSE-PPI on the ab initio reconstruction of PPI networks, we carried out several tests to reconstruct PPI networks for sets of a few dozen proteins from the STRING database (version 11. 5)  [58]. For each test, we set one or two proteins as "seeds" and collected known partners on the basis of high confidence for physical interaction according to STRING. We then calculated SENSE-PPI predictions for a comparative analysis with STRING data. Figures  2  and 3  show four examples of these tests chosen to illustrate the general characteristics of SENSE-PPI behavior: 1. False positives are frequently related to indirect interactions, that is non-physical interactions with proteins sharing a common partner in the network (Figure  2A ); 2.
SENSE-PPI often misses interactions for weakly connected proteins (Figure  2B ); 3. it successfully identifies interactions involved in functionally distinguished subnetworks (Figure  2A  and Figure  3 ); 4. it successfully predicts PPIs in other species (Figure  2C ). These four sets group interacting human proteins (Figure  2AB ), C. elegans proteins (Figure  2C ) and M. musculus proteins (Figure  3 ). The tests were carried out on the model trained on the D-SCRIPT human dataset. Three sets of proteins are analyzed in Figure  2  where heatmaps display confidence scores for STRING (lower triangle) and prediction scores for SENSE-PPI (upper triangle). Although we used the same color spectrum for both sources, there is no direct correlation between the SENSE-PPI scale and the STRING scale, since our model was trained on binary labels and STRING provides confidence estimates that were not taken into account during SENSE-PPI training (except for the fact that we only used STRING high-scoring interactions in the training set).
musculus. In the mouse, as in human, these two subnetworks are not supposed to share common partners since the two complexes involved assure drastic di↵erences in functions and subcellular localizations. We compared SENSE-PPI to the D-SCRIPT and Topsy-Turvy models  [52]  (see also  Table 1) , where training was performed for all models on the human D-SCRIPT dataset. For each seed protein, we chose four partners with the highest confidence score in STRING. Figure  3  shows two heatmaps where network reconstruction shows two separate clusters in SENSE-PPI predictions (lower triangle). D-SCRIPT (upper triangle, Figure  3A ) correctly predicts the RFC-Rad17 complex network but fails on the C1-Serping1 complex. Topsy-Turvy improves D-SCRIPT predictions on the scoring values of interaction pairs in the RFC-Rad17 complex by being more confident in the predictions.
Discussion Current PPI experimental datasets are still far away from having reached completion, even on well-studied model organisms such as yeast or humans  [55, 14] , and their curation is an active area of research  [46]. Today, physical interaction networks obtained by high-throughput techniques are found to include numerous non-8 functional PPI  [22]  and at the same time, many missing true interactions. This is the main reason that an ab initio computational reconstruction of PPIs would provide invaluable information. We introduced SENSE-PPI, a deep learning model for predicting protein-protein interactions based entirely on sequence information. Trained in a single species, SENSE-PPI predicts the proteome within the same species and generalizes to other species. Trained on physical interactions between proteins with stable structures, it generalizes to proteins interacting with IDPs.
e. the quadratic number of potential pairwise interactions among the proteins) is handled e ciently by the trained architecture. As expected, SENSE-PPI shows that the computational issue can be overcome, and that PPIs of tens of thousands of proteins can now be handled. Indeed, the identification of protein partners in a 100-protein dataset takes to takes SENSE-PPI around 2 minutes with a single NVIDIA GeForce RTX 3090. Given that the computational time for ESM2 embeddings scales linearly in the number of proteins (the tensors are computed only once for each protein and then reused) and the prediction time scales quadratically, we can can estimate that it takes around 2 hours to predict the partners of a set of 1,000 proteins and around 3 to 4 hours for 10,000. Another major advantage of SENSE-PPI over other structure-based approaches to protein interactions, including docking, is that SENSE-PPI achieves far better results based solely on sequences, opening up new directions for development.
Guo's yeast dataset: a dataset with proteins shared in training and testing The Guo dataset  [12]  comprises 2,497 proteins and 11,188 interactions, with an equal number of positive and negative samples. The 5,943 positive interactions were extracted from the DIP 20070219 database  [45]. Sequences are more than 50 amino acids long and the majority of them exhibit pairwise sequence identities < 40%  [12]. The negative samples were generated through a random pairing of proteins that appeared in the positive data but lacked any evidence of interaction. Pairs were subsequently filtered based on the subcellular localization of the proteins, thereby excluding non-interacting pairs residing in the same location. No further filtering was realized in this dataset to allow a fair comparison with models already evaluated on it.
The D-SCRIPT datasets from human and other species A large human dataset was constructed to test the D-SCRIPT deep learning model for the prediction of PPIs  [53]. The positive interactions were extracted from STRING (version 11)  [56] , a database encompassing diverse PPI networks and consolidating comprehensive information from various primary sources. They are high-confidence interactions supported by a positive experimental evidence score in STRING. Protein sequences are greater than 50 and smaller than 800 amino acids in length, and protein pairs exhibit pairwise sequence identities < 40%. By construction, any two protein pairs A-B and C-D in the dataset are non-redundant, that is both pairs of sequences A, C, and B, D have < 40% sequence identity respectively. Eliminating redundancy is crucial as it prevents the model from relying solely on sequence similarity when predicting interactions.
To establish an appropriate balance between positive and negative PPIs, negative interactions have been generated by randomly picking two proteins from distinguished pairs in the non-redundant set of positive interactions. These negative samples were created in a 1:10 positive-negative ratio with the aim of better approximating the frequency of positive interactions observed in nature. These construction criteria lead to a primary dataset consisting of 47,932 positive protein interactions and 479,320 negative protein interactions for H. sapiens. These interactions were divided into training and testing sets using an 80% -20% ratio, respectively. Additionally, we have used 4 smaller datasets of M. musculus, D. melanogaster, C. elegans and S. cerevisiae from [53] made using the procedure described above and containing 5,000 positive and 50,000 negative interactions each.
The SENSE-PPI human dataset We constructed a second human PPI dataset directly from the STRING database (version 11. 5)  [58]  by focusing exclusively on physical interactions from H. sapiens. Similar to the D-SCRIPT dataset, we only considered proteins in a length range from 50 to 800 amino acids in order to optimize computational resources and to avoid potential biases introduced by very short or very long sequences. A STRING confidence score threshold 10 of 0. 5 was applied and interactions obtained by homology transfer were excluded. We also removed redundant interactions through a clustering strategy that first uses MMseqs2 to cluster proteins at a 40% sequence identity, and then that eliminates redundant pairs as done for the D-SCRIPT dataset. The most important di↵erence between the human D-SCRIPT dataset and the human SENSE-PPI dataset relies on a more stringent condition for constructing negative pairs for training, named here the "neighboring-exclusion" condition.
Indeed, a second round of clustering is performed on the proteins in the non-redundant dataset using a 40% sequence identity threshold. Then, a random pair of proteins, A-B, is added to the negative set if no interaction between A and B is known in STRING, and B does not share its cluster with any known interactors of A. This condition ensures that negative pairs are chosen both on the basis of the absence of known interactions and by minimizing the potential biases introduced by homology in shared clusters. As above, in order to reflect the natural frequency of interactions observed in biological systems, the positive-to-negative ratio in the number of protein pairs was set to 1:10. This ratio aims to approximate the relative scarcity of true positive interactions, thus ensuring a more representative distribution of positive and negative examples in the dataset.
The IDPpi dataset describing the interactions between structured proteins and IDPs The IDPpi dataset  [39]  contains interactions with intrinsically disordered proteins (IDPs). It is organized in five distinguished datasets corresponding to the testing sets in  [39]. Each subset comprises 3,500-4,000 pairs of proteins from H. sapiens where one of the partners is intrinsically disordered. Pairs were extracted from the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database  [47]. Negative interactions were defined through random sampling: two proteins (one that is an IDP and one that has a stable structure) were selected randomly and were assumed to be non-interacting unless they were already known as interacting. The positive-to-negative ratio of each testing set is 1:1.
Human-Virus PPI dataset This dataset  [61]  was designed in order to predict interactions between H. sapiens and di↵erent viruses. Interacting protein pairs were extracted from the Human-Virus Protein-Protein Interactions database (HVPPI)  [65]  and negative pairs were constructed using the protocol of the dissimilarity negative sampling, which uses a sequence similarity-based method to explore protein pairs that are unlikely to interact  [61]. The training set includes 7,371 positive and 117,326 negative interactions between human and viral proteins and contains 16,933 human and 676 viral proteins in total. Viruses span over many families, and the full list of species included in training can be found in the SENSE-PPI repository (data/human virus folder). We constructed two test sets. The first set consists of interactions involving proteins from the Epstein-Barr virus.
This set encompasses 99 viral proteins and 9,426 human proteins, of which only 596 human proteins demonstrate interaction with Epstein-Barr proteins. The set contains a total of 1,308 positive interactions and 14,428 negative interactions. The second dataset exclusively comprises interactions featuring proteins associated with Influenza viruses of type A, B, and C. This dataset includes 4,080 positive pairs and 15,724 negative pairs, incorporating a total of 95 viral proteins and 10,594 human proteins. Among these, 1,533 human proteins are involved in interactions with Influenza virus proteins. Notably, all proteins from both Influenza and Epstein-Barr viruses are excluded from the training process. However, it's important to mention that all human proteins present in the test data have been included in the training set at least once.
Evaluation measures. To properly evaluate SENSE-PPI performance we rely on a ground truth set by reference databases such as the STRING database and the following quantities: known interactions that are identified by SENSE-PPI (true positives, TP), interactions identified by SENSE-PPI which are not known (false positives, FP), interactions that are not found by SENSE-PPI but are known (false negatives, FN), and interactions that are not known and are not detected by SENSE-PPI (true negatives, TN). We use four standard measures of performance: recall (sensitivity) = T P/(T P + F N), precision (positive predictive value) = T P/(T P + F P ), F1-Score = 2 T P/(2 T P + F P + F N), and the Matthews' correlation coe cient MCC = (T P • T N F P • F N)/K where K = p (T P + F P )(T P + F N)(T N + F P )(T N + F N).
Design of the SENSE-PPI architecture Recent advancements in deep learning for protein science have lead to the introduction of pre-trained language models  [33] , which have been developed for natural language processing to learn compressed, informed, and abstract data representations that are later transferred to proteins by adapting them to amino acid sequences. PLMs have demonstrated tremendous potential for a broad range of protein-related problems, such as predicting 3D shapes, interactions, mutational outcomes, and subcellular localizations  [42, 1, 15, 40]. They have been trained on large amounts of sequence data, such as the UniProt database  [6]. Here, we use ESM2  [25]  which is a state-of-the-art general-purpose PLM that has been challenged to predict structure, contact sites, and other protein properties.