dataesr/scientific-paragraphs-categorization huggingface dataset:
53 However, these proteins are not present in Cryptosporidium. Rhoptries and micronemes are likely to hold the key to understanding Cryptosporidium invasion, and the hyperLOPIT dataset greatly expanded our knowledge on their content. One particularly interesting rhoptry candidate (cgd3_1330) possesses a formin homology domain, that might trigger the actin polymerization observed during Cryptosporidium invasion. 6, 54, 55 We also identified numerous additional microneme proteins featuring likely adhesin domains, and a CLAMP (claudin-like apicomplexan microneme protein) homo-log (cgd4_2350). 56 Two proteins ascribed to the micronemes using antibody reagents (cgd1_3550 and cgd2_1590) show different hyperLOPIT assignment and may require further study. 57 The microneme proteome includes five thrombospondin (TSP) domain proteins (CpTSP7-11) out of the 12 previously predicted.
58 Interestingly, TSP proteins are also present in the rhoptry (CpTSP12), and three are part of the yet uncharacterized hyperLOPIT cluster 8 (TRAPC1 and CpTSP3 and 6). Work in Toxoplasma suggested two subpopulations of micronemes. 59, 60 e sought to test this by tagging both TRAPC1 (cgd1_3500) and CpTSP6 (cgd6_2310) but were unable to in multiple attempts. Cryptosporidium elaborates an interface featuring multiple rings, bands, tight junctions, and a membranous metabolite exchanger named the feeder organelle. In this study, we define the protein content of the dense granules (79 proteins) and demonstrate that following secretion these proteins are found at this interface and are presumably involved in its formation and function. The insulinase protein INS-16 (cgd3_4270) was recently assigned to dense granule-like organelles using an antibody but was not observed in our dataset.
17 The four Cryptosporidium proteins tested thus far do not appear to have such tropism; however, we note HSP101 in the dense granule proteome, which is associated with translocation in Plasmodium 62 and could function similarly in Cryptosporidium. Using the hyperLOPIT dataset, we discovered a previously uncharacterized apicomplexan secretory organelle, the SGs. Initially we suspected them to be a precursor of one of the other secretory compartments; however, further analysis showed them to be morphologically distinct, independently secreted, to have no overlap in protein content with rhoptries, micronemes, or dense granules, and to follow a unique temporal pattern of biogenesis. We studied two of these proteins and found one to be delivered to the host-parasite interface, as are proteins secreted from dense granules and rhoptries (Figure  7C ).
Materials availability All unique/stable reagents generated in this study are available from the lead contact with a completed Materials Transfer Agreement. Data and code availability d The mass-spectrometry-based proteomics data have been deposited to the ProteomeXchange Consortium via PRIDE with the dataset identifier PXD015269. The data is also available through cryptoDB. d No original code was used in this study. d Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.
LC-MS/MS analysis of peptides LC-MS/MS analyses were performed on an OrbitrapÔ FusionÔ LumosÔ TribridÔ mass spectrometer coupled to a Dionex UltimateÔ 3000 RSLCnano system (Thermo Fisher Scientific) as previously described. 41 The data were acquired in the positiveion data-dependent acquisition mode with the SPS-MS 3 method with a total run time of 120 min. The peptide samples were resolubilized in 20 ml of the sample loading solution (0. 1% aqueous formic acid). Approximately 1 mg of peptides was loaded onto a micro-precolumn (C18 PepMap 100, 300 mm i. d. 3 5 mm, 5 mm particle size, 100 A ˚pore size, Thermo Fisher Scientific) with the sample loading solution for 3 min. Following the loading step, the valve was switched to the inject position, and the peptides were fractionated on an analytical Proxeon EASY-Spray column (PepMap, RSLC C18, 50 cm 3 75 mm i.
QUANTIFICATION AND STATISTICAL ANALYSIS Raw LC-MS data processing and quantification Raw LS-MS/MS data were processed with Proteome Discoverer v2. 5 (Thermo Fisher Scientific). The spectra were searched against the annotated protein sequences of Cryptosporidium parvum isolate Iowa II (3,944 entries retrieved from CryptoDB. org release 52 on 27. 05. 2021). 75 Common contaminant proteins -e. g. , human keratins, bovine serum albumin, porcine trypsin -from the common Repository of Adventitious Proteins (cRAP, 115 entries, adapted from https://www. thegpm. org/crap/) were added to the database. The Sequest HT search engine was used. The precursor and fragment mass tolerances were set to 10 ppm and 0. 6 Da, respectively. The enzyme was set to trypsin with up to two missed cleavages allowed. The static modifications were carbamidomethylation of cysteine and TMT6plex at the peptide N-terminus and lysine side chain.
The dynamic modifications were TMT6plex at the side chains of serine, and threonine, oxidation of methionine, and deamidation of asparagine and glutamine. In addition, the dynamic modifications of protein N-terminus considered were acetylation, the loss of methionine, and the combination of the two. The false discovery rate of peptide-to-spectrum matches (PSMs) was validated by Percolator v3. 05. 0 and only high-confidence peptides (FDR threshold 1%) of a minimum length of 6 amino acid residues were used for protein identification. The unmatched spectra were further searched against the same database with MS Amanda 2. 0 using the same search parameters, except for not considering the protein N-terminal dynamic modifications. The PSM FDR was validated by Percolator as above. Strict parsimony was applied for protein grouping.
The search results were merged globally by search engine, and the protein FDR was controlled at 5% cut-off. TMT reporter ion abundances were obtained in Proteome Discoverer Reporter Ions Quantifier node using the most confident centroid method for peak integration with 20 p. p. m. tolerance window. The isotopic impurity correction as per the manufacturer's specification (TMT10plex Lot No. RJ239873) was applied. The results of data processing by Proteome Discoverer were exported as tab-delimited text files and processed further through an in-house data analysis pipeline in R version R4. 1 (R Core Team), using Bioconductor packages MSnbase (v R2. 18. 0), pRoloc (v R1. 32. 0), and tidyverse (v R1. 3. 1). For protein quantification, the PSM-level output from Proteome Discoverer was filtered to retain only unique and unambiguous PSMs that were mapped to a single protein group not containing contaminant proteins, were matched to rank 1 peptide, and had no more than 50% precursor isolation interference, at least 50% of SPS precursor ions matched to sequence fragments, and an average reporter ion signal-to-noise ratio of at least 5.
Dimensionality reduction and unsupervised clustering The normalized 20plex dataset was visualized by principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) as described earlier by Barylyuk et al. 25, 41  Briefly, the data were PCA-transformed, reduced to top 14 principal components explaining approximately 99% of the variance and whitened, and embedded into two dimensions by running t-SNE for 5000 iterations with the parameters 'theta' and 'perplexity' set to 0 and 42, respectively. Unsupervised clustering of proteins was performed on the normalized abundance data using the HDBSCAN* algorithm  68  available through the Python library 'hdbscan' (https://hdbscan. readthedocs. io/en/latest/index. html). We used the distance to the 7 th nearest neighbor to define the core distance for each protein ('min_samples = 7') and allowed a minimum of 10 proteins per cluster ('min_ cluster_size = 10').
ACKNOWLEDGMENTS We thank  Fabio Tosini  and  Honorine Ward  for sharing antibodies, and we thank  Mike Deery  for proteomic data acquisition. This work was funded in part by grants from the  National Institutes of Health  ( R01AI112427  and  R01AI148249 ) and the  Pennsylvania Department of Health  to B. S. and from the  Wellcome Trust  ( 214298/Z/18/Z ) to R. F. W. , an  EMBO postdoctoral fellowship  to  A. G. ( ALTF 58-2018 ), a  Leverhulme Early Career Fellowship  co-funded by the  Isaac Newton Trust  ( ECF-2015-562 ) to K. B. , and a  Todd-Bird Junior Research Fellowship from Oxford New College  to O. M. C.
tion. For example, protein family analysis of the initial release of about 365,000 structures  10, 16  , covering the proteomes of humans and 20 model organisms, suggested that 92% of predicted domains within this set match existing domain superfamilies. However, comparing all 214 million structures against each other using current methods would take approximately 10 years on a 64-core machine. To speed up the process of clustering amino acid sequences, a linear time algorithm, Linclust 17 , has been proposed to reduce the computational time significantly. However, such methods have yet to be applied to clustering by protein structural similarity. Here, we analysed the AlphaFold Protein Structure Database, which contains predicted structures for 214 million proteins across the tree of life.
Proteins are key to all cellular processes and their structure is important in understanding their function and evolution. Sequence-based predictions of protein structures have increased in accuracy  1  , and over 214 million predicted structures are available in the AlphaFold database  2. However, studying protein structures at this scale requires highly efficient methods. Here, we developed a structural-alignmentbased clustering algorithm-Foldseek cluster-that can cluster hundreds of millions of structures. Using this method, we have clustered all of the structures in the AlphaFold database, identifying 2. 30 million non-singleton structural clusters, of which 31% lack annotations representing probable previously undescribed structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AlphaFold database.
Although our knowledge of protein sequences has grown considerably over the past years, reaching over hundreds of millions of sequences, the knowledge of their 3D structures has lagged behind owing to the lack of highly scalable experimental methods. Improvements in methods for predicting structure from sequences  1, 3, 4  now enable the scalable prediction of protein structures for the known protein universe. The AlphaFold Protein Structure Database (AFDB) is a publicly available data repository of protein structures and their confidence metrics, predicted using the AlphaFold2 AI system  1, 2. The AlphaFold-predicted structures have been generally assessed to be of high quality when the predicted local distance difference test (pLDDT) confidence metrics are accounted for, despite remaining inferior to experimentally determined structures  5.
Clustering proteins by their structure is a crucial tool for analysing structural databases as it enables the grouping of remotely related proteins. Identifying distant relationships might provide valuable insights into protein structure evolution and funcdatabase into 2. 30 million clusters with 31% of clusters-representing 4% of protein sequences-not matching previously known structural or domain family annotations. We found that 532,478 clusters have representatives present in all of the tree of life and we found several species-specific structural clusters that could contain examples of de novo gene birth events. Finally, we used structural comparisons to predict domain families and their relationships identifying putative remote homologies that expand the evolutionary coverage of previously known families.
Structure-based clustering of the AFDB The AFDB covers over 214 million predicted protein structures and has grown in several stages (Fig. 1a ). The initial release focused on 20 key model organisms, while subsequent updates provided predictions for the Swiss-Prot dataset of the Universal Protein Resource  18  (UniProt) and proteomes relevant to global health, taken from priority lists compiled by the World Health Organisation. The current update covers most of the TrEMBL dataset of UniProt. The AFDB parses and archives these data and makes them accessible through bulk download options, programmatic access end points and interactive web pages. The programmatic access, in particular, facilitated the integration of AlphaFold models into other biological data repositories, such as Protein Data Bank Europe (PDBe)  19  , UniProt 18 , Pfam 20 , InterPro  21  and Ensembl  22.
To gain insights into the 214,684,311 structures of the AlphaFold UniProt v. 3 database we developed a scalable clustering approach in two steps as depicted in Fig. 1b. The first step involved using MMseqs2 (ref. 23) to cluster the database on the basis of 50% sequence identity and a 90% sequence alignment overlap of both sequences, resulting in 52,327,413 clusters. For each cluster, the protein structure with the highest confidence (that is, the highest pLDDT score) was.
3 ). As anticipated, an increase in LDDT corresponds to an increase in functional similarity. With increasing LDDT from 0. 5 to 0. 9, there is an increase in the percentage of perfectly consistent clusters-from 29. 2% to 93. 8% in Pfam, and from 40. 3% to 81. 3% for EC (level 4), respectively. Structural similarity between two sequences can often be traced back to either shared evolutionary roots (homologues), or it can be a result of convergent evolution (analogues). We therefore investigated the evolutionary relationships within clusters produced by our method using the Evolutionary Classification of Protein Domains (ECOD) database  24. This hierarchical domain database delineates the evolutionary relationships between protein domains. This analysis showed that 97. 4% of pairwise compared cluster members are conserved at the H-group (homology) level (Methods).
Clusters of unknown structure and function The availability of predicted structures covering a large fraction of the known protein universe enables us to examine what fraction of this structural space is novel. We tried to uncover structurally and functionally unknown protein clusters in the AFDB dataset-defined as 'dark clusters'. We first identified 1,135,118 (49% of AFDB clusters) clusters that were found to be at least partially similar to previously known structures in the PDB (Methods). The representative proteins of the remaining clusters were next annotated to the Pfam database by MMseqs2 search, resulting in 883,788 (38% of AFDB clusters) dark clusters (Methods). Finally, we identified clusters containing members with Pfam or TIGRFAM  25  annotations in the UniProt/TrEMBL and Swiss-Prot database.
Bacterial and human immunity protein links Note that, even if some biological processes were primarily restricted to eukarya or more recently diverged clades, we could find cluster representatives that were present in bacterial species. For example, most human proteins that are annotated to the nucleus (GO:0005634) are in clusters mapped to eukarya as their LCA. However, we found exceptions including, for example, a histone-related cluster (Fig. 3c ) supporting the previously reported evolutionary link between eukaryotic and bacterial histones  26. Similarly, we found several immunity-related proteins with structural similar proteins present in bacteria. These include TNFRSF4 (UniProt: P43489) with similar structures in bacteria due to common cysteine-rich repeat regions that overlap with the TNFR/NGFR cysteine-rich region domain annotations in InterPro (IPR001368).
We also found bacterial structures that are related to the human CD4 like protein B4E1T0 (Extended Data Fig. 7a ), although these can also be annotated by sequence matching to the immunoglobulin-like domain family in InterPro (IPR013783). The structural similarity between human and bacterial proteins may also inform on their function in bacteria. The human bactericidal permeability-increasing (BPI) protein (B4DKH6) is a key component of the innate immune system and is known to have a strong affinity for negatively charged lipopolysaccharides found in Gram-negative bacteria. In our analyses, this protein clusters with bacterial structures (Fig 3c ), for example, the protein A0A2D5ZNG0, which aligns with the human protein at a TM-score of 0. 81 normalized to the length of the human protein. Moreover, searching for partial hits by Foldseek identified that YceB from E.
coli and other gram-negative bacteria has structural similarity to the C-terminal region of human BPI (Extended Data Fig. 7b ). The E. coli YceB protein is a tubular putative lipid-binding protein without a well-characterized function. This structural similarity may suggest a role of YceB homologues in regulating the outer membrane. Our analysis identified a cluster containing the human protein AIM2 (O14862), which recognizes pathogenic double-stranded DNA  27  and leads to the formation of the AIM2 inflammasome. When searching the NR database using NCBI BLAST  28  , we found no bacterial hits for the human AIM2 gene. However, three structures in 'Candidatus Lokiarchaeota archaeon' and one in the bacterium Clostridium sp. from an uncultured source (UniProt: A0A1C5UEQ5) were identified as similar to human AIM2 in our analysis.
The bacterial protein (Uni-Prot: A0A1C5UEQ5), encoded on a contig of length 138,559 (GenBank: FMFM01000010), is unlikely to be a contaminant due to its length  29. UniProt A0A1C5UEQ5 is not unique, as many homologous sequences, mostly labelled as 'hypothetical protein', were found in the NR database from mostly uncultured human gut bacterial sequences with higher than 90% sequence identity. We predicted the structure of one homologous protein that is 64% identical to UniProt A0A1C5UEQ5 (Extended Data Fig. 8 )-which originates from a cultured Lachnospiraceae bacterium that is part of the Culturable Genome Reference  30  of the human gut-using ColabFold  31  and confirmed that it has a similar structure DNA-binding domain structure (TM score of 0. 97 and 0. 56 in relation to UniProt A0A1C5UEQ5 and human AIM, respectively).
Domain prediction by structure search The clusters defined above group structurally similar proteins at full length. Proteins are sometimes composed of different regions or domains that can fold independently, with a growing collection of such domain families being catalogued in databases such as Pfam 20 shown. b, The distribution of selected GO terms across the human lineage of the LCA based on the analysis of human protein-containing clusters (abundance is normalized per GO category). c, Three example structures from the human clusters that are conserved across humans and bacteria, among the eukaryote GO-annotated clusters. A histone protein with a nucleus GO annotation, which was found to be conserved at the cellular organism level and supports the previously reported evolutionary connection between eukaryotic and bacterial histones (left)  26.
Structural similarity in distant domains The network clustering procedure used above also enables the identification of pairs of predicted domain families that share some structural similarity. Among such pairs, we found around 500 connections between clusters enriched with a Pfam annotation and other domains without clear annotations, providing examples of potential functional annotations. From these, we focused on connected domain families enriched in proteins from different kingdoms (Fig. 5 ). The Frag1-like domains exemplify the strength of structural-based similarity searching (Fig. 5a ). The Frag1/DRAM/Sfk1 Pfam domain (PF10277) annotates proteins with a six-α-helix bundle transmembrane region observed in eukaryotic species. In our analysis, a domain family enriched for this Pfam annotation was linked to two additional families enriched in bacterial and archeal sequences, one enriched for a domain of unknown function (DUF998; PF06197) and a second not annotated.
The three families are structurally identical, typically forming a six-α-helix bundle, despite the very low sequence similarity between the sequences forming these. We also found a cluster enriched for the anthrax_toxA Pfam (PF03497; Fig. 5b ), more specifically, the annotated domains contained structures similar to the oedema factor, a calmodulin-activated adenylyl cyclase  32. The oedema factor is one of the three components forming the bacterial anthrax toxin system. Our analysis identified a structurally similar putative domain family enriched in eukaryotic proteins (Fig. 5b ). Specifically, several algae proteins were found to have structures that had partial matches to the oedema-factor-domain-related structures. This raises the possibility that algae might be using similar toxin systems.
Discussion The orders-of-magnitude increase in available structural models raises challenges in data management and analysis of such large volumes. This difficulty is amplified by the fact that the repository of publicly available structures, consisting of the combined databases of AFDB and the ESMatlas  38  , is approaching a billion entries. For this reason, we developed a clustering procedure that can scale to billions of structures, identifying 2. 30 million non-singleton clusters of which 31% do not have similarity to previously known structures or domain annotations. These clusters annotate only 4% of protein sequences, indicating that the vast majority of the protein structural space has been at least partially annotated. As the criteria used include partial hits to known structures or domain annotations, the degree of understudied structural space is probably underestimated.
In addition to the full-length protein clustering, we used Foldseek's local hit matches to predict and cluster protein regions into putative domain families. The protein region clusters tend to overlap well with previous definitions of domain families as annotated in the Pfam database and led to the identification of over 10,000 unassigned domain-level clusters that should be enriched in putative novel domain families. We did not perform exhaustive searches with other sequence-based domain family annotations that could annotate additional clusters with previous knowledge. Note that we considered only the representatives of Foldseek clusters when performing the domain prediction. As the domain prediction requires multiple observations on the same structural region, additional domains are expected to be detected if each structure was searched against a larger set of structures.
The pipeline then proceeds with an ungapped alignment algorithm that rescores the structures on the basis of the shared diagonal between members and representatives using 3Di and amino acid information. The sequences that meet the defined alignment criteria, such as E-value, alignment coverage, sequence identity, alignment LDDT  43  or TM score  44  , are clustered using the MMseqs2 clustering module (default using the set-cover algorithm). After this step, the structures that have been assigned already are removed from the set and the remaining representative member hits are aligned using Foldseek's structural Gotoh-Smith-Waterman algorithm  15  , and all passing hits are clustered as well. The remaining cluster representatives are successively clustered by three cascaded steps of prefiltering, structural Smith-Waterman alignment and clustering.
Distinguishing homologues from analogues Structural similarity between two sequences can be attributed to either common evolutionary ancestry (homologues) or convergent evolution (analogues). We investigated the association between cluster members, computed by our pipeline on the basis of structural similarity, and homology relationships using the ECOD database  24. ECOD is a hierarchical domain database that describes the evolutionary relationships between pairs of protein domains. Its hierarchical levels from root to leaf are classified as: A-group (same architecture), X-group (possible homology), H-group (homology), T-group (topology) and F-group (sequence similarity). Analogues are expected to occur between members of different X-groups, whereas homologues should be found within the H-group.