Toxin Data Quality: A Critical Examination of Bacterial Exotoxins and Animal Toxins

doi:10.21203/rs.3.rs-4376594/v1

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Toxin Data Quality: A Critical Examination of Bacterial Exotoxins and Animal Toxins

https://doi.org/10.21203/rs.3.rs-4376594/v1

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Objective

Existing toxins data sets include a mixture of proteins and toxin peptides. In this study we present two curated data sets of toxic proteins free of associated proteins: bacterial exotoxins and animal toxins. Our stringent selection criteria resulted in two data sets with only toxins that directly target or disrupt vital molecular mechanisms of their target organism. To gain insight in their properties and differences, we compared both sets of toxins to controls, and used simple biophysical features such as protein length, and amino acid composition distinguishing between evolutionary kingdoms (phyla). This approach should reveal if there is a need to consider differences present in toxin sets depending on their origin.

Results

Our analysis revealed biophysical differences between animal and bacterial toxins that should not be ignored. Both toxin groups exhibited preference amino acid usage depending on their origin, together with higher cysteine content compared to their respective controls. The animal toxins set contains, on average, significantly shorter sequences than bacterial toxins, and had their isoelectric point shifted towards acidic pH values. We show that animal toxins and bacterial toxins possess intrinsic differences in general biophysical properties, which reinforce the necessity of segregating these datasets to ensure reliability of bioinformatics models aimed at understanding and predicting toxin characteristics.

Animal toxins

bacteria toxins

amino acid usage

exotoxins

Over the past decade, numerous toxin data resources have emerged, each with a specific focus, such as animal toxins or venoms [1–4]⁠, toxins from plants [5]⁠, fungi [6–8]⁠ or bacteria [9–13]⁠, or toxic compounds [4, 5]⁠. These resources are either highly specialized(e.g. bacterial secretion system specific) and/or contain non-toxic components (e.g. outer membrane proteins or chaperones). For several of them, their creation relied on a keyword text search within public sequence databases such as UniProt [14]⁠, SWISS-PROT [15]⁠, or NCBI [16]⁠.

When talking about toxins, we need to go into their definitions and classification, which can differ based on the field. For microbiologists, protein bacterial toxins are called exotoxins, and are sub-classified in Type I to III based on their location of action: extracellular (Type I and II), intracellular (Type III). Type I toxins interact with cell surface receptors, Type II disrupt cell membranes, and Type III enter the cell's cytoplasm either through binding (AB toxins) or injection (effector proteins). We expand the classification to a Type IV toxin category responsible for degrading the extracellular matrix (ECM), essential for cellular microenvironment management [17]⁠. In the field of animal toxins, these are broadly classified into two types [18]⁠: passively delivered true poisons and actively delivered venoms. This categorization echoes the distinction between bacterial exotoxins and endotoxins, with a similar dichotomy of delivery. Our study focuses specifically on the protein components of animal venoms, drawing parallels with bacterial exotoxins.

We introduce a curated data set of bacterial exotoxins and compare them to an animal toxins set, to bridge the gap between specialized and unspecific resources. The analysis includes their nearest counterpart of non-toxic proteins: secreted proteins. By shedding light on shared traits and distinguishing features, we provide high quality data useful for development of machine learning models.

Animal and bacterial toxins differ in similarity of sequence

The initial datasets comprised 6,810 animal and 2,417 bacterial toxins (See Table 1). Applying a sequence similarity threshold (SST) of 1.0, which eliminates identical sequences, retained about 75% of bacterial and 94% of animal proteins. More stringent reduction at SST 0.25 led to comparable numbers in both sets (1,056 bacterial, 1,125 animal), despite the initial threefold higher count of animal toxins. This indicates a a higher level of diversity in the bacterial sequences than in the animal toxins sequences.

In terms of overlap, no shared toxin clusters were found between animal and bacterial proteins at SST 1.0. Even at the lowest SST (0.25), less than 1.2% of the toxin clusters included both animal and bacterial toxins.

Table 1: Sequence similarity clusters.

		Sequence Similarity Threshold (SST)
Number (% remaining) clusters	Raw data	1.0	0.75	0.50	0.25
All toxins (%)	9281 (100%)	8216 (89%)	4103 (44%)	2762 (30%)	2206 (24%)
Animal toxins (%)	6810 (100%)	6373 (94%)	2754 (40%)	1569 (23%)	1125 (17%)
Bacterial toxins (%)	2471 (100%)	1843 (75%)	1347 (55%)	1190 (48%)	1056 (43%)
Cluster overlap	0	0	2	3	25

Absolute number and percentage of remaining protein clusters in the animal toxins, bacterial toxins and both sets (All). In brackets, percentages compared to original unfiltered data (Raw data. Sequence similarity reduction was applied using the MMseqs2 at the thresholds of 1.00, 0.75, 0.5 and 0.25 (modus 0) [45]. The last row (Cluster overlap) shows how many clusters contain both animal and bacterial toxins

Sequence similarity at low thresholds implies but does not guarantee different structures between proteins [19]⁠. It's likely that animal and bacterial toxins with such low similarities will differ in function.

Toxins use specific amino acids. We analyzed amino acid compositions using a metric termed Surprise, which measures the deviation of an amino acid's (AA) composition from the mean in standard deviation units (Eqn. 1). Bacterial toxins exhibited the highest Surprise values for histidine (H) and arginine (R) (Fig. 1A). In contrast, the most abundant amino acids in bacterial secreted proteins (control group) were glycine (G), alanine (A), and aspartic acid (D). For animal toxins (Fig. 1B), most notable were cysteine (C) and lysine (K), whereas glutamine (Q), serine (S), and histidine (H) were more prevalent in non-toxic animal proteins, indicating an inverse trend compared to bacteria.

Direct comparison between animal and bacterial toxins (Fig. 1C) revealed cysteine (C) as most over-represented in animal toxins; with glutamine (Q) and alanine (A) being under-represented. This trend held for non-toxic secreted proteins (Fig. 1D), with cysteine over- and alanine under-represented. Cysteine stood out for all data sets. The prevalence of cysteine in animal toxins corroborates earlier findings [20]⁠. Its relevance in toxins might be related to cystein’s contribution to thermal stability [21]⁠ and protein stabilization [22]⁠. Additionally, its increased frequency in smaller proteins has been confirmed [23]⁠. Discussions of other amino acid variations are omitted for brevity.

Toxins shorter than secreted controls

Animal proteins in our sets were shorter than the bacterial proteins, and toxins were shorter than non-toxins (Fig. 2). Short length in animal toxins corroborates with what has been observed for sea anemones, snakes and spiders [24–26]⁠ . Our datasets showing bacterial proteins as longer than animal proteins is contrary to studies stating that eukaryotic proteins are longer than prokaryotic [27–29]⁠. This difference was explained by our datasets focus on secreted proteins. An expert-curated dataset employed to develop SignalP 6.0 [30]⁠⁠ confirmed our findings: secreted proteins are indeed shorter in animals than in bacteria (See Additional file 2, Fig. S2).

Evaluation of biophysical properties: aromaticity and pI

All sets (toxins and secreted non-toxins) were similar in aromaticity; this might be related to secretion (See Additional file 3, Fig. S4). In contrast, for the isoelectric point (pI) distributions appeared bimodal with a dip around a neutral pH of 7.4 (See Additional file 3, Fig. S3), however, all toxins distributions shifted toward lower pH values compared to non-toxic proteins. This trend was stronger in bacteria (Fig. S3A) than animals (Fig. S3B), with about a third of all bacterial toxins having pI-values below 5.

Aromaticity and isoelectric points have not been thoroughly investigated for toxins. The bimodal pI distribution in other proteins with a minimum around neutral pH has been explained as a means to prevent aggregation [31, 32]⁠ . Several bacterial toxins, specifically type III from AB type, are known to react to acid conditions in the endosome just before their insertion in the cytoplasm, which could explain the differences with animal toxins, which do not seem to require acid conditions for their activity. However, more research is needed to be able to define the real reason between the isoelectric point distributions shown by toxins.

Data set : bacterial toxins and controls

The dataset was constructed by extracting representatives of Type I, Type II, and Type III toxins from UniProtKB/Swiss-Prot (RRID:SCR_021164) [15]⁠ and PubMed (RRID:SCR_004846) [16] after selecting them from literature reviews for each type of toxin. For Type I, we included the representatives for the Superantigen family and Enterotoxins [33]⁠.Type II included representatives of pore-forming toxins [34]⁠ and phospholipases [35]⁠. Type III toxins included AB toxins and effector proteins. For AB toxins coded in different open reading frames, the binding subunit (B) was removed. Although, it is essential for binding, localization, and delivery of the active toxin into the cytosol [36]⁠, it has no known toxicity-related enzymatic activity inside the target cell. When both subunits are encoded in one gene and secreted as one protein (e.g. Tetanus toxin), the full protein was included. Effector proteins from bacterial secretion systems were extracted from existing specialized datasets for effector proteins [37–40]⁠⁠. A curation step included the removal of structural proteins, and chaperones, as they do not contain known toxicity. We have listed example proteins removed (See Additional file 1, Table S1) and kept (Aee Additional file 1, Table S2) from available resources, and all sources used for the revision and extraction of proteins (See Additional file 2, Table S3). We assumed that all effector proteins have a function in the manipulation of the cell metabolism. Therefore, Type III toxins include all known and predicted effector proteins from the secretion systems known to translocate proteins directly into the target cell, which include T3SS, T4SS, and T6SS. However, as before, we excluded structural proteins (See Additional file 1, Table S1) and proteins involved in the translocation.

Based on our operational definition of toxins, we have expanded the classification of bacterial toxins to a Type IV, which included bacterial proteins degrading extracellular matrix (ECM). By degrading the ECM they have a potential effect on cell behavior. Representatives of Type IV toxins included enzymes with direct effect on ECM proteins such as collagenases, siacylases, and metalloproteases.

For bacterial secreted proteins (control) with no known toxin activity, we selected all secreted proteins, non-membrane associated, and no outer membrane vesicles (OMV) associated from monoderms and diderms, available in the PSORTb 3.0 database (accessed June 2021) [41]⁠ . This database was chosen because its classification considers the potential membrane association of secreted proteins, which other databases do not. This allows us to select the most similar proteins to bacterial toxins. We removed all proteins that contained “Fragment” within the identification.

Data set: animal toxins and controls

We based the dataset of animal venom toxins on the expert-curated Tox-Prot [42]⁠, itself is a subset of UniProt (accessed on Dec 2022). We included venom proteins from jellyfish, snails, centipedes, insects, arachnids, fish, mammals and snakes. We added proteins retrieved from our own studies on three-finger toxins (3FTs) in snakes [43]⁠ and of our own recent annotation of hymenopteran venoms [44]⁠.

For the control set of secreted, animal proteins, we selected all secreted [KW-0964], non-toxic proteins [NOT KW-0800] from the same organism as in the animal venoms, based on their Organism ID as defined in UniProt [14]⁠ (accessed July 2023). For animals, we chose UniProt as better suited because of the better prediction of subcellular localization for eukaryotic proteins. We removed all proteins that contained “Fragment” within the identification.

Data set comparison:

To address a possible bias from sequence redundancy, we excluded identical sequences within each of the sets through the alignment method MMseqs2 [45]⁠. We applied the default parameter setting from the easy-cluster option at a sequence similarity threshold of 1.00, and alignment coverage modus of 0 [45]⁠. At modus 0, the alignments cover both the query- and the target sequence at the selected coverage threshold. We kept only the cluster-representatives for each cluster.

To investigate the remaining sequence diversity within the sets, we used MMseqs2 clustered at three different thresholds of 0.75, 0.50, and 0.25. Subsequently we calculated the number of clusters (n)that were similar given a particular threshold (thresh) as follows:

For each data set, we calculated the amino acid composition (percentage of any of the 20 native amino acids), average sequence length (number of residues per protein), the aromaticity (percentage of phenylalanine, tryptophan and tyrosine) and the isoelectric points (pI) after Bjellqvist [46]⁠. The pI, amino acid composition, average sequence length and aromaticity were calculated after removal of duplicates from the raw data using Biopython (RRID:SCR_007173).

To visualize the differences in amino acid composition between datasets, we calculated the Surprise (Eqn. 1) per amino acid, defined as follows:

with AA as one of the 20 amino acids; µ_data1, as the average percentage of AA in data set 1, and µ_{mergeddatasets} as the average for data set 1 minus background (both sets combined); while σ_{mergeddatasets}described the standard deviation.

We first computed the amino acid composition in the single and merged datasets. Then, we generated a normal distribution by bootstrapping with 10000 samples to calculate the mean and standard deviations. Finally, the Surprise values determined the height of each amino acid letter (one-letter code) using the python package Logomaker [47]⁠⁠ .

Limitations

The following limitations need to be considered: Our bacterial data sets are biased as a result of databases enriched in bacteria that can be cultured in the laboratory and their relevance as human pathogens. Future analyses addressing the biases in data sets available, like metagenomic data, or computational predictions for identification of toxins from unculturable organisms, will be valuable approaches. What our data cannot tell us is if these differences will also be valid for toxins from other bacteria, fungi, or plants.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and analysed during the current study are available in the LMU repository Open Data LMU (RRID:SCR_025005) under the DOI: https://doi.org/10.5282/ubm/data.423. If updated, the repository will allow access to the updated and previous version.

Competing interests

The authors declare that they have no competing interests.

Funding

This research was made possible by the generous funding from BMBF (German Federal Ministry of Education and Research (RRID:SCR_005066)) grant number SSTDBB-16DKWN136A and SSTDBB-16DKWN136B to LJ and BR; and the Humbolt Foundation to IK.

Authors' contributions

M.L., B.R. and L.J. conceptualized the study. I.K. and L.J. curated the data. T.K. and L.J. performed the formal analysis, the investigation and wrote the original draft. I.K., B.R. and L.J. were responsible for the resources and funding acquisition. I.K., M.L., B.K. and L.J. contributed to the methodology. L.J., B.R. and I.K. were responsible for the project administration. T.K. visualized the data and prepare the code and data under FAIR principles. L.J., M.L. and B.R. supervised the project. M.L., T.K. ,I.K. ,L.J. and B.R. reviewed and edited the draft. All authors read and approved the final manuscript.

Acknowledgements

We would like to express gratitude to Tim Karl (TUM) for his invaluable assistance with hardware and software issues (ROSTLAB (RRID:SCR_000792) infrastructure); to Inga Weise and Nikita Kugut (TUM) for their logistical support during the grant application, and to Thomas Gudermann (LMU) for his support during the start of a new research area. Thanks to Celine Marquet for her active scientific input and counseling during the grant application. Thanks to Henrik Nielsen (DTU Copenhagen) and his team for making the SignalP tools and data sets easily available (SignalP (RRID:SCR_015644)). We also want to thank all researchers who share their experimental data in public databases, and all those who maintain these valuable resources.

Amino acids: A, alanine; R, arginine; N, asparagine; D, aspartic acid; C, cysteine; E, glutamic acid; Q, glutamine; G, glycine; H, histidine; I, isoleucine; K, lysine; L, leucine; M, methionine; F, phenylalanine; P, proline; S, serine; T, threonine; W tryptophan; V, valine; ECM, extra cellular matrix; IQR, inter quartile range; OMV, Outer Membrane Vesicles; pI, isoelectric point; pKa, acid dissociation constant; T3SS, Type 3 secretion system; T4SS, Type 4 secretion system, T6SS, Type 6 secretion system, SST, sequence similarity threshold.

Kuzmenkov AI, Krylov NA, Chugunov AO, Grishin E V., Vassilevski AA. Kalium: A database of potassium channel toxins from scorpion venom. Database. 2016;2016.
Wood DLA, Miljenović T, Cai S, Raven RJ, Kaas Q, Escoubas P, et al. ArachnoServer: A database of protein toxins from spiders. BMC Genomics. 2009;10.
He QY, He QZ, Deng XC, Yao L, Meng E, Liu ZH, et al. ATDB: A uni-database platform for animal toxins. Nucleic Acids Res. 2008;36 SUPPL. 1.
Moriyama F, Abe S, Harada T, Tanaka T. Database of regulated chemicals, pathogens, and toxins. J Environ Saf. 2017;8:65–71.
Günthardt BF, Hollender J, Hungerbühler K, Scheringer M, Bucheli TD. Comprehensive Toxic Plants-Phytotoxins Database and Its Application in Assessing Aquatic Micropollution Potential. J Agric Food Chem. 2018;66:7577–88.
Lu T, Yao B, Zhang C. DFVF: database of fungal virulence factors. Database. 2012;2012.
Sinha RP, Singh SP, Häder DP. Database on mycosporines and mycosporine-like amino acids (MAAs) in fungi, cyanobacteria, macroalgae, phytoplankton and animals. J Photochem Photobiol B Biol. 2007;89:29–35.
Nielsen KF, Smedsgaard J. Fungal metabolite screening: database of 474 mycotoxins and fungal metabolites for dereplication by standardised liquid chromatography–UV–mass spectrometry methodology. J Chromatogr A. 2003;1002:111–36.
Chakraborty A, Ghosh S, Chowdhary G, Maulik U, Chakrabarti S. DBETH: A database of bacterial exotoxins for human. Nucleic Acids Res. 2012;40.
Xie Y, Wei Y, Shen Y, Li X, Zhou H, Tai C, et al. TADB 2.0: An updated database of bacterial type II toxin-antitoxin loci. Nucleic Acids Res. 2018;46.
Barbosa LCB, Garrido SS, Marchetto R. BtoxDB: A comprehensive database of protein structural data on toxin-antitoxin systems. Comput Biol Med. 2015;58.
Zhou CE, Smith J, Lam M, Zemla A, Dyer MD, Slezak T. MvirDB - A microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Res. 2007;35 SUPPL. 1.
Arnold R, Brandmaier S, Kleine F, Tischler P, Heinz E, Behrens S, et al. Sequence-Based Prediction of Type III Secreted Proteins. PLoS Pathog. 2009;5.
Bateman A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47.
Bairoch A, Apweiler R. The SWISS-PROT Protein Sequence Data Bank and Its New Supplement TREMBL. Nucleic Acids Res. 1996;24:21–5.
Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, et al. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2022;50:D20–6.
Sainio A, Järveläinen H. Extracellular matrix-cell interactions: Focus on therapeutic applications. Cell Signal. 2020;66:109487.
Jackson TNW, Fry BG. A tricky trait: Applying the fruits of the “function debate” in the philosophy of biology to the “venom debate” in the science of toxinology. Toxins (Basel). 2016;8:1–14.
Rost B. Twilight zone of protein sequence alignments. Protein Eng vol. 1999;12:85–94.
Chen N, Xu S, Zhang Y, Wang F. Animal protein toxins: origins and therapeutic applications. Biophys Reports. 2018;4:233.
Veno J, Rahman RNZRA, Masomian M, Ali MSM, Kamarudin NHA. Insight into improved thermostability of cold-adapted staphylococcal lipase by glycine to cysteine mutation. Molecules. 2019;24.
Hovde CJ, Marr JC, Hoffmann ML, Hackett SP, Chi Y ‐i, Crum KK, et al. Investigation of the role of the disulphide bond in the activity and structure of staphylococcal enterotoxin C1. Mol Microbiol. 1994;13:897–909.
White SH. Amino Acid Preferences of Small Proteins Implications for Protein Stability and Evolution. J Mol Biol. 1992;227:991–5.
Diochot S, Lazdunski M. Sea anemone toxins affecting potassium channels. Prog Mol Subcell Biol. 2009;46:99–122.
Ojeda PG, Ramírez D, Alzate-Morales J, Caballero J, Kaas Q, González W. toxins Computational Studies of Snake Venom Toxins. https://doi.org/10.3390/toxins10010008.
Escoubas P, Sollod B, King GF. Venom landscapes: Mining the complexity of spider venoms via a combined cDNA and mass spectrometric approach. Toxicon. 2006;47:650–63.
Tiessen A, Pérez-Rodríguez P, Delaye-Arredondo L. Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes. BMC Res Notes. 2012;5:1–23.
Nevers Y, Glover N, Dessimoz C, Lecompte O. Protein length distribution is remarkably consistent across Life. bioRxiv. 2021;:2021.12.03.470944.
Brocchieri L, Karlin S. Protein length in eukaryotic and prokaryotic proteomes. Nucleic Acids Res. 2005;33:3390.
Teufel F, Almagro Armenteros JJ, Johansen AR, Gíslason MH, Pihl SI, Tsirigos KD, et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol. 2022;40:1023–5.
Schwartz R, Ting CS, King J. Whole proteome pI values correlate with subcellular localizations of proteins for organisms within the three domains of life. Genome Res. 2001;11:703–9.
Kiraga J, Mackiewicz P, Mackiewicz D, Kowalczuk M, Biecek P, Polak N, et al. The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms. BMC Genomics. 2007;8:163.
PROFT T, Fraser JD. Bacterial superantigens. Clin Exp Immunol. 2003;133:299–306.
Alouf JE. Pore-Forming Bacterial Protein Toxins: An Overview. In: Current Topics in Microbiology and Immunology. 2001. p. 1–14.
Flores-Díaz M, Monturiol-Gross L, Naylor C, Alape-Girón A, Flieger A. Bacterial Sphingomyelinases and Phospholipases as Virulence Factors. Microbiol Mol Biol Rev. 2016;80:597–628.
Henkel JS, Baldwin MR, Barbieri JT. Toxins from bacteria. In: Exs. 2010. p. 1–29.
Alvarez-Martinez CE, Christie PJ. Biological Diversity of Prokaryotic Type IV Secretion Systems. Microbiol Mol Biol Rev. 2009;73:775–808.
Coburn B, Sekirov I, Finlay BB. Type III secretion systems and disease. Clinical Microbiology Reviews. 2007;20:535–49.
Wang Y, Tang Y, Lin C, Zhang J, Mai J, Jiang J, et al. Crosstalk between the ancestral type VII secretion system ESX-4 and other T7SS in Mycobacterium marinum. iScience. 2021;25.
Lasica AM, Ksiazek M, Madej M, Potempa J. The Type IX Secretion System (T9SS): Highlights and Recent Insights into Its Structure and Function. Frontiers in Cellular and Infection Microbiology. 2017;7 MAY.
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–15.
Jungo F, Bairoch A. Tox-Prot, the toxin protein annotation program of the Swiss-Prot protein knowledgebase. Toxicon. 2005;45:293–301.
Koludarov I, Senoner T, Jackson TNW, Dashevsky D, Heinzinger M, Aird SD, et al. Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily. Nat Commun. 2023;14.
Koludarov I, Velasque M, Timm T, Lochnit G, Heinzinger M, Vilcinskas A, et al. Bee core venom genes predominantly originated before aculeate stingers evolved. bioRxiv. 2022;:2022.01.21.477203.
Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35:1026–8.
Bjellqvist B, Hughes GJ, Pasquali C, Paquet N, Ravier F, Sanchez J ‐C, et al. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis. 1993;14:1023–31.
Tareen A, Kinney JB. Logomaker: Beautiful sequence logos in Python. Bioinformatics. 2020;36:2272–4.

No competing interests reported.

AdditionalFile1.pdf
Additional Files: Additional file 1: File format: pdf Title of data: Examples of toxin curation Description: Table S1: Example proteins removed because they do not fulfill our definition of toxins. Table S2: Example proteins kept because they fulfill our definition of toxins.
AdditionalFile2.pdf
Additional file 2: File format: pdf Title of data: Publications of bacterial toxins Description: Table S3: Publications consulted for the creation of the bacterial toxin dataset. Not all databases or links in the publications were accessible. Several links to the datasets were broken.
AdditionalFile3.pdf
Additional file 3: File format: pdf Title of data: Supplementary analysis of datasets Description: Figure S1: More extensive length analysis including density plots. Figure S2: Comparative length analysis of an established dataset that includes both secreted and non-secreted proteins. Figure S3: Analysis of protein isoelectric points in toxins and control proteins. Figure S4: Analysis of protein aromaticity in toxins and control proteins.

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You are reading this latest preprint version

Toxin Data Quality: A Critical Examination of Bacterial Exotoxins and Animal Toxins

Status:

Version 1

Abstract

Introduction

Results and Discussion

Methods

Limitations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

Abbreviations

References

Additional Declarations

Supplementary Files

Status:

Version 1