Functional classification of protein toxins as a basis for bioinformatic screening

VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction

Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle for protein-based therapies. Thus, there is an urgent need for accurate in silico methods for determining toxic proteins to filter the pool of potential candidates. At the same time, it is imperative to precisely identify non-toxic proteins to expand the possibilities for protein-based biologics. To address this challenge, we proposed an ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer models on a large, experimentally validated, curated dataset of protein and peptide toxicities. The primary steps in the VISH-Pred framework are to efficiently estimate protein toxicities taking just the protein sequence as input, employing an under sampling technique to handle the humongous class-imbalance in the data and learning representations from fine-tuned ESM2 protein language models which are then fed to machine learning techniques such as Lightgbm and XGBoost. The VISH-Pred framework is able to correctly identify both peptides/proteins with potential toxicity and non-toxic proteins, achieving a Matthews correlation coefficient of 0.737, 0.716 and 0.322 and F1-score of 0.759, 0.696 and 0.713 on three non-redundant blind tests, respectively, outperforming other methods by over $10\%$ on these quality metrics. Moreover, VISH-Pred achieved the best accuracy and area under receiver operating curve scores on these independent test sets, highlighting the robustness and generalization capability of the framework. By making VISH-Pred available as an easy-to-use web server, we expect it to serve as a valuable asset for future endeavors aimed at discerning the toxicity of peptides and enabling efficient protein-based therapeutics.

In Silico-Based Design of a Hybrid Peptide with Antimicrobial Activity against Multidrug-Resistant Pseudomonas aeruginosa Using a Spider Toxin Peptide

The escalating prevalence of antibiotic-resistant bacteria poses an immediate and grave threat to public health. Antimicrobial peptides (AMPs) have gained significant attention as a promising alternative to conventional antibiotics. Animal venom comprises a diverse array of bioactive compounds, which can be a rich source for identifying new functional peptides. In this study, we identified a toxin peptide, Lycotoxin-Pa1a (Lytx-Pa1a), from the transcriptome of the Pardosa astrigera spider venom gland. To enhance its functional properties, we employed an in silico approach to design a novel hybrid peptide, KFH-Pa1a, by predicting antibacterial and cytotoxic functionalities and incorporating the amino-terminal Cu(II)- and Ni(II) (ATCUN)-binding motif. KFH-Pa1a demonstrated markedly superior antimicrobial efficacy against pathogens, including multidrug-resistant (MDR) Pseudomonas aeruginosa, compared to Lytx-Pa1a. Notably, KFH-Pa1a exerted several distinct mechanisms, including the disruption of the bacterial cytoplasmic membrane, the generation of intracellular ROS, and the cleavage and inhibition of bacterial DNA. Additionally, the hybrid peptide showed synergistic activity when combined with conventional antibiotics. Our research not only identified a novel toxin peptide from spider venom but demonstrated in silico-based design of hybrid AMP with strong antimicrobial activity that can contribute to combating MDR pathogens, broadening the utilization of biological resources by incorporating computational approaches.

Bioinformatic and literature assessment of toxicity and allergenicity of a CRISPR-Cas9 engineered gene drive to control Anopheles gambiae the mosquito vector of human malaria

BACKGROUND

Population suppression gene drive is currently being evaluated, including via environmental risk assessment (ERA), for malaria vector control. One such gene drive involves the dsxFCRISPRh transgene encoding (i) hCas9 endonuclease, (ii) T1 guide RNA (gRNA) targeting the doublesex locus, and (iii) DsRed fluorescent marker protein, in genetically-modified mosquitoes (GMMs). Problem formulation, the first stage of ERA, for environmental releases of dsxFCRISPRh previously identified nine potential harms to the environment or health that could occur, should expressed products of the transgene cause allergenicity or toxicity.

METHODS

Amino acid sequences of hCas9 and DsRed were interrogated against those of toxins or allergens from NCBI, UniProt, COMPARE and AllergenOnline bioinformatic databases and the gRNA was compared with microRNAs from the miRBase database for potential impacts on gene expression associated with toxicity or allergenicity. PubMed was also searched for any evidence of toxicity or allergenicity of Cas9 or DsRed, or of the donor organisms from which these products were originally derived.

RESULTS

While Cas9 nuclease activity can be toxic to some cell types in vitro and hCas9 was found to share homology with the prokaryotic toxin VapC, there was no evidence from previous studies of a risk of toxicity to humans and other animals from hCas9. Although hCas9 did contain an 8-mer epitope found in the latex allergen Hev b 9, the full amino acid sequence of hCas9 was not homologous to any known allergens. Combined with a lack of evidence in the literature of Cas9 allergenicity, this indicated negligible risk to humans of allergenicity from hCas9. No matches were found between the gRNA and microRNAs from either Anopheles or humans. Moreover, potential exposure to dsxFCRISPRh transgenic proteins from environmental releases was assessed as negligible.

CONCLUSIONS

Bioinformatic and literature assessments found no convincing evidence to suggest that transgenic products expressed from dsxFCRISPRh were allergens or toxins, indicating that environmental releases of this population suppression gene drive for malaria vector control should not result in any increased allergenicity or toxicity in humans or animals. These results should also inform evaluations of other GMMs being developed for vector control and in vivo clinical applications of CRISPR-Cas9.

Trends in ingredients added to infant formula: FDA's experiences in the GRAS notification program

While human milk is considered the optimal source of nutrition for infants for the first six and twelve months of age, with continued benefit of breastfeeding with complementary foods, a safe alternative, nutritionally adequate to support infant growth and development, is necessary. In the United States, the Food and Drug Administration (FDA) establishes the requirements necessary to demonstrate the safety of infant formula within the framework of the Federal Food, Drug, and Cosmetic Act. FDA's Center for Food Safety and Applied Nutrition/Office of Food Additive Safety evaluates the safety and lawfulness of individual ingredients used in infant formula, whereas the Office of Nutrition and Food Labeling oversees the safety of infant formula. Most infant formula ingredients are either from sources with history of safe consumption by infants or are like components in human milk. Information demonstrating the regulatory status of all ingredients is required in submissions for new infant formulas, and ingredient manufacturers often use the Generally Recognized as Safe (GRAS) Notification program to establish ingredient regulatory status. We provide an overview of ingredients used in infant formula evaluated through the GRAS Notification program to highlight trends and discuss the data and information used to reach these GRAS conclusions.

The Amazonian Tropical Bites Research Initiative, a hope for resolving zoonotic neglected tropical diseases in the One Health era

BACKGROUND

Neglected tropical diseases (NTDs) disproportionately affect populations living in resource-limited settings. In the Amazon basin, substantial numbers of NTDs are zoonotic, transmitted by vertebrate (dogs, bats, snakes) and invertebrate species (sand flies and triatomine insects). However, no dedicated consortia exist to find commonalities in the risk factors for or mitigations against bite-associated NTDs such as rabies, snake envenoming, Chagas disease and leishmaniasis in the region. The rapid expansion of COVID-19 has further reduced resources for NTDs, exacerbated health inequality and reiterated the need to raise awareness of NTDs related to bites.

METHODS

The nine countries that make up the Amazon basin have been considered (Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Surinam and Venezuela) in the formation of a new network.

RESULTS

The Amazonian Tropical Bites Research Initiative (ATBRI) has been created, with the aim of creating transdisciplinary solutions to the problem of animal bites leading to disease in Amazonian communities. The ATBRI seeks to unify the currently disjointed approach to the control of bite-related neglected zoonoses across Latin America.

CONCLUSIONS

The coordination of different sectors and inclusion of all stakeholders will advance this field and generate evidence for policy-making, promoting governance and linkage across a One Health arena.

ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning

MOTIVATION

Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from therapeutic peptides is their toxicity toward human cells, and few available algorithms for predicting toxicity are specially designed for short-length peptides.

RESULTS

We present ToxIBTL, a novel deep learning framework by utilizing the information bottleneck principle and transfer learning to predict the toxicity of peptides as well as proteins. Specifically, we use evolutionary information and physicochemical properties of peptide sequences and integrate the information bottleneck principle into a feature representation learning scheme, by which relevant information is retained and the redundant information is minimized in the obtained features. Moreover, transfer learning is introduced to transfer the common knowledge contained in proteins to peptides, which aims to improve the feature representation capability. Extensive experimental results demonstrate that ToxIBTL not only achieves a higher prediction performance than state-of-the-art methods on the peptide dataset, but also has a competitive performance on the protein dataset. Furthermore, a user-friendly online web server is established as the implementation of the proposed ToxIBTL.

AVAILABILITY AND IMPLEMENTATION

The proposed ToxIBTL and data can be freely accessible at http://server.wei-group.net/ToxIBTL. Our source code is available at https://github.com/WLYLab/ToxIBTL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural and functional characterization of Mpp75Aa1.1, a putative beta-pore forming protein from Brevibacillus laterosporus active against the western corn rootworm

The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, is a major corn pest of significant economic importance in the United States. The continuous need to control this corn maize pest and the development of field-evolved resistance toward all existing transgenic maize (Zea mays L.) expressing Bacillus thuringiensis (Bt) insecticidal proteins against WCR has prompted the development of new insect-protected crops expressing distinct structural classes of insecticidal proteins. In this current study, we describe the crystal structure and functional characterization of Mpp75Aa1.1, which represents the first corn rootworm (CRW) active insecticidal protein member of the ETX_MTX2 sub-family of beta-pore forming proteins (β-PFPs), and provides new and effective protection against WCR feeding. The Mpp75Aa1.1 crystal structure was solved at 1.94 Å resolution. The Mpp75Aa1.1 is processed at its carboxyl-terminus by WCR midgut proteases, forms an oligomer, and specifically interacts with putative membrane-associated binding partners on the midgut apical microvilli to cause cellular tissue damage resulting in insect death. Alanine substitution of the surface-exposed amino acids W206, Y212, and G217 within the Mpp75Aa1.1 putative receptor binding domain I demonstrates that at least these three amino acids are required for WCR activity. The distinctive spatial arrangement of these amino acids suggests that they are part of a receptor binding epitope, which may be unique to Mpp75Aa1.1 and not present in other ETX_MTX2 proteins that do not have WCR activity. Overall, this work establishes that Mpp75Aa1.1 shares a mode of action consistent with traditional WCR-active Bt proteins despite significant structural differences.

PathFams: statistical detection of pathogen-associated protein domains

Background

A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal.

Results

To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors.

Conclusions

We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at https://pathfams.uwaterloo.ca.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07982-8.

Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi

Plant diseases caused by fungal pathogens are typically initiated by molecular interactions between 'effector' molecules released by a pathogen and receptor molecules on or within the plant host cell. In many cases these effector-receptor interactions directly determine host resistance or susceptibility. The search for fungal effector proteins is a developing area in fungal-plant pathology, with more than 165 distinct confirmed fungal effector proteins in the public domain. For a small number of these, novel effectors can be rapidly discovered across multiple fungal species through the identification of known effector homologues. However, many have no detectable homology by standard sequence-based search methods. This study employs a novel comparison method (RemEff) that is capable of identifying protein families with greater sensitivity than traditional homology-inference methods, leveraging a growing pool of confirmed fungal effector data to enable the prediction of novel fungal effector candidates by protein family association. Resources relating to the RemEff method and data used in this study are available from https://figshare.com/projects/Effector_protein_remote_homology/87965.

Proteomic Analysis of the Venom of Jellyfishes Rhopilema esculentum and Sanderia malayensis

Venomics, the study of biological venoms, could potentially provide a new source of therapeutic compounds, yet information on the venoms from marine organisms, including cnidarians (sea anemones, corals, and jellyfish), is limited. This study identified the putative toxins of two species of jellyfish—edible jellyfish Rhopilema esculentum Kishinouye, 1891, also known as flame jellyfish, and Amuska jellyfish Sanderia malayensis Goette, 1886. Utilizing nano-flow liquid chromatography tandem mass spectrometry (nLC–MS/MS), 3000 proteins were identified from the nematocysts in each of the above two jellyfish species. Forty and fifty-one putative toxins were identified in R. esculentum and S. malayensis, respectively, which were further classified into eight toxin families according to their predicted functions. Amongst the identified putative toxins, hemostasis-impairing toxins and proteases were found to be the most dominant members (>60%). The present study demonstrates the first proteomes of nematocysts from two jellyfish species with economic and environmental importance, and expands the foundation and understanding of cnidarian toxins.

Repurposing approved drugs on the pathway to novel therapies

The time and cost of developing new drugs have led many groups to limit their search for therapeutics to compounds that have previously been approved for human use. Many "repurposed" drugs, such as derivatives of thalidomide, antibiotics, and antivirals have had clinical success in treatment areas well beyond their original approved use. These include applications in treating antibiotic-resistant organisms, viruses, cancers and to prevent burn scarring. The major theoretical justification for reusing approved drugs is that they have known modes of action and controllable side effects. Coadministering antibiotics with inhibitors of bacterial toxins or enzymes that mediate multidrug resistance can greatly enhance their activity. Drugs that control host cell pathways, including inflammation, tumor necrosis factor, interferons, and autophagy, can reduce the "cytokine storm" response to injury, control infection, and aid in cancer therapy. An active compound, even if previously approved for human use, will be a poor clinical candidate if it lacks specificity for the new target, has poor solubility or can cause serious side effects. Synergistic combinations can reduce the dosages of the individual components to lower reactivity. Preclinical analysis should take into account that severely ill patients with comorbidities will be more sensitive to side effects than healthy trial subjects. Once an active, approved drug has been identified, collaboration with medicinal chemists can aid in finding derivatives with better physicochemical properties, specificity, and efficacy, to provide novel therapies for cancers, emerging and rare diseases.

Predicting bacterial virulence factors - evaluation of machine learning and negative data strategies

Bacterial proteins dubbed virulence factors (VFs) are a highly diverse group of sequences, whose only obvious commonality is the very property of being, more or less directly, involved in virulence. It is therefore tempting to speculate whether their prediction, based on direct sequence similarity (seqsim) to known VFs, could be enhanced or even replaced by using machine-learning methods. Specifically, when trained on a large and diverse set of VFs, such may be able to detect putative, non-trivial characteristics shared by otherwise unrelated VF families and therefore better predict novel VFs with insignificant similarity to each individual family. We therefore first reassess the performance of dimer-based Support Vector Machines, as used in the widely used MP3 method, in light of seqsim-only and seqsim/dimer-hybrid classifiers. We then repeat the analysis with a novel, considerably more diverse data set, also addressing the important problem of negative data selection. Finally, we move on to the real-world use case of proteome-wide VF prediction, outlining different approaches to estimating specificity in this scenario. We find that direct seqsim is of unparalleled importance and therefore should always be exploited. Further, we observe strikingly low correlations between different feature and classifier types when ranking proteins by VF likeness. We therefore propose a 'best of each world' approach to prioritize proteins for experimental testing, focussing on the top predictions of each classifier. Further, classifiers for individual VF families should be developed.

Discovery of novel bacterial toxins by genomics and computational biology

Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes.