Rational attenuation of elapid long-chain neurotoxin by targeted residue substitution and disulfide architecture simplification: implications for receptor interaction and immune recognition

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  Rational attenuation of elapid long-chain neurotoxin by targeted residue substitution and disulfide architecture simplification: implications for receptor interaction and immune recognition Abstract Elapid long-chain neurotoxins (LNTxs) exert their toxicity through high-affinity binding to nicotinic acetylcholine receptors (nAChRs). Specific residues mediate this process, including basic (Lys23, Arg33, Arg36, Lys49), structural (Cys26, Cys30), and aromatic (Phe29) amino acids. To investigate the functional and immunogenic implications of modifying these residues, we designed two recombinant variants of a consensus LNTx. The first variant, rLcNTx V1, contains charge-reversal mutations at four key basic positions, while the second, rLcNTx V2, lacks the segment corresponding to the fifth disulfide bond. We expressed both variants in Escherichia coli Origami cells. rLcNTx V1 was recovered in a soluble form, but rLcNTx V2 was present only in inclusion bodies. We assessed the binding ...

MultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification

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MultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification

Abstract

Understanding the structural and functional diversity of toxin proteins is critical for elucidating macromolecular behavior, mechanistic variability, and structure-driven bioactivity. Traditional approaches have primarily focused on binary toxicity prediction, offering limited resolution into distinct modes of action of toxins. Here, we present MultiTox, an ensemble stacking framework for the classification of toxin proteins based on their molecular mode of action: neurotoxins, cytotoxins, hemotoxins, and enterotoxins. We curated a comprehensive dataset of 24,756 proteins (20,361 toxins and 4395 non-toxins) and extracted high-dimensional ESM-2 embeddings that encode evolutionary, structural, and biochemical features. The two-tier stacking framework integrates LGBM, MLP, ET, KNN, and QDA as base classifiers and XGBoost as a meta classifier. MultiTox achieved an overall accuracy of 91.07 %, an F1-score of 90.73 %, and a Matthews Correlation Coefficient (MCC) of 91.61 %. Class-wise accuracies were 93.75 % (neurotoxins), 87.79 % (cytotoxins), 98.80 % (hemotoxins), 97.02 % (enterotoxins), and 95.83 % (toxins vs. non-toxins). SHAP-based interpretation and correlation with known physicochemical descriptors revealed class-specific features linked to biologically meaningful patterns in structural motifs, hydrophobicity, and solvent accessibility. Functional annotations using InterProScan, clusters of orthologs, and secretion signal analysis identified toxin class-specific signatures related to folding, localization, and host interactions. We deployed a public web server (https://cosylab.iiitd.edu.in/multitox/) for real-time and batch-mode predictions. MultiTox provides a scalable and biologically interpretable framework for protein classification, bridging sequence data with functional insights.
Sharma, H., Thakur, M. S., Barala, A., Khan, M. S., Bhagat, S., & Bagler, G. (2025). MultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification. International Journal of Biological Macromolecules, 327, 147399. https://doi.org/10.1016/j.ijbiomac.2025.147399