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| Format: | Artículo científico |
| Sprache: | en |
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ACS chemical neuroscience
2025
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| Online-Zugang: | https://pubmed.ncbi.nlm.nih.gov/40441695/ |
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| author | Do, Hung Nguyen Kubicek-Sutherland, Jessica Z Gnanakaran, Sandrasegaram |
| author_facet | Do, Hung Nguyen Kubicek-Sutherland, Jessica Z Gnanakaran, Sandrasegaram Do, Hung Nguyen Kubicek-Sutherland, Jessica Z Gnanakaran, Sandrasegaram |
| collection | PubMed - marine biology |
| contents | Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning. Do, Hung Nguyen Kubicek-Sutherland, Jessica Z Gnanakaran, Sandrasegaram Conotoxins Humans Receptors, Nicotinic Supervised Machine Learning Machine Learning Conus Snail Protein Binding Amino Acid Sequence Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is with fatality rates of up to ∼65% from a single sting, which is caused mostly by the activity of α-conotoxins against human nicotinic acetylcholine receptors (nAChRs). While sequence-based machine learning (ML) classifiers have been trained to identify targets of conotoxins binding voltage-gated ion channels, no ML model has been built to predict the subtype-specific nAChR targets of α-conotoxins. Here, we trained an ML model in a semi-supervised manner to predict the specificity of α-conotoxin binding toward different human nAChR subtypes to overcome the challenge of limited data in subtype-specific nAChR targets of α-conotoxins and the issue that one α-conotoxin can bind multiple nAChR subtypes with high selectivity. We considered additional features of sequences of α-conotoxins in training our ML model, including the secondary structure propensities and electrostatic properties, which resulted in better prediction capability for the ML model. Notably, we identify that most α-conotoxins bind to α3β2, α1γδ, and α7 subtypes of human nAChRs. Our findings from this study provide a framework for predicting targets of various kinds of toxins. |
| format | Artículo científico |
| id | pubmed_40441695 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | ACS chemical neuroscience |
| record_format | pubmed |
| spellingShingle | Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning. Do, Hung Nguyen Kubicek-Sutherland, Jessica Z Gnanakaran, Sandrasegaram Conotoxins Humans Receptors, Nicotinic Supervised Machine Learning Machine Learning Conus Snail Protein Binding Amino Acid Sequence Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning. Do, Hung Nguyen Kubicek-Sutherland, Jessica Z Gnanakaran, Sandrasegaram Conotoxins Humans Receptors, Nicotinic Supervised Machine Learning Machine Learning Conus Snail Protein Binding Amino Acid Sequence Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is with fatality rates of up to ∼65% from a single sting, which is caused mostly by the activity of α-conotoxins against human nicotinic acetylcholine receptors (nAChRs). While sequence-based machine learning (ML) classifiers have been trained to identify targets of conotoxins binding voltage-gated ion channels, no ML model has been built to predict the subtype-specific nAChR targets of α-conotoxins. Here, we trained an ML model in a semi-supervised manner to predict the specificity of α-conotoxin binding toward different human nAChR subtypes to overcome the challenge of limited data in subtype-specific nAChR targets of α-conotoxins and the issue that one α-conotoxin can bind multiple nAChR subtypes with high selectivity. We considered additional features of sequences of α-conotoxins in training our ML model, including the secondary structure propensities and electrostatic properties, which resulted in better prediction capability for the ML model. Notably, we identify that most α-conotoxins bind to α3β2, α1γδ, and α7 subtypes of human nAChRs. Our findings from this study provide a framework for predicting targets of various kinds of toxins. |
| title | Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning. |
| topic | Conotoxins Humans Receptors, Nicotinic Supervised Machine Learning Machine Learning Conus Snail Protein Binding Amino Acid Sequence |
| url | https://pubmed.ncbi.nlm.nih.gov/40441695/ |