Prediction of Specificity of α‑Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning

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 Conus geographus, 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.