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Updated: Aug 24, 2026

Synthesis and Structure Determination of µ-Conotoxin PIIIA Isomers with Different Disulfide Connectivities
Published on: October 2, 2018
Prediction of disulfide-bonded cysteines in proteomes with a hidden neural network
Pier Luigi Martelli1, Piero Fariselli, Rita Casadio
1Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna, Italy.
Abstract:
A hidden neural network-based method is used to predict the bonding state of cysteines starting from the residue sequence of the protein chain. The method scores as high as 89% and 86% per cysteine residue and per protein, respectively, and in this overcomes other predictors of the same category. We then explore the efficacy of our predictor in computing the disulfide content of the whole proteome of Escherichia coli (K12 and O157), Aeropirum pernix, Thermotoga maritima, and Homo sapiens. We find that the percentage of extracellular disulfide containing proteins is higher than that of intracellular one, and that the human proteome is by far the one with the highest content of sulfur-sulfur linkages in proteins.
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