DiANNA 1.1: An Extension of the DiANNA Web Server for Ternary Cysteine Classification

Abstract

DiANNA is a recent state-of-the-art artificial neural network and web server, which determines the cysteine oxidation state and disulfide connectivity of a protein, given only its amino acid sequence. Version 1.0 of DiANNA uses a feed-forward neural network to determine which cysteines are involved in a disulfide bond, and employs a novel architec-ture neural network to predict which half-cystines are covalently bound to which other half-cystines. In version 1.1 of DiANNA, described here, we extend functionality by applying a support vector machine with spectrum kernel for the cysteine clas-sification problem—to determine whether a cysteine is reduced (free in sulfhydryl state), half-cystine (involved in a disulfide bond) or bound to a metallic ligand. In the latter case,DiANNApredicts the ligand among iron, zinc, cadmium and carbon