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Laccases (E.C. are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production (Osma, Toca-Herrera et al. 2010, Piscitelli, Pezzella et al. 2010). Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols (Giardina, Faraco et al. 2010, Reiss, Ihssen et al. 2013). Despite acting on a wide range of compounds as a family, individual laccases often exhibit distinctive and varied substrate ranges (Reiss, Ihssen et al. 2013). This is likely due to laccases being treated as one type of enzyme when they are actually involved in many metabolic roles across diverse taxa such as fungi, plants, insects, and bacteria (Messerschmidt 1997, Alexandre and Zhulin 2000, Mayer and Staples 2002, Dittmer, Suderman et al. 2004). Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges or enzyme properties. It has been suggested that the roles and substrates of various laccases are related to their optimal pH. This is consistent with the observation that fungal laccases usually prefer acidic conditions, whereas plant and bacterial laccases prefer basic conditions (Reiss, Ihssen et al. 2013). We hypothesize that a descriptor based supervised learning can provide a better classification system of lacasses. A precedent for such an attempt has been demonstrated for feruloyl esterases (Udatha, Kouskoumvekaki et al. 2011).