Predicting subcellular localization of proteins using machine-learned classifiers.

MOTIVATION: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. RESULTS: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.

Acetaminophen toxicosis in a cat.

A seven month old domestic shorthaired male cat was presented with a known history of acetaminophen ingestion. Clinical findings included icterus, depression, hypothermia, tachypnea and pronounced edema of the head and neck. Treatment was aimed at providing substrate to assist in conjugation of the drug and reversing methemoglobinemia. Administration of oral acetylcysteine, ascorbic acid and IV fluids was insufficient in this case due to a delay in initiation of treatment. The salient postmortem findings were icterus, subcutaneous and pulmonary edema and evidence of hemolysis in the liver, spleen and urinary tract.The pathophysiology of the toxicosis and the current recommendations for treatment are reviewed.