Prediction of Protein Aggregation Propensities using GOR Method

Abstract

The original GOR method published by Garnier, Osguthorpe, and Robson in 1978 was one of the first successful methods to predict protein secondary structure from amino acid sequence [1]. The method is based on information theory. The analysis of frequencies of occurrence of secondary structure for singlets and doublets of residues in a protein database enables prediction of secondary structure for new amino acid sequences. Because of these simple physical assumptions and the ability to predict the probability of formation of beta-strands for each residue in protein sequence GOR method has a conceptual advantage over other later developed methods such as PHD, PSIPRED, and others that are based on Machine Learning methods that have a “black box” nature. The GOR method has been continuously improved and modified for 30 years with the last GOR V version published in 2002 [2], its modification in 2006 [3] and the GOR V server developed in 2005 [4]. Recently we applied GOR method to chameleon sequences in protein folding simulations, and for prediction of protein aggregation propensities [5]. Fibril prone regions of polypeptides were predicted by estimation the probability to form beta-strand secondary structure. The probability of amino acid to form beta-strand corresponds to its aggregation propensity. Our preliminary studies show that the GOR method is a promising and efficient alternative to other protein aggregation predicting tools. A web server for prediction of protein aggregation propensities is currently being developed.