Many protein regions and some entire proteins have no definite tertiary structure, presenting instead as dynamic, disorder ensembles under different physiochemical circumstances. These proteins and regions are known as Intrinsically Unstructured Proteins (IUP). IUP have been associated with a wide range of protein functions and play essential roles in diseases characterized by protein misfolding and aggregation. Identifying IUP is an important but difficult task in today?s structural and functional genomics. We exact useful features from polypeptide sequences and develop machine learning algorithms for the above task. We compare our IUP predictor with PONDR ? (mainly neural-network-based predictors), disEMBL (also based on neural networks) and Globplot (based on IUP propensity). We find that augmenting features derived from physiochemical properties of amino acids (such as 9-gram encoding scheme and hydrophobicity) and using ensemble methods proved beneficial. The IUP predictor is a viable alternative software tool for identifying IUP regions and proteins.
['Mary Qu Yang', 'Yang J']
IUP: Intrinsically Unstructured Protein predictor - A software tool for analyzing polypeptide sequences