title: Predicting protein disorder by analyzing amino acid sequence.
abstract: BACKGROUND
  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, along with roles in diseases characterized by protein misfolding and aggregation.
  
  
  RESULTS
  Identifying IUP is important task in structural and functional genomics. We exact useful features from sequences and develop machine learning algorithms for the above task. We compare our IUP predictor with PONDRs (mainly neural-network-based predictors), disEMBL (also based on neural networks) and Globplot (based on disorder propensity).
  
  
  CONCLUSIONS
  We find that augmenting features derived from physiochemical properties of amino acids (such as hydrophobicity, complexity etc.) and using ensemble method proved beneficial. The IUP predictor is a viable alternative software tool for identifying IUP protein regions and proteins.
authors.affiliation: Harvard Medical School, Harvard University, Cambridge, MA 02115, USA. jyang@bwh.Harvard.edu
authors.forename: Jack Y
authors.initials: JY
authors.lastname: Yang
authors.forename: Mary Qu
authors.initials: MQ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D008958:Models, Molecular; D010363:Pattern Recognition, Automated / Q000379:methods; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins