title: Mining alpha-helix-forming molecular recognition features with cross species sequence alignments.
abstract: Previously described algorithms for mining alpha-helix-forming molecular recognition elements (MoREs), described by Oldfield et al. (Oldfield, C. J., Cheng, Y., Cortese, M. S., Brown, C. J., Uversky, V. N., and Dunker, A. K. (2005) Comparing and combining predictors of mostly disordered proteins, Biochemistry 44, 1989-2000), also known as molecular recognition features (MoRFs) (Mohan, A., Oldfield, C. J., Radivojac, P., Vacic, V., Cortese, M. S., Dunker, A. K., and Uversky, V. N. (2006) Analysis of Molecular Recognition Features (MoRFs), J. Mol. Biol. 362, 1043-1059), revealed that regions undergoing disorder-to-order transition are involved in many molecular recognition events and are crucial for protein-protein interactions. However, these algorithms were developed using a training data set of a limited size. Here we propose to improve the prediction algorithms by (1) including additional alpha-MoRF examples and their cross species homologues in the positive training set, (2) carefully extracting monomer structure chains from the Protein Data Bank (PDB) as the negative training set, (3) including attributes from recently developed disorder predictors, secondary structure predictions, and amino acid indices, and (4) constructing neural network based predictors and performing validation. Over 50 regions which undergo disorder-to-order transition that were identified in the PDB together with a set of corresponding cross species homologues of each structure-based example were included in a new positive training set. Over 1500 attributes, including disorder predictions, secondary structure predictions, and amino acid indices, were evaluated by the conditional probability method. The top attributes, including VSL2 and VL3 disorder predictions and several physicochemical propensities of amino acid residues, were used to develop the feed forward neural networks. The sensitivity, specificity, and accuracy of the resulting predictor, alpha-MoRF-PredII, were 0.87 +/- 0.10, 0.87 +/- 0.11, and 0.87 +/- 0.08 over 10 cross validations, respectively. We present the results of these analyses and validation examples to discuss the potential improvement of the alpha-MoRF-PredII prediction accuracy.
authors.affiliation: Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
authors.forename: Yugong
authors.initials: Y
authors.lastname: Cheng
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Oldfield
authors.forename: Jingwei
authors.initials: J
authors.lastname: Meng
authors.forename: Pedro
authors.initials: P
authors.lastname: Romero
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Uversky
authors.forename: A Keith
authors.initials: AK
authors.lastname: Dunker
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D030562:Databases, Protein; D006801:Humans; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D016415:Sequence Alignment; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins