title: Improved prediction of the number of residue contacts in proteins by recurrent neural networks.
abstract: Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searches. Here we use an ensemble of bi-directional recurrent neural network architectures and evolutionary information to improve the state-of-the-art in contact prediction using a large corpus of curated data. The ensemble is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. The ensemble achieves performances ranging from 70.1% to 73.1% depending on the radius adopted to discriminate contacts (6Ato 12A). These performances represent gains of 15% to 20% over the base line statistical predictors always assigning an aminoacid to the most numerous state, 3% to 7% better than any previous method. Combination of different radius predictors further improves the performance. SERVER: http://promoter.ics.uci.edu/BRNN-PRED/.
authors.affiliation: Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA.
authors.forename: G
authors.initials: G
authors.lastname: Pollastri
authors.forename: P
authors.initials: P
authors.lastname: Baldi
authors.forename: P
authors.initials: P
authors.lastname: Fariselli
authors.forename: R
authors.initials: R
authors.lastname: Casadio
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein; D015394:Molecular Structure; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins