NDLI: Improved Prediction of the Number of Residue Contacts in Proteins by Recurrent Neural Networks (2001)

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Improved Prediction of the Number of Residue Contacts in Proteins by Recurrent Neural Networks (2001)

Content Provider	CiteSeerX
Author	Pollastri, Gianluca Casadio, Rita Baldi, Pierre Fariselli, Pietro
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 (6 A to 12 A). 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. Keywords: protein structure prediction, protein contacts, contact map, recurrent neural networks, solvent accessibility, evolutionary information.
File Format	PDF
Journal	Bioinformatics
Publisher Date	2001-01-01
Access Restriction	Open
Subject Keyword	Contact Number Residue Distribution Remote Homology Search Protein Structure Prediction Previous Method Numerous State Different State Protein Structure Large Corpus Contact Prediction Protein Folding Evolutionary Information Recurrent Neural Network Solvent Accessibility Residue Contact Different Radius Predictor Bi-directional Recurrent Neural Network Architecture Average Value Protein Contact Base Line Statistical Predictor Ensemble Achieves Performance Contact Map
Content Type	Text

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