Prediction of the Number of Residue Contacts in Proteins

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 di erent 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 (6A to 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 di erent radius predictors further improves the performance.

• Type: Conference
• Year: 2000
• Where: ISMB
• Authors: Piero Fariselli, Rita Casadio
• Keywords: Computational Biology, Ensemble Achieves Performances, Erent Radius Predictors, ISMB 2000, Residue Contacts