DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning

Background: Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequence is still very challenging and largely unsolved. Results: We developed a new method to integrate the classification power of machine learning with evolutionary signals embedded in protein families in order to improve protein domain boundary prediction. The method first extracts putative domain boundary signals from a multiple sequence alignment between a query sequence and its homologs. The putative sites are then classified and scored by support vector machines in conjunction with input features such as sequence profiles, secondary structures, solvent accessibilities around the sites and their positions. The method was evaluated on a domain benchmark by 10-fold cross-validation and 60% of true domain boundaries can be recalled at a precision of 60%. The trade-off between the precision and recall can...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Jesse Eickholt, Xin Deng, Jianlin Cheng
• Keywords: Artificial Intelligence, BMCBI 2011, Domain Boundary, Protein, Protein Domains