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Open Access Highly Accessed Research article

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

Jesse Eickholt1, Xin Deng1 and Jianlin Cheng123*

Author Affiliations

1 Department of Computer Science, University of Missouri, Columbia, MO 65211, USA

2 Informatics Institute, University of Missouri, Columbia, MO 65211, USA

3 C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA

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BMC Bioinformatics 2011, 12:43  doi:10.1186/1471-2105-12-43

Published: 1 February 2011



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.


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 be adjusted according to specific needs by using different decision thresholds on the domain boundary scores assigned by the support vector machines.


The good prediction accuracy and the flexibility of selecting domain boundary sites at different precision and recall values make our method a useful tool for protein structure determination and modelling. The method is available at webcite.