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a bioinformatics approach for accurate prediction of type VI secreted effectors

Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data.

We extracted a wide range of features from T6SE protein sequences and comprehensively analyzed the prediction performance of these features through unsupervised and supervised learning. By integrating these features, we subsequently developed a two-layer SVM-based ensemble model with fine-grain optimized parameters, to identify potential T6SEs.

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If you find our work useful for your research work, please cite:
Bastion6 Development Team
Lithgow Group
Infection and Immunity Program
Biomedicine Discovery Institute
Faculty of Medicine, Nursing and Health Sciences
Monash University
Melbourne, VIC 3800, Australia
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