MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information

作者：Pai Priyadarshini P; Mondal Sukanta^*

来源：Journal of Biomolecular Structure & Dynamics, 2016, 34(10): 2069-2083.

DOI：10.1080/07391102.2015.1106978

• position-specific scoring matrix
• consensus approach
• 10-fold cross-validation
• first-line host defense
• imbalanced benchmark data set
• random forest
• support vector machines

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein-mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

• 出版日期2016