Discrimination of insoluble-carbohydrate binding proteins and their binding sites using a 3D motif detection method

We apply a 3D motif detection approach to insoluble-carbohydrate binding modules (CBMs), a class of proteins that share a common binding activity but lack a common sequence motif or fold. Key features of insoluble-carbohydrate binding sites were incorporated into a 3D motif detection algorithm, and used in a linear discriminant analysis. Our method effectively discriminated all known type A CBMs from a non-redundant structural dataset and correctly detected known binding sites without any false positives. The algorithm was used to screen a structural database of homology-modeled proteins from tobacco, and the results were experimentally validated using an affinity purification assay and mass spectrometric protein identification. The algorithm correctly predicted CBMs and binding sites not included in the training set, and predicted a previously unknown binding site in the PR-5d protein family. This work highlights the potential of 3D motif detection methods for use in large-scale functional annotation.