On the use of structure and sequence-based features for protein classification and retrieval

The need to retrieve or classify proteins using structure or sequence-based similarity underlies many biomedical applications. In drug discovery, researchers search for proteins that share specific chemical properties as sources for new treatment. With folding simulations, similar intermediate structures might be indicative of a common folding pathway. Here we present two normalized, stand-alone representations of proteins that enable fast and efficient object retrieval based on sequence or structure. To create our sequence-based representation, we take the profiles returned by the PSI-BLAST alignment algorithm and create a normalized summary using a discrete wavelet transform. For our structural representation, we transform each 3D structure into a normalized 2D distance matrix and apply a 2D wavelet decomposition to generate our descriptor. We also create a hybrid representation by concatenating together the above descriptors. We evaluate the generality of our models by using them as indices for database retrieval experiments as well as feature vectors for classification. We find that our methods provide excellent performance when compared with the state-of-the-art for each task. Our results show that the sequence-based representation is generally superior to the structure-based representation and that in the classification context, the hybrid strategy affords a significant improvement over sequence or structure.