Sequence and structure are complementary pieces of information that can be used to infer protein function. We study and compare sequence, structure and sequence-structure integrative kernels to recognize proteins with enzymatic function. Using a support-vector machine, we show that kernels that combine sequence and structure information typically perform better (AUC 0.73) at this task than kernels that exploit either type of information exclusively. We find that the feature space of structure kernels complements that of sequence kernels, making both sources of similarity more accessible to kernel methods
['Isye Arieshanti', 'Mikael Bodén', 'Stefan Maetschke', 'Fabian A. Buske']
Detecting sequence and structure homology via an integrative kernel: A case-study in recognizing enzymes