Computed Protonation Properties: Unique Capabilities for Protein Functional Site Prediction

Leonel F. Murga[1],[2] (
Ying Wei[1] (
Mary Jo Ondrechen[1] (

[1]Department of Chemistry & Chemical Biology and Institute for Complex Scientific Software, Northeastern University, Boston, MA 02115 USA
[2]Present Address: Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454 USA


Prediction of protein functional sites from 3D structure is an important problem, particularly as structural genomics projects produce hundreds of structures of unknown function, including novel folds and the structures of orphan sequences. The present paper shows how computed protonation properties provide unique and powerful capabilities for the prediction of catalytic sites from the 3D structure alone. These protonation properties of the ionizable residues in a protein may be computed from the 3D structure using the calculated electrical potential function. In particular, the shapes of the theoretical microscopic titration curves (THEMATICS) enable selection of the residues involved in catalysis or small molecule recognition with good sensitivity and precision. Results are shown for 169 annotated enzymes in the Catalytic Site Atlas (CSA). Performance, as measured by residue recall and precision, is clearly better than that of other 3D-structure-based methods. When compared with methods based on sequence alignments and structural comparisons, THEMATICS performance is competitive for well-characterized enzymes. However THEMATICS performance does not degrade in the absence of similarity, as do the alignment-based methods, even if there are few or no sequence homologues or few or no proteins of similar structure. It is further shown that the protonation properties perform well on open, unbound structures, even if there is substantial conformational change upon ligand binding.

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Japanese Society for Bioinformatics