From Protein Structure to Function with Bioinformatics.pdf

210 E.C. Meng et al.8.5 Docking for Functional AnnotationUltimately, the ligand specificity and catalytic capabilities of a protein depend onthe arrangement of atoms in its binding or active site(s). While 3D motif approachesseek to associate spatial patterns of atoms directly with functions, one can imagineusing the structure of a protein to select likely ligands, then using the resulting predictionsof binding specificity to help infer the protein’s molecular function. Incomputational docking, many small organic molecule structures are each positionedwithin a protein’s binding site and scored by complementarity. Hundreds tothousands of poses may be evaluated for each compound. The top-scoring moleculesfrom a database search are predicted as the most likely to bind the protein andthe best candidates for testing experimentally.This process of computational molecular recognition may sound straightforward,but in practice there are many challenges. One is the large number of metabolitesthat may need to be considered as potential ligands. Another is the difficultyof evaluating structural complementarity rapidly yet accurately enough to distinguishtrue ligands from similar structures that do not bind. For accurate ranking, itmay be necessary to allow conformational flexibility during docking, furtherincreasing the computational burden. Traditionally, database docking has beenapplied to the discovery of drug leads. The need for accurate rankings is evengreater in functional annotation than in lead discovery, because screening experimentallyfor an unknown catalytic activity is much more difficult than screening forbinding, and because the goal is to annotate thousands of proteins in an automatedor semi-automated fashion. By contrast, lead discovery applications typically focuson one or a few well-characterized targets.Despite these challenges, this approach has recently begun to receive considerableattention (Macchiarulo et al. 2004; Paul et al. 2004; Kalyanaraman et al. 2005;Tyagi and Pleiss 2006; Favia et al. 2008). In two published examples of functionprediction by docking (Hermann et al. 2007; Song et al. 2007), the protein of interestwas first identified as a member of a functionally diverse superfamily ofenzymes. This allowed narrowing the search space of potential substrates. However,different approaches were used in each study to obtain accurate rankings of thedocked molecules.One study focused on a family of proteins that, based on sequence information,could be recognized as members of the enolase superfamily but not reliablyannotated with more detailed functions (Song et al. 2007). Members of theenolase superfamily all catalyze abstraction of a proton from a carbon adjacent toa carboxylate group, but their substrates and overall reactions vary significantly(Babbitt and Gerlt 2000). As no experimentally determined structures were availablefor the family, a homology model was constructed for one of the sequencesbased on the most similar known structure (35% sequence identity), an Ala-Gluepimerase. N-succinyl amino acid racemases also clustered near the unknownfamily in sequence similarity space, so dipeptides and N-succinyl amino acids wereincluded in the libraries for computational and experimental screening. Despite