Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.

Efficient computation of root mean square deviations under rigid transformations.

The computation of root mean square deviations (RMSD) is an important step in many bioinformatics applications. If approached naively, each RMSD computation takes time linear in the number of atoms. In addition, a careful implementation is required to achieve numerical stability, which further increases runtimes. In practice, the structural variations under consideration are often induced by rigid transformations of the protein, or are at least dominated by a rigid component. In this work, we show how RMSD values resulting from rigid transformations can be computed in constant time from the protein's covariance matrix, which can be precomputed in linear time. As a typical application scenario is protein clustering, we will also show how the Ward-distance which is popular in this field can be reduced to RMSD evaluations, yielding a constant time approach for their computation.

On the applicability of elastic network normal modes in small-molecule docking.

Incorporating backbone flexibility into protein-ligand docking is still a challenging problem. In protein-protein docking, normal mode analysis (NMA) has become increasingly popular as it can be used to describe the collective motions of a biological system, but the question of whether NMA can also be useful in predicting the conformational changes observed upon small-molecule binding has only been addressed in a few case studies. Here, we describe a large-scale study on the applicability of NMA for protein-ligand docking using 433 apo/holo pairs of the Astex data sets. On the basis of sets of the first normal modes from the apo structure, we first generated for each paired holo structure a set of conformations that optimally reproduce its C(α) trace with respect to the underlying normal mode subspace. Using AutoDock, GOLD, and FlexX we then docked the original ligands into these conformations to assess how the docking performance depends on the number of modes used to reproduce the holo structure. The results of our study indicate that, even for such a best-case scenario, the use of normal mode analysis in small-molecule docking is restricted and that a general rule on how many modes to use does not seem to exist or at least is not easy to find.

Identification of CYP106A2 as a regioselective allylic bacterial diterpene hydroxylase.

The cytochrome P450 monooxygenase CYP106A2 from Bacillus megaterium ATCC 13368 catalyzes hydroxylations of a variety of 3-oxo-Δ(4) -steroids such as progesterone and deoxycorticosterone (DOC), mainly in the 15ß-position. We combined a high-throughput screening and a rational approach for identifying new substrates of CYP106A2. The diterpene resin acid abietic acid was found to be a substrate and was docked into the active site of a CYP106A2 homology model to provide further inside into the structural basis of the regioselectivity of hydroxylation. The products of the hydroxylation reaction were analyzed by HPLC and the V(max) and K(m) values were calculated. The corresponding reaction products were analyzed by NMR spectroscopy and identified as 12α- and 12ß-hydroxyabietic acid. CYP106A2 was therefore identified as the first reported bacterial cytochrome P450 diterpene hydroxylase. Furthermore, an effective whole-cell catalyst for the selective allylic 12α- and 12ß-hydroxylation was applied to produce the hydroxylated products.