Chem-Bio Informatics Journal Vol. 17(2017)

CYP3A4 contributes to the metabolism of more than 30% of drugs in clinical use. Predicting the sites of metabolism (SOM) by CYP3A4, as well as the binding modes, for target compounds is important for the design of metabolically more stable drugs. Precisely predicting the structures of CYP3A4–ligand complexes is enormously challenging owing to the high number of conformational possibilities with its numerous binding substrates. We previously described a method for predicting the SOM of carbamazepine by means of docking and molecular dynamics (MD) simulations starting from multiple initial structures. To validate our method, we have now applied it to tolterodine, which is more flexible than carbamazepine. In addition, we evaluated the effectiveness of two methods for selecting the initial structures for MD. In analyzing the MD trajectories, we calculated the frequency with which carbon atoms at each of four groups of the tolterodine molecule approached to within a certain cutoff distance of the heme iron, and we also calculated binding free energy. We found that compared to the other three groups, the position to the experimentally determined SOM was close to the heme most frequently and had the lowest average ∆Gbinding. For selecting the MD initial structures, clustering on the basis of protein–ligand interaction fingerprints (PLIF) was substantially more robust at predicting accessibility compared with clustering based on root-mean-square deviations. These findings demonstrate that our method is applicable for a flexible ligand and that PLIF clustering is a promising method for selecting structures for MD. We succeeded to predict the experimentally determined SOM of tolterodine together with the appropriate binding mode. The predicted binding mode is useful to design metabolically more stable compounds.

Several researchers have focused on the inference of genetic networks as a process for extracting useful information from gene expression data. Their work has led to the proposal of a number of methods for genetic network inference. Yet the genetic networks inferred by these methods often contain large numbers of false-positive regulations along with the true-positives. One effective way to reduce the number of erroneous regulations is to apply inference methods that use a priori knowledge on the properties of the genetic networks. The existing inference methods adopting this approach generally use a priori knowledge and the observed gene expression data simultaneously to determine whether or not the target genetic network actually contains each of the candidate regulations. In this study, we establish a new framework for “using a priori knowledge after genetic network inference.” The framework uses a priori knowledge only to modify the genetic network that has already been inferred by the other inference method. Based on this framework, we propose a new inference method that uses multiple kinds of a priori knowledge about genetic networks. The proposed method effectively combines multiple kinds of knowledge and computes the confidence values of regulations. Here, we confirm the effectiveness of the proposed method by applying it to artificial and actual genetic network inference problems. While only a small improvement is gained from the use of multiple kinds of a priori knowledge, we can improve the performance of many other existing inference methods by combining them with the method we propose here.

Fragment molecular orbital (FMO) calculations were performed for explicitly solvated single-stranded DNA (ssDNA), ssDNA binding protein, and their complex in order to assess the solvent effects on the solutes and thereby to find optimal solvation conditions for FMO calculation. A series of solvated structures were generated with different solvent thicknesses. The structures were subjected to FMO calculation at MP2/6-31G* to obtain the net charges and internal energies of the solutes and the solute–solvent interaction energies as functions of the solvent thickness. In all cases, the properties showed complete or marginal convergence at ca. 6 Ǻ, regardless whether or not the system charge was neutralized. This suggested that the first and second solvent shells mainly determine the electronic structure of a solute while the outer solvent including ions has only minor effects, consistent with several preceding reports. In light of this, and considering safety as a factor, we conclude that a solvent shell thickness of ca. 8 Ǻ suffices for FMO calculation of the solutes.

Widespread arguments of the hydrogen bond are criticized with respect to theories of protein folding and interactions of proteins with their specific ligands. Contrary to popular belief, by no means does the hydrogen bond play an important role in determining the conformation of proteins and the interactions of proteins with specific substrates. Stereotypical thinking on the hydrogen bond constitutes a myth in modern science and seriously restricts the success of structure-based drug design.