CYP 2D6 binding affinity predictions using multiple ligand and protein conformations.

Because of the large flexibility and malleability of Cytochrome P450 enzymes (CYPs), in silico prediction of CYP binding affinities to drugs and other xenobiotic compounds is a true challenge. In the current work, we use an iterative linear interaction energy (LIE) approach to compute CYP binding affinities from molecular dynamics (MD) simulation. In order to improve sampling of conformational space, we combine results from simulations starting with different relevant protein-ligand geometries. For calculated binding free energies of a set of thiourea compounds binding to the flexible CYP 2D6 isoform, improved correlation with experiment was obtained by combining results of MD runs starting from distinct protein conformations and ligand-binding orientations. This accuracy was obtained from relatively short MD simulations, which makes our approach computationally attractive for automated calculations of ligand-binding affinities to flexible proteins such as CYPs.

Improved ligand-protein binding affinity predictions using multiple binding modes.

Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.

Structural rationalization of novel drug metabolizing mutants of cytochrome P450 BM3.

Three newly discovered drug metabolizing mutants of cytochrome P450 BM3 (van Vugt-Lussenburg et al., Identification of critical residues in novel drug metabolizing mutants of Cytochrome P450 BM3 using random mutagenesis, J Med Chem 2007;50:455-461) have been studied at an atomistic level to provide structural explanations for a number of their characteristics. In this study, computational methods are combined with experimental techniques. Molecular dynamics simulations, resonance Raman and UV-VIS spectroscopy, as well as coupling efficiency and substrate-binding experiments, have been performed. The computational findings, supported by the experimental results, enable structural rationalizations of the mutants. The substrates used in this study are known to be metabolized by human cytochrome P450 2D6. Interestingly, the major metabolites formed by the P450 BM3 mutants differ from those formed by human cytochrome P450 2D6. The computational findings, supported by resonance Raman data, suggest a conformational change of one of the heme propionate groups. The modeling results furthermore suggest that this conformational change allows for an interaction between the negatively charged carboxylate of the heme substituent and the positively charged nitrogen of the substrates. This allows for an orientation of the substrates favorable for formation of the major metabolite by P450 BM3.

Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450.

BACKGROUND: Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s). OBJECTIVE: This review discusses the recent advances of these methods and the implications that are specific for P450s. METHODS: Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand-P450 interactions. CONCLUSION: P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.

Identification of critical residues in novel drug metabolizing mutants of cytochrome P450 BM3 using random mutagenesis.

Previously, we've described a site-directed triple mutant of cytochrome P450 BM3 (BM3) that is able to convert various drugs (van Vugt-Lussenburg, B. M. A., et al. Biochem. Biophys. Res. Commun. 2006, 346, 810-818). In the present study, random mutagenesis was used to improve the activity of this mutant. With three generations of error-prone PCR, mutants were obtained with 200-fold increased turnover toward drug substrates dextromethorphan and 3,4-methylenedioxymethylamphetamine. The initial activities of these mutants were up to 90-fold higher than that of human P450 2D6. These highly active drug metabolizing enzymes have great potential for biotechnology. Using sequencing analysis, the mutations responsible for the increase in activity were determined. The mutations that had the greatest effects on the activity were F81I, E267V, and particularly L86I, which is not located in the active site. Computer modeling studies were used to rationalize the effects of the mutations. This study shows that random mutagenesis can be used to identify novel critical residues, and to increase our insight into P450s.

Improving structure-based virtual screening by multivariate analysis of scoring data.

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.

Comparison of murine and human estrogen sulfotransferase inhibition in vitro and in silico--implications for differences in activity, subunit dimerization and substrate inhibition.

It is well established that various endocrine disrupting compounds (EDCs) can inhibit human estrogen sulfotransferase (SULT1E1). In this study, we investigate murine SULT1E1 inhibition in vitro and in silico and compare this to data for the human enzyme. 34 potential EDCs were screened for their ability to inhibit both murine and human SULT1E1 and IC(50) values were determined for 14 of the inhibitory EDCs. Only estrone, dienestrol and enterolactone showed significant differences in affinity between the human and murine SULT1E1. Extensive molecular modelling was performed using molecular dynamics (MD) simulations. During the MD simulations the ligands moved away from the catalytically active position, something which was not observed when simulating the unit cell of the crystal structure. This finding suggests that catalytically inactive binding modes, other than the one observed in the crystal structures, are possible in SULT1E1. The ligands stayed longer in the catalytically active position in mSULT1E1, which is likely a result of simultaneous hydrogen bond formation on both sides of the binding pocket, which does not seem to be possible in hSULT1E1. The ligands in the human protein moved to a sub-pocket near the entrance of the active site, which offers hydrogen bond formation possibilities with Asp22 and Lys85 as well as favourable hydrophobic interactions. The ligands moved more randomly in mSULT1E1. These observations offer a possible explanation for the substrate inhibition only observed in hSULT1E1.

Are automated molecular dynamics simulations and binding free energy calculations realistic tools in lead optimization? An evaluation of the linear interaction energy (LIE) method.

An extensive evaluation of the linear interaction energy (LIE) method for the prediction of binding affinity of docked compounds has been performed, with an emphasis on its applicability in lead optimization. An automated setup is presented, which allows for the use of the method in an industrial setting. Calculations are performed for four realistic examples, retinoic acid receptor gamma, matrix metalloprotease 3, estrogen receptor alpha, and dihydrofolate reductase, focusing on different aspects of the procedure. The obtained LIE models are evaluated in terms of the root-mean-square (RMS) errors from experimental binding free energies and the ability to rank compounds appropriately. The results are compared to the best empirical scoring function, selected from a set of 10 scoring functions. In all cases, good LIE models can be obtained in terms of free-energy RMS errors, although reasonable ranking of the ligands of dihydrofolate reductase proves difficult for both the LIE method and scoring functions. For the other proteins, the LIE model results in better predictions than the best performing scoring function. These results indicate that the LIE approach, as a tool to evaluate docking results, can be a valuable asset in computational lead optimization programs.