Identification of Plasmodium falciparum spermidine synthase active site binders through structure-based virtual screening.

Seven novel binders, binding in the active site of Plasmodium falciparum spermidine synthase, were identified by structure-based virtual screening. The binding of these compounds was experimentally verified by NMR techniques. Spermidine synthase, an enzyme involved in the polyamine pathway, has been suggested as a target for treating malaria. The virtual screening protocol combined 3D pharmacophore filtering, docking, and scoring, focusing on finding compounds predicted to form interactions mimicking those of a previously known binder. The virtual screen resulted in the selection of 28 compounds that were acquired and tested from 2.6 million starting structures. Two of the seven binders were predicted to bind in the amino substrate binding pocket. Both of these showed stronger binding upon addition of methylthioadenosine, one of the two products of the enzyme, and a known binder and inhibitor. The five other compounds were predicted to bind in the part of the active site where the other substrate, decarboxylated S-adenosylmethionine, binds. These five compounds all competed for binding with methylthioadenosine.

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.

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.

A triple mutant of the Drosophila ERR confers ligand-induced suppression of activity.

The steroid hormone (NR3) subfamily of nuclear receptors was until recently believed to be restricted to deuterostomes. However, a novel nuclear receptor belonging to the NR3 subfamily was recently identified in the Drosophila melanogaster genome, indicating the existence of an ancestor before the evolutionary split of deuterostomes and protostomes. This receptor, termed the Drosophila estrogen-related receptor (dERR), most closely resembles the human and mouse estrogen-related receptors (ERRs) in both the DNA binding domain (DBD) (approximately 85% identical) and the ligand binding domain (LBD) (approximately 35% identical). Here we describe the functional analysis and rational design of ligand responsive dERR mutants created by protein engineering of the LBD. On the basis of homology modeling, three amino acid residues in the LBD were identified and mutated to enable ligand-dependent suppression of transcriptional activity. Our results show that the Y295A/T333I/Y365L triple mutant is significantly suppressed by the known ERR inverse agonists 4-hydroxytamoxifen (OHT) and diethylstilbestrol (DES), in comparison to the wild-type dERR receptor, which was inefficiently suppressed by these substances. The coactivator mGRIP-1 (mouse glucocorticoid receptor interacting protein 1) was shown to significantly increase the activity of the triple mutant in transfection experiments, and the addition of OHT resulted in an efficient suppression of the activity. Accordingly, the ability to functionally interact with a coactivator is still maintained by the Y295A/T333I/Y365L mutant. These findings demonstrate the potential of using rational design and engineering of the LBD to study the function of a nuclear receptor lacking identified ligands.