Improved 3D-QSAR CoMFA of the dopamine transporter blockers with multiple conformations using the genetic algorithm

Ligand and structure-based approaches for the identification of SIRT1 activators

SIRT1 is a NAD(+)-dependent deacetylase that involved in various important metabolic pathways. Combined ligand and structure-based approach was utilized for identification of SIRT1 activators. Pharmacophore models were developed using DISCOtech and refined with GASP module of Sybyl X software. Pharmacophore models were composed of two hydrogen bond acceptor (HBA) atoms, two hydrogen bond donor (HBD) sites and one hydrophobic (HY) feature. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods. Model-2 was selected as best model among the model 1-3, based on ROC and GH score value, and found reliable in identification of SIRT1 activators. Model-2 (3D search query) was searched against Zinc database. Several compounds with different chemical scaffold were retrieved as hits. Currently, there is no experimental SIRT1 3D structure available, therefore, we modeled SIRT1 protein structure using homology modeling. Compounds with Qfit value of more than 86 were selected for docking study into the SIRT1 homology model to explore the binding mode of retrieved hits in the active allosteric site. Finally, in silico ADMET prediction study was performed with two best docked compounds. Combination of ligand and structure-based modeling methods identified active hits, which may be good lead compounds to develop novel SIRT1 activators.

Pharmacophore modeling, virtual screening, docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors

Protein kinase B (PKB) is a key mediator of proliferation and survival pathways that are critical for cancer growth. Therefore, inhibitors of PKB are useful agents for the treatment of cancer. Herein, we describe pharmacophore-based virtual screening combined with docking study as a rational strategy for identification of novel hits or leads. Pharmacophore models of PKB β inhibitors were established using the DISCOtech and refined with GASP from compounds with IC50 values ranging from 2.2 to 246nM. The best pharmacophore model consists of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) site and two hydrophobic (HY) features. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods indicated that the model-3 was statistically valuable and reliable in identifying PKB β inhibitors. Pharmacophore model as a 3D search query was searched against NCI database. Several compounds with different structures (scaffolds) were retrieved as hits. Molecules with a Qfit value of more than 95 and three other known inhibitors were docked in the active site of PKB to further explore the binding mode of these compounds. Finally in silico pharmacokinetic and toxicities were predicted for active hit molecules. The hits reported here showed good potential to be PKB β inhibitors.

3D-QSAR studies of triazolopyrimidine derivatives of Plasmodium falciparum dihydroorotate dehydrogenase inhibitors using a combination of molecular dynamics, docking, and genetic algorithm-based methods

A series of 35 triazolopyrimidine analogues reported as Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors were optimized using quantum mechanics methods, and their binding conformations were studied by docking and 3D quantitative structure-activity relationship studies. Genetic algorithm-based criteria was adopted for selection of training and test sets while maintaining structural diversity of training and test sets, which is also very crucial for model development and validation. Both the comparative molecular field analyses ([Formula: see text], [Formula: see text]) and comparative molecular similarity indices analyses ([Formula: see text], [Formula: see text]) show excellent correlation and high predictive power. Furthermore, molecular dynamics simulations were performed to explore the binding mode of the two of the most active compounds of the series, 10 and 14. Harmonization in the two simulation results validate the analysis and therefore applicability of docking parameters based on crystallographic conformation of compound 14 bound to receptor molecule. This work provides useful information about the inhibition mechanism of this class of molecules and will assist in the design of more potent inhibitors of PfDHODH.

QSAR study and synthesis of new phenyltropanes as ligands of the dopamine transporter (DAT)

The dopamine transporter (DAT) plays a pivotal role in the regulation of dopamine neurotransmission, and is involved in a number of physiological functions and brain disorders. Furthermore the DAT analysis by molecular imaging techniques is a useful tool for the diagnosis and follow up treatment of diseases involving the DAT. In order to predict the affinity of new derivatives for the DAT, different QSAR molecular modeling models based on cocaine were compared. We have evaluated in these models tropane derivatives synthesized with original synthons which coupled properties of both fluorine and iodine atoms. One compound showed a high in vitro affinity and selectivity for the DAT (K(i)=0.87±0.04 nM). This compound should be radiolabeled with radioiodine for further investigations by SPECT.

Singular value decomposition analysis of the torsional angles of dopamine reuptake inhibitor GBR 12909 analogs: effect of force field and charges

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis of large, flexible molecules, such as the dopamine reuptake inhibitor GBR 12909 (1), is complicated by the fact that they can take on a wide range of closely-related conformations. The first step in the analysis is to classify the conformers into groups. Over 600 conformers each of a piperazine (2) and piperidine (3) analog of 1 were generated by random search conformational analysis using the Merck Molecular Force Field (MMFF94). Singular value decomposition (SVD) was used to group the conformers of 2 and 3 by the similarity of their non-ring torsional angles. SVD uncovered subtle differences in their conformer populations due to that fact that the conformers separate along different principal components, and ultimately to the fact that different torsional angles are the chief contributors to these components. The results were compared to our previous SVD analysis (Fiorentino, et al., Journal of Computational Chemistry, 2006, 27, 609-620) of conformer populations of 2 and 3 generated by the Tripos force field and Gasteiger-Hückel charges. Except for the dominant contribution of angle B3 to principal component 8 seen with both force fields, the angles which are chiefly responsible for the grouping of the conformers of 2 and 3 are different with both force fields. This illustrates that SVD is useful in identifying unique groupings of conformers in large data sets of flexible molecules-a first step in selecting representative conformers for 3D-QSAR modeling studies.

Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

BACKGROUND

Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm.

RESULTS

The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1.

CONCLUSION

On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.

Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.