Rational design of berberine-based FtsZ inhibitors with broad-spectrum antibacterial activity.

Inhibition of the functional activity of Filamenting temperature-sensitive mutant Z (FtsZ) protein, an essential and highly conserved bacterial cytokinesis protein, is a promising approach for the development of a new class of antibacterial agents. Berberine, a benzylisoquinoline alkaloid widely used in traditional Chinese and native American medicines for its antimicrobial properties, has been recently reported to inhibit FtsZ. Using a combination of in silico structure-based design and in vitro biological assays, 9-phenoxyalkyl berberine derivatives were identified as potent FtsZ inhibitors. Compared to the parent compound berberine, the derivatives showed a significant enhancement of antibacterial activity against clinically relevant bacteria, and an improved potency against the GTPase activity and polymerization of FtsZ. The most potent compound 2 strongly inhibited the proliferation of Gram-positive bacteria, including methicillin-resistant S. aureus and vancomycin-resistant E. faecium, with MIC values between 2 and 4 µg/mL, and was active against the Gram-negative E. coli and K. pneumoniae, with MIC values of 32 and 64 µg/mL respectively. The compound perturbed the formation of cytokinetic Z-ring in E. coli. Also, the compound interfered with in vitro polymerization of S. aureus FtsZ. Taken together, the chemical modification of berberine with 9-phenoxyalkyl substituent groups greatly improved the antibacterial activity via targeting FtsZ.

Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening.

The Filamenting temperature-sensitive mutant Z (FtsZ), an essential GTPase in bacterial cell division, is highly conserved among Gram-positive and Gram-negative bacteria and thus considered an attractive target to treat antibiotic-resistant bacterial infections. In this study, a new class of FtsZ inhibitors bearing the pyrimidine-quinuclidine scaffold was identified from structure-based virtual screening of natural product libraries. Iterative rounds of in silico studies and biological evaluation established the preliminary structure-activity relationships of the new compounds. Potent FtsZ inhibitors with low micromolar IC50 and antibacterial activity against S. aureus and E. coli were found. These findings support the use of virtual screening and structure-based design for the rational development of new antibacterial agents with innovative mechanisms of action.

Anticancer steroids: linking natural and semi-synthetic compounds.

Steroids, a widespread class of natural organic compounds occurring in animals, plants and fungi, have shown great therapeutic value for a broad array of pathologies. The present overview is focused on the anticancer activity of steroids, which is very representative of a rich structural molecular diversity and ability to interact with various biological targets and pathways. This review encompasses the most relevant discoveries on steroid anticancer drugs and leads through the last decade and comprises 668 references.

Docking and scoring with ICM: the benchmarking results and strategies for improvement.

Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC = 82.2 and ROC((2%)) = 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.

Validation of the AmpC ß-lactamase binding site and identification of inhibitors with novel scaffolds.

AmpC ß-lactamase confers resistance to ß-lactam antibiotics in multiple Gram-negative bacteria. Therefore, identification of non-ß-lactam compounds that inhibit the enzyme is considered crucial to the development of novel antibacterial therapies. Given the highly solvent-exposed active site, it is important to study the induced-fit movements and water-mediated interactions to improve docking accuracy and virtual screening enrichments in structure-based design of new AmpC inhibitors. Here, we tested multiple models of the AmpC binding site to investigate the importance of conserved water molecules and binding site plasticity on molecular docking. The results indicate that at least one conserved water molecule greatly improves the binding pose predictions and virtual screening enrichments of known noncovalent AmpC inhibitors. The best model was tested prospectively in the virtual screening of about 6 million commercially available compounds. Sixty-one chemically diverse top-scoring compounds were experimentally tested, which led to the identification of seven previously unknown inhibitors. These findings validate the essential features of the AmpC binding site for molecular recognition and are useful for further optimization of identified inhibitors.

Unusual arginine formations in protein function and assembly: rings, strings, and stacks.

Protein-protein interfaces are often stabilized by a small number of dominant contacts, exemplified by the overrepresentation of arginine residues at oligomerization interfaces. Positively charged arginines are most commonly involved in ion pairs of opposite charge; however, previous work of Scheraga and co-workers described the stable, close range interaction between guanidinium pairs in a solvated environment. To extend this work, we searched over 70 thousand protein structures and complexes for unusual formations of arginine residues supported by the electron density. Symmetry transformations were used to generate full assemblies. Clusters of four to eight arginine residues with C(ζ)-C(ζ) distances <5 Å, organized as rings with four to eight members, stacks of two arginines, and strings of stacked arginines, are commonly located at the interfaces of oligomeric proteins. The positive charge is properly balanced by negatively charged counterions in about 90% of the cases. We also observed planar stacking of guanidinium groups, bridged by hydrogen bonds and interactions with water molecules. The guanidinium groups are commonly involved in five hydrogen bonds with water molecules and acceptor groups from surrounding amino acids. Water molecules have a bridging effect on the arginine pairs, but in some cases, small molecular weight chemicals in the crystallization buffer may be misinterpreted as water molecules. In summary, despite electrostatic repulsion, arginines do form various clusters that are exposed to interact with and potentially be controlled or switched by charged metabolites, membrane lipids, nucleic acids, or side chains of other proteins. Control of the stability of arginine clusters may play an important role in protein-protein oligomerization, molecular recognition, and ligand binding.

Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach.

CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure-function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists.

Molecular recognition and drug-lead identification: what can molecular simulations tell us?

Molecular recognition and ligand binding involving proteins underlie the most important life processes within the cell, such as substrate transport, catalysis, signal transmission, receptor trafficking, gene regulation, switching on and off of biochemical pathways. Despite recent successes in predicting the structures of many protein-substrate complexes, the dynamic aspects of binding have been largely neglected by computational/theoretical investigations. Recently, several groups have started tackling these problems with the use of experimental and simulation methods and developed models describing the variation of protein dynamics upon complex formation, shedding light on how substrate or inhibitor binding can alter protein flexibility and function. The study of ligand-induced dynamic variations has also been exploited to review the concept of allosteric changes, in the absence of major conformational changes. In this context, the study of the influence of protein motions on signal transduction and on catalytic activities has been used to develop pharmacophore models based on ensembles of protein conformations. These models, taking flexibility explicitly into account, are able to distinguish active inhibitors versus nonactive drug-like compounds, to define new molecular motifs and to preferentially identify specific ligands for a certain protein target. The application of these methods holds great promise in advancing structure-based drug discovery and medicinal chemistry in general, opening up the possibility to explore broader chemical spaces than is normally done in an efficient way. In this review, examples illustrating the extent to which simulations can be used to understand these phenomena will be presented along with examples of methodological developments to increase physical understanding of the processes and improve the possibility to rationally design new molecules.

Dynamics-Based Discovery of Allosteric Inhibitors: Selection of New Ligands for the C-terminal Domain of Hsp90.

The study of allosteric functional modulation in dynamic proteins is attracting increasing attention. In particular, the discovery of new allosteric sites may generate novel opportunities and strategies for drug development, overcoming the limits of classical active-site oriented drug design. In this paper, we report on the results of a novel, ab initio, fully computational approach for the discovery of allosteric inhibitors based on the physical characterization of signal propagation mechanisms in proteins and apply it to the important molecular chaperone Hsp90. We first characterize the allosteric "hot spots" involved in interdomain communication pathways from the nucleotide-binding site in the N-domain to the distal C-domain. On this basis, we develop dynamic pharmacophore models to screen drug libraries in the search for small molecules with the functional and conformational properties necessary to bind these "hot spot" allosteric sites. Experimental tests show that the selected moelcules bind the Hsp90 C-domain, exhibit antiproliferative activity in different tumor cell lines, while not affecting proliferation of normal human cells, destabilize Hsp90 client proteins, and disrupt association with several cochaperones known to bind the N- and M-domains of Hsp90. These results prove that the hits alter Hsp90 function by affecting its conformational dynamics and recognition properties through an allosteric mechanism. These findings provide us with new insights on the discovery and development of new allosteric inhibitors that are active on important cellular pathways through computational biology. Though based on the specific case of Hsp90, our approach is general and can readily be extended to other target proteins and pathways.

An efficient steroid pharmacophore-based strategy to identify new aromatase inhibitors.

Aromatase, an enzyme involved in the conversion of androgens into estrogens, is an important target for the endocrine treatment of breast cancer. Aromatase inhibition is usually achieved with steroids structurally related to the substrate of catalysis or, alternatively, with azole non-steroid compounds. Substituted androstenedione derivatives with Delta(1), Delta(6) and Delta(1,6) unsaturations and 6-alkyl/6-phenyl aliphatic substitutions, are among the most potent steroid aromatase inhibitors known to date. In this paper we have combined the common pharmacophoric and shape features of these molecules into a new pharmacophore model, useful for virtual screening of large compound databases. Small subsets of the best fitting anti-aromatase candidates were extracted from the NCI database and experimentally tested on an in vitro assay with human placental aromatase. New potent aromatase inhibitors were identified such as compounds 8 and 14. Considering the lack of a crystal structure for the aromatase enzyme, this ligand-based method is a valuable tool for the virtual screening of new aromatase inhibitors.

Fast three dimensional pharmacophore virtual screening of new potent non-steroid aromatase inhibitors.

Suppression of estrogen biosynthesis by aromatase inhibition is an effective approach for the treatment of hormone sensitive breast cancer. Third generation non-steroid aromatase inhibitors have shown important benefits in recent clinical trials with postmenopausal women. In this study we have developed a new ligand-based strategy combining important pharmacophoric and structural features according to the postulated aromatase binding mode, useful for the virtual screening of new potent non-steroid inhibitors. A small subset of promising drug candidates was identified from the large NCI database, and their antiaromatase activity was assessed on an in vitro biochemical assay with aromatase extracted from human term placenta. New potent aromatase inhibitors were discovered to be active in the low nanomolar range, and a common binding mode was proposed. These results confirm the potential of our methodology for a fast in silico high-throughput screening of potent non-steroid aromatase inhibitors.

Biochemical and computational insights into the anti-aromatase activity of natural catechol estrogens.

High levels of endogenous estrogens are associated with increased risks of breast cancer. Estrogen levels are mainly increased by the activity of the aromatase enzyme and reduced by oxidative/conjugative metabolic pathways. In this paper, we demonstrate for the first time that catechol estrogen metabolites are potent aromatase inhibitors, thus establishing a link between aromatase activity and the processes involved in estrogen metabolism. In particular, the anti-aromatase activity of a set of natural hydroxyl and methoxyl estrogen metabolites was investigated using biochemical methods and subsequently compared with the anti-aromatase potency of estradiol and two reference aromatase inhibitors. Catechol estrogens proved to be strong inhibitors with an anti-aromatase potency two orders of magnitude higher than estradiol. A competitive inhibition mechanism was found for the most potent molecule, 2-hydroxyestradiol (2-OHE(2)) and a rational model identifying the interaction determinants of the metabolites with the enzyme is proposed based on ab initio quantum-mechanical calculations. A strong relationship between activity and electrostatic properties was found for catechol estrogens. Moreover, our results suggest that natural catechol estrogens may be involved in the control mechanisms of estrogen production.

Combining computational and biochemical studies for a rationale on the anti-aromatase activity of natural polyphenols.

Aromatase, an enzyme of the cytochrome P450 family, is a very important pharmacological target, particularly for the treatment of breast cancer. The anti-aromatase activity of a set of natural polyphenolic compounds was evaluated in vitro. Strong aromatase inhibitors including flavones, flavanones, resveratrol, and oleuropein, with activities comparable to that of the reference anti-aromatase drug aminoglutethimide, were identified. Through the application of molecular modeling techniques based on grid-independent descriptors and molecular interaction fields, the major physicochemical features associated with inhibitory activity were disclosed, and a putative virtual active site of aromatase was proposed. Docking of the inhibitors into a 3D homology model structure of the enzyme defined a common binding mode for the small molecules under investigation. The good correlation between computational and biological results provides the first rationalization of the anti-aromatase activity of polyphenolic compounds. Moreover, the information generated in this approach should be further exploited for the design of new aromatase inhibitors.