Computer based screening of compound databases: 1. Preselection of benzamidine-based thrombin inhibitors

Molecular modeling and in vitro study on pyrocatechol as potential pharmacophore of CD151 inhibitor

CD151 has been recognized as a prognostic marker, the therapeutic target of breast cancers, but less explored for small molecule inhibitors due to lack of a validated model. The 3-D structure of CD151 large extracellular loop (LEL) was modeled using the LOMETS server and validated by the Ramachandran plot. The validated structure was employed for molecular docking and structure-based pharmacophore analysis. Druglikeness was evaluated by the ADMET description protocol. Antiproliferative activity was evaluated by MTT, BrdU incorporation, flow cytometry, and cell death ELISA^PLUS assay. This study predicted the best model for CD151-LEL with 94.1% residues in favored regions and Z score -2.79 kcal/mol using the threading method. The web-based receptor cavity method identified one functional target site, which was suitable for the binding of aromatic and heterocyclic compounds. Molecular docking study identified pyrocatechol (PCL) and 5-fluorouracil (FU) as potential leads of CD151-LEL. The pharmacophore model identified interaction points of modeled CD151-LEL with PCL and FU. Also, the analysis of ADMET properties revealed the drug-likeness of PCL and FU. The viability of MDA-MB 231 cells was significantly reduced with PCL and FU but less affected MCF-12A, normal healthy breast epithelial cell line. With 50% toxic concentration, both PCL and FU significantly inhibited 82.46 and 87.12% proliferation, respectively, of MDA-MB 231 cells by altering morphology and inducing G1 cell cycle arrest and apoptosis. In addition, PCL and FU inhibited the CD151 expression by 4.5-and 4.8-folds, respectively. This study suggests the further assessment of pyrocatechol as a potential lead of CD151 in breast cancer at the molecular level.

Ligand-based pharmacophore modeling and docking studies on vitamin D receptor inhibitors

In recent years, pharmacophore modeling and molecular docking approaches have been extensively used to characterize the structural requirements and explore the conformational space of a ligand in the binding pocket of the selected target protein. Herein, we report a pharmacophore modeling and molecular docking of 45 compounds comprising of the indole scaffold as vitamin D receptor (VDR) inhibitors. Based on the selected best hypothesis (DRRRR.61), an atom-based three-dimensional quantitative structure-activity relationships model was developed to rationalize the structural requirement of biological activity modulating components. The developed model predicted the binding affinity for the training set and test set with R² _(training)  = 0.8869 and R² _(test)  = 0.8139, respectively. Furthermore, molecular docking and dynamics simulation were performed to understand the underpinning of binding interaction and stability of selected VDR inhibitors in the binding pocket. In conclusion, the results presented here, in the form of functional and structural data, agreed well with the proposed pharmacophores and provide further insights into the development of novel VDR inhibitors with better activity.

Structure-based design of small-molecule protein–protein interaction modulators: the story so far

As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.

A dynamic target-based pharmacophoric model mapping the CD4 binding site on HIV-1 gp120 to identify new inhibitors of gp120-CD4 protein-protein interactions

A dynamic target-based pharmacophoric model mapping the CD4 binding site on HIV-1 gp120 was built and used to identify new hits able to inhibit gp120-CD4 protein-protein interactions. Two compounds showed micromolar inhibition of HIV-1 replication in cells attributable to an interference with the entry step of infection, by direct interaction with gp120. Inactivity of compounds toward a M475I strain suggested specific contacts with the Phe43 cavity of gp120.

Targets looking for drugs: a multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery

Pharmacophoresthree-dimensional (3D) arrangements of essential features enabling a molecule to exert a particular biological effectconstitute a very useful tool in drug design both in hit discovery and hit-to-lead optimization process. Two basic approaches for pharmacophoric model generation can be used by chemists, depending on the availability or not of the target 3D structure. In view of the rapidly growing number of protein structures that are now available, receptor-based pharmacophore generation methods are becoming more and more used. Since most of them require the knowledge of the 3D structure of the ligand-target complex, they cannot be applied when no compounds targeting the binding site of interest are known. Here, a GRID-based procedure for the generation of receptor-based pharmacophores starting from the knowledge of the sole protein structure is described and successfully applied to address three different tasks in the field of medicinal chemistry.

Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores

The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.

Identification of novel allosteric regulators of human-erythrocyte pyruvate kinase

Erythrocyte pyruvate kinase (PK) is an important glycolytic enzyme, and manipulation of its regulatory behavior by allosteric modifiers is of interest for medicinal purposes. Human-erythrocyte PK was expressed in Rosetta cells and purified on an Ni-NTA column. A search of the small-molecules database of the National Cancer Institute (NCI), using the UNITY software, led to the identification of several compounds with similar pharmacophores as fructose-1,6-bisphosphate (FBP), the natural allosteric activator of the human kinases. The compounds were subsequently docked into the FBP binding site using the programs FlexX and GOLD, and their interactions with the protein were analyzed with the energy-scoring function of HINT. Seven promising candidates, compounds 1-7, were obtained from the NCI, and subjected to kinetics analysis, which revealed both activators and inhibitors of the R-isozyme of PK (R-PK). The allosteric effectors discovered in this study could prove to be lead compounds for developing medications for the treatment of hemolytic anemia, sickle-cell anemia, hypoxia-related diseases, and other disorders arising from erythrocyte PK malfunction.

In silico three-dimensional pharmacophores for aiding the discovery of the Pfmrk (Plasmodium cyclin-dependent protein kinases) specific inhibitors for the therapeutic treatment of malaria

The resurgence of malaria and lack of effective antimalarial drugs affect millions of people worldwide every year, causing several million deaths. With the emergence of structure-based drug design methodologies, a major thrust in drug discovery efforts has shifted towards targeting specific proteins in parasites that are involved in their metabolic pathways. Although cyclin-dependent kinases (CDKs), due to their direct role in cell cycle regulations, have been targeted for the development of cancer therapeutics, CDKs for Plasmodium falciparum have only been recently identified to be attractive for the discovery of antimalarials. One of the plasmodium CDK targets is Pfmrk. Being a putative homolog of Cdk7 and, thus, having the possibility of dual functions, both in cell cycle control and gene expression within the parasite, pfrmk has become an interesting antimalarial chemotherapeutic target. This review discusses how in silico methodologies, without the knowledge of the X-ray crystallographic structure of Pfmrk, particularly based on the development of pharmacophores on known inhibitors can aid the discovery and design of Pfmrk-specific inhibitors through virtual screening of compound databases and provides insights into the understanding of the mechanism of binding in the active site of this enzyme.

Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening

For the successful identification and docking of new ligands to a protein target by virtual screening, the essential features of the protein and ligand surfaces must be captured and distilled in an efficient representation. Since the running time for docking increases exponentially with the number of points representing the protein and each ligand candidate, it is important to place these points where the best interactions can be made between the protein and the ligand. This definition of favorable points of interaction can also guide protein structure-based ligand design, which typically focuses on which chemical groups provide the most energetically favorable contacts. In this paper, we present an alternative method of protein template and ligand interaction point design that identifies the most favorable points for making hydrophobic and hydrogen-bond interactions by using a knowledge base. The knowledge-based protein and ligand representations have been incorporated in version 2.0 of SLIDE and resulted in dockings closer to the crystal structure orientations when screening a set of 57 known thrombin and glutathione S-transferase (GST) ligands against the apo structures of these proteins. There was also improved scoring enrichment of the dockings, meaning better differentiation between the chemically diverse known ligands and a approximately 15,000-molecule dataset of randomly-chosen small organic molecules. This approach for identifying the most important points of interaction between proteins and their ligands can equally well be used in other docking and design techniques. While much recent effort has focused on improving scoring functions for protein-ligand docking, our results indicate that improving the representation of the chemistry of proteins and their ligands is another avenue that can lead to significant improvements in the identification, docking, and scoring of ligands.

Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design

The number of protein structures is currently increasing at an impressive rate. The growing wealth of data calls for methods to efficiently exploit structural information for medicinal and pharmaceutical purposes. Given the three-dimensional (3D) structure of a validated protein target, the identification of functionally relevant binding sites and the analysis ('mapping') of these sites with respect to molecular recognition properties are important initial tasks in structure-based drug design. To address these tasks, a variety of computational tools have been developed. Approaches to identify binding pockets include geometric analyses of protein surfaces, comparisons of protein structures, similarity searches in databases of protein cavities, and docking scans to reveal areas of high ligand complementarity. In the context of binding-site analysis, powerful data mining tools help to retrieve experimental information about related protein-ligand complexes. To identify interaction hot spots, various potential functions and knowledge-based approaches are available for mapping binding regions. The results may subsequently be used to guide virtual screenings for new ligands via pharmacophore searches or docking simulations.

SuperStar: improved knowledge-based interaction fields for protein binding sites

SuperStar is an empirical method for identifying interaction sites in proteins, based entirely on experimental information about non-bonded interactions occurring in small-molecule crystal structures, taken from the IsoStar database. We describe recent modifications and additions to SuperStar, validating the results on a test set of 122 X-ray structures of protein-ligand complexes. In this validation, propensity maps are generated for all the binding sites of these proteins, using four different probes: a charged NH(+)(3) nitrogen atom, a carbonyl oxygen atom, a hydroxyl oxygen atom and a methyl carbon atom. Next, the maps are compared with the experimentally observed positions of ligand atoms of these types. A peak-searching algorithm is introduced that highlights potential interaction hot spots. For the three hydrogen-bonding probes - NH(+)(3) nitrogen atom, carbonyl oxygen atom and hydroxyl oxygen atom - the average distance from the ligand atom to the nearest SuperStar peak is 1.0-1.2 A (0.8-1.0 A for solvent-inaccessible ligand atoms). For the methyl carbon atom probe, this distance is about 1.5 A, probably because interactions to methyl groups are much less directional. The most important addition to SuperStar is the enabling of propensity maps around metal centres - Ca(2+), Mg(2+) and Zn(2+) - in protein binding sites. The results are validated on a test set of 24 protein-ligand complexes that have a metal ion in their binding site. Coordination geometries are derived automatically, using only the protein atoms that coordinate to the metal ion. The correct coordination geometry is derived in approximately 75 % of the cases. If the derived geometry is assumed during the SuperStar calculation, the average distance from a ligand atom coordinating to the metal ion to the nearest peak in the propensity map for an oxygen probe is 0.87(7) A. If the correct coordination geometry is imposed, this distance reduces to 0.59(7)A. This indicates that the SuperStar predictions around metal-binding sites are at least as good as those around other protein groups. Using clustering techniques, a non-redundant set of probes is selected from the set of probes available in the IsoStar database. The performance in SuperStar of all these probes is tested on the test set of protein-ligand complexes. With the exception of the "ether oxygen" probe and the "any NH(+)" probe, all new probes perform as well as the four probes introduced first.