Recent advances in structure-based virtual screening of G-protein coupled receptors

Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions

The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.

Understanding Membrane Protein Drug Targets in Computational Perspective

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity

Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.

Discovery of antiviral molecules for dengue: In silico search and biological evaluation

BACKGROUND

Dengue disease is a global disease that has no effective treatment. The dengue virus (DENV) NS2B/NS3 protease complex is a target for designing specific antivirals due to its importance in viral replication and its high degree of conservation.

METHODS

NS2B/NS3 protease complex structural information was employed to find small molecules that are capable of inhibiting the activity of the enzyme complex. This inhibitory activity was probed with in vitro assays using a fluorescent substrate and the complex NS2B/NS3 obtained by recombinant DNA techniques. HepG2 cells infected with dengue virus serotype 2 were used to test the activity against dengue virus replication.

RESULTS

A total of 210,903 small molecules from PubChem were docked in silico to the NS2B/NS3 structure (PDB: 2FOM) to find molecules that were capable of inhibiting this protein complex. Five of the best 500 leading compounds, according to their affinity values (-11.6 and -13.5 kcal/mol), were purchased. The inhibitory protease activity on the recombinant protein and antiviral assays was tested.

CONCLUSIONS

Chemicals CID 54681617, CID 54692801 and CID 54715399 were strong inhibitors of NS2B/NS3, with IC50 values (μM) and percentages of viral titer reductions of 19.9, 79.9%; 17.5, 69.8%; and 9.1, 73.9%, respectively. Multivariate methods applied to the molecular descriptors showed two compounds that were structurally different from other DENV inhibitors.

GENERAL SIGNIFICANCE

This discovery opens new possibilities for obtaining drug candidates against Dengue virus.

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2 Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2 kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5 Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.

How well do the substrates KISS the enzyme? Molecular docking program selection for feruloyl esterases

Molecular docking is the most commonly used technique in the modern drug discovery process where computational approaches involving docking algorithms are used to dock small molecules into macromolecular target structures. Over the recent years several evaluation studies have been reported by independent scientists comparing the performance of the docking programs by using default ‘black box’ protocols supplied by the software companies. Such studies have to be considered carefully as the docking programs can be tweaked towards optimum performance by selecting the parameters suitable for the target of interest. In this study we address the problem of selecting an appropriate docking and scoring function combination (88 docking algorithm-scoring functions) for substrate specificity predictions for feruloyl esterases, an industrially relevant enzyme family. We also propose the ‘Key Interaction Score System’ (KISS), a more biochemically meaningful measure for evaluation of docking programs based on pose prediction accuracy.

Comparison of three preprocessing filters efficiency in virtual screening: identification of new putative LXRbeta regulators as a test case

In silico screening methodologies are widely recognized as efficient approaches in early steps of drug discovery. However, in the virtual high-throughput screening (VHTS) context, where hit compounds are searched among millions of candidates, three-dimensional comparison techniques and knowledge discovery from databases should offer a better efficiency to finding novel drug leads than those of computationally expensive molecular dockings. Therefore, the present study aims at developing a filtering methodology to efficiently eliminate unsuitable compounds in VHTS process. Several filters are evaluated in this paper. The first two are structure-based and rely on either geometrical docking or pharmacophore depiction. The third filter is ligand-based and uses knowledge-based and fingerprint similarity techniques. These filtering methods were tested with the Liver X Receptor (LXR) as a target of therapeutic interest, as LXR is a key regulator in maintaining cholesterol homeostasis. The results show that the three considered filters are complementary so that their combination should generate consistent compound lists of potential hits.

Using ligand-based virtual screening to allosterically stabilize the activated state of a GPCR

G-protein coupled receptors play an essential role in many biological processes. Despite an increase in the number of solved X-ray crystal structures of G-protein coupled receptors, capturing a G-protein coupled receptor in its activated state for structural analysis has proven to be difficult. An unexplored paradigm is stabilization of one or more conformational states of a G-protein coupled receptor via binding a small molecule to the intracellular loops. A short tetrazole peptidomimetic based on the photoactivated state of rhodopsin-bound structure of Gt(alpha)(340-350) was previously designed and shown to stabilize the photoactivated state of rhodopsin, the G-protein coupled receptor involved in vision. A pharmacophore model derived from the designed tetrazole tetrapeptide was used for ligand-based virtual screening to enhance the possible discovery of novel scaffolds. Maybridge Hitfinder and National Cancer Institute diversity libraries were screened for compounds containing the pharmacophore. Forty-seven compounds resulted from virtually screening the Maybridge library, whereas no hits resulted with the National Cancer Institute library. Three of the 47 Maybridge compounds were found to stabilize the MII state. As these compounds did not inhibit binding of transducin to photoactivated state of rhodopsin, they were assumed to be allosteric ligands. These compounds are potentially useful for crystallographic studies where complexes with these compounds might capture rhodopsin in its activated conformational state.