Current Topics in Medicinal Chemistry - Volume 17, Issue 21, 2017

The eternal or ultimate goal of medicinal chemistry is to find most effective ways to treat various diseases and extend human beings’ life as long as possible. Human being is a biological entity. To realize such an ultimate goal, the inputs or breakthroughs from the advances in biological science are no doubt most important that may even drive medicinal science into a revolution. In this review article, we are to address this from several different angles.

As a subset of glycosyltransferases, the family of sialyltransferases catalyze transfer of sialic acid (Sia) residues to terminal non-reducing positions on oligosaccharide chains of glycoproteins and glycolipids, utilizing CMP-Neu5Ac as the activated sugar nucleotide donor. In the four known sialyltransferase families (ST3Gal, ST6Gal, ST6GalNAc and ST8Sia), the ST8Sia family catalyzes synthesis of α2, 8-linked sialic/polysialic acid (polySia) chains according to their acceptor specificity. We have determined the 3D structural models of the ST8Sia family members, designated ST8Sia I (1), II(2), IV(4), V(5), and VI(6) using the Phyre2 server. Accuracy of these predicted models are based on the ST8Sia III crystal structure as the calculated template. The common structural features of these models are: (1) Their parallel templates and disulfide bonds are buried within the enzymes and are predominately surrounded by helices; (2) The anti-parallel β-sheets are located at the N-terminal region of the enzymes; (3) The mono-sialytransferases (mono-STs), ST8Sia I and ST8Sia VI, contain only a single pair of disulfide bonds, and there are no anti-parallel β-sheets in ST8Sia VI; (4) The Nterminal region of all of the mono-STs are located some distant away from their core structure; (5) These conformational features show that the 3D structures of the mono-STs are less compact than the two polySTs, ST8Sia II and ST8Sia IV, and the oligo-ST, ST8Sia III. These structural features relate to the catalytic specificity of the monoSTs; (6) In contrast, the more compact structural features of ST8Sia II, ST8Sia IV and ST8Sia III relate to their ability to catalyze the processive synthesis of oligo- (ST8Sia III) and polySia chains (ST8Sia II & ST8Sia IV); (7) Although ST8Sia II, III and IV have similar conformations in their corresponding polysialyltransferase domain (PSTD) and polybasic region (PBR) motifs, the structure of ST8Sia III is less compact than ST8Sia II and ST8Sia IV, and the amino acid components of the several three-residue-loops in the two motifs of ST8Sia III are different from that in ST8Sia II and ST8Sia IV. This is likely the structural basis for why ST8Sia III is an oligoST and not able to polysialylate and; (8) In contrast, essentially all amino acids within the threeresidue- loops in the PSTD of ST8Sia II and ST8Sia IV are highly conserved, and many amino acids in the loops and the helices of these two motifs are critical for NCAM polysialylation, as determined by mutational analysis and confirmed by our recent NMR results. In summary, these new findings provide further insights into the molecular mechanisms underlying polyST-NCAM recognition, polySTpolySia/ oligoSia interactions, and polysialylation of NCAM.

G Protein-Coupled Receptors (GPCRs) are the most common therapeutic targets for drug discovery by the pharmaceutical industries. Since 2007, several three-dimensional X-ray crystallographic structures of ligand-activated GPCRs have been determined in their agonist-bound or inverse agonist-bound states, providing a wealth of fundamental resources for the investigation of the atomic-level mechanism of receptor activation and deactivation. A number of computational methods, such as conventional and enhanced sampling Molecular Dynamic (MD) simulations have been applied to investigate the receptor dynamics bound with ligands of different functional types (i.e., agonist and inverse agonist). In this article, we reviewed recent efforts in characterizing the dynamical activation and deactivation mechanisms of GPCRs induced by different functional types of ligands.

Post translational modification (PTM) is one of the critical levels in regulation of gene expression that determines the fate of proteins after translation in eukaryotic cells. Since the detection of PTM sites in proteins is useful for diagnosis and prevention of diseases, numerous web-tool predictors are introduced to predict various types of PTMs. In this paper, an attempt is made to summarize a number of server predictors for the prediction of PTM sites.

BRI1 and BAK1 are Receptor-Like Kinases (RLKs), one of the largest gene families in plants participating in various cell signal transduction from cell surface to cytoplasm with oligomerization and phosphorylation to regulate plant growth, development, immunity, and environmental responses. Based on the recent investigations on the BRI1 and BAK1 and other RLKs involving in the receptor complex formation, transphosphorylation, phosphorylation sites identification, downstream substrates identification, and so on, it is recovered that the receptors oligomerization and phosphorylation integrate multiple distinct signaling to realize signaling modulation, divergence, convergence, and specificity. The studies of the complex formation and phosphorylation of BRI1 and BAK1 uncovered the potential signaling transduction interface primarily composed of the RLKs. The general novel model will be helpful to understand the plant cell signal transduction networks.

Enzymes are biological catalysts that play an important role in determining the patterns of chemical transformations pertaining to life. Many milestones have been achieved in unraveling the mechanisms in which the enzymes orchestrate various cellular processes using experimental and computational approaches. Experimental studies generating nearly all possible mutations of target enzymes have been aided by rapid computational approaches aiming at enzyme functional classification, understanding domain organization, functional site identification. The functional architecture, essentially, is involved in binding or interaction with ligands including substrates, products, cofactors, inhibitors, providing for their function, such as in catalysis, ligand mediated cell signaling, allosteric regulation and post-translational modifications. With the increasing availability of enzyme information and advances in algorithm development, computational approaches have now become more capable of providing precise inputs for enzyme engineering, and in the process also making it more efficient. This has led to interesting findings, especially in aberrant enzyme interactions, such as hostpathogen interactions in infection, neurodegenerative diseases, cancer and diabetes. This review aims to summarize in retrospection – the mined knowledge, vivid perspectives and challenging strides in using available experimentally validated enzyme information for characterization. An analytical outlook is presented on the scope of exploring future directions.

The last couple of decades has witnessed that an amalgamation of multidisciplinary branches of science come together in the form of ‘Bioinformatics’ and made a substantial impact on the drug designing process. The applicability of Bioinformatics approaches has been able to lower down the overall cost and time of drug discovery and development. The Computer Aided Drug Designing System (CADDS) using extensive applicability of Bioinformatics has been recognized as one step ahead to carry out the primary high throughput virtual screening as an economically viable solution to the problem. The present article discusses the applicability of various Bioinformatics tools for virtual screening and molecular dynamics of selected molecules/ active ingredients derived from herbs, semi-synthetic and synthetic compounds, to predict their possible therapeutic interventions in diabetes induced neuropathy and neurodegenerative disorders. The article ends by summarizing the application of the virtual screening, lead optimization and predictions of bioavailability and bioactivity in the experimental drug development research.

(2R,3R)-2,3-Butanediol has many industrial applications, such as it is used as an antifreeze agent and low freezing point fuel. In addition, it is particularly important to provide chiral groups in drugs. In recent years, this valuable bio-based chemical has attracted increasing attention, and significant progress has been made in the development of microbial cell factories for (2R,3R)-2,3-butanediol production. This article reviews recent advances and challenges in microbial routes to (2R,3R)-2,3- butanediol production, and highlights the metabolic engineering and synthetic biological approaches used to improve titers, yields, productivities, and optical purities. Finally, a systematic and integrative strategy for developing high-performance microbial cell factories is proposed.

Addiction is a chronic, relapsing disease of the brain that includes drug-induced compulsive seeking behavior and consumption of drugs. Dopamine (DA) is considered to be critical in drug addiction due to reward mechanisms in the midbrain. In this article, we review the major animal models in addictive drug experiments in vivo and in vitro. We discuss the relevance of the structure and pharmacological function of DA receptors. To improve the understanding of the role of DA receptors in reward pathways, specific brain regions, including the Ventral tegmental area, Nucleus accumbens, Prefrontal cortex, and Habenula, are highlighted. These factors contribute to the development of novel therapeutic targets that act at DA receptors. In addiction, the development of neuroimaging method will increase our understanding of the mechanisms underlying drug addiction.

System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors.