Current Topics in Medicinal Chemistry - Volume 17, Issue 14, 2017

Background: The mammalian bombesin receptor family comprises three G proteincoupled receptors: the neuromedin B receptor, the gastrin-releasing peptide receptor (BB2), and the bombesin receptor subtype 3. BB2 receptor plays a role in gastrointestinal functions; however, at present the role of this subtype in physiological and pathological conditions is unknown due to the lack of specific binders for all subclasses of bombesin receptors. Results: Here, we present a study focused on the properties of the peptoid bombesin antagonist called PD176252, and other structural analogues with the aim to elucidate causes of their different affinity towards the BB2 receptor. Conclusion: By means of computational techniques, based on QSAR, docking and homology building, supported by experimental data (X-ray diffraction and NMR spectroscopy) fresh insights on binding modes of this class of biological targets were achieved.

Introduction: Histone deacetylase (HDAC) enzymes control the acetylation status of transcription factors that regulate chromatin structure and gene function. The transcriptional regulatory factors that distinguish histone acetylation and deacetylation patterns by pharmacological HDAC inhibition (HDACi) have not yet been studied. Methods: We analysed sequencing datasets derived from human aortic endothelial cells (HAECs) stimulated with the HDAC inhibitors, Trichostatin A (TSA) and suberoylanilide hydroxamic acid (SAHA). We integrated gene expression and histone acetylation profiles with the transcription factor binding site (TFBS) database derived from the Encyclopedia of DNA Elements (ENCODE) project. Results: Overall, TFBS signatures observed in SAHA and TSA stimulated cells were analogous. Histone acetylation was observed at transcription factor binding sites of target genes associated with the silencing factors NRSF, EZH2 and SUZ12. Histone deacetylation was a prominent property of HDACi and correlated with changes in the expression of genes regulated by proteins in transcriptional control such as histone acetyltransferase P300 and lysine demethylase JARID1A, as well as the regulatory factors cMYC, YY1 and STAT family proteins. Conclusion: We identified several transcription factors and coregulators implicated in the regulation of histone modification at target genes mediated by pharmacological HDAC inhibition.

Background: Tuberculosis, one of the oldest diseases affecting human beings, is still considered as a world public health problem by the World Health Organization. Method & Material: Therefore, there is a need for new and more powerful analytical methods for early illness diagnosis. With this idea in mind, the development of a High Fundamental Frequency (HFF) piezoelectric immunosensor for the sensitive detection of tuberculosis was undertaken. A 38 kDa protein secreted by Mycobacterium tuberculosis was first selected as the target biomarker. Then, specific monoclonal antibodies (MAbs) were obtained. Myc-31 MAb, which showed the highest affinity to the analyte, was employed to set up a reference enzyme-linked immunosorbent assay (ELISA) with a limit of detection of 14 ng mL-1 of 38 kDa antigen. Results & Discussion: For the development of the HFF piezoelectric immunosensor, 100 MHz quartz crystals were used as transducer elements. The gold electrode surface was functionalized by covalent immobilization of the target biomarker through mixed self-assembled monolayers (mSAM) of carboxylic alkane thiols. A competitive immunoassay based on Myc-31 MAb was integrated with the transducer as sensing bio-recognition event. Reliable assay signals were obtained using low concentrations of antigen for functionalization and MAb for the competitive immunoassay. Under optimized conditions, the HFF immunosensor calibration curve for 38 kDa determination showed a limit of detection as low as 11 ng mL-1 of the biomarker. The high detectability attained by this immunosensor, in the picomolar range, makes it a promising tool for the easy, direct and sensitive detection of the tuberculosis biomarker in biological fluids such as sputum.

Background: A high-throughput virtual screening pipeline has been extended from single energetically minimized structure Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring to ensemble-average MM/GBSA rescoring. The correlation coefficient (R2) of calculated and experimental binding free energies for a series of antithrombin ligands has been improved from 0.36 to 0.69 when switching from the single-structure MM/GBSA rescoring to ensemble-average one. The electrostatic interactions in both solute and solvent are identified to play an important role in determining the binding free energy after the decomposition of the calculated binding free energy. The increasing negative charge of the compounds provides a more favorable electrostatic energy change but creates a higher penalty for the solvation free energy. Such a penalty is compensated by the electrostatic energy change, which results in a better binding affinity. A highly hydrophobic ligand is determined by the docking program to be a non-specific binder. Results: Our results have demonstrated that it is very important to keep a few top poses for rescoring, if the binding is non-specific or the binding mode is not well determined by the docking calculation.

Background: Mitigation of unwanted effects is a major issue to be addressed in drug discovery. The human genome shares a considerable degree of sequence identity and structural homology whereby multiple binding pockets of similar composition is seen across different family of proteins. This distribution of similar binding pockets in different proteins is a major reason for cross reactivity upon ligand binding. Therefore, identifying such off-targets on a case-to-case basis in the early stages of drug discovery pipeline is very much essential. Especially for developing specific ligands against isozymes such as glycogen phosphorylase it is a pre-requisite owing to its implications. The inhibitors of glycogen phosphorylase (GPi) control the blood glucose level effectively by inhibiting the hepatic GP (hGP). But due to its high degree of sequence identity with the muscle form (mGP) and similar binding pocket, these ligands cross react resulting in impaired muscle function due to prolonged relaxation. Therefore, molecular targeting or optimizing the lead to a specific target (hGP) is very essential. In the current work, a comparative docking approach was adopted wherein suitable ligands were designed based on the chemical descriptors present in lig18 binding site of mGP and hGP followed by virtual docking. Results: The docking results showed that lig18 optimized compound (lig18_7) had better affinity to hGP and mGP respectively. lig18_7 interacted with residues Arg171 and Asn172 that are present specifically in hGP indicating specificity. Using this improved comparative docking approach isoform- specific ligands can be effectively developed.

Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.