Advances in visual representation of molecular potentials

Design of Two New Sulfur Derivatives of Perezone: In Silico Study Simulation Targeting PARP-1 and In Vitro Study Validation Using Cancer Cell Lines

Poly-ADP-Ribose Polymerase (PARP-1) is an overexpressed enzyme in several carcinomas; consequently, the design of PARP-1 inhibitors has acquired special attention. Hence, in the present study, three compounds (8-10) were produced through a Michael addition protocol, using phenylmethanethiol, 5-fluoro-2-mercaptobenzyl alcohol, and 4-mercaptophenylacetic acid, respectively, as nucleophiles and perezone as the substrate, expecting them to be convenient candidates that inhibit PARP-1. It is convenient to note that in the first stage of the whole study, the molecular dynamics (MD) simulations and the quantum chemistry studies of four secondary metabolites, i.e., perezone (1), perezone angelate (2), hydroxyperezone (3), and hydroxyperezone monoangelate (4), were performed, to investigate their interactions in the active site of PARP-1. Complementarily, a docking study of a set of eleven sulfur derivatives of perezone (5-15) was projected to explore novel compounds, with remarkable affinity to PARP-1. The molecules 8-10 provided the most adequate results; therefore, they were evaluated in vitro to determine their activity towards PARP-1, with 9 having the best IC50 (0.317 µM) value. Additionally, theoretical calculations were carried out using the density functional theory (DFT) with the hybrid method B3LYP with a set of base functions 6-311++G(d,p), and the reactivity properties were compared between the natural derivatives of perezone and the three synthesized compounds, and the obtained results exhibited that 9 has the best properties to bind with PARP-1. Finally, it is important to mention that 9 displays significant inhibitory activity against MDA-MB-231 and MCF-7 cells, i.e., 145.01 and 83.17 µM, respectively.

Synthesis, Cytotoxic Activity and In Silico Study of Novel Dihydropyridine Carboxylic Acids Derivatives

To aid the possible prevention of multidrug resistance in tumors and cause lower toxicity, a set of sixteen novel dihydropyridine carboxylic acids derivatives 3a-p were produced; thus, the activation of various ynones with triflic anhydride was performed, involving a nucleophilic addition of several bis(trimethylsilyl) ketene acetals, achieving good yields requiring easy workup. The target molecules were unequivocally characterized by common spectroscopic methods. In addition, two of the tested compounds (3a, and 3b) were selected to perform in silico studies due to the highest cytotoxic activity towards the HCT-15 cell line (7.94 ± 1.6 μM and 9.24 ± 0.9 μM, respectively). Employing theoretical calculations with density functional theory (DFT) using the B3LYP/6-311++G(d,p) showed that the molecular parameters correlate adequately with the experimental results. In contrast, predictions employing Osiris Property Explorer showed that compounds 3a and 3b present physicochemical characteristics that would likely make it an orally active drug. Moreover, the performance of Docking studies with proteins related to the apoptosis pathway allowed a proposal of which compounds could interact with PARP-1 protein. Pondering the obtained results (synthesis, in silico, and cytotoxic activity) of the target compounds, they can be judged as suitable antineoplastic agent candidates.

Streptococcus pneumoniae Genome-wide Identification and Characterization of BOX Element-binding Domains

The BOX elements are short repetitive DNA sequences that distribute randomly in intergenic regions of the Streptococcus pneumoniae genome. The function and origin of such elements are still unknown, but they were found to modulate expression of neighboring genes. Evidences suggested that the modulation's mechanism can be fulfilled by sequence-specific interaction of BOX elements with transcription factor family proteins. However, the type and function of these BOX-binding proteins still remain largely unexplored to date. In the current study we described a synthetic protocol to investigate the recognition and interaction between a highly conserved site of BOX elements and the DNA-binding domains of a variety of putative transcription factors in the pneumococcal genome. With the protocol we were able to predict those high-affinity domain binders of the conserved BOX DNA site (BOX DNA) in a high-throughput manner, and analyzed sequence-specific interaction in the domainDNA recognition at molecular level. Consequently, a number of putative transcription factor domains with both high affinity and specificity for the BOX DNA were identified, from which the helix-turn-helix (HTH) motif of a small heat shock factor was selected as a case study and tested for its binding capability toward the double-stranded BOX DNA using fluorescence anisotropy analysis. As might be expected, a relatively high affinity was detected for the interaction of HTH motif with BOX DNA with dissociation constant at nanomolar level. Molecular dynamics simulation, atomic structure examination and binding energy analysis revealed a complicated network of intensive nonbonded interactions across the complex interface, which confers both stability and specificity for the complex architecture.

Alignment free comparison: similarity distribution between the DNA primary sequences based on the shortest absent word

This work proposes an alignment free comparison model for the DNA primary sequences. In this paper, we treat the double strands of the DNA rather than single strand. We define the shortest absent word of the double strands between the DNA sequences and some properties are studied to speed up the algorithm for searching the shortest absent word. We present a novel model for comparison, in which the similarity distribution is introduced to describe the similarity between the sequences. A distance measure is deduced based on the Shannon entropy meanwhile is used in phylogenetic analysis. Some experiments show that our model performs well in the field of sequence analysis.

Progress in structure-based drug design against influenza A virus

INTRODUCTION

The 2009-H1N1 influenza pandemic has prompted new global efforts to develop new drugs and drug design techniques to combat influenza viruses. While there have been a number of attempts to provide drugs to treat influenza, drug resistance has been a major problem with only four drugs currently approved by the FDA for its treatment.

AREAS COVERED

In this review, the drug-resistant problem of influenza A viruses is discussed and summarized. The article also introduces the experimental and computational structures of drug targeting proteins, neuraminidases, and of the M2 proton channel. Furthermore, the article illustrates the latest drug candidates and techniques of computer-aided drug design with examples of their application, including virtual in silico screening and scoring, AutoDock and evolutionary technique AutoGrow.

EXPERT OPINION

Structure-based drug design is the inventive process for finding new drugs based on the structural knowledge of the biological target. Computer-aided drug design strategies and techniques will make drug discovery more effective and economical. It is anticipated that the recent advances in structure-based drug design techniques will greatly help scientists to develop more powerful and specific drugs to fight the next generation of influenza viruses.