Medicinal Chemistry - Volume 11, Issue 3, 2015

Facing the explosive growth of biological sequence data, such as those of protein/peptide and DNA/RNA, generated in the post-genomic age, many bioinformatical and mathematical approaches as well as physicochemical concepts have been introduced to timely derive useful informations from these biological sequences, in order to stimulate the development of medical science and drug design. Meanwhile, because of the rapid penetrations from these disciplines, medicinal chemistry is currently undergoing an unprecedented revolution. In this minireview, we are to summarize the progresses by focusing on the following six aspects. (1) Use the pseudo amino acid composition or PseAAC to predict various attributes of protein/peptide sequences that are useful for drug development. (2) Use pseudo oligonucleotide composition or PseKNC to do the same for DNA/RNA sequences. (3) Introduce the multi-label approach to study those systems where the constituent elements bear multiple characters and functions. (4) Utilize the graphical rules and “wenxiang” diagrams to analyze complicated biomedical systems. (5) Recent development in identifying the interactions of drugs with its various types of target proteins in cellular networking. (6) Distorted key theory and its application in developing peptide drugs.

Due to the low toxicity, easy synthesis, rapid elimination, and less side effect, more and more peptide inhibitors are emerging as the effective drugs that are clinically used in therapies of a number of diseases. At the same time the computer-aided drug design (CADD) methods have remarkably developed. In this mini review the newly developed peptide inhibitors and drugs are introduced, including peptide vaccines for cancers, peptide inhibitors for HIV, Alzheimer’s disease and related diseases, and the peptides as the leading compounds of drugs. The recent progress in the theory and methodology of peptide inhibitor design is reviewed. (1) The flexible protein-peptide docking model is introduced, in which the peptide structures are treated as segment-flexible chains using genetic algorithm and special force field parameters. (2) The “Wenxiang diagram” is illustrated for protein-peptide interaction analysis that has been successfully used in the coiled-coil interaction analysis. (3) The “Distorted key” theory is reviewed, which is an effective method to convert the peptide inhibitors to the small chemical drugs. (4) The amino acid property-based peptide prediction method (AABPP) is described that is a twolevel QSAR prediction network for the bioactivity prediction of peptide inhibitors. (5) Finally, several types of molecular interactions between protein and peptide ligands are summarized, including cation-π interactions; polar hydrogen-π interactions; and π-π stocking interactions.

The elucidation of the interactions between a protein and a ligand or proteins is a key issue for understanding of functional processes of various proteins, enzymes and receptors in biological organisms, system biology and drug design. One of the most important matters in these problems is the accurate estimation of binding free energy between a protein and a ligand or proteins. In the present review, we overview various techniques and introduce a new technique for estimation of binding free energy calculations. We also discuss possible ways to incorporate the effect of fect of solvent.

Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of novel methods for disease diagnosis and forensics. The experimental methods currently used to identify PPIs are both time-consuming and expensive, and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. These obstacles could be overcome by developing computational approaches to predict PPIs and validate the obtained experimental results. In this work, we will describe the recent advances in predicting protein-protein interaction from the following aspects: i) the benchmark dataset construction, ii) the sequence representation approaches, iii) the common machine learning algorithms, and iv) the cross-validation test methods and assessment metrics.

A protein contact map is a simplified, two-dimensional version of the three-dimensional protein structure. Protein contact map is proved to be crucial in forming the three-dimensional structure. Contact map prediction has now become an indispensable and promising intermediate step towards final three-dimensional structure prediction, while directed sequence-structure prediction hits its bottlenecks. In this article, different evaluation scores of prediction efficiency are compared. Next, the state of the art and future perspectives of contact map methods are reviewed and special attention is paid to those relying on machine learning algorithms. Details of neural network based methods as well as a list of machine learning based methods are given. Finally, bottlenecks and potential improvements of contact map predictions are discussed.

Aberrant regulation of epigenetic pathways causes many diseases including aging, cancer, diabetes, viral pathogenesis, drug addiction etc. and it has been estimated that epigenetic aberrations are at least ten to forty times more frequent in cancers than genetic mutations. Present epigenetic modulators hold great promise for a variety of diseases, and important tools for biological applications but these molecules have many dose limiting toxicities and existing paradigms lack desired efficacy. Synthesis and biological studies of epigenetic modulators have been attractive targets for medicinal and synthetic organic chemists in recent years. This review article provides deep insight into the new and under explored epigenetic modulators. These molecules have the potential to be used as unique template with novel pharmacophores.

Melanocortins, i.e., melanocyte stimulating hormones (MSH) are peptides with strong antiinflammatory effects. The most investigated aspects of γ2-MSH are related to cardiovascular effects and natriuresis, with limited research available about its anti-inflammatory and cytoprotective effects. The aims of this study were: 1) to examine the effects of γ2-MSH and its derivative [D-Trp2]-γ2-MSH on the acetaminophen model of liver damage in CBA mice; 2) to evaluate the modulation of γ2-MSH hepatoprotection by melanocortin subtypes 3 and 4 receptor antagonists SHU 9119 and HS 024; 3) to define the importance of central MSH pharmacophore region (HFRW) by using antisense peptides LVKAT and VKAT. In this study, specific antagonists and antisense peptides were used to target central pharmacophore region of γ2-MSH and [D-Trp⁸]-γ2-MSH, enabling the evaluation of hepatoprotection from the standpoint of the receptor and pharmacophore blockade. The criteria for monitoring the effects of the hormones on the liver damage were alanine transaminase, aspartate transaminase activities (U/L), and pathohistological scoring of liver necrosis (scale 0-5). γ2-MSH (0.24 mg/kg) indicated hepatoprotective effects in comparison to control (p < 0.001). In contrast, [D-Trp2]-γ2-MSH did not show any hepatoprotective effects. Application of antagonists SHU 9119 and HS 024, and antisense peptides LVKAT and VKAT, also did not show any hepatoprotective effects. In fact, when combined with γ2-MSH, it annulled its hepatoprotective effect. The results provide evidence for hepatoprotective and antiinflammatory effects of the γ2-MSH in the liver.

The search for newer histone deacetylase (HDAC) inhibitors has attracted a great deal of interest of medicinal chemists worldwide, especially after the first HDAC inhibitor (Zolinza^®, widely known as SAHA or Suberoylanilide hydroxamic acid) was approved by the FDA for the treatment of Tcell lymphoma in 2006. As a continuity of our ongoing research in this area, we designed and synthesized a series of 5-aryl-1,3,4-thiadiazole-based hydroxamic acids as analogues of SAHA and evaluated their biological activities. Most of the compounds in this series, e.g. compounds with 5-aryl moiety being 2- furfuryl (5a), 5-bromofuran-2-yl (5b), 5-methylfuran-2-yl (5c), thiophen-2-yl (5d), 5-methylthiophen-2-yl (5f) and pyridyl (5g-i), were found to have potent anticancer cytotoxicity with IC50 values of generally 5- to 10-fold lower than that of SAHA in 4 human cancer cell lines assayed. Those compounds with potent cytotoxicity were also found to have strong HDAC inhibition effects. Docking studies revealed that compounds 5a and 5d displayed high affinities towards HDAC2 and 8.

The continual emergence of bacterial resistance problems to current clinical drugs has brought a severe threat against human being's health; and the development of novel antimicrobial agents for selectively inhibiting the constantly evolved bacterial targets has also been continually promoted, with challenging processes like marathon race. FabH, which initiated the fatty acid biosynthesis cycle, provided considerable new opportunities in novel antibacterial drug discovery. Based on our previous findings that o-hydroxybenzylamine derivatives demonstrated potent FabH inhibitory and antimicrobial activities, computer-assistant drug design was introduced and then a series of novel nitrobenzotrifluoride-containing ohydroxybenzylamine derivatives (3a-3x) was designed and synthesized. Most of them were more potent than the corresponding urea analogues, with compound 3d being the most potent member. Furthermore, the structure-activity relationship of all synthesized o-hydroxybenzylamine derivatives as FabH inhibitors was studied, and inhibitory potency of top antimicrobial compounds against the aminoacylation of S. aureus tyrosyl-tRNA synthetase was also evaluated.