Prediction of potential drug targets based on simple sequence properties

<p>Abstract</p> <p>Background</p> <p>During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.</p> <p>Results</p> <p>Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research.</p> <p>Conclusion</p> <p>We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.</p

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Characterization of High-Fat, Diet-Induced, Non-alcoholic Steatohepatitis with Fibrosis in Rats

An ideal animal model is necessary for a clear understanding of the etiology, pathogenesis, and mechanisms of human non-alcoholic steatohepatitis (NASH) and for facilitating the design of effective therapy for this condition. We aimed to establish a rat model of NASH with fibrosis by using a high-fat diet (HFD). Male Sprague–Dawley (SD) rats were fed a HFD consisting of 88 g normal diet, 10 g lard oil, and 2 g cholesterol. Control rats were fed normal diet. Rats were killed at 4, 8, 12, 16, 24, 36, and 48 weeks after HFD exposure. Body weight, liver weight, and epididymal fat weight were measured. Serum levels of fasting glucose, triglyceride, cholesterol, alanine aminotransferase (ALT), free fatty acids (FFA), insulin, and tumor necrosis factor-alpha (TNF-α) were determined. Hepatic histology was examined by H&E stain. Hepatic fibrosis was assessed by VG stain and immunohistochemical staining for transforming growth factor beta 1 (TGF-β1), and alpha-smooth-muscle actin (α-SMA). The liver weight and liver index increased from week 4, when hepatic steatosis was also observed. By week 8, the body weight and epididymal fat weight started increasing, which was associated with increased serum levels of FFA, cholesterol, and TNF-α, as well as development of simple fatty liver. The serum ALT level increased from week 12. Steatohepatitis occurred from weeks 12 through 48. Apparent hepatic perisinosodial fibrosis did not occur until week 24, and progressed from week 36 to 48 with insulin resistance. Therefore, this novel model may be potentially useful in NASH study

A Global Characterization and Identification of Multifunctional Enzymes

Multi-functional enzymes are enzymes that perform multiple physiological functions. Characterization and identification of multi-functional enzymes are critical for communication and cooperation between different functions and pathways within a complex cellular system or between cells. In present study, we collected literature-reported 6,799 multi-functional enzymes and systematically characterized them in structural, functional, and evolutionary aspects. It was found that four physiochemical properties, that is, charge, polarizability, hydrophobicity, and solvent accessibility, are important for characterization of multi-functional enzymes. Accordingly, a combinational model of support vector machine and random forest model was constructed, based on which 6,956 potential novel multi-functional enzymes were successfully identified from the ENZYME database. Moreover, it was observed that multi-functional enzymes are non-evenly distributed in species, and that Bacteria have relatively more multi-functional enzymes than Archaebacteria and Eukaryota. Comparative analysis indicated that the multi-functional enzymes experienced a fluctuation of gene gain and loss during the evolution from S. cerevisiae to H. sapiens. Further pathway analyses indicated that a majority of multi-functional enzymes were well preserved in catalyzing several essential cellular processes, for example, metabolisms of carbohydrates, nucleotides, and amino acids. What’s more, a database of known multi-functional enzymes and a server for novel multi-functional enzyme prediction were also constructed for free access at http://bioinf.xmu.edu.cn/databases/MFEs/index.htm

Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

The Yeast Pif1 Helicase Prevents Genomic Instability Caused by G-Quadruplex-Forming CEB1 Sequences In Vivo

In budding yeast, the Pif1 DNA helicase is involved in the maintenance of both nuclear and mitochondrial genomes, but its role in these processes is still poorly understood. Here, we provide evidence for a new Pif1 function by demonstrating that its absence promotes genetic instability of alleles of the G-rich human minisatellite CEB1 inserted in the Saccharomyces cerevisiae genome, but not of other tandem repeats. Inactivation of other DNA helicases, including Sgs1, had no effect on CEB1 stability. In vitro, we show that CEB1 repeats formed stable G-quadruplex (G4) secondary structures and the Pif1 protein unwinds these structures more efficiently than regular B-DNA. Finally, synthetic CEB1 arrays in which we mutated the potential G4-forming sequences were no longer destabilized in pif1Δ cells. Hence, we conclude that CEB1 instability in pif1Δ cells depends on the potential to form G-quadruplex structures, suggesting that Pif1 could play a role in the metabolism of G4-forming sequences

Host-parasite co-metabolic activation of antitrypanosomal aminomethyl-benzoxaboroles

<div><p>Recent development of benzoxaborole-based chemistry gave rise to a collection of compounds with great potential in targeting diverse infectious diseases, including human African Trypanosomiasis (HAT), a devastating neglected tropical disease. However, further medicinal development is largely restricted by a lack of insight into mechanism of action (MoA) in pathogenic kinetoplastids. We adopted a multidisciplinary approach, combining a high-throughput forward genetic screen with functional group focused chemical biological, structural biology and biochemical analyses, to tackle the complex MoAs of benzoxaboroles in <i>Trypanosoma brucei</i>. We describe an oxidative enzymatic pathway composed of host semicarbazide-sensitive amine oxidase and a trypanosomal aldehyde dehydrogenase TbALDH3. Two sequential reactions through this pathway serve as the key underlying mechanism for activating a series of 4-aminomethylphenoxy-benzoxaboroles as potent trypanocides; the methylamine parental compounds as pro-drugs are transformed first into intermediate aldehyde metabolites, and further into the carboxylate metabolites as effective forms. Moreover, comparative biochemical and crystallographic analyses elucidated the catalytic specificity of TbALDH3 towards the benzaldehyde benzoxaborole metabolites as xenogeneic substrates. Overall, this work proposes a novel drug activation mechanism dependent on both host and parasite metabolism of primary amine containing molecules, which contributes a new perspective to our understanding of the benzoxaborole MoA, and could be further exploited to improve the therapeutic index of antimicrobial compounds.</p></div