Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.

Hybrid Diphenylalkyne-Dipeptide Oligomers Induce Multistrand β-Sheet Formation

Functionalized diphenylalkynes provide a template for the presentation of protein-like surfaces composed of multistrand β-sheets. The conformational properties of three-, four-, and seven-stranded systems have been investigated in the solid- and solution-state. This class of molecule may be suitable for the mediation of therapeutically relevant protein-protein interactions.

β-Strand mimetic foldamers rigidified through dipolar repulsion

Many therapeutically relevant protein-protein interactions contain hot-spot regions on secondary structural elements, which contribute disproportionately to binding enthalpy. Mimicry of such α-helical regions has met with considerable success, however the analogous approach for the β-strand has received less attention. Presented herein is a foldamer for strand mimicry in which dipolar repulsion is a central determinant of conformation. Computation as well as solution- and solid-phase data are consistent with an ensemble weighted almost exclusively in favor of the desired conformation.

Low-dose salinomycin induces anti-leukemic responses in AML and MLL

Development of anti-cancer drugs towards clinical application is costly and inefficient. Large screens of drugs, efficacious for non-cancer disease, are currently being used to identify candidates for repurposing based on their anti-cancer properties. Here, we show that low-dose salinomycin, a coccidiostat ionophore previously identified in a breast cancer screen, has anti-leukemic efficacy. AML and MLLr cell lines, primary cells and patient samples were sensitive to submicromolar salinomycin. Most strikingly, colony formation of normal hematopoietic cells was unaffected by salinomycin, demonstrating a lack of hemotoxicity at the effective concentrations. Furthermore, salinomycin treatment of primary cells resulted in loss of leukemia repopulation ability following transplantation, as demonstrated by extended recipient survival compared to controls. Bioinformatic analysis of a 17-gene signature identified and validated in primary MLLr cells, uncovered immunomodulatory pathways, hubs and protein interactions as potential transducers of low dose salinomycin treatment. Additionally, increased protein expression of p62/Sqstm1, encoded for by one of the 17 signature genes, demonstrates a role for salinomycin in aggresome/vesicle formation indicative of an autophagic response.
Together, the data support the efficacy of salinomycin as an anti-leukemic at non-hemotoxic concentrations. Further investigation alone or in combination with other therapies is warranted for future clinical trial.

Mapping protein-DNA interactions using ChIP-sequencing

Chromatin immunoprecipitation (ChIP) allows enrichment of genomic regions which are associated with specific transcription factors, histone modifications, and indeed any other epitopes which are present on chromatin. The original ChIP methods used site-specific PCR and Southern blotting to confirm which regions of the genome were enriched, on a candidate basis. The combination of ChIP with genomic tiling arrays (ChIP-chip) allowed a more unbiased approach to map ChIP-enriched sites. However, limitations of microarray probe design and probe number have a detrimental impact on the coverage, resolution, sensitivity, and cost of whole-genome tiling microarray sets for higher eukaryotes with large genomes. The combination of ChIP with high-throughput sequencing technology has allowed more comprehensive surveys of genome occupancy, greater resolution, and lower cost for whole genome coverage. Herein, we provide a comparison of high-throughput sequencing platforms and a survey of ChIP-seq analysis tools, discuss experimental design, and describe a detailed ChIP-seq method.Chromatin immunoprecipitation (ChIP) allows enrichment of genomic regions which are associated with specific transcription factors, histone modifications, and indeed any other epitopes which are present on chromatin. The original ChIP methods used site-specific PCR and Southern blotting to confirm which regions of the genome were enriched, on a candidate basis. The combination of ChIP with genomic tiling arrays (ChIP-chip) allowed a more unbiased approach to map ChIP-enriched sites. However, limitations of microarray probe design and probe number have a detrimental impact on the coverage, resolution, sensitivity, and cost of whole-genome tiling microarray sets for higher eukaryotes with large genomes. The combination of ChIP with high-throughput sequencing technology has allowed more comprehensive surveys of genome occupancy, greater resolution, and lower cost for whole genome coverage. Herein, we provide a comparison of high-throughput sequencing platforms and a survey of ChIP-seq analysis tools, discuss experimental design, and describe a detailed ChIP-seq method.

Mapping Protein-DNA Interactions Using ChIP-exo and Illumina-Based Sequencing

Chromatin immunoprecipitation (ChIP) provides a means of enriching DNA associated with transcription factors, histone modifications, and indeed any other proteins for which suitably characterized antibodies are available. Over the years, sequence detection has progressed from quantitative real-time PCR and Southern blotting to microarrays (ChIP-chip) and now high-throughput sequencing (ChIP-seq). This progression has vastly increased the sequence coverage and data volumes generated. This in turn has enabled informaticians to predict the identity of multi-protein complexes on DNA based on the overrepresentation of sequence motifs in DNA enriched by ChIP with a single antibody against a single protein. In the course of the development of high-throughput sequencing, little has changed in the ChIP methodology until recently. In the last three years, a number of modifications have been made to the ChIP protocol with the goal of enhancing the sensitivity of the method and further reducing the levels of nonspecific background sequences in ChIPped samples. In this chapter, we provide a brief commentary on these methodological changes and describe a detailed ChIP-exo method able to generate narrower peaks and greater peak coverage from ChIPped material.

Effects of roziglitazone and interactions with growth-regulating factors in ventricular cell hypertrophy

Chronic administration of thiazolidinediones might predispose to cardiac hypertrophy. The aim was to investigate direct effects of rosiglitazone in rat ventricular cardiomyocytes maintained in vitro (24 h). Rosiglitazone (=10-5 M) did not increase protein synthesis and produced small inconsistent increases in cellular protein. In the presence of serum (10% v/v), but not insulin-like growth factor (IGF-1, 10-8 M) or insulin (1 U/ml), an interaction with rosiglitazone to stimulate protein synthesis was observed. The hypertrophic responses to noradrenaline (5×10-6 M), PMA (10-7 M) and ET-1 (10-7 M) were not attenuated by rosiglitazone. Rosiglitazone (10-7 M) did not influence protein synthesis in response to insulin (1 U/ml) and elevated glucose (2.5×10-2 M) alone or in combination, but attenuated the increase in protein mass observed in response to elevated glucose alone. In re-differentiated cardiomyocytes, a model of established hypertrophy, rosiglitazone (10-8 M–10-6 M) increased protein synthesis. Together, these data indicate that rosiglitazone does not initiate cardiomyocyte hypertrophy directly in vitro. However, during chronic administration, the interaction of rosiglitazone with locally-derived growth-regulating factors may make a modest contribution to cardiac remodelling and influence the extent of compensatory hypertrophy of the compromised rat heart.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.