Novel cytotoxic exhibition mode of antimicrobial peptide anoplin in MEL cells,the cell line of murine Friend leukemia virus‐induced leukemic cells

Anoplin is a recently discovered antimicrobial peptide (AMP) isolated from the venom sac of the spider wasp Anoplius samariensis, and it is one of the shortest α‐helical AMP found naturally to date consisting of only ten amino acids. Previous results showed that anoplin exhibits potent antimicrobial activity but little hemolytic activity. In this study, we synthesized anoplin, studied its cytotoxicity in Friend virus‐induced leukemia cells [murine erythroleukemia (MEL) cells], and proposed its possible mechanism. Our results showed that anoplin could inhibit the proliferation of MEL cells in a dose‐dependent and time‐dependent manner via disrupting the integrity of cell membrane, which indicated that anoplin exerts its cytotoxicity efficacy. In addition, the cell cycle distribution of MEL cells was arrested in the G₀/G₁ phase significantly. However, anoplin could not induce obvious apoptosis in MEL cells, as well as anoplin could not induce visible changes on morphology and quantity in the bone marrow cells isolated from normal mice. All of these results indicate that anoplin, as generally believed, is a selective AMP, a value characteristic in the design of safe therapeutic agents. The cytotoxicity of anoplin on MEL cells was mainly attributable to the plasma membrane perturbation and also to the intracellular events such as the arrest of cell cycle. Although this is an initial study that explored the activity of anoplin in vitro rather than in vivo, with the increasing resistance of conventional chemotherapy, there is no doubt that anoplin has desirable feature to be developed as a novel and selective anticancer agent. Copyright © 2013 European Peptide Society and John Wiley & Sons, Ltd.     

Absolute Quantification of E1, Ubiquitin-Like Proteins and Nedd8–MLN4924 Adduct by Mass Spectrometry

Ubiquitin (Ub) and ubiquitin-like (Ubl) proteins regulate a variety of important cellular processes by forming covalent conjugates with target proteins or lipids. Ubl conjugation is catalyzed by a cascade of proteins including activating enzymes (E1), conjugating enzymes (E2), and in many cases ligation enzymes (E3). The discovery of MLN4924 (Brownell et al., Mol Cell 37: 102–111, 1), an investigational small molecule that is a mechanism-based inhibitor of NEDD8-activating enzyme (NAE), reveals a promising strategy of targeting E1/Ubl pathway for therapeutic purposes. In order to better understand, the biochemical dynamics of Ubl conjugation in cells and tissues, we have developed a mass spectrometry-based method to quantify E1 and Ubls using isotope-labeled proteins as internal standards. Furthermore, we have used the described method to quantify levels of the covalent Nedd8-inhibitor adduct formed in MLN4924 treated cells and tissues. The Nedd8–MLN4924 adduct is a tight-binding inhibitor of NAE, and its cellular concentration represents an indirect pharmacodynamic readout of NAE/Nedd8 pathway inhibition.     

A conservation and rigidity based method for detecting critical protein residues

Background

Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins.

Results

We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins.

Conclusions

Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces.

     

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

Background

We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.

Results

Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.

Conclusions

Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

     

Statins in cardiometabolic disease: what makes pitavastatin different?

The term cardiometabolic disease encompasses a range of lifestyle-related conditions, including Metabolic syndrome (MetS) and type 2 diabetes (T2D), that are characterized by different combinations of cardiovascular (CV) risk factors, including dyslipidemia, abdominal obesity, hypertension, hyperglycemia/insulin resistance, and vascular inflammation. These risk factors individually and interdependently increase the risk of CV and cerebrovascular events, and represent one of the biggest health challenges worldwide today. CV diseases account for almost 50% of all deaths in Europe and around 30% of all deaths worldwide. Furthermore, the risk of CV death is increased twofold to fourfold in people with T2D. Whilst the clinical management of CV disease has improved in Western Europe, the pandemic of obesity and T2D reduces the impact of these gains. This, together with the growing, aging population, means the number of CV deaths is predicted to increase from 17.1 million worldwide in 2004 to 23.6 million in 2030. The recommended treatment for MetS is lifestyle change followed by treatment for the individual risk factors. Numerous studies have shown that lowering low-density lipoprotein-cholesterol (LDL-C) levels using statins can significantly reduce CV risk in people with and without T2D or MetS. However, the risk of major vascular events in those attaining the maximum levels of LDL-C-reduction is only reduced by around one-third, which leaves substantial residual risk. Recent studies suggest that low high-density lipoprotein-cholesterol (HDL-C) (<1 .0 mmol/l; 40 mg/dl) and high triglyceride levels (≥1.7 mmol/l; 150 mg/dl) are independent risk factors for CV disease and that the relationship between HDL-C and CV risk persists even when on-treatment LDL-C levels are low (<1.7 mmol/l; 70 mg/dl). European guidelines highlight the importance of reducing residual risk by targeting these risk factors in addition to LDL-C. This is particularly important in patients with T2D and MetS because obesity and high levels of glycated hemoglobin are directly related to low levels of HDL-C and high triglyceride. Although most statins have a similar low-density lipoprotein-lowering efficacy, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects (for example, high-density lipoprotein-elevating efficacy), adverse event profiles, and drug-drug interactions. The choice of statin should therefore depend on the needs of the individual patient. The following reviews will discuss the potential benefits of pitavastatin versus other statins in the treatment of patients with dyslipidemia and MetS or T2D, focusing on its effects on HDL-C quantity and quality, its potential impact on atherosclerosis and CV risk, and its metabolic characteristics that reduce the risk of drug interactions. Recent controversies surrounding the potentially diabetogenic effects of statins will also be discussed.     

Hopeahainol A attenuates memory deficits by targeting β‐amyloid in APP/PS1 transgenic mice

Increasing evidence demonstrates that amyloid beta (Aβ) elicits mitochondrial dysfunction and oxidative stress, which contributes to the pathogenesis of Alzheimer's disease (AD). Identification of the molecules targeting Aβ is thus of particular significance in the treatment of AD. Hopeahainol A (HopA), a polyphenol with a novel skeleton obtained from Hopea hainanensis, is potentially acetylcholinesterase‐inhibitory and anti‐oxidative in H₂O₂‐treated PC12 cells. In this study, we reported that HopA might bind to Aβ_1–42 directly and inhibit the Aβ_1–42 aggregation using a combination of molecular dynamics simulation, binding assay, transmission electron microscopic analysis and staining technique. We also demonstrated that HopA decreased the interaction between Aβ_1–42 and Aβ‐binding alcohol dehydrogenase, which in turn reduced mitochondrial dysfunction and oxidative stress in vivo and in vitro. In addition, HopA was able to rescue the long‐term potentiation induction by protecting synaptic function and attenuate memory deficits in APP/PS1 mice. Our data suggest that HopA might be a promising drug for therapeutic intervention in AD.     

Predicting beta-turns in proteins using support vector machines with fractional polynomials

Background

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.

Results

We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.

Conclusions

In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

     

Integrating domain similarity to improve protein complexes identification in TAP-MS data

Background

Detecting protein complexes in protein-protein interaction (PPI) networks plays an important role in improving our understanding of the dynamic of cellular organisation. However, protein interaction data generated by high-throughput experiments such as yeast-two-hybrid (Y2H) and tandem affinity-purification/mass-spectrometry (TAP-MS) are characterised by the presence of a significant number of false positives and false negatives. In recent years there has been a growing trend to incorporate diverse domain knowledge to support large-scale analysis of PPI networks.

Methods

This paper presents a new algorithm, by incorporating Gene Ontology (GO) based semantic similarities, to detect protein complexes from PPI networks generated by TAP-MS. By taking co-complex relations in TAP-MS data into account, TAP-MS PPI networks are modelled as bipartite graph, where bait proteins consist of one set of nodes and prey proteins are on the other. Similarities between pairs of bait proteins are computed by considering both the topological features and GO-driven semantic similarities. Bait proteins are then grouped in to sets of clusters based on their pair-wise similarities to produce a set of 'seed' clusters. An expansion process is applied to each 'seed' cluster to recruit prey proteins which are significantly associated with the same set of bait proteins. Thus, completely identified protein complexes are then obtained.

Results

The proposed algorithm has been applied to real TAP-MS PPI networks. Fifteen quality measures have been employed to evaluate the quality of generated protein complexes. Experimental results show that the proposed algorithm has greatly improved the accuracy of identifying complexes and outperformed several state-of-the-art clustering algorithms. Moreover, by incorporating semantic similarity, the proposed algorithm is more robust to noises in the networks.