Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues

Estimation of PM10-bound As, Cd, Ni and Pb levels by means of statistical modelling: PLSR and ANN approaches

Air quality assessment regarding metals and metalloids using experimental measurements is expensive and time consuming due to the cost and time required for the analytical determination of the levels of these pollutants. According to the European Union (EU) Air Quality Framework Directive (Directive 2008/50/EC), other alternatives, such as objective estimation techniques, can be considered for ambient air quality assessment in zones and agglomerations where the level of pollutants is below a certain concentration value known as the lower assessment threshold. These conditions occur in urban areas in Cantabria (northern Spain). This work aims to estimate the levels of As, Cd, Ni and Pb in airborne PM10 at two urban sites in the Cantabria region (Castro Urdiales and Reinosa) using statistical models as objective estimation techniques. These models were developed based on three different approaches: partial least squares regression (PLSR), artificial neural networks (ANNs) and an alternative approach consisting of principal component analysis (PCA) coupled with ANNs (PCA-ANN). Additionally, these models were externally validated using previously unseen data. The results show that the models developed in this work based on PLSR and ANNs fulfil the EU uncertainty requirements for objective estimation techniques and provide an acceptable estimation of the mean values. As a consequence, they could be considered as an alternative to experimental measurements for air quality assessment regarding the aforementioned pollutants in the study areas while saving time and resources.The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the Project CMT2010-16068. The authors also thank the Regional Environment Ministry of the Cantabria Government for providing the PM10 samples at the Castro Urdiales and Reinosa sites

Apelin Enhances Directed Cardiac Differentiation of Mouse and Human Embryonic Stem Cells

Apelin is a peptide ligand for an orphan G-protein coupled receptor (APJ receptor) and serves as a critical gradient for migration of mesodermal cells fated to contribute to the myocardial lineage. The present study was designed to establish a robust cardiac differentiation protocol, specifically, to evaluate the effect of apelin on directed differentiation of mouse and human embryonic stem cells (mESCs and hESCs) into cardiac lineage. Different concentrations of apelin (50, 100, 500 nM) were evaluated to determine its differentiation potential. The optimized dose of apelin was then combined with mesodermal differentiation factors, including BMP-4, activin-A, and bFGF, in a developmentally specific temporal sequence to examine the synergistic effects on cardiac differentiation. Cellular, molecular, and physiologic characteristics of the apelin-induced contractile embryoid bodies (EBs) were analyzed. It was found that 100 nM apelin resulted in highest percentage of contractile EB for mESCs while 500 nM had the highest effects on hESCs. Functionally, the contractile frequency of mESCs-derived EBs (mEBs) responded appropriately to increasing concentration of isoprenaline and diltiazem. Positive phenotype of cardiac specific markers was confirmed in the apelin-treated groups. The protocol, consisting of apelin and mesodermal differentiation factors, induced contractility in significantly higher percentage of hESC-derived EBs (hEBs), up-regulated cardiac-specific genes and cell surface markers, and increased the contractile force. In conclusion, we have demonstrated that the treatment of apelin enhanced cardiac differentiation of mouse and human ESCs and exhibited synergistic effects with mesodermal differentiation factors

Liver Is Able to Activate Naïve CD8+ T Cells with Dysfunctional Anti-Viral Activity in the Murine System

The liver possesses distinct tolerogenic properties because of continuous exposure to bacterial constituents and nonpathogenic food antigen. The central immune mediators required for the generation of effective immune responses in the liver environment have not been fully elucidated. In this report, we demonstrate that the liver can indeed support effector CD8+ T cells during adenovirus infection when the T cells are primed in secondary lymphoid tissues. In contrast, when viral antigen is delivered predominantly to the liver via intravenous (IV) adenovirus infection, intrahepatic CD8+ T cells are significantly impaired in their ability to produce inflammatory cytokines and lyse target cells. Additionally, intrahepatic CD8+ T cells generated during IV adenovirus infection express elevated levels of PD-1. Notably, lower doses of adenovirus infection do not rescue the impaired effector function of intrahepatic CD8+ T cell responses. Instead, intrahepatic antigen recognition limits the generation of potent anti-viral responses at both priming and effector stages of the CD8+ T cell response and accounts for the dysfunctional CD8+ T cell response observed during IV adenovirus infection. These results also implicate that manipulation of antigen delivery will facilitate the design of improved vaccination strategies to persistent viral infection

The Congenital Cataract-Linked G61C Mutation Destabilizes γD-Crystallin and Promotes Non-Native Aggregation

γD-crystallin is one of the major structural proteins in human eye lens. The solubility and stability of γD-crystallin play a crucial role in maintaining the optical properties of the lens during the life span of an individual. Previous study has shown that the inherited mutation G61C results in autosomal dominant congenital cataract. In this research, we studied the effects of the G61C mutation on γD-crystallin structure, stability and aggregation via biophysical methods. CD, intrinsic and extrinsic fluorescence spectroscopy indicated that the G61C mutation did not affect the native structure of γD-crystallin. The stability of γD-crystallin against heat- or GdnHCl-induced denaturation was significantly decreased by the mutation, while no influence was observed on the acid-induced unfolding. The mutation mainly affected the transition from the native state to the intermediate but not that from the intermediate to the unfolded or aggregated states. At high temperatures, both proteins were able to form aggregates, and the aggregation of the mutant was much more serious than the wild type protein at the same temperature. At body temperature and acidic conditions, the mutant was more prone to form amyloid-like fibrils. The aggregation-prone property of the mutant was not altered by the addition of reductive reagent. These results suggested that the decrease in protein stability followed by aggregation-prone property might be the major cause in the hereditary cataract induced by the G61C mutation

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

Herpes Simplex Virus Dances with Amyloid Precursor Protein while Exiting the Cell

Herpes simplex type 1 (HSV1) replicates in epithelial cells and secondarily enters local sensory neuronal processes, traveling retrograde to the neuronal nucleus to enter latency. Upon reawakening newly synthesized viral particles travel anterograde back to the epithelial cells of the lip, causing the recurrent cold sore. HSV1 co-purifies with amyloid precursor protein (APP), a cellular transmembrane glycoprotein and receptor for anterograde transport machinery that when proteolyzed produces A-beta, the major component of senile plaques. Here we focus on transport inside epithelial cells of newly synthesized virus during its transit to the cell surface. We hypothesize that HSV1 recruits cellular APP during transport. We explore this with quantitative immuno-fluorescence, immuno-gold electron-microscopy and live cell confocal imaging. After synchronous infection most nascent VP26-GFP-labeled viral particles in the cytoplasm co-localize with APP (72.8+/−6.7%) and travel together with APP inside living cells (81.1+/−28.9%). This interaction has functional consequences: HSV1 infection decreases the average velocity of APP particles (from 1.1+/−0.2 to 0.3+/−0.1 µm/s) and results in APP mal-distribution in infected cells, while interplay with APP-particles increases the frequency (from 10% to 81% motile) and velocity (from 0.3+/−0.1 to 0.4+/−0.1 µm/s) of VP26-GFP transport. In cells infected with HSV1 lacking the viral Fc receptor, gE, an envelope glycoprotein also involved in viral axonal transport, APP-capsid interactions are preserved while the distribution and dynamics of dual-label particles differ from wild-type by both immuno-fluorescence and live imaging. Knock-down of APP with siRNA eliminates APP staining, confirming specificity. Our results indicate that most intracellular HSV1 particles undergo frequent dynamic interplay with APP in a manner that facilitates viral transport and interferes with normal APP transport and distribution. Such dynamic interactions between APP and HSV1 suggest a mechanistic basis for the observed clinical relationship between HSV1 seropositivity and risk of Alzheimer's disease