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Data-driven Approach to Detect and Predict Adverse Drug Reactions

BACKGROUND Many factors that directly or indirectly cause adverse drug reaction (ADRs) varying from pharmacological, immunological and genetic factors to ethnic, age, gender, social factors as well as drug and disease related ones. On the other hand, advanced methods of statistics, machine learning and data mining allow the users to more effectively analyze the data for descriptive and predictive purposes. The fast changes in this field make it difficult to follow the research progress and context on ADR detection and prediction. METHODS A large amount of articles on ADRs in the last twenty years is collected. These articles are grouped by recent data types used to study ADRs: omics, social media and electronic medical records (EMRs), and reviewed in terms of the problem addressed, the datasets used and methods. RESULTS Corresponding three tables are established providing brief information on the research for ADRs detection and prediction. CONCLUSION The data-driven approach has shown to be powerful in ADRs detection and prediction. The review helps researchers and pharmacists to have a quick overview on the current status of ADRs detection and prediction.

A new piece in the puzzle of the novel avian-origin influenza A (H7N9) virus

Using phylogenetic analysis on newly available sequences, we characterize A/chicken/Jiangsu/RD5/2013(H10N9) as currently closest precursor strain for the NA segment in the novel avian-origin H7N9 virus responsible for an outbreak in China. We also show that the internal segments of this precursor strain are closely related to those of the presumed precursor for the HA segment, A/duck/Zhejiang/12/2011(H7N3), which indicates that the sources of both HA and NA donors for the reassortant virus are of regional and not migratory-bird origin and highlights the role of chicken already in the early reassortment events.ReviewersThis article was reviewed by Prof Xiufan Liu (nominated by Dr Purificacion Lopez-Garcia) and Prof Sandor Pongor.

Computational Study of Drug Binding Affinity to Influenza A Neuraminidase Using Smooth Reaction Path Generation (SRPG) Method

Assessment of accurate drug binding affinity to a protein remains a challenge for in silico drug development. In this research, we used the smooth reaction path generation (SRPG) method to calculate binding free energies and determine potential of mean forces (PMFs) along the smoothed dissociation paths of influenza A neuraminidase and its variants with oseltamivir (Tamiflu) and zanamivir (Relenza) inhibitors. With the gained results, we found that the binding free energies of neuraminidase A/H5N1 in WT and two mutants (including H274Y and N294S) with oseltamivir and zanamivir show good agreement with experimental results. Additionally, the thermodynamic origin of the drug resistance of the mutants was also discussed from the PMF profiles.

Computational study on ice growth inhibition of Antarctic bacterium antifreeze protein using coarse grained simulation

Antarctic bacterium antifreeze proteins (AFPs) protect and support the survival of cold-adapted organisms by binding and inhibiting the growth of ice crystals. The mechanism of the anti-freezing process in a water environment at low temperature of Antarctic bacterium AFPs remains unclear. In this research, we study the effects of Antarctic bacterium AFPs by coarse grained simulations solution at a temperature range from 262 to 273 K. The results indicated that Antarctic bacterium AFPs were fully active in temperatures greater than 265 K. Additionally, the specific temperature ranges at which the water molecules become completely frozen, partially frozen, and not frozen were identified.

Improving the performance of the coupled reference interaction site model-hyper-netted chain (RISM-HNC)/simulation method for free energy of solvation.

The coupled reference interaction site model-hyper-netted chain (RISM-HNC)/ simulation methodology determines solvation free energies as a function of the set of all radial distribution functions of solvent atoms about atomic solute sites. These functions are determined from molecular dynamics (MD) or Monte Carlo (MC) simulations rather than from solving the RISM and HNC equations iteratively. Previous applications of the method showed that it can predict relative free energies of solvation for small solutes accurately. However, the errors scale with the system size. In this study, we propose the use of the hard-sphere free energy as the reference and a linear response approximation to improve the performance, i.e., accuracy and robustness, of the method, particularly removing the size dependency of the error. The details of the new formalism are presented. To validate the proposed formalism, solvation free energies of N-methylacetamide and methylamine are computed using the new RISM-HNC-based expressions in addition to a linear response expression, which are compared to previous thermodynamic integration and thermodynamic perturbation results performed with the same force field. Additionally, free energies of solvation for cyclohexane, pyridine, benzene and derivatives, and other small organic molecules are calculated and compared to experimental values.

Systems Pharmacology: A Unified Framework for Prediction of Drug-Target Interactions.

BACKGROUND Drug discovery is one important issue in medicine and pharmacology area. Traditional methods using target-based approach are usually time-consuming and ineffective. Recently, the problems are approached in a system-level view and therefore it is called systems pharmacology. This research field deals with the problems in drug discovery by integrating various kinds of biomedical and pharmacological data and using advanced computational methods. Ultimately, the problems are more effectively solved. One of the most important problem in systems pharmacology is prediction of drug-target interactions. METHODS In this review, we are going to summarize various computational methods for this problem. RESULTS More importantly, we formed a unified framework for the problem. In addition, to study human health and disease in a more systematically and effectively, we also presented an integrated scheme for a wider problem of prediction of disease-gene-drug associations. CONCLUSION By presenting the unified framework and the integrated scheme, underlying computational methods for problems in systems pharmacology can be understood and complex relationships among diseases, genes and drugs can be identified effectively.

A Perspective on Rational Designs of a Hemagglutinin Based Universal Influenza Vaccine.

BACKGROUND The influenza virus is one of the most critical threats to public health with major economic impact. Though annual influenza vaccination is currently the most effective prevention strategy against flu epidemics and pandemics, the mutational evolution of the influenza virus tends to reduce the effectiveness of strain-specific vaccines. METHODS For past decades, a broad spectrum of potentially universal influenza vaccines has been thoroughly investigated to suppress different strains and subtypes of influenza virus concomitantly. Universal influenza vaccines were attempted to be designed to target conserved regions of surface receptors to provide the necessary preventive strategy against new influenza outbreaks. CONCLUSIONS Notably, the influenza hemagglutinin (HA) receptor has evolutionary conserved domains that can serve as basis for the rational design of a universal influenza vaccine. In this review, we examine recent studies on HA-based universal influenza vaccines and address their molecular mechanism.

Bioinformatics Approach in Plant Genomic Research

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.

Computational approach for selection of epitope-based dengue vaccine targets

High antigenic variability in the envelope (E) protein of different virus strains has been a major obstacle in designing effective vaccines for Dengue virus (DENV). To maintain their biological function, some parts of viral proteins remain stable during evolution thus one possible approach to solve this problem is to recognize specific regions within different protein sequences of E that have the tendency to stay constant through evolution. These regions may possess some special attributes to become a vaccine candidate against dengue virus. In this study, a computational approach was utilized to identify and analyze highly conserved amino acid sequences of the DENV E protein. Sequences of 9 amino acids or more were specifically focused due to their immune-relevant as T-cell determinants. Different bioinformatics tools were responsible for revealing conserved regions in the DENV E protein and constructing the phylogenetic tree from the sequence database. The tools also predicted immunogenicity of the identified vaccine targets. Ultimately, two peptide regions of at least 9 amino acids were chosen due to their high conserved attribute in more than 95% of all collected DENV sequences. Moreover, both of them was found to be immune-relevant by their correspondence to known or putative HLA-restricted T cell determinants. The conserved attribute of these sequences through the entire analysis of this study supports their potential as candidates for further in vitro experiments for rational design a universal vaccine which has longer and broader impact.

Steered Molecular Dynamics Simulation in Rational Drug Design

Conventional de novo drug design is time consuming, laborious, and resource intensive. In recent years, emerging in silico approaches have been proven to be critical to accelerate the process of bringing drugs to market. Molecular dynamics (MD) simulations of single molecule and molecular complexes have been commonly applied to achieve accurate binding modes and binding energies of drug-receptor interactions. A derivative of MD, namely, steered molecular dynamics (SMD), has been demonstrated as a promising tool for rational drug design. In this paper, we review various studies over the last 20 years using SMD simulations, thus paving the way to determine the relationship between protein structure and function. In addition, the paper highlights the use of SMD simulation for in silico drug design. We also aim to establish an understanding on the key interactions which play a crucial role in the stabilization of peptide-ligand interfaces, the binding and unbinding mechanism of the ligand-protein complex, the mechanism of ligand translocating via membrane, and the ranking of different ligands on receptors as therapeutic candidates.