Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development

Vaxign is the first web-based vaccine design system that predicts vaccine targets based on genome sequences using the strategy of reverse vaccinology. Predicted features in the Vaxign pipeline include protein subcellular location, transmembrane helices, adhesin probability, conservation to human and/or mouse proteins, sequence exclusion from genome(s) of nonpathogenic strain(s), and epitope binding to MHC class I and class II. The precomputed Vaxign database contains prediction of vaccine targets for >70 genomes. Vaxign also performs dynamic vaccine target prediction based on input sequences. To demonstrate the utility of this program, the vaccine candidates against uropathogenic Escherichia coli (UPEC) were predicted using Vaxign and compared with various experimental studies. Our results indicate that Vaxign is an accurate and efficient vaccine design program

Self-Calibrated Warping for Mass Spectra Alignment

With recent advances in mass spectrometry (MS) technologies, it is now possible to study protein profiles over a wide range of molecular weights in small biological specimens. However, MS spectra are usually not aligned or synchronized between samples. To ensure the consistency of the subsequent analysis, spectrum alignment is necessary to align the spectra such that the same biological entity would show up at the same m/z value for different samples. Although a variety of alignment algorithms have been proposed in the past, most of them are developed based on chromatographic data and do not address some of the unique characteristics of the serum or other body fluid MS data. In this work, we propose a self-calibrated warping (SCW) algorithm to address some of the challenges associated with serum MS data alignment. In addition, we compare the proposed algorithm with five existing representative alignment methods using a clinical surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) data set

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.
&#xa

Presence of Putative Repeat-in-Toxin Gene tosA in Escherichia coli Predicts Successful Colonization of the Urinary Tract

Uropathogenic Escherichia coli (UPEC) strains, which cause the majority of uncomplicated urinary tract infections (UTIs), carry a unique assortment of virulence or fitness genes. However, no single defining set of virulence or fitness genes has been found in all strains of UPEC, making the differentiation between UPEC and fecal commensal strains of E. coli difficult without the use of animal models of infection or phylogenetic grouping. In the present study, we consider three broad categories of virulence factors simultaneously to better define a combination of virulence factors that predicts success in the urinary tract. A total of 314 strains of E. coli, representing isolates from fecal samples, asymptomatic bacteriuria, complicated UTIs, and uncomplicated bladder and kidney infections, were assessed by multiplex PCR for the presence of 15 virulence or fitness genes encoding adhesins, toxins, and iron acquisition systems. The results confirm previous reports of gene prevalence among isolates from different clinical settings and identify several new patterns of gene associations. One gene, tosA, a putative repeat-in-toxin (RTX) homolog, is present in 11% of fecal strains but 25% of urinary isolates. Whereas tosA-positive strains carry an unusually high number (11.2) of the 15 virulence or fitness genes, tosA-negative strains have an average of only 5.4 virulence or fitness genes. The presence of tosA was predictive of successful colonization of a murine model of infection, even among fecal isolates, and can be used as a marker of pathogenic strains of UPEC within a distinct subset of the B2 lineage

Macro-level factors impacting geographic disparities in cancer screening

Abstract Objectives Examine how differences in state regulatory environments predict geographic disparities in the utilization of cancer screening. Data sources/setting 100% Medicare fee-for-service population data from 2001-2005 was developed as multi-year breast (BC) and colorectal cancer (CRC) screening utilization rates in each county in the US. Study design A comprehensive set of supply and demand predictors are used in a multilevel model of county-level cancer screening utilization in the context of state regulatory markets. States dictate insurance mandates/regulations and whether alternative providers (nurse practitioners) can provide preventive care services supplied by MDs. Controlling statistically for the supply of both types of providers, we study the joint effects of two private insurance regulations: one mandating that insureds with serious or chronic health conditions may receive continuity of care from their established physician(s) after changing health insurance plans, and another mandating that external grievance review is an option for all health plan coverage/denial decisions. These private insurance plan regulations are expected to affect the degree of beneficial spillovers from managed care practices, which may have increased area-wide cancer screening rates. Principal findings The two private insurance regulations under study were significant predictors impacted by local market conditions. Managed care spillovers in local markets were significantly associated with higher BC screening rates, but only in states lacking the two forms of regulation under study. Spillovers were significantly associated with higher CRC cancer screening rates everywhere, but much higher in the unregulated states. Area poverty dampened screening rates, but less so for CRC screening in the states with these regulations. Conclusions Two state insurance regulations that empowered consumers with more autonomy to make informed utilization decisions varied across states, and exhibited significant associations with screening rates, which varied with the degree of managed care penetration or poverty in the state’s counties. Beneficial spillover effects from managed care practices and negative influences from area poverty are not uniform across the United States. Both variables had stronger associations with CRC than BC screening utilization, as did state regulatory variables. CRC screening by endoscopy was more subject to market and regulatory factors than BC screening

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented

VO: Vaccine Ontology

The collaborative, community-based Vaccine Ontology (VO) was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Currently VO covers a variety of aspects of the vaccine domain, with an emphasis on classification of vaccines and vaccine components, and on host immune response to vaccines. VO can be used for a number of applications, e.g., ontology-based vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI)

Postoperative pain management in children: Guidance from the pain committee of the European Society for Paediatric Anaesthesiology (ESPA Pain Management Ladder Initiative)

The main remit of the European Society for Paediatric Anaesthesiology (ESPA) Pain Committee is to improve the quality of pain management in children. The ESPA Pain Management Ladder is a clinical practice advisory based upon expert consensus to help to ensure a basic standard of perioperative pain management for all children. Further steps are suggested to improve pain management once a basic standard has been achieved. The guidance is grouped by the type of surgical procedure and layered to suggest basic, intermediate, and advanced pain management methods. The committee members are aware that there are marked differences in financial and personal resources in different institutions and countries and also considerable variations in the availability of analgesic drugs across Europe. We recommend that the guidance should be used as a framework to guide best practice

A Quantitative Study of the Effects of Guest Flexibility on Binding Inside a Coordination Cage Host.

We have performed a systematic investigation of the effects of guest flexibility on their ability to bind in the cavity of a coordination cage host in water, using two sets of isomeric aliphatic ketones that differ only in the branching patterns of their alkyl chains. Apart from the expected increase in binding strength for C9 over C7 ketones associated with their greater hydrophobic surface area, within each isomeric set there is a clear inverse correlation between binding free energy and guest flexibility, associated with loss of conformational entropy. This can be parameterized by the number of rotatable C-C bonds in the guest, with each additional rotatable bond resulting in a penalty of around 2 kJ mol(-1) in the binding free energy, in good agreement with values obtained from protein/ligand binding studies. We used the binding data for the new flexible guests to improve the scoring function that we had previously developed that allowed us to predict binding constants of relatively rigid guests in the cage cavity using the molecular docking programme GOLD (Genetic Optimisation of Ligand Docking). This improved scoring function resulted in a significant improvement in the ability of GOLD to predict binding constants for flexible guests, without any detriment to its ability to predict binding for more rigid guests

Distinguishing Binders from False Positives by Free Energy Calculations: Fragment Screening Against the Flap Site of HIV Protease

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand–receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets