Near-native protein loop sampling using nonparametric density estimation accommodating sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.     

Gene flow in environmental Legionella pneumophila leads to genetic and pathogenic heterogeneity within a Legionnaires’ disease outbreak

Background

Legionnaires’ disease is a severe form of pneumonia caused by the environmental bacterium Legionella pneumophila. Outbreaks commonly affect people with known risk factors, but the genetic and pathogenic complexity of L. pneumophila within an outbreak is not well understood. Here, we investigate the etiology of the major Legionnaires’ disease outbreak that occurred in Edinburgh, UK, in 2012, by examining the evolutionary history, genome content, and virulence of L. pneumophila clinical isolates.

Results

Our high resolution genomic approach reveals that the outbreak was caused by multiple genetic subtypes of L. pneumophila, the majority of which had diversified from a single progenitor through mutation, recombination, and horizontal gene transfer within an environmental reservoir prior to release. In addition, we discover that some patients were infected with multiple L. pneumophila subtypes, a finding which can affect the certainty of source attribution. Importantly, variation in the complement of type IV secretion systems encoded by different genetic subtypes correlates with virulence in a Galleria mellonella model of infection, revealing variation in pathogenic potential among the outbreak source population of L. pneumophila.

Conclusions

Taken together, our study indicates previously cryptic levels of pathogen heterogeneity within a Legionnaires’ disease outbreak, a discovery that impacts on source attribution for future outbreak investigations. Furthermore, our data suggest that in addition to host immune status, pathogen diversity may be an important influence on the clinical outcome of individual outbreak infections.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0504-1) contains supplementary material, which is available to authorized users.     

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.     

Biosensor approach to psychopathology classification

We used a multi-round, two-party exchange game in which a healthy subject played a subject diagnosed with a DSM-IV (Diagnostic and Statistics Manual-IV) disorder, and applied a Bayesian clustering approach to the behavior exhibited by the healthy subject. The goal was to characterize quantitatively the style of play elicited in the healthy subject (the proposer) by their DSM-diagnosed partner (the responder). The approach exploits the dynamics of the behavior elicited in the healthy proposer as a biosensor for cognitive features that characterize the psychopathology group at the other side of the interaction. Using a large cohort of subjects (n = 574), we found statistically significant clustering of proposers' behavior overlapping with a range of DSM-IV disorders including autism spectrum disorder, borderline personality disorder, attention deficit hyperactivity disorder, and major depressive disorder. To further validate these results, we developed a computer agent to replace the human subject in the proposer role (the biosensor) and show that it can also detect these same four DSM-defined disorders. These results suggest that the highly developed social sensitivities that humans bring to a two-party social exchange can be exploited and automated to detect important psychopathologies, using an interpersonal behavioral probe not directly related to the defining diagnostic criteria.     

Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data

MOTIVATION: Surface-enhanced laser desorption and ionization (SELDI) time of flight (TOF) is a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order to locate the peaks and quantify their intensities, several pre-processing steps are required. Though different approaches to perform pre-processing have been proposed, there is no systematic study that compares their performance. RESULTS: In this article, we present the results of a systematic comparison of various popular packages for pre-processing of SELDI-TOF data. We evaluate their performance in terms of two of their primary functions: peak detection and peak quantification. Regarding peak quantification, the performance of the algorithms is measured in terms of reproducibility. For peak detection, the comparison is based on sensitivity and false discovery rate. Our results show that for spectra generated with low laser intensity, the software developed by Ciphergen Biosystems (ProteinChip Software 3.1 with the additional tool Biomarker Wizard) produces relatively good results for both peak quantification and detection. On the other hand, for the data produced with either medium or high laser intensity, none of the methods show uniformly better performances under both criteria. Our analysis suggests that an advantageous combination is the use of the packages MassSpecWavelet and PROcess, the former for peak detection and the latter for peak quantification.     

Glucose transporter asymmetries in the bovine blood-brain barrier

The transport of glucose across the mammalian blood-brain barrier is mediated by the GLUT1 glucose transporter, which is concentrated in the endothelial cells of the cerebral microvessels. Several studies supported an asymmetric distribution of GLUT1 protein between the luminal and abluminal membranes (1:4) with a significant proportion of intracellular transporters. In this study we investigated the activity and concentration of GLUT1 in isolated luminal and abluminal membrane fractions of bovine brain endothelial cells. Glucose transport activity and glucose transporter concentration, as determined by cytochalasin B binding, were 2-fold greater in the luminal than in the abluminal membranes. In contrast, Western blot analysis using a rabbit polyclonal antibody raised against the C-terminal 20 amino acids of GLUT1 indicated a 1:5 luminal:abluminal distribution. Western blot analysis with antibodies raised against either the intracellular loop of GLUT1 or the purified erythrocyte protein exhibited luminal:abluminal ratios of 1:1. A similar ratio was observed when the luminal and abluminal fractions were exposed to the 2-N-4[(3)H](1-azi-2,2,2,-trifluoroethyl)benzoxyl-1,3-bis-(d-mannos-4-yloxyl)-2-propylamine ([(3)H]ATB-BMPA) photoaffinity label. These observations suggest that either an additional glucose transporter isoform is present in the luminal membrane of the bovine blood-brain barrier or the C-terminal epitope of GLUT1 is "masked" in the luminal membrane but not in the abluminal membranes.     

Citrate synthase from Antarctic ciliates: adaptation to low temperatures and comparison with temperate ciliates

The activity of citrate synthase (CS), a mitochondrial enzyme in the energy metabolism pathway, was studied in Euplotes focardii (Hypotrichia) and Uronema marinum (Scuticociliatia), isolated from samples of Antarctic seawater and, for comparison in E. vannus and U. nigricans, related ciliates from the Adriatic. The temperature-dependent characteristics of CS were investigated in the range 0–55°C, to evaluate mechanisms of thermal regulation, such as optimal temperature levels, activation energy, and kinetic parameters. CS activity was up to double that recorded in temperate organisms at reaction temperatures between 0 and 10°C. The optimal temperature for enzymatic activity was independent of Tamb. Activation energy for CS was significantly lower in Antarctic ciliates than in temperate ones, indicating a significant increase in the reaction rate. KM at various temperatures of the reaction substrates indicated the higher affinity of CS for acetyl-CoA in both Antarctic organisms at lower temperatures. The data for thermal profiles for KM, showing greater differences between E. focardii and E. vannus than between U. marinum and U. nigricans, support the distinctive physiological characteristics of each species. E. focardii is an endemic and psychrophilic species, whereas U. marinum is a typically ubiquitous species, more adaptable to environmental changes.     

Differential effects of hypoxia-ischemia on subunit expression and tyrosine phosphorylation of the NMDA receptor in 7- and 21-day-old rats

The effect of cerebral hypoxia-ischemia (HI) on levels and tyrosine phosphorylation of the NMDA receptor was examined in 7- (P7) and 21 (P21)-day-old rats. Unilateral HI was administered by ligation of the right common carotid artery and exposure to an atmosphere of 8% O2/92% N2 for 2 (P7) or 1.5 (P21) h. This duration of HI produces significant infarction in nearly all of the survivors with damage being largely restricted to the cortex, striatum, and hippocampus of the hemisphere ipsilateral to the carotid artery ligation. NR2A levels in the right hemisphere of P7 pups were markedly reduced after 24 h of recovery, while NR1 and NR2B remained unchanged. In contrast, NR2B, but not NR2A, was reduced after HI at P21. At both ages, HI resulted in a transient increase in tyrosine phosphorylation of a number of forebrain proteins that peaked between 1 and 6 h of recovery. At both P7 and P21, tyrosine phosphorylation of NR2B was enhanced 1 h after HI and had returned to basal levels by 24 h. HI induced an increase in tyrosine phosphorylation of NR2A in 21 day, but not in 7-day-old animals. The differential effects of HI on the NMDA receptor at different post-natal ages may contribute to changing sensitivity to hypoxia-ischemia.