SIMS: A Hybrid Method for Rapid Conformational Analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution for each. These include the automatic determination of ﾑﾑactiveﾒﾒ residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose- Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems

Population biology of malaria within the mosquito: density-dependent processes and potential implications for transmission-blocking interventions

<p>Abstract</p> <p>Background</p> <p>The combined effects of multiple density-dependent, regulatory processes may have an important impact on the growth and stability of a population. In a malaria model system, it has been shown that the progression of <it>Plasmodium berghei </it>through <it>Anopheles stephensi </it>and the survival of the mosquito both depend non-linearly on parasite density. These processes regulating the development of the malaria parasite within the mosquito may influence the success of transmission-blocking interventions (TBIs) currently under development.</p> <p>Methods</p> <p>An individual-based stochastic mathematical model is used to investigate the combined impact of these multiple regulatory processes and examine how TBIs, which target different parasite life-stages within the mosquito, may influence overall parasite transmission.</p> <p>Results</p> <p>The best parasite molecular targets will vary between different epidemiological settings. Interventions that reduce ookinete density beneath a threshold level are likely to have auxiliary benefits, as transmission would be further reduced by density-dependent processes that restrict sporogonic development at low parasite densities. TBIs which reduce parasite density but fail to clear the parasite could cause a modest increase in transmission by increasing the number of infectious bites made by a mosquito during its lifetime whilst failing to sufficiently reduce its infectivity. Interventions with a higher variance in efficacy will therefore tend to cause a greater reduction in overall transmission than a TBI with a more uniform effectiveness. Care should be taken when interpreting these results as parasite intensity values in natural parasite-vector combinations of human malaria are likely to be significantly lower than those in this model system.</p> <p>Conclusions</p> <p>A greater understanding of the development of the malaria parasite within the mosquito is required to fully evaluate the impact of TBIs. If parasite-induced vector mortality influenced the population dynamics of <it>Plasmodium </it>species infecting humans in malaria endemic regions, it would be important to quantify the variability and duration of TBI efficacy to ensure that community benefits of control measures are not overestimated.</p

Modelling Transmission of Vector-Borne Pathogens Shows Complex Dynamics When Vector Feeding Sites Are Limited

The relationship between species richness and the prevalence of vector-borne disease has been widely studied with a range of outcomes. Increasing the number of host species for a pathogen may decrease infection prevalence (dilution effect), increase it (amplification), or have no effect. We derive a general model, and a specific implementation, which show that when the number of vector feeding sites on each host is limiting, the effects on pathogen dynamics of host population size are more complex than previously thought. The model examines vector-borne disease in the presence of different host species that are either competent or incompetent (i.e. that cannot transmit the pathogen to vectors) as reservoirs for the pathogen. With a single host species present, the basic reproduction ratio R0 is a non-monotonic function of the population size of host individuals (H), i.e. a value exists that maximises R0. Surprisingly, if a reduction in host population size may actually increase R0. Extending this model to a two-host species system, incompetent individuals from the second host species can alter the value of which may reverse the effect on pathogen prevalence of host population reduction. We argue that when vector-feeding sites on hosts are limiting, the net effect of increasing host diversity might not be correctly predicted using simple frequency-dependent epidemiological models

Comparison between observed children's tooth brushing habits and those reported by mothers

<p>Abstract</p> <p>Background</p> <p>Information bias can occur in epidemiological studies and compromise scientific outcomes, especially when evaluating information given by a patient regarding their own health. The oral habits of children reported by their mothers are commonly used to evaluate tooth brushing practices and to estimate fluoride intake by children. The aim of the present study was to compare observed tooth-brushing habits of young children using fluoridated toothpaste with those reported by mothers.</p> <p>Methods</p> <p>A sample of 201 mothers and their children (aged 24-48 months) from Montes Claros, Brazil, took part in a cross-sectional study. At day-care centres, the mothers answered a self-administered questionnaire on their child's tooth-brushing habits. The structured questionnaire had six items with two to three possible answers. An appointment was then made with each mother/child pair at day-care centres. The participants were asked to demonstrate the tooth-brushing practice as usually performed at home. A trained examiner observed and documented the procedure. Observed tooth brushing and that reported by mothers were compared for overall agreement using Cohen's Kappa coefficient and the McNemar test.</p> <p>Results</p> <p>Cohen's Kappa values comparing mothers' reports and tooth brushing observed by the examiner ranged from poor-to-good (0.00-0.75). There were statistically significant differences between observed tooth brushing habits and those reported by mothers (p < 0.001). When observed by the examiner, the frequencies of dentifrice dispersed on all bristles (35.9%), children who brushed their teeth alone (33.8%) and those who did not rinse their mouths during brushing (42.0%) were higher than those reported by the mothers (12.1%, 18.9% and 6.5%, respectively; p < 0.001).</p> <p>Conclusions</p> <p>In general, there was low agreement between observed tooth brushing and mothers' reports. Moreover, the different methods of estimation resulted in differences in the frequencies of tooth brushing habits, indicative of reporting bias. Data regarding children's tooth-brushing habits as reported by mothers should be considered with caution in epidemiological surveys on fluoridated dentifrice use and the risk of dental fluorosis.</p

Particulate Matter-Induced Lung Inflammation Increases Systemic Levels of PAI-1 and Activates Coagulation Through Distinct Mechanisms

Exposure of human populations to ambient particulate matter (PM) air pollution significantly contributes to the mortality attributable to ischemic cardiovascular events. We reported that mice treated with intratracheally instilled PM develop a prothrombotic state that requires the release of IL-6 by alveolar macrophages. We sought to determine whether exposure of mice to PM increases the levels of PAI-1, a major regulator of thrombolysis, via a similar or distinct mechanism. mice but was absent in mice treated with etanercept, a TNF-α inhibitor. Treatment with etanercept did not prevent the PM-induced tendency toward thrombus formation.Mice exposed to inhaled PM exhibited a TNF-α-dependent increase in PAI-1 and an IL-6-dependent activation of coagulation. These results suggest that multiple mechanisms link PM-induced lung inflammation with the development of a prothrombotic state

Wind direction and proximity to larval sites determines malaria risk in Kilifi District in Kenya

Studies of the fine-scale spatial epidemiology of malaria consistently identify malaria hotspots, comprising clusters of homesteads at high transmission intensity. These hotspots sustain transmission, and may be targeted by malaria-control programmes. Here we describe the spatial relationship between the location of Anopheles larval sites and human malaria infection in a cohort study of 642 children, aged 1–10-years-old. Our data suggest that proximity to larval sites predict human malaria infection, when homesteads are upwind of larval sites, but not when homesteads are downwind of larval sites. We conclude that following oviposition, female Anophelines fly upwind in search for human hosts and, thus, malaria transmission may be disrupted by targeting vector larval sites in close proximity, and downwind to malaria hotspots

Manifold Learning for Human Population Structure Studies

The dimension of the population genetics data produced by next-generation sequencing platforms is extremely high. However, the “intrinsic dimensionality” of sequence data, which determines the structure of populations, is much lower. This motivates us to use locally linear embedding (LLE) which projects high dimensional genomic data into low dimensional, neighborhood preserving embedding, as a general framework for population structure and historical inference. To facilitate application of the LLE to population genetic analysis, we systematically investigate several important properties of the LLE and reveal the connection between the LLE and principal component analysis (PCA). Identifying a set of markers and genomic regions which could be used for population structure analysis will provide invaluable information for population genetics and association studies. In addition to identifying the LLE-correlated or PCA-correlated structure informative marker, we have developed a new statistic that integrates genomic information content in a genomic region for collectively studying its association with the population structure and LASSO algorithm to search such regions across the genomes. We applied the developed methodologies to a low coverage pilot dataset in the 1000 Genomes Project and a PHASE III Mexico dataset of the HapMap. We observed that 25.1%, 44.9% and 21.4% of the common variants and 89.2%, 92.4% and 75.1% of the rare variants were the LLE-correlated markers in CEU, YRI and ASI, respectively. This showed that rare variants, which are often private to specific populations, have much higher power to identify population substructure than common variants. The preliminary results demonstrated that next generation sequencing offers a rich resources and LLE provide a powerful tool for population structure analysis

Visual adaptation enhances action sound discrimination

Prolonged exposure, or adaptation, to a stimulus in one modality can bias, but also enhance, perception of a subsequent stimulus presented within the same modality. However, recent research has also found that adaptation in one modality can bias perception in another modality. Here we show a novel crossmodal adaptation effect, where adaptation to a visual stimulus enhances subsequent auditory perception. We found that when compared to no adaptation, prior adaptation to visual, auditory or audiovisual hand actions enhanced discrimination between two subsequently presented hand action sounds. Discrimination was most enhanced when the visual action ‘matched’ the auditory action. In addition, prior adaptation to a visual, auditory or audiovisual action caused subsequent ambiguous action sounds to be perceived as less like the adaptor. In contrast, these crossmodal action aftereffects were not generated by adaptation to the names of actions. Enhanced crossmodal discrimination and crossmodal perceptual aftereffects may result from separate mechanisms operating in audiovisual action sensitive neurons within perceptual systems. Adaptation induced crossmodal enhancements cannot be explained by post-perceptual responses or decisions. More generally, these results together indicate that adaptation is a ubiquitous mechanism for optimizing perceptual processing of multisensory stimuli