Journal Articles

Drugs on demand

Nature - Tue, 11/24/2015 - 00:00

Drugs on demand

Nature 527, 7579 (2015). doi:10.1038/527410b

Controversy in Brazil over access to a purported cancer cure could set a harmful precedent.

Categories: Journal Articles

Brazilian courts tussle over unproven cancer treatment

Nature - Tue, 11/24/2015 - 00:00

Brazilian courts tussle over unproven cancer treatment

Nature 527, 7579 (2015). http://www.nature.com/doifinder/10.1038/527420a

Author: Heidi Ledford

Patients demand access to compound despite lack of clinical testing.

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The fragile framework

Nature - Tue, 11/24/2015 - 00:00

The fragile framework

Nature 527, 7579 (2015). http://www.nature.com/doifinder/10.1038/527427a

Authors: Richard Monastersky & Nick Sousanis

A Nature comic examines the 25-year quest for a climate treaty. Can nations unite to save Earth’s climate?

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Is the 2 °C world a fantasy?

Nature - Tue, 11/24/2015 - 00:00

Is the 2 °C world a fantasy?

Nature 527, 7579 (2015). http://www.nature.com/doifinder/10.1038/527436a

Author: Jeff Tollefson

Countries have pledged to limit global warming to 2 °C, and climate models say that is still possible. But only with heroic — and unlikely — efforts.

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A Systematic Bayesian Integration of Epidemiological and Genetic Data

PLoS Computational Biology - Mon, 11/23/2015 - 17:00

by Max S. Y. Lau, Glenn Marion, George Streftaris, Gavin Gibson

Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process.
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Computational Modelling of Metastasis Development in Renal Cell Carcinoma

PLoS Computational Biology - Mon, 11/23/2015 - 17:00

by Etienne Baratchart, Sébastien Benzekry, Andreas Bikfalvi, Thierry Colin, Lindsay S. Cooley, Raphäel Pineau, Emeline J Ribot, Olivier Saut, Wilfried Souleyreau

The biology of the metastatic colonization process remains a poorly understood phenomenon. To improve our knowledge of its dynamics, we conducted a modelling study based on multi-modal data from an orthotopic murine experimental system of metastatic renal cell carcinoma. The standard theory of metastatic colonization usually assumes that secondary tumours, once established at a distant site, grow independently from each other and from the primary tumour. Using a mathematical model that translates this assumption into equations, we challenged this theory against our data that included: 1) dynamics of primary tumour cells in the kidney and metastatic cells in the lungs, retrieved by green fluorescent protein tracking, and 2) magnetic resonance images (MRI) informing on the number and size of macroscopic lesions. Critically, when calibrated on the growth of the primary tumour and total metastatic burden, the predicted theoretical size distributions were not in agreement with the MRI observations. Moreover, tumour expansion only based on proliferation was not able to explain the volume increase of the metastatic lesions. These findings strongly suggested rejection of the standard theory, demonstrating that the time development of the size distribution of metastases could not be explained by independent growth of metastatic foci. This led us to investigate the effect of spatial interactions between merging metastatic tumours on the dynamics of the global metastatic burden. We derived a mathematical model of spatial tumour growth, confronted it with experimental data of single metastatic tumour growth, and used it to provide insights on the dynamics of multiple tumours growing in close vicinity. Together, our results have implications for theories of the metastatic process and suggest that global dynamics of metastasis development is dependent on spatial interactions between metastatic lesions.
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Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns

PLoS Computational Biology - Mon, 11/23/2015 - 17:00

by Andrea Maesani, Pavan Ramdya, Steeve Cruchet, Kyle Gustafson, Richard Benton, Dario Floreano

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.
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Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

BMC Structural Biology - Mon, 11/23/2015 - 07:00
Background: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. Results: To address this problem, we developed eRank PPI , an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank PPI employs multiple features including interface probability estimates calculated by eFindSite PPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank PPI consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. Conclusions: eRank PPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.
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Coev-web: a web platform designed to simulate and evaluate coevolving positions along a phylogenetic tree

BMC Bioinformatics - Mon, 11/23/2015 - 07:00
Background: Available methods to simulate nucleotide or amino acid data typically use Markov models to simulate each position independently. These approaches are not appropriate to assess the performance of combinatorial and probabilistic methods that look for coevolving positions in nucleotide or amino acid sequences. Results: We have developed a web-based platform that gives a user-friendly access to two phylogenetic-based methods implementing the Coev model: the evaluation of coevolving scores and the simulation of coevolving positions. We have also extended the capabilities of the Coev model to allow for the generalization of the alphabet used in the Markov model, which can now analyse both nucleotide and amino acid data sets. The simulation of coevolving positions is novel and builds upon the developments of the Coev model. It allows user to simulate pairs of dependent nucleotide or amino acid positions. Conclusions: The main focus of our paper is the new simulation method we present for coevolving positions. The implementation of this method is embedded within the web platform Coev-web that is freely accessible at http://coev.vital-it.ch/, and was tested in most modern web browsers.
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MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions

BMC Bioinformatics - Mon, 11/23/2015 - 07:00
Background: The alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem. Results: We propose a novel and efficient algorithm called, MSAIndelFR, for multiple sequence alignment using the information on the predicted locations of IndelFRs and the computed average log–loss values obtained from IndelFR predictors, each of which is designed for a different protein fold. We demonstrate that the introduction of a new variable gap penalty function based on the predicted locations of the IndelFRs and the computed average log–loss values into the proposed algorithm substantially improves the protein alignment accuracy. This is illustrated by evaluating the performance of the algorithm in aligning sequences belonging to the protein folds for which the IndelFR predictors already exist and by using the reference alignments of the four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABRE (SABmark 1.65). Conclusions: We have proposed a novel and efficient algorithm, the MSAIndelFR algorithm, for multiple protein sequence alignment incorporating a new variable gap penalty function. It is shown that the performance of the proposed algorithm is superior to that of the most–widely used alignment algorithms, Clustal W2, Clustal Omega, Kalign2, MSAProbs, MAFFT, MUSCLE, ProbCons and Probalign, in terms of both the sum–of–pairs and total column metrics.
Categories: Journal Articles

High-resolution crystal structures leverage protein binding affinity predictions

ABSTRACT

Predicting protein binding affinities from structural data has remained elusive, a difficulty owing to the variety of protein binding modes. Using the structure-affinity-benchmark (SAB, 144 cases with bound/unbound crystal structures and experimental affinity measurements), prediction has been undertaken either by fitting a model using a handfull of predefined variables, or by training a complex model from a large pool of parameters (typically hundreds). The former route unnecessarily restricts the model space, while the latter is prone to overfitting. We design models in a third tier, using 12 variables describing enthalpic and entropic variations upon binding, and a model selection procedure identifying the best sparse model built from a subset of these variables. Using these models, we report three main results. First, we present models yielding a marked improvement of affinity predictions. For the whole dataset, we present a model predicting Kd within 1 and 2 orders of magnitude for 48% and 79% of cases, respectively. These statistics jump to 62% and 89% respectively, for the subset of the SAB consisting of high resolution structures. Second, we show that these performances owe to a new parameter encoding interface morphology and packing properties of interface atoms. Third, we argue that interface flexibility and prediction hardness do not correlate, and that for flexible cases, a performance matching that of the whole SAB can be achieved. Overall, our work suggests that the affinity prediction problem could be partly solved using databases of high resolution complexes whose affinity is known. Proteins 2015. © 2015 Wiley Periodicals, Inc.

Categories: Journal Articles

Interaction between bound water molecules and local protein structures: A statistical analysis of the hydrogen bond structures around bound water molecules

ABSTRACT

Water molecules play an important role in protein folding and protein interactions through their structural association with proteins. Examples of such structural association can be found in protein crystal structures, and can often explain protein functionality in the context of structure. We herein report the systematic analysis of the local structures of proteins interacting with water molecules, and the characterization of their geometric features. We first examined the interaction of water molecules with a large local interaction environment by comparing the preference of water molecules in three regions, namely, the protein–protein interaction (PPI) interfaces, the crystal contact (CC) interfaces, and the non-interfacial regions. High preference of water molecules to the PPI and CC interfaces was found. In addition, the bound water on the PPI interface was more favorably associated with the complex interaction structure, implying that such water-mediated structures may participate in the shaping of the PPI interface. The pairwise water-mediated interaction was then investigated, and the water-mediated residue–residue interaction potential was derived. Subsequently, the types of polar atoms surrounding the water molecules were analyzed, and the preference of the hydrogen bond acceptor was observed. Furthermore, the geometries of the structures interacting with water were analyzed, and it was found that the major structure on the protein surface exhibited planar geometry rather than tetrahedral geometry. Several previously undiscovered characteristics of water–protein interactions were unfolded in this study, and are expected to lead to a better understanding of protein structure and function. Proteins 2015. © 2015 Wiley Periodicals, Inc.

Categories: Journal Articles

Conformational fluctuations of the AXH monomer of Ataxin-1

ABSTRACT

In this paper, we report the results of molecular dynamics simulations of AXH monomer of Ataxin-1. The AXH domain plays a crucial role in Ataxin-1 aggregation, which accompanies the initiation and progression of Spinocerebellar ataxia type 1. Our simulations involving both classical and replica exchange molecular dynamics, followed by principal component analysis of the trajectories obtained, reveal substantial conformational fluctuations of the protein structure, especially in the N-terminal region. We show that these fluctuations can be generated by thermal noise since the free energy barriers between conformations are small enough for thermally stimulated transitions. In agreement with the previous experimental findings, our results can be considered as a basis for a future design of ataxin aggregation inhibitors that will require several key conformations identified in the present study as molecular targets for ligand binding. Proteins 2015. © 2015 Wiley Periodicals, Inc.

Categories: Journal Articles

Visualizing global properties of a molecular dynamics trajectory

ABSTRACT

Molecular dynamics (MD) trajectories are very large data sets that contain substantial information about the dynamic behavior of a protein. Condensing these data into a form that can provide intuitively useful understanding of the molecular behavior during the trajectory is a substantial challenge that has received relatively little attention. Here, we introduce the sigma-r plot, a plot of the standard deviation of intermolecular distances as a function of that distance. This representation of global dynamics contains within a single, one-dimensional plot, the average range of motion between pairs of atoms within a macromolecule. Comparison of sigma-r plots calculated from 10 ns trajectories of proteins representing the four major SCOP fold classes indicates diversity of dynamic behaviors which are recognizably different among the four classes. Differences in domain structure and molecular weight also produce recognizable features in sigma-r plots, reflective of differences in global dynamics. Plots generated from trajectories with progressively increasing simulation time reflect the increased sampling of the structural ensemble as a function of time. Single amino acid replacements can give rise to changes in global dynamics detectable through comparison of sigma-r plots. Dynamic behavior of substructures can be monitored by careful choice of interatomic vectors included in the calculation. These examples provide demonstrations of the utility of the sigma-r plot to provide a simple measure of the global dynamics of a macromolecule. Proteins 2015. © 2015 Wiley Periodicals, Inc.

Categories: Journal Articles

Salmon approval heralds rethink of transgenic animals

Nature - Mon, 11/23/2015 - 00:00

Salmon approval heralds rethink of transgenic animals

Nature 527, 7579 (2015). http://www.nature.com/doifinder/10.1038/527417a

Author: Heidi Ledford

Long-awaited decision by US government authorizes the first genetically engineered animal to be sold as food.

Categories: Journal Articles

Climate optimism builds ahead of Paris talks

Nature - Mon, 11/23/2015 - 00:00

Climate optimism builds ahead of Paris talks

Nature 527, 7579 (2015). http://www.nature.com/doifinder/10.1038/527418a

Author: Jeff Tollefson

Emission pledges raise hopes for an international treaty.

Categories: Journal Articles

Supramolecular Packing Controls H2 Photocatalysis in Chromophore Amphiphile Hydrogels

Journal of American Chemical Society - Sat, 11/21/2015 - 08:00

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b10027
Categories: Journal Articles

FoCo: a simple and robust quantification algorithm of nuclear foci

BMC Bioinformatics - Fri, 11/20/2015 - 19:00
Background: The number of γH2AX foci per nucleus is an accepted measure of the number of DNA double-strand breaks in single cells. One of the experimental techniques for γH2AX detection in cultured cells is immunofluorescent labelling of γH2AX and nuclei followed by microscopy imaging and analysis. Results: In this study, we present the algorithm FoCo for reliable and robust automatic nuclear foci counting in single cell images. FoCo has the following advantages with respect to other software packages: i) the ability to reliably quantify even densely distributed foci, e.g., on images of cells subjected to radiation doses up to 10 Gy, ii) robustness of foci quantification in the sense of suppressing out-of-focus background signal, and iii) its simplicity. FoCo requires only 5 parameters that have to be adjusted by the user. Conclusions: FoCo is an open-source user-friendly software with GUI for individual foci counting, which is able to produce reliable and robust foci quantifications even for low signal/noise ratios and densely distributed foci.
Categories: Journal Articles
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