Toll-like receptor stimulation differentially regulates vasoactive intestinal peptide type 2 receptor in macrophages

Vasoactive intestinal peptide (VIP) was originally isolated as a vasodilator intestinal peptide, then as a neuropeptide. In the immune system, VIP is described as an endogenous macrophage-deactivating factor. VIP exerts its immunological actions in a paracrine and/or autocrine manner, through specific receptors. However, very little is known about the molecular regulation of VIP type 2 receptor (VPAC2) in the immune system. We now report that different toll-like receptor (TLR) ligands selectively regulate the VPAC2 receptor gene and show a gene repression system controlled by key protein kinase signalling cascades in macrophages. VPAC2 gene expression is regulated by gram-positive (TLR2 ligands) and gram-negative bacteria wall constituents (TLR4 ligands). Moreover, VPAC2 is tightly regulated: TLR2- or TLR2/6- but not TLR2/1-mediated mechanisms are responsible for the induction of VPAC2. TLR stimulation by viral or bacterial nucleic acids did not modify the VPAC2 mRNA levels. Remarkably, imiquimod – a synthetic TLR7 ligand – led to a potent up-regulation of VPAC2 gene expression. TLR5 stimulation by flagellin present in gram-positive and gram-negative bacteria did not affect VPAC2 mRNA. The p38 mitogen-activated protein kinase (MAPK) activity accounted for the TLR4-mediated induction of VPAC2 gene expression. Surprisingly, our data strongly suggest for the first time a tightly repressed control of VPAC2 mRNA induction by elements downstream of MAPK kinase 1/2, PI3K/Akt, and particularly Jun-NH2-terminal kinase signalling pathways

Altruism can proliferate through group/kin selection despite high random gene flow

The ways in which natural selection can allow the proliferation of cooperative behavior have long been seen as a central problem in evolutionary biology. Most of the literature has focused on interactions between pairs of individuals and on linear public goods games. This emphasis led to the conclusion that even modest levels of migration would pose a serious problem to the spread of altruism in group structured populations. Here we challenge this conclusion, by analyzing evolution in a framework which allows for complex group interactions and random migration among groups. We conclude that contingent forms of strong altruism can spread when rare under realistic group sizes and levels of migration. Our analysis combines group-centric and gene-centric perspectives, allows for arbitrary strength of selection, and leads to extensions of Hamilton's rule for the spread of altruistic alleles, applicable under broad conditions.Comment: 5 pages, 2 figures. Supplementary material with 50 pages and 26 figure

To hit or not to hit, that is the question -genome-wide structure-based druggability predictions for <i>pseudomonas aeruginosa </i>proteins

Pseudomonas aeruginosa is a Gram-negative bacterium known to cause opportunistic infections in immune-compromised or immunosuppressed individuals that often prove fatal. New drugs to combat this organism are therefore sought after. To this end, we subjected the gene products of predicted perturbative genes to structure-based druggability predictions using DrugPred. Making this approach suitable for large-scale predictions required the introduction of new methods for calculation of descriptors, development of a workflow to identify suitable pockets in homologous proteins and establishment of criteria to obtain valid druggability predictions based on homologs. We were able to identify 29 perturbative proteins of P. aeruginosa that may contain druggable pockets, including some of them with no or no drug-like inhibitors deposited in ChEMBL. These proteins form promising novel targets for drug discovery against P. aeruginosa

Structures of the first representatives of Pfam family PF06938 (DUF1285) reveal a new fold with repeated structural motifs and possible involvement in signal transduction

The crystal structures of SPO0140 and Sbal_2486 revealed a two-domain structure that adopts a novel fold. Analysis of the interdomain cleft suggests a nucleotide-based ligand with a genome context indicating signaling as a possible role for this family

Average RMSE differences for different types of mutations, prediction methods and their combinations with ENCoM.

<p>We calculate the RMSE difference (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003569#pcbi.1003569.e049" target="_blank">Eq. 8</a>) for different subsets of the data (columns) and different methods (rows). Methods followed by a ‘+’ denote linear combinations of the named method with ENCoM. The heatmap values (shown within cells) denote the bootstrapped (10000 iterations) average RMSE difference with respect to random and are color coded according to the map on the upper left (lower values in red signify better predictions). For example the values in the leftmost column representing all data comes from the subtraction of the averages in the corresponding box plots in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003569#pcbi-1003569-g004" target="_blank">Figure 4</a> for the non-combined methods and the random model. Different subsets of types of mutations are show according to certain properties of the amino-acids or residues involved: buried (less than 30% solvent-exposed surface area) or exposed (otherwise), small (A,N,D,C, G, P,S, and V) or big (otherwise), polar (R,N,D,E,Q,H,K,S,T and Y) or non-polar including hydrophobic and aromatic (otherwise). Both methods and subsets of the data are clustered according to similarities in the RMSE difference profiles. Cases where the combination of a method with ENCoM is beneficial, i.e., the RMSE difference is lower than either method in isolation are denoted by a ‘*’ next to their values within the cell.</p

Prediction of domain motions.

<p>The best overlap found within the 10 slowest internal motion modes for different NMA models on domain movements. Legend: All (all cases, N = 248), I (Independent movements, N = 130), C (Coupled movements, N = 117), A (apo form, N = 124) and H (holo form, N = 124). AI (apo form independent, N = 130), HI (holo independent, N = 130), AC (apo coupled, N = 116) and HC (holo coupled, N = 116). Box plots generated from 10000 resampling bootstrapping iterations. ENCoM/ENCoM_ns outperform ANM and STeM on all types of motion.</p

A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations

<div><p>Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with C_α−only representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.</p></div

Prediction of loop conformational change.

<p>The best overlap found within the 10 slowest internal motion modes for different NMA models on loop movements. Acronyms are the same as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003569#pcbi-1003569-g002" target="_blank">Figure 2</a>. Number of cases: All (488), I (252), C (236), A (244) and H (244). AI (126), HI (126), AC (118) and HC (118). Box plots generated from 10000 resampling bootstrapping iterations. ENCoM/ENCoM_ns outperform ANM and STeM on all types of motion. The prediction of loop motions is much harder and here the difference between ENCoM and ENCoM_ns are more pronounced.</p

Self-consistency error.

<p>The error calculated the magnitude of the biases in the prediction of forward and back mutations. Box plots were generated from 10000 resampling bootstrapping iterations for the 57 proteins pairs in the Thiltgen dataset. ENCoM/ENCoM_ns are the methods with lowest self-consistency errors.</p

Performance of different parameter sets on the prediction of mutations, b-factors and motions.

<p>We present as a parallel plot the bootstrapped median RMSE for stabilizing and destabilizing mutation, average best overlap for domains and loop movements as well as self-consistency bias and errors. In the right-most four columns with include the logarithm of the 4 alpha variables. Different parameter sets are colored based on b-factors correlations (red gradient) or domain movement overlaps (blue gradient). The black line represent the specific set of parameters used in ENCoM while the dashed line represents the values for ENCoM using the set of alpha parameters employed in STeM. There is a dichotomy in parameter space such that most sets of parameters are either good at predicting b-factors or overlap and mutations.</p