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  • 1. Barbier, Matthieu
    et al.
    Watson, James R.
    Stockholm University, Faculty of Science, Stockholm Resilience Centre. Oregon State University, USA.
    The Spatial Dynamics of Predators and the Benefits and Costs of Sharing Information2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 10, e1005147Article in journal (Refereed)
    Abstract [en]

    Predators of all kinds, be they lions hunting in the Serengeti or fishermen searching for their catch, display various collective strategies. A common strategy is to share information about the location of prey. However, depending on the spatial characteristics and mobility of predators and prey, information sharing can either improve or hinder individual success. Here, our goal is to investigate the interacting effects of space and information sharing on predation efficiency, represented by the expected rate at which prey are found and consumed. We derive a feeding functional response that accounts for both spatio-temporal heterogeneity and communication, and validate this mathematical analysis with a computational agent-based model. This agent-based model has an explicit yet minimal representation of space, as well as information sharing about the location of prey. The analytical model simplifies predator behavior into a few discrete states and one essential tradeoff, between the individual benefit of acquiring information and the cost of creating spatial and temporal correlation between predators. Despite the absence of an explicit spatial dimension in these equations, they quantitatively predict the predator consumption rates measured in the agent-based simulations across the explored parameter space. Together, the mathematical analysis and agent-based simulations identify the conditions for when there is a benefit to sharing information, and also when there is a cost.

  • 2.
    Basile, Walter
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Sachenkova, Oxana
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Light, Sara
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Linköping University, Sweden.
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Kungliga Tekniska Högskolan, Sweden.
    High GC content causes orphan proteins to be intrinsically disordered2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 3, e1005375Article in journal (Refereed)
    Abstract [en]

    De novo creation of protein coding genes involves the formation of short ORFs from noncoding regions; some of these ORFs might then become fixed in the population These orphan proteins need to, at the bare minimum, not cause serious harm to the organism, meaning that they should for instance not aggregate. Therefore, although the creation of short ORFs could be truly random, the fixation should be subjected to some selective pressure. The selective forces acting on orphan proteins have been elusive, and contradictory results have been reported. In Drosophila young proteins are more disordered than ancient ones, while the opposite trend is present in yeast. To the best of our knowledge no valid explanation for this difference has been proposed. To solve this riddle we studied structural properties and age of proteins in 187 eukaryotic organisms. We find that, with the exception of length, there are only small differences in the properties between proteins of different ages. However, when we take the GC content into account we noted that it could explain the opposite trends observed for orphans in yeast (low GC) and Drosophila (high GC). GC content is correlated with codons coding for disorder promoting amino acids. This leads us to propose that intrinsic disorder is not a strong determining factor for fixation of orphan proteins. Instead these proteins largely resemble random proteins given a particular GC level. During evolution the properties of a protein change faster than the GC level causing the relationship between disorder and GC to gradually weaken.

  • 3.
    Björklund, Åsa K.
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Ekman, Diana
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Expansion of Protein Domain Repeats2006In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 2, no 8, 959-970 p.Article in journal (Refereed)
    Abstract [en]

    Many proteins, especially in eukaryotes, contain tandem repeats of several domains from the same family. These repeats have a variety of binding properties and are involved in protein-protein interactions as well as binding to other ligands such as DNA and RNA. The rapid expansion of protein domain repeats is assumed to have evolved through internal tandem duplications. However, the exact mechanisms behind these tandem duplications are not well-understood. Here, we have studied the evolution, function, protein structure, gene structure, and phylogenetic distribution of domain repeats. For this purpose we have assigned Pfam-A domain families to 24 proteomes with more sensitive domain assignments in the repeat regions. These assignments confirmed previous findings that eukaryotes, and in particular vertebrates, contain a much higher fraction of proteins with repeats compared with prokaryotes. The internal sequence similarity in each protein revealed that the domain repeats are often expanded through duplications of several domains at a time, while the duplication of one domain is less common. Many of the repeats appear to have been duplicated in the middle of the repeat region. This is in strong contrast to the evolution of other proteins that mainly works through additions of single domains at either terminus. Further, we found that some domain families show distinct duplication patterns, e. g., nebulin domains have mainly been expanded with a unit of seven domains at a time, while duplications of other domain families involve varying numbers of domains. Finally, no common mechanism for the expansion of all repeats could be detected. We found that the duplication patterns show no dependence on the size of the domains. Further, repeat expansion in some families can possibly be explained by shuffling of exons. However, exon shuffling could not have created all repeats.

  • 4. Dunn, Benjamin
    et al.
    Morreaunet, Maria
    Roudi, Yasser
    Stockholm University, Nordic Institute for Theoretical Physics (Nordita).
    Correlations and Functional Connections in a Population of Grid Cells2015In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 11, no 2, e1004052Article in journal (Refereed)
    Abstract [en]

    We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving rats. We evaluate pairwise correlations between these cells and, using a maximum entropy kinetic pairwise model (kinetic Ising model), study their functional connectivity. Even when we account for the covariations in firing rates due to overlapping fields, both the pairwise correlations and functional connections decay as a function of the shortest distance between the vertices of the spatial firing pattern of pairs of grid cells, i.e. their phase difference. They take positive values between cells with nearby phases and approach zero or negative values for larger phase differences. We find similar results also when, in addition to correlations due to overlapping fields, we account for correlations due to theta oscillations and head directional inputs. The inferred connections between neurons in the same module and those from different modules can be both negative and positive, with a mean close to zero, but with the strongest inferred connections found between cells of the same module. Taken together, our results suggest that grid cells in the same module do indeed form a local network of interconnected neurons with a functional connectivity that supports a role for attractor dynamics in the generation of grid pattern.

  • 5.
    Frånberg, Mattias
    et al.
    Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Stockholm University, Science for Life Laboratory (SciLifeLab). Karolinska Institutet, Sweden.
    Strawbridge, Rona J.
    Hamster, Anders
    de Faire, Ulf
    Lagergren, Jens
    Sennblad, Bengt
    Fast and general tests of genetic interaction for genome-wide association studies2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 6, e1005556Article in journal (Refereed)
    Abstract [en]

    A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 . 10(-09)) as well as replicate the interaction (p = 6.97 . 10(-07)). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

  • 6.
    Giardina, Federica
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics. Los Alamos National Laboratory, United States of America .
    Romero-Severson, Ethan Obie
    Albert, Jan
    Britton, Tom
    Stockholm University, Faculty of Science, Department of Mathematics.
    Leitner, Thomas
    Inference of Transmission Network Structure from HIV Phylogenetic Trees2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 1, e1005316Article in journal (Refereed)
    Abstract [en]

    Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance- based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

  • 7. Gutierrez-Arenas, Omar
    et al.
    Eriksson, Olivia
    Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA).
    Hellgren Kotaleski, Jeanette
    Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden; Karolinska Institutet, Sweden;.
    Segregation and Crosstalk of D1 Receptor-Mediated Activation of ERK in Striatal Medium Spiny Neurons upon Acute Administration of Psychostimulants2014In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 1, e1003445- p.Article in journal (Refereed)
    Abstract [en]

    The convergence of corticostriatal glutamate and dopamine from the midbrain in the striatal medium spiny neurons (MSN) triggers synaptic plasticity that underlies reinforcement learning and pathological conditions such as psychostimulant addiction. The increase in striatal dopamine produced by the acute administration of psychostimulants has been found to activate not only effectors of the AC5/cAMP/PKA signaling cascade such as GluR1, but also effectors of the NMDAR/Ca2+/RAS cascade such as ERK. The dopamine-triggered effects on both these cascades are mediated by D1R coupled to Golf but while the phosphorylation of GluR1 is affected by reductions in the available amount of Golf but not of D1R, the activation of ERK follows the opposite pattern. This segregation is puzzling considering that D1R-induced Golf activation monotonically increases with DA and that there is crosstalk from the AC5/cAMP/PKA cascade to the NMDAR/Ca2+/RAS cascade via a STEP (a tyrosine phosphatase). In this work, we developed a signaling model which accounts for this segregation based on the assumption that a common pool of D1R and Golf is distributed in two D1R/Golf signaling compartments. This model integrates a relatively large amount of experimental data for neurons in vivo and in vitro. We used it to explore the crosstalk topologies under which the sensitivities of the AC5/cAMP/PKA signaling cascade to reductions in D1R or Golf are transferred or not to the activation of ERK. We found that the sequestration of STEP by its substrate ERK together with the insensitivity of STEP activity on targets upstream of ERK (i.e. Fyn and NR2B) to PKA phosphorylation are able to explain the experimentally observed segregation. This model provides a quantitative framework for simulation based experiments to study signaling required for long term potentiation in MSNs.

  • 8.
    Holme, Petter
    Stockholm University, Faculty of Social Sciences, Department of Sociology. Umeå University, Sweden; Sungkyunkwan University, South Korea.
    Epidemiologically Optimal Static Networks from Temporal Network Data2013In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 9, no 7, e1003142Article in journal (Refereed)
    Abstract [en]

    One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets.

  • 9.
    Kasson, Peter M.
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Lindahl, Erik
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Pande, Vijay S.
    Atomic-Resolution Simulations Predict a Transition State for Vesicle Fusion Defined by Contact of a Few Lipid Tails2010In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, no 6, e1000829- p.Article in journal (Refereed)
    Abstract [en]

    Membrane fusion is essential to both cellular vesicle trafficking and infection by enveloped viruses. While the fusion protein assemblies that catalyze fusion are readily identifiable, the specific activities of the proteins involved and nature of the membrane changes they induce remain unknown. Here, we use many atomic-resolution simulations of vesicle fusion to examine the molecular mechanisms for fusion in detail. We employ committor analysis for these million-atom vesicle fusion simulations to identify a transition state for fusion stalk formation. In our simulations, this transition state occurs when the bulk properties of each lipid bilayer remain in a lamellar state but a few hydrophobic tails bulge into the hydrophilic interface layer and make contact to nucleate a stalk. Additional simulations of influenza fusion peptides in lipid bilayers show that the peptides promote similar local protrusion of lipid tails. Comparing these two sets of simulations, we obtain a common set of structural changes between the transition state for stalk formation and the local environment of peptides known to catalyze fusion. Our results thus suggest that the specific molecular properties of individual lipids are highly important to vesicle fusion and yield an explicit structural model that could help explain the mechanism of catalysis by fusion proteins.

  • 10. Kenah, Eben
    et al.
    Britton, Tom
    Stockholm University, Faculty of Science, Department of Mathematics.
    Halloran, M. Elizabeth
    Longini, Ira M.
    Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 4Article in journal (Refereed)
    Abstract [en]

    Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology.

  • 11.
    Leimar, Olof
    et al.
    Stockholm University, Faculty of Science, Department of Zoology.
    Dall, Sasha R. X.
    Hammerstein, Peter
    McNamara, John M.
    Genes as Cues of Relatedness and Social Evolution in Heterogeneous Environments2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 6, e1005006Article in journal (Refereed)
    Abstract [en]

    There are many situations where relatives interact while at the same time there is genetic polymorphism in traits influencing survival and reproduction. Examples include cheater-cooperator polymorphism and polymorphic microbial pathogens. Environmental heterogeneity, favoring different traits in nearby habitats, with dispersal between them, is one general reason to expect polymorphism. Currently, there is no formal framework of social evolution that encompasses genetic polymorphism. We develop such a framework, thus integrating theories of social evolution into the evolutionary ecology of heterogeneous environments. We allow for adaptively maintained genetic polymorphism by applying the concept of genetic cues. We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats. An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness. We show that epistatic modifiers of the cue polymorphism can evolve to make optimal use of the information in the genetic cue, in analogy with a Bayesian decision maker. Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.

  • 12.
    Lindén, Martin
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Sens, Pierre
    Laboratoire de Physico-Chimie Théorique, CNRS/UMR 7083, ESPCI, Paris, France.
    Phillips, Rob
    Department of Applied Physics and Division of Biology, California Institute of Technology, Pasadena, California, United States of America, and Laboratoire de Physico-Chimie Théorique, CNRS/UMR 7083, ESPCI, Paris, France.
    Entropic tension in crowded membranes.2012In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 3, e1002431- p.Article in journal (Refereed)
    Abstract [en]

    Unlike their model membrane counterparts, biological membranes are richly decorated with a heterogeneous assembly of membrane proteins. These proteins are so tightly packed that their excluded area interactions can alter the free energy landscape controlling the conformational transitions suffered by such proteins. For membrane channels, this effect can alter the critical membrane tension at which they undergo a transition from a closed to an open state, and therefore influence protein function in vivo. Despite their obvious importance, crowding phenomena in membranes are much less well studied than in the cytoplasm. Using statistical mechanics results for hard disk liquids, we show that crowding induces an entropic tension in the membrane, which influences transitions that alter the projected area and circumference of a membrane protein. As a specific case study in this effect, we consider the impact of crowding on the gating properties of bacterial mechanosensitive membrane channels, which are thought to confer osmoprotection when these cells are subjected to osmotic shock. We find that crowding can alter the gating energies by more than [Formula: see text] in physiological conditions, a substantial fraction of the total gating energies in some cases. Given the ubiquity of membrane crowding, the nonspecific nature of excluded volume interactions, and the fact that the function of many membrane proteins involve significant conformational changes, this specific case study highlights a general aspect in the function of membrane proteins.

  • 13. Lundqvist, Mikael
    et al.
    Compte, Albert
    Lansner, Anders
    Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
    Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network2010In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, no 6, e1000803- p.Article in journal (Refereed)
    Abstract [en]

    Attractor neural networks are thought to underlie working memory functions in the cerebral cortex. Several such models have been proposed that successfully reproduce firing properties of neurons recorded from monkeys performing working memory tasks. However, the regular temporal structure of spike trains in these models is often incompatible with experimental data. Here, we show that the in vivo observations of bistable activity with irregular firing at the single cell level can be achieved in a large-scale network model with a modular structure in terms of several connected hypercolumns. Despite high irregularity of individual spike trains, the model shows population oscillations in the beta and gamma band in ground and active states, respectively. Irregular firing typically emerges in a high-conductance regime of balanced excitation and inhibition. Population oscillations can produce such a regime, but in previous models only a non-coding ground state was oscillatory. Due to the modular structure of our network, the oscillatory and irregular firing was maintained also in the active state without fine-tuning. Our model provides a novel mechanistic view of how irregular firing emerges in cortical populations as they go from beta to gamma oscillations during memory retrieval.

  • 14.
    Luo, Jinghui
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Maréchal, Jean-Didier
    Wärmländer, Sebastian
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Gräslund, Astrid
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Perálvarez-Marín, Alex
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    In silico analysis of the apolipoprotein E and the amyloid beta peptide interaction: misfolding induced by frustration of the salt bridge network2010In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, no 2, e1000663- p.Article in journal (Refereed)
    Abstract [en]

    The relationship between Apolipoprotein E (ApoE) and the aggregation processes of the amyloid beta (A beta) peptide has been shown to be crucial for Alzheimer's disease (AD). The presence of the ApoE4 isoform is considered to be a contributing risk factor for AD. However, the detailed molecular properties of ApoE4 interacting with the A beta peptide are unknown, although various mechanisms have been proposed to explain the physiological and pathological role of this relationship. Here, computer simulations have been used to investigate the process of A beta interaction with the N-terminal domain of the human ApoE isoforms (ApoE2, ApoE3 and ApoE4). Molecular docking combined with molecular dynamics simulations have been undertaken to determine the A beta peptide binding sites and the relative stability of binding to each of the ApoE isoforms. Our results show that from the several ApoE isoforms investigated, only ApoE4 presents a misfolded intermediate when bound to A beta. Moreover, the initial alpha-helix used as the A beta peptide model structure also becomes unstructured due to the interaction with ApoE4. These structural changes appear to be related to a rearrangement of the salt bridge network in ApoE4, for which we propose a model. It seems plausible that ApoE4 in its partially unfolded state is incapable of performing the clearance of A beta, thereby promoting amyloid forming processes. Hence, the proposed model can be used to identify potential drug binding sites in the ApoE4-A beta complex, where the interaction between the two molecules can be inhibited.

  • 15.
    Murail, Samuel
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Howard, Rebecca J.
    Broemstrup, Torben
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Bertaccini, Edward J.
    Harris, R. Adron
    Trudell, James R.
    Lindahl, Erik
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Molecular mechanism for the dual alcohol modulation of cys loop receptors2012In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 10, e1002710- p.Article in journal (Refereed)
    Abstract [en]

    Cys-loop receptors constitute a superfamily of pentameric ligand-gated ion channels (pLGICs), including receptors for acetylcholine, serotonin, glycine and gamma-aminobutyric acid. Several bacterial homologues have been identified that are excellent models for understanding allosteric binding of alcohols and anesthetics in human Cys-loop receptors. Recently, we showed that a single point mutation on a prokaryotic homologue (GLIC) could transform it from a channel weakly potentiated by ethanol into a highly ethanol-sensitive channel. Here, we have employed molecular simulations to study ethanol binding to GLIC, and to elucidate the role of the ethanol-enhancing mutation in GLIC modulation. By performing 1-mu s simulations with and without ethanol on wild-type and mutated GLIC, we observed spontaneous binding in both intra-subunit and inter-subunit transmembrane cavities. In contrast to the glycine receptor GlyR, in which we previously observed ethanol binding primarily in an inter-subunit cavity, ethanol primarily occupied an intra-subunit cavity in wild-type GLIC. However, the highly ethanol-sensitive GLIC mutation significantly enhanced ethanol binding in the inter-subunit cavity. These results demonstrate dramatic effects of the F(14')A mutation on the distribution of ligands, and are consistent with a two-site model of pLGIC inhibition and potentiation.

  • 16. Rocha, Luis E. C.
    et al.
    Liljeros, Fredrik
    Stockholm University, Faculty of Social Sciences, Department of Sociology.
    Holme, Petter
    Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts2011In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 7, no 3, e1001109- p.Article in journal (Refereed)
    Abstract [en]

    Sexual contact patterns, both in their temporal and network structure, can influence the spread of sexually transmitted infections (STI). Most previous literature has focused on effects of network topology; few studies have addressed the role of temporal structure. We simulate disease spread using SI and SIR models on an empirical temporal network of sexual contacts in high-end prostitution. We compare these results with several other approaches, including randomization of the data, classic mean-field approaches, and static network simulations. We observe that epidemic dynamics in this contact structure have well-defined, rather high epidemic thresholds. Temporal effects create a broad distribution of outbreak sizes, even if the per-contact transmission probability is taken to its hypothetical maximum of 100%. In general, we conclude that the temporal correlations of our network accelerate outbreaks, especially in the early phase of the epidemics, while the network topology (apart from the contact-rate distribution) slows them down. We find that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network. Thus, temporal structures are needed alongside network topology to fully understand the spread of STIs. On a side note, our simulations further suggest that the specific type of commercial sex we investigate is not a reservoir of major importance for HIV.

  • 17.
    Skwark, Marcin J.
    et al.
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Aalto University, Finland.
    Raimondi, Daniele
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). University Libre Brussels, Belgium.
    Michel, Mirco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
    Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns2014In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 11, e1003889Article in journal (Refereed)
    Abstract [en]

    Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for b-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.

  • 18. Tully, Philip J.
    et al.
    Lindén, Henrik
    Hennig, Matthias H.
    Lansner, Anders
    Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology (KTH), Sweden; Karolinska Institute, Sweden.
    Spike-Based Bayesian-Hebbian Learning of Temporal Sequences2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 5, e1004954Article in journal (Refereed)
    Abstract [en]

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  • 19. Yazdi, Samira
    et al.
    Stein, Matthias
    Elinder, Fredrik
    Andersson, Magnus
    Lindahl, Erik
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). KTH Royal Institute of Technology, Sweden.
    The Molecular Basis of Polyunsaturated Fatty Acid Interactions with the Shaker Voltage-Gated Potassium Channel2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 1, e1004704Article in journal (Refereed)
    Abstract [en]

    Voltage-gated potassium (K-V) channels are membrane proteins that respond to changes in membrane potential by enabling K+ ion flux across the membrane. Polyunsaturated fatty acids (PUFAs) induce channel opening by modulating the voltage-sensitivity, which can provide effective treatment against refractory epilepsy by means of a ketogenic diet. While PUFAs have been reported to influence the gating mechanism by electrostatic interactions to the voltage-sensor domain (VSD), the exact PUFA-protein interactions are still elusive. In this study, we report on the interactions between the Shaker K-V channel in open and closed states and a PUFA-enriched lipid bilayer using microsecond molecular dynamics simulations. We determined a putative PUFA binding site in the open state of the channel located at the protein-lipid interface in the vicinity of the extracellular halves of the S3 and S4 helices of the VSD. In particular, the lipophilic PUFA tail covered a wide range of non-specific hydrophobic interactions in the hydrophobic central core of the protein-lipid interface, while the carboxylic head group displayed more specific interactions to polar/charged residues at the extracellular regions of the S3 and S4 helices, encompassing the S3-S4 linker. Moreover, by studying the interactions between saturated fatty acids (SFA) and the Shaker K-V channel, our study confirmed an increased conformational flexibility in the polyunsaturated carbon tails compared to saturated carbon chains, which may explain the specificity of PUFA action on channel proteins.

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