Jürgen Pfeffer

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals.

Sampling from Social Networks with Attributes

Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.

Lazer et al. (2009): Computational Social Science

Computational Social Science ist als wissenschaftliche Disziplin relativ jung. Der im Februar 2009 in einer Ausgabe des Science Magazins von 15 ForscherInnen (David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, Marshall Van Alstyne) veroffentlichte Artikel wird vielfach als ihre Grundungspublikation zitiert, obwohl in den Jahren davor bereits unter diesem Begriff geforscht wurde.

Policy visions of big data: views from the Global South

Abstract Government intentions stand at the heart of debates about how big data can and should be used in the Global South. This paper provides new insights by examining the political and economic visions promoted by emerging power governments in Brazil, India and China (the BICs). Doing so is crucial as these countries not only comprise some of the world’s largest populations, but have also demonstrated their initiative in national and international promotion of big data politics. Drawing on a content analysis of strategic and legal documents discussing the use of big data, we identify potential areas for big data cooperation among the BICs by determining the compatibility of national policy visions. Three visions are apparent: data as a force for political liberation or repression, for improving public services and for facilitating development. Successful BIC cooperation is likely related to the latter two, but less probable for the liberation/repression vision. We conclude by identifying the implications of BIC engagement with big data for the Global South more broadly.

Cost Matters: A New Example-Dependent Cost-Sensitive Logistic Regression Model

Connectivity and automation are evermore part of today’s cars. To provide automation, many gauges are integrated in cars to collect physical readings. In the automobile industry, the gathered multiple datasets can be used to predict whether a car repair is needed soon. This information gives drivers and retailers helpful information to take action early. However, prediction in real use cases shows new challenges: misclassified instances have not equal but different costs. For example, incurred costs for not predicting a necessarily needed tire change are usually higher than predicting a tire change even though the car could still drive thousands of kilometers. To tackle this problem, we introduce a new example-dependent cost sensitive prediction model extending the well-established idea of logistic regression. Our model allows different costs of misclassified instances and obtains prediction results leading to overall less cost. Our method consistently outperforms the state-of-the-art in example-dependent cost-sensitive logistic regression on various datasets. Applying our methods to vehicle data from a large European car manufacturer, we show cost savings of about 10%.

zooRank: Ranking Suspicious Entities in Time-Evolving Tensors

Most user-based websites such as social networks (Twitter, Facebook) and e-commerce websites (Amazon) have been targets of group fraud (multiple users working together for malicious purposes). How can we better rank malicious entities in such cases of group-fraud? Most of the existing work in group anomaly detection detects lock-step behavior by detecting dense blocks in matrices, and recently, in tensors. However, there is no principled way of scoring the users based on their participation in these dense blocks. In addition, existing methods do not take into account temporal features while detecting dense blocks, which are crucial to uncover bot-like behaviors. In this paper (a) we propose a systematic way of handling temporal information; (b) we give a list of axioms that any individual suspiciousness metric should satisfy; (c) we propose zooRank, an algorithm that finds and ranks suspicious entities (users, targeted products, days, etc.) effectively in real-world datasets. Experimental results on multiple real-world datasets show that zooRank detected and ranked the suspicious entities with high accuracy, while outperforming the baseline approach.

Simulating the Dynamics of Socio-Economic Systems

To the two traditional modes of doing science, in vivo (observation) and in vitro (experimentation), has been added “in silico”: computer simulation. It has become routine in the natural sciences, as well as in systems planning and business process management (Baines et al. 2004; Laguna and Marklund 2013; Paul et al. 1999) to recreate the dynamics of physical systems in computer code. The code is then executed to give outputs that describe how a system evolves from given inputs. Simulation models of simple physical processes, like boiling water or materials rupturing, give precise outputs that reliably match the outcomes of the actual physical system. However, as Winsberg (2010, p. 71) argues, scientists who rely on simulations do so because they “assume as background knowledge that we already know a great deal about how to build good models of the very features of the target system that we are interested in learning about.”

Chlorophyll-Derivative Modulation of Rhodopsin Signaling Properties through Evolutionarily Conserved Interaction Pathways

Retinal is the light-absorbing chromophore that is responsible for the activation of visual pigments and light-driven ion pumps. Evolutionary changes in the intermolecular interactions of the retinal with specific amino acids allow for adaptation of the spectral characteristics, referred to as spectral tuning. However, it has been proposed that a specific species of dragon fish has bypassed the adaptive evolutionary process of spectral tuning and replaced it with a single evolutionary event: photosensitization of rhodopsin by chlorophyll derivatives. Here, by using a combination of experimental measurements and computational modeling to probe retinal-receptor interactions in rhodopsin, we show how the binding of the chlorophyll derivative, chlorin-e6 (Ce6) in the intracellular domain (ICD) of the receptor allosterically excites G-protein coupled receptor class A (GPCR-A) conserved long-range correlated fluctuations that connect distant parts of the receptor. These long-range correlated motions are associated with regulating the dynamics and intermolecular interactions of specific amino acids in the retinal ligand-binding pocket that have been associated with shifts in the absorbance peak maximum (λmax) and hence, spectral sensitivity of the visual system. Moreover, the binding of Ce6 affects the overall global properties of the receptor. Specifically, we find that Ce6-induced dynamics alter the thermal stability of rhodopsin by adjusting hydrogen-bonding interactions near the receptor active-site that consequently also influences the intrinsic conformational equilibrium of the receptor. Due to the conservation of the ICD residues amongst different receptors in this class and the fact that all GPCR-A receptors share a common mechanism of activation, it is possible that the allosteric associations excited in rhodopsin with Ce6 binding are a common feature in all class A GPCRs.