31 research outputs found
Recommended from our members
Identifying and quantifying heterogeneity in high content analysis: Application of heterogeneity indices to drug discovery
One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology. © 2014 Gough et al
Effect of coupling asymmetry on mean-field solutions of direct and inverse Sherrington-Kirkpatrick model
We study how the degree of symmetry in the couplings influences the
performance of three mean field methods used for solving the direct and inverse
problems for generalized Sherrington-Kirkpatrick models. In this context, the
direct problem is predicting the potentially time-varying magnetizations. The
three theories include the first and second order Plefka expansions, referred
to as naive mean field (nMF) and TAP, respectively, and a mean field theory
which is exact for fully asymmetric couplings. We call the last of these simply
MF theory. We show that for the direct problem, nMF performs worse than the
other two approximations, TAP outperforms MF when the coupling matrix is nearly
symmetric, while MF works better when it is strongly asymmetric. For the
inverse problem, MF performs better than both TAP and nMF, although an ad hoc
adjustment of TAP can make it comparable to MF. For high temperatures the
performance of TAP and MF approach each other
Using Entropy Maximization to Understand the Determinants of Structural Dynamics beyond Native Contact Topology
Comparison of elastic network model predictions with experimental data has provided important insights on the dominant role of the network of inter-residue contacts in defining the global dynamics of proteins. Most of these studies have focused on interpreting the mean-square fluctuations of residues, or deriving the most collective, or softest, modes of motions that are known to be insensitive to structural and energetic details. However, with increasing structural data, we are in a position to perform a more critical assessment of the structure-dynamics relations in proteins, and gain a deeper understanding of the major determinants of not only the mean-square fluctuations and lowest frequency modes, but the covariance or the cross-correlations between residue fluctuations and the shapes of higher modes. A systematic study of a large set of NMR-determined proteins is analyzed using a novel method based on entropy maximization to demonstrate that the next level of refinement in the elastic network model description of proteins ought to take into consideration properties such as contact order (or sequential separation between contacting residues) and the secondary structure types of the interacting residues, whereas the types of amino acids do not play a critical role. Most importantly, an optimal description of observed cross-correlations requires the inclusion of destabilizing, as opposed to exclusively stabilizing, interactions, stipulating the functional significance of local frustration in imparting native-like dynamics. This study provides us with a deeper understanding of the structural basis of experimentally observed behavior, and opens the way to the development of more accurate models for exploring protein dynamics
Mechanical Strength of 17 134 Model Proteins and Cysteine Slipknots
A new theoretical survey of proteins' resistance to constant speed stretching
is performed for a set of 17 134 proteins as described by a structure-based
model. The proteins selected have no gaps in their structure determination and
consist of no more than 250 amino acids. Our previous studies have dealt with
7510 proteins of no more than 150 amino acids. The proteins are ranked
according to the strength of the resistance. Most of the predicted top-strength
proteins have not yet been studied experimentally. Architectures and folds
which are likely to yield large forces are identified. New types of potent
force clamps are discovered. They involve disulphide bridges and, in
particular, cysteine slipknots. An effective energy parameter of the model is
estimated by comparing the theoretical data on characteristic forces to the
corresponding experimental values combined with an extrapolation of the
theoretical data to the experimental pulling speeds. These studies provide
guidance for future experiments on single molecule manipulation and should lead
to selection of proteins for applications. A new class of proteins, involving
cystein slipknots, is identified as one that is expected to lead to the
strongest force clamps known. This class is characterized through molecular
dynamics simulations.Comment: 40 pages, 13 PostScript figure
Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data
Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data
Protein 3D Structure Computed from Evolutionary Sequence Variation
The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing
Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches
Cascading activity is commonly found in complex systems with directed
interactions such as metabolic networks, neuronal networks, or disease spreading
in social networks. Substantial insight into a system's organization
can be obtained by reconstructing the underlying functional network architecture
from the observed activity cascades. Here we focus on Bayesian approaches and
reduce their computational demands by introducing the Iterative Bayesian (IB)
and Posterior Weighted Averaging (PWA) methods. We introduce a special case of
PWA, cast in nonparametric form, which we call the normalized count (NC)
algorithm. NC efficiently reconstructs random and small-world functional network
topologies and architectures from subcritical, critical, and supercritical
cascading dynamics and yields significant improvements over commonly used
correlation methods. With experimental data, NC identified a functional and
structural small-world topology and its corresponding traffic in cortical
networks with neuronal avalanche dynamics
Exploring the Conformational Transitions of Biomolecular Systems Using a Simple Two-State Anisotropic Network Model
Biomolecular conformational transitions are essential to biological functions. Most experimental methods report on the long-lived functional states of biomolecules, but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect experimentally. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed experimentally. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a physically reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the experimental structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the minimum energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biological interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom molecular dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides experimentally testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results. © 2014 Das et al
Varieties of living things: Life at the intersection of lineage and metabolism
publication-status: Publishedtypes: Articl