A method for probabilistic mapping between protein structure and function taxonomies through cross training

<p>Abstract</p> <p>Background</p> <p>Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>) and classes of functional classification databases (e.g. PROSITE <abbrgrp><abbr bid="B2">2</abbr></abbrgrp>), structure and function of proteins could be probabilistically related.</p> <p>Results</p> <p>We demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees.</p> <p>Conclusion</p> <p>We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions.</p

The IkB–NF-kB Signaling Module: Temporal Control and Selective Gene Activation

Nuclear localization of the transcriptional activator NF-κB (nuclear factor κB) is controlled in mammalian cells by three isoforms of NF-κB inhibitor protein: IκBα, -β, and -ɛ. Based on simplifying reductions of the IκB–NF-κB signaling module in knockout cell lines, we present a computational model that describes the temporal control of NF-κB activation by the coordinated degradation and synthesis of IκB proteins. The model demonstrates that IκBα is responsible for strong negative feedback that allows for a fast turn-off of the NF-κB response, whereas IκBβ and -ɛ function to reduce the system's oscillatory potential and stabilize NF-κB responses during longer stimulations. Bimodal signal-processing characteristics with respect to stimulus duration are revealed by the model and are shown to generate specificity in gene expression

A 3D Monte Carlo Analysis of the Role of Dyadic Space Geometry in Spark Generation

AbstractIn multiple biological systems, vital intracellular signaling processes occur locally in minute periplasmic subspaces often referred to as signaling microdomains. The number of signaling molecules in these microdomains is small enough to render the notion of continuous concentration changes invalid, such that signaling events are better described using stochastic rather than deterministic methods. Of particular interest is the dyadic cleft in the cardiac myocyte, where short-lived, local increases in intracellular Ca2+ known as Ca2+ sparks regulate excitation-contraction coupling. The geometry of dyadic spaces can alter in disease and development and display significant interspecies variability. We created and studied a 3D Monte Carlo model of the dyadic cleft, specifying the spatial localization of L-type Ca2+ channels and ryanodine receptors. Our analysis revealed how reaction specificity and efficiency are regulated by microdomain geometry as well as the physical separation of signaling molecules into functional complexes. The spark amplitude and rise time were found to be highly dependent on the concentration of activated channels per dyadic cleft and on the intermembrane separation, but not very sensitive to other cleft dimensions. The role of L-type Ca2+ channel and ryanodine receptor phosphorylation was also examined. We anticipate that this modeling approach may be applied to other systems (e.g., neuronal growth cones and chemotactic cells) to create a general description of stochastic events in Ca2+ signaling

Dynamic Properties of Network Motifs Contribute to Biological Network Organization

Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property—stability or robustness to small perturbations—is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks