Browsing by Subject "Proteins--Conformation"

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Simulation studies of biopolymers under spatial and topological constraints
(2008-05) Huang, Lei, 1978-; Makarov, Dmitrii E.
The translocation of a biopolymer through a narrow pore exists in universal cellular processes, such as the translocations of nascent proteins through ribosome and the degradation of protein by ATP-dependent proteases. However, the molecular details of these translocation processes remain unclear. Using computer simulations we study the translocations of a ubiquitin-like protein into a pore. It shows that the mechanism of co-translocational unfolding of proteins through pores depends on the pore diameter, the magnitude of pulling force and on whether the force is applied at the N- or the C-terminus. Translocation dynamics depends on whether or not polymer reversal is likely to occur during translocation. Although it is of interest to compare the timescale of polymer translocation and reversal, there are currently no theories available to estimate the timescale of polymer reversal inside a pore. With computer simulations and approximate theories, we show how the polymer reversal depends on the pore size, r, and the chain length, N. We find that one-dimensional transition state theory (TST) using the polymer extension along the pore axis as a reaction coordinate adequately predicts the exponentially strong dependence of the reversal rate on r and N. Additionally, we find that the transmission factor (the ratio of the exact rate and the TST rate) has a much weaker power law dependence on r and N. Finite-size effects are observed even for chains with several hundred monomers. If metastable states are separated by high energy-barriers, transitions between them will be rare events. Instead of calculating the relative energy by studying those transitions, we can calculate absolute free energy separately to compare their relative stability. We proposed a method for calculating absolute free energy from Monte Carlo or molecular dynamics data. Additionally, the diffusion of a knot in a tensioned polymer is studied using simulations and it can be modeled as a one-dimensional free diffusion problem. The diffusion coefficient is determined by the number of monomers involved in a knot and its tension dependence shows a maximum due to two dominating factors: the friction from solvents and “local friction” from interactions among monomers in a compact knot.
Towards a comprehensive human protein-protein interaction network
(2005) Ramani, Arun Kumar; Marcotte, Edward M.
Obtaining a reliable interaction data set describing the human interactome is a milestone yet to be reached. The past few years has seen tremendous progress in elucidating the yeast interactome. Experimental approaches for obtaining large-scale protein interaction data coupled with powerful computational methods for combining these data sets and for predicting functional relations between genes have been successful in tackling the yeast interactome. The concerted development of visualization techniques and the progress in the field of network biology has provided us with tools to evaluate, analyze, and interpret the interactome. Although techniques are being scaled to tackle mammalian genomes, as witnessed by the first protein interaction networks for fly and worm we are far from a complete map of the human interactome. Human genes create additional challenges due to molecular complexity, tissue specificity, and alternate splicing. It therefore becomes important to build well-annotated benchmarks and accuracy measures to evaluate new data. Here, we describe three methods that provide a framework to build a comprehensive human interactome. We have developed a novel algorithm for predicting protein interaction partners based on comparing the position of proteins in their respective phylogenetic trees. We establish two tests of the accuracy of human protein interaction data sets and integrate the small-scale human interaction data sets using a Log likelihood framework. The benchmarks and the consolidated interaction set will provide a basis for determining the quality of future large-scale human protein interaction assays. Lastly, based on patterns of conserved co-expression of human gene pairs and their orthologs from 5 different organisms (A. thaliana, M. musculus, D. melanogaster, C. elegans, and Yeast) we predict protein interactions, and test them against the benchmarks established by us. By combining the existing interaction data sets, we build a network of 61,974 interactions between 9,642 human proteins and cluster the network to show examples representative of the quality of the interactions in the network. The methods, benchmarks and the Log likelihood framework, we hope, would enable us to build a comprehensive human interactome.