Accepted Posters

Category 'B'- Biophysics '
Poster B1
Molecular Mechanics Analysis of Minimal Energy RNA Conformational Change Pathways
Keith Van Nostrand- University of Rochester
Scott D. Kennedy (Assistance Professor, Biochemistry and Biophysics); Douglas H. Turner (Professor, Chemistry); David H. Mathews (Assistant Professor, Biochemistry and Biophysics);
Short Abstract: Nudged Elastic Band and targeted molecular dynamics were used to model low potential energy pathways for the conformational change of an AA non-canonical pair, which was shown to undergo conformational exchange by NMR. The pathways revealed a reaction coordinate along which free energy changes were determined by umbrella sampling.
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Poster B2
pKa's of Ionizable Groups and Energetics of Protein Conformational Transitions
Zofia Pilat- Uniwersity of Warsaw
Jan Antosiewicz (University of Warsaw, Physics);
Short Abstract: We demonstrate (using Drosophila engrailed homeodomain test-case) that when interacting ionizable groups titrate in the same pH region,it is not possible to evaluate free energy of proteins unfolding (or any other conformational transition) with satisfactory accuracy based only on pKa's of the groups in folded and unfolded states respectively.
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Poster B3
Key Role of Flexibility of Aromatic Residues in Ligand Trafficking in the case of Acetylcholinesterases
Pavan Ghatty- ORNL
No additional authors
Short Abstract: The high and conserved aromatic content in the 20A deep narrow active-site gorge in Acetylcholinesterases of various species is an interesting observation made in literature. We studied the flexibility of those residues using Molecular Dynamics simulations.
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Poster B4
Protein kinase substrate prediction: Using protein kinase structural features to predict substrates
Greg Ziegler- Purdue University
Michael Gribskov (Purdue University, Department of Biological Sciences);
Short Abstract: We have extracted physiochemical descriptors from the residues found around the substrate binding groove of protein kinases and paired these with features from the target sequences that bind to the kinase. This information is used for training a machine learning algorithm to predict novel protein kinase substrates.
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Poster B5
Yana Gofman- GKSS Research Center
Turkan Haliloglu (Bogazici University, Polymer Research Center and Chemical Engineering Department); Nir Ben-Tal (Tel-Aviv University, Department of Biochemistry);
Short Abstract: We have previously developed and tested a Monte Carlo simulations model for investigation of linear ?-helical peptides with membranes. Encouraged by good correlation between the simulations and empiric data, we established a web-server to allow external users to perform simulations of their peptides of interest in membrane and water environments.
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Poster B6
Bridging the Gaps: Atomic Simulation of Macromolecular Environment Brings Together Protein Docking, Interaction Kinetics and Crowding Effects
Xiaofan Li- Cancer Research UK London Research Institute
Iain Moal (Cancer Research UK London Research Institute, Biomolecular Modelling Laboratory); Paul Bates (Cancer Research UK London Research Institute, Biomolecular Modelling Laboratory);
Short Abstract: We developed a computational package capable of simulating a crowded macromolecular environment. For the first time protein docking can be investigated with environmental proteins, and retention-time based binding scores can be fitted to experimental k-on. These observations provide us further insights into protein-binding mechanisms and the influence of macromolecular crowding.
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Accepted Posters

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