15 research outputs found

    Salsbury Group research Presentation

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    <p>Annual overview of our research to the Physics Department; a flavor of our group mostly for the undergrads</p

    Salsbury Group 2016 Research Presentation

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    Annual research overview; sort of big pictur

    VisualStatistics

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    <p>Python scripts for visualizing macromolecules and uncertainty</p

    HDBSCAN and Amorim-Hennig for MD

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    Scripts for performing first-pass non-parametric clustering on Molecular Dynamics trajectories using HDBSCAN and ntelligent Minkowski-Weighted K-Means (iMWK-means) with explicit rescaling followed by K-Means. These scripts depend on code from https://sourceforge.net/projects/unsupervisedpy/ and https://github.com/lmcinnes/hdbsca

    Catdcd Interface

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    <p>CatDCD is a versatile tool that can be cumbersome to use for non-experienced users as it is available primarily through a command line interface. The purpose of this program is to provide an easy to use interface for CatDCD and make it much simpler to perform batch tasks to process lots of data. For questions, comments, or the source code, send an email to the developer.</p> <p>Running the Application:<br>The first time running the application, it is imperative to run the configuration editor to make sure that the folder paths are correct.<br>Configuration<br>To edit/check the configuration parameters click on the runtime menu Tools>Configuration or use the shortcut key (Command-E)<br>There are three configurable parameters:<br>CatdcdExecutableFolderLocation<br>The location of catdcd. This can either be the version distributed with VMD (show package contents - Contents/vmd/plugins/MACOSXX86/bin/catdcd5.1)<br>VMDStartupApplicationLocation<br>The location of the VMD startup.command file on Mac. The default location on a Mac is, for example /Applications/VMD\ 1.9.2.app/Contents/MacOS/startup.command<br>TCLScriptFolderLocation<br>This is really just the location of the getAtomSeelctionIndices.tcl script used to index the atom selection in that operational mode.<br>Normal Usage<br>The file path controls are all compatible with drag and drop functionality so the user can drag pdb, dcd, or other files directly into the GUI controls and it will update the file path automatically.<br>Select PDB file<br>Select DCD file(s)<br>Decide operational mode:<br>1to 1 will map an output dcd file to every input to is useful execute multiple runs at once.<br>Many to 1 will concatenate the dcd files into a single output trajectory. Useful for combining different simulations together.<br>Set frame parameters<br>Determine whether or not to index. If so, input desired VMD compatible atom selection (as seen from the command line. For example, one must escape certain characters e.g. "type C1\' "<br>Select output file(s) (Note: an additional folder with the report name will be created to contain the analysis output)<br>If desired, request a log file (it will have the same file name as the first output file with a different file extension.<br>Execute<br>Rinse & Repeat<br>Quit when complete</p> <p> </p> <p> </p> <p> </p

    Shifting interfaces: changes in protein-protein and protein-DNA interfaces probed via molecular dynamics

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    <p>This is a copy of a poster presented at the 2013 Biophysics Society. The postdoc, Dr, Lacramioara Negureanu did not attend, so Professor Salsbury presented the work.</p

    Villin Headpiece Simulations

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    3 separate simulations each of 6µs concatenated together are contained herein.  Each trajectory was run with ACEMD using a CHARMM 27 force field.  The proteins (2RJY) were first solvated to 0.15 mol/L of NaCL and set in a water box modeled as a TIP3P water model.  Proteins were then equilibrated (conjugate gradient minimization) and then heated from the crystal structure to unfold.  Folding simulations were run after a subsequent equilibration. While the simulations do not find the native folded state,  they sample many partially folded intermediates and offer insight into the folding pathway of villin headpiece.  Simulations included here have had the water and ions removed for space considerations and they have been aligned to remove translation and rotation resulting from diffusion

    A Matlab script to perform PCA on molecular dynamics trajectories

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    A Matlab script to perform principal component analysis on molecular dynamics trajectories and construct free energy surfaces using predominant principal components

    Python Implementation of Quality Threshold Clustering for Molecular Dynamics

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    A python implementation of Heyer, 1999's Quality Threshold clustering algorithm specialized for molecular dynamics trajectories

    Combining Molecular Dynamics and Biopolymer Docking

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    <p>Presentation given at  2nd Workshop on High-Throughput Molecular Dynamics 2015 at the Barcelona biomedical research park </p> <p>Abstract:</p> <p>High-throughput docking is most reliable when no structural change occurs in the receptor (i.e., rigid docking). However, in vivo (and in vitro) ligand binding and protein-protein interactions usually result in conformational change. To determine if we can overcome the limits of current docking software, we have performed ensemble docking runs, gathering sets of receptor and ligand conformations from microsecond-timescale all-atom molecular dynamics simulations. Here we provide insights and guidelines for others who plan to do likewise. As a case study, we present results from ensemble docking of a therapeutic polymer of FdUMP (5-fluoro-2′-deoxyuridine-5′-O-monophosphate) – a topoisomerase-1 (Top1), apoptosis-inducing poison – to Albumin and Vitronectin, showing that partially folded states of the FdUMP strand have the lowest free energy of binding to both proteins. We also present an example of this method applied to protein-protein interactions with two nucleotide binding proteins from Bacillus subtilis along with their interactions with ATP in complex. From these latter investigations, we predict a 3D structure in good agreement with results from residue mutation experiments. Finally, we propose a methodology for visualizing the uncertainty in these data – a method that can be applied to computational biology results in general.</p> <p> </p
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