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  • aabi
    Member
    • May 2016
    • 35

    Training in Molecular Modeling and Molecular Dynamics @ NIH, USA, Nov 29-Dec 2, 2016



    Training in Molecular Modeling and Molecular Dynamics
    Nov 29-Dec 2, 2016

    Where?
    National Institutes of Health
    9000 Rockville Pike
    Building 60, Room 162
    Bethesda, MD 20892, USA

    Background:
    Predicting the effect of a mutation on the structure and function of a protein is not just for researchers with super-computer facilities. Thanks to public cloud computing options, anyone with basic molecular biology background can setup and run compute intensive computational modeling and dynamics experiments.

    Objectives:
    Participants will use popular open source tools and techniques necessary for conducting successful molecular modeling and dynamics experiments... in the cloud.

    Hands-on Skills/Tools Taught
    • Ab initio protein structure modeling: QUARK / Rosetta
    • Remote homology detection: HHpred
    • Fragment-based protein structure modeling: Phyre2
    • Homology-based protein structure modeling: I-TASSER, MODELLER
    • Protein structure quality analysis: PROCHECK, WHAT_IF, Verify3D, PDB-REDO
    • Protein structure refinement: ModRefiner, ModLoop, Ramachandran plot
    • Macromolecular visualization: VMD, USCF Chimera
    • Molecular dynamics: NAMD

    Highlights
    • Cloud-based, high performance computing platform
    • Cloud image freely provided to participants
    • Training provided by active NIH researchers
    • Cookbook style bound manual for all exercises
    • Direct, after training support through exclusive forum membership
    • Continuing Educational Credits

    For more information and registration, please visit the following page;
    Information and Registration
    .
    .
    .

Latest Articles

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  • GATTACAT
    Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by GATTACAT
    Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
    07-01-2026, 11:43 AM
  • SEQadmin2
    Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by SEQadmin2


    I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

    Here are nine questions we think about, in roughly the order they matter, before...
    06-18-2026, 07:11 AM

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