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MD Simulations with NAMD (And VMD) MD Simulations with NAMD (and VMD) João V. Ribeiro Research Programmer NIH Center for Macromolecular Modeling and Bioinformatics University of Illinois at Urbana-Champaign www.ks.uiuc.edu/~jribeiro [email protected] PRACE/BioExcel Spring School 2019 HPC for Life Sciences Sweden A Brief History (and Future) of NAMD and VMD Number of Citations per Year VMD NAMD Hours Until Next Citation (VMD + NAMD) NAMD in a Nutshell • Developed in C++, CUDA (GPU), and Charm++ • Performance Scales to Hundreds of Thousands of IEEE Fernbach Award 2012 - Cores and Hundreds of GPUs “For outstanding contributions to Large Systems the development of widely used - parallel software for large - Enhanced Sampling biomolecular systems simulation” • Large Variety of User Defined Forces and Biased Simulations • TCL Script as Input File - Allows Scripting in the Input File - Workflow Control - Method Development at Higher Level • Close Relationship with VMD - Preparation - QwikMD - Analysis - Cross Correlation, Clustering… E.Coli Chemosensory Array Protocell - Visualization- Ray Tracing NAMD: http://www.ks.uiuc.edu/Research/namd/ VMD: https://www.ks.uiuc.edu/Research/vmd/ Main NAMD Developers and Contributors David Hardy Julio Maia Jim Philips Ryan McGreevy Senior Research Programmer Research Programmer NCSA Blue Waters Research Programmer Jérôme Hénin Brian Radak Wei Jiang Giacomo Fiorin Institut de Biologie Research Programmer Argonne Lab Temple University Physico-Chimique (Paris) NAMD Developer Workshop I NAMD Developer Workshop - Chicago, IL 2016 II NAMD Developer Workshop - Chicago, IL 2017 III NAMD Developer Workshop - Urbana, IL 2018 NAMD Developer Workshop I NAMD Developer Workshop - Chicago, IL 2016 II NAMD Developer Workshop - Chicago, IL 2017 Upcoming NAMD Developer Workshop August 19-20 2019 Urbana Illinois III NAMD Developer Workshop - Urbana, IL 2018 http://www.ks.uiuc.edu/Training/Workshop/Urbana2019/ Hands-On NAMD • 55 Workshops • 50+ Tutorials - 5 New Tutorials - 1800+ pages of Tutorials • 12 Case Studies • Hands-On Workshop on Enhanced Sampling and Free-Energy Calculation - September (2019) - to be Announced Training - https://www.ks.uiuc.edu/Training/ Previous Workshop Streams - https://www.youtube.com/user/tcbguiuc/playlists Hands-On Workshops NAMD 2.13 - What’s New • Stochastic Velocity Rescaling Thermostat • Replica Exchange with Solute Scaling (REST2) • Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation • Interleaved Double-Wide Sampling for Alchemical FEP • Constant-pH MD • Gaussian accelerated MD (GaMD) • τ-Random acceleration MD (τRAMD) • Improved Support for Lone pair and Polarizable Drude Force Field • Scaling on Summit Supercomputer • Support for billion-atom systems NAMD: https://www.ks.uiuc.edu/Research/namd/2.13/features.html NAMD 2.13 - What’s New • Stochastic Velocity Rescaling Thermostat • Replica Exchange with Solute Scaling (REST2) • Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation • Interleaved Double-Wide Sampling for Alchemical FEP • Constant-pH MD • Gaussian accelerated MD (GaMD) • τ-Random acceleration MD (τRAMD) • Improved Support for Lone pair and Polarizable Drude Force Field • Scaling on Summit Supercomputer Support for billion-atom systems • NAMD pre-2.13, STMV matrix, 2fs timesteps 5x2x2 STMV ≈ 21M atoms 7x6x5 STMV ≈ 224M atoms NAMD: https://www.ks.uiuc.edu/Research/namd/2.13/features.html NAMD On Summit: http://www.ks.uiuc.edu/Research/namd/2.13/NAMD-IBM-Journal-Manuscript-Revised.pdf NAMD 2.13 GPU Performance Improvements • Simulation Parameters: - Integration Time Step: 1 fs - Cutoff: 12 - Switch: 10 - CHARMM Force Field • Ivy Bridge system: dual socket Intel Xeon CPU E5-2690 v2 @ 3.00 GHz, 20 total cores. • Haswell system: dual socket Intel Xeon CPU E5-2698 v3 @ 2.30 GHz, 32 total cores • Skylake system: dual socket Intel Xeon Gold 6148 CPU @ 2.4 GHz, 40 total cores. Apolipoprotein A-I NAMD: http://www.ks.uiuc.edu/Research/namd/benchmarks/ NAMD 2.13 GPU Performance Improvements • Simulation Parameters: - Integration Time Step: 1 fs - Cutoff: 12 - Switch: 10 - CHARMM Force Field • Ivy Bridge system: dual socket Intel Xeon CPU E5-2690 v2 @ 3.00 GHz, 20 total cores. • Haswell system: dual socket Intel Xeon CPU E5-2698 v3 @ 2.30 GHz, 32 total cores • Skylake system: dual socket Intel Xeon Gold 6148 CPU @ 2.4 GHz, 40 total cores. Satellite Tobacco Mosaic Virus NAMD: http://www.ks.uiuc.edu/Research/namd/benchmarks/ Exemplary NAMD Features • User Defined Forces - Grid Forces - Interactive Molecular Dynamics - Steered Molecular Dynamics • Accelerated Sampling Methods - Replica Exchange • Collective Variable (Colvars) - Biased Simulations - Enhanced Sampling • Free-Energy Calculation Methods - Free-Energy Perturbation - Adaptative Biasing Force - Constant pH Simulations . • Hybrid QM/MM Simulations Complete List of NAMD Features: https://www.ks.uiuc.edu/Research/namd/2.13/ug/ Grid Forces • Addition Potential Term • Arbitrary Shape and Magnitude • Three Dimensional Grid with Scaling factor on Each Voxel • Use VMD to “Translate” Density Data into Potential Grid UEM(R) = ∑ wjVEM(rj), j Trabuco et al. Structure (2008) Trabuco et al. Methods (2009) Wehmer et al. PNAS (2017) Grid Forces - https://www.ks.uiuc.edu/Research/namd/2.13/ug/node41.html MDFF- https://www.ks.uiuc.edu/Research/mdff/ MDFF Tutorial - https://www.ks.uiuc.edu/Training/Tutorials/#mdff Grid Forces for Molecular Dynamics Flexible Fitting • Addition Potential Term • Arbitrary Shape and Magnitude • Three Dimensional Grid with Scaling factor on Each Voxel • Use VMD to “Translate” Density Data into Potential Grid - Molecular Dynamics Flexible Fitting UEM(R) = ∑ wjVEM(rj), j Φ(r) − Φthr if ξ 1 − , Φ(r) ≥ Φthr ( Φmax − Φthr ) VEM(r) = if ξ, Φ(r) < Φthr Trabuco et al. Structure (2008) Trabuco et al. Methods (2009) Wehmer et al. PNAS (2017) Grid Forces - https://www.ks.uiuc.edu/Research/namd/2.13/ug/node41.html MDFF- https://www.ks.uiuc.edu/Research/mdff/ MDFF Tutorial - https://www.ks.uiuc.edu/Training/Tutorials/#mdff Molecular Dynamics Flexible Fitting (Ribosome-bound YidC) Electron APS Microscope Synchrotron Match through MD EM density crystallographic map Supercomputer structure Molecular Dynamics Flexible Fitting (MDFF) Integrating experimental data to produce models of biomolecular complexes with atomic detail E.Coli Chemosensory Array Trabuco et al. Structure (2008) Trabuco et al. Methods (2009) Wehmer et al. PNAS (2017) Cassidy et al. eLife (2015) Chemotaxis http://www.ks.uiuc.edu/Research/chemotaxis/ Proteasome MDFF- https://www.ks.uiuc.edu/Research/mdff/ MDFF Tutorial - https://www.ks.uiuc.edu/Training/Tutorials/#mdff Molecular Dynamics Flexible Fitting (MDFF) Integrating experimental data to produce models of biomolecular complexes with atomic detail E.Coli Chemosensory Array Trabuco et al. Structure (2008) Trabuco et al. Methods (2009) Wehmer et al. PNAS (2017) Cassidy et al. eLife (2015) Chemotaxis http://www.ks.uiuc.edu/Research/chemotaxis/ Proteasome MDFF- https://www.ks.uiuc.edu/Research/mdff/ MDFF Tutorial - https://www.ks.uiuc.edu/Training/Tutorials/#mdff Molecular Dynamics Flexible Fitting (MDFF) Integrating experimental data to produce models of biomolecular complexes with atomic detail E.Coli Chemosensory Array Cascade MDFF High Resolution Density Maps Trabuco et al. Structure (2008) Trabuco et al. Methods (2009) Wehmer et al. PNAS (2017) Cassidy et al. eLife (2015) Chemotaxis http://www.ks.uiuc.edu/Research/chemotaxis/ Proteasome MDFF- https://www.ks.uiuc.edu/Research/mdff/ MDFF Tutorial - https://www.ks.uiuc.edu/Training/Tutorials/#mdff Interactive Modeling with MDFF GUI • Apply forces to manually manipulate structure into the density • Useful for difficult to fit structures with large conformational changes Set up and run interactive (or traditional) Analyze interactive simulations MDFF/xMDFF simulations in real-time Modeling Large Complex Membrane Systems Vesicle Construction Coarse Grain Protein CG Protein Placement Combine Lipid + Protein Distribution of proteins across the membrane surface (dense environment) • Ability the handle a variety of protein geometries • Proper orientation of proteins in relation to the membrane surface • Generalizable and automated method for membranes of arbitrary shape Embedding proteins into the membrane • Account for surface area occupied by proteins in inner and outer leaflets • Proper lipid packing around embedded proteins Modeling Large Complex Membrane Systems Vesicle Construction Coarse Grain Protein CG Protein Placement Combine Lipid + Protein Distribution of proteins across the membrane surface (dense environment) • Ability the handle a variety of protein geometries • Proper orientation of proteins in relation to the membrane surface • Generalizable and automated method for membranes of arbitrary shape Embedding proteins into the membrane • Account for surface area occupied by proteins in inner and outer leaflets • Proper lipid packing around embedded proteins Modeling Large Complex Membrane Systems Vesicle Construction Coarse Grain Protein CG Protein Placement Combine Lipid + Protein Distribution of proteins across the membrane surface (dense environment) • Ability the handle a variety of protein geometries • Proper orientation of proteins in relation to the membrane surface • Generalizable and automated method for membranes of arbitrary shape Embedding proteins into the membrane • Account for surface area occupied by proteins in inner and outer leaflets • Proper lipid packing around embedded proteins Modeling Large Complex Membrane Systems Vesicle Construction
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