Enhanced Sampling and Free Energy Applications in Biomolecular Modeling

Transcription

1 Enhanced Sampling and Free Energy Applications in Biomolecular Modeling Emad Tajkhorshid NIH Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Computational Biophysics Workshop - Urbana, Sep 2017

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3 NIH P41 Center for Macromolecular Modeling and Bioinforma9cs University of Illinois at Urbana-Champaign MD papers 103,000 VMD users 19,000 NAMD users 17,000 NIH funded 1.4 million web visitors 228,000 tutorial views

4 Serving a Large and Fast Growing Community Deploying Center s flagship programs NAMD and VMD on all major computational platforms from commodity computers to supercomputers Consistently adding user-requested features simulation, visualization, and analysis Covering broad range of scales (orbitals to cells) and data types Enhanced software accessibility QwikMD, interactive MDFF, fftk, simulation in the Cloud, remote visualization

5 Exploiting State of the Art Hardware Technology Software available and optimized on all national supercomputing platforms (even before they come online) Decade-long, highly productive relationship with NVIDIA The first CUDA Center of Excellence funded by NVIDIA Consistently exploring opportunities for new hardware technology Remote visualization Virtual Reality Handheld devices

6 Technology Made Highly Accessible to the Community interactive MDFF QwikMD VMD Plugin for Setup and Analysis of NAMD Simulations Developed primarily for experimental users

7 Vigorous Training Through Hands-On Workshops 47 Workshops on Computational Biophysics In addition to 5 others: Online Workshops on Simulating Membrane Channels In-residence workshops for visiting researchers Local workshops on hardware and coding Researchers Trained Since 2003 High school students to professional faculty Computational to experimental backgrounds National to international and minority communities 1636 Pages of Self-Study Tutorial Material Slides, recorded lectures, and video tutorials also available

8 Microscopic View of Molecular Phenomena Mechanisms in Molecular Biology Molecular Basis of Disease Drug Design Nano-biotechnology Binding of a small molecule to a binding site Y. Wang & E.T. PNAS 2010

9 Microscopic View of Molecular Phenomena Mechanisms in Molecular Biology Molecular Basis of Disease Drug Design Nano-biotechnology Drug binding to a GPCR Dror,, Shaw, PNAS, 108: (2011)

10 Microscopic View of Molecular Phenomena Nano-biotechnology Functionalized nanosurface with antibodies HIV subtype identification Lab Chip 2012 Created by nanobio Node tools

11 Most Detailed and Dynamic Microscopic View S. Mansoor,, E. Tajkhorshid, E. Gouaux, Nature, 2016.

12 Battling the Timescale non-equilibrium MD simulations Free Energy Methods Enhanced Sampling Techniques 12

13 Battling the Timescale - Case I Steered Molecular Dynamics is a non-equilibrium method by nature A wide variety of events that are inaccessible to conventional molecular dynamics simulations can be probed. The system will be driven, however, away from equilibrium, resulting in problems in describing the energy landscape associated with the event of interest. Second law of thermodynamics W ΔG

14 Steered Molecular Dynamics constant force (250 pn) constant velocity (30 Å/ns)

15 Jarzynski s Equality Transition between two equilibrium states λ = λ i T λ = λ(t) T work W heat Q λ = λ f e - βwp(w) ΔG W W ΔG Δ G = G G f i p(w) C. Jarzynski, Phys. Rev. Lett., 78, 2690 (1997) C. Jarzynski, Phys. Rev. E, 56, 5018 (1997) e βw = e βδg In principle, it is possible to obtain free energy surfaces from repeated non-equilibrium experiments. β = 1 k T B

16 Constructing the Potential of Mean Force 4 trajectories v = 0.03, Å/ps k = 150 pn/å f ( t) = k[ z( t) z0 vt] W ( t) dt" vf( t" ) = t 0

17 Three fold higher barriers periplasm cytoplasm SF NPA AqpZ 22.8 kcal/mol GlpF 7.3 kcal/mol Y. Wang, K. Schulten, and E. Tajkhorshid Structure 13, 1107 (2005)

18 Battling the Timescale - Case II Biased (nonequilibrium) simulations J. Li,, E. Tajkhorshid. (2015) COSB, 31: Neurotransmitter Uptake» Norepinephrine, serotonin, dopamine, glutamate, Gastrointestinal Tract» Active absorption of nutrients» Secretion of ions Kidneys» Reabsorption» Secretion Pharmacokinetics of all drugs» Absorption, distribution, elimination» Multi-drug resistance in cancer cells

19 COMPLEX Diverse Structural Transitions Involved Na-coupled Secondary Neurotransmitter Transporter Secondary Phosphate Antiporter ATP-Driven Primary ABC Exporter Non-equilibrium methods are required.

20 Complex Processes Require Complex Treatments Empirical search for reaction coordinates and biasing protocols Work Optimized Protocol Mahmoud Moradi Path-Refining Algorithms Reaction Coordinate Free Energy Calculations M. Moradi and ET (2013) PNAS, 110: M. Moradi and ET (2014) JCTC, 10: M. Moradi, G. Enkavi, and ET (2015) Nature Comm., 6:8393.

21 Aggressive Search of the Space Inward-Facing Op9mal Path Refined TMD TMD Outward-Facing 21

22 Non-equilibrium Driven Molecular Dynamics: Applying a time-dependent external force to induce the transition Along various pathways/mechanisms (collective variables) Harmonic constant IniTal state Biasing potental Collec9ve variables: RMSD, distance, R g, angle, orienta9on quaternion Final state Total simulaton Tme M. Moradi and ET (2013) PNAS, 110: M. Moradi and ET (2014) JCTC, 10: M. Moradi, G. Enkavi, and ET (2015) Nature Comm., 6:8393.

23 Progressively Optimizing the Biasing Protocol/Collective Variable using non-equilibrium Work as a Measure of the Path Quality Work Mechanism work (kcal/mol) (a) β (b) work (kcal/mol) (c) t (ns) γ Example set taken from a subset of 20 ns biased simulatons (d) α

24 Mechanistic Insight From Transition Pathways in ABC exporters from Non-Equilibrium Simulations work (kcal/mol) (a) t (ns) } α γ β γ α β d α γ β α β γ d γ α β d α β γ } β d α γ β α γ d β α γ } β γ α γ β α d γ β α β d γ α d β γ α α β β α γ (β,γ) α Cytoplasm OF IF-c IF-o γ α β Periplasm M. Moradi and ET (2013) PNAS, 110: M. Moradi and ET (2014) JCTC, 10:

25 NBD Doorknob Mechanism M. Moradi and ET (2013) PNAS, 110:

26 Describing a Complete Cycle (Adding Substrate) Requiring a Combination of Multiple Collective Variables 30 r x 20 ns 30 r x 20 ns IF apo IF bound 12 replicas x 40 ns (H1/H7) 50 replicas x 20 ns (10 Hs) 150 replicas 12 replicas x 40 ns (H1/H7) 24 replicas x 20 ns (H1/H7) 200 replicas (2D) x 5 ns 50 replicas x 20 ns OF apo 30 r x 20 ns 30 r x 20 ns 30 r x 20 ns OF bound

27 Simula'on*protocols* Transition Technique Collective Variables # of Replicas Runtime 1 BEUS (Q 1,Q 7 ) ns = 0.5 s 2 IF a OF a SMwST {Q} ns = 1 s 3 BEUS {Q} ns = 1 s 4 BEUS Z IF a IF Pi ns = 1.2 s 5 b BEUS ({Q}, Z Pi ) ns = 1.2 s 6 BEUS Z OF a OF Pi ns = 1.2 s 7 b BEUS ({Q}, Z Pi ) ns = 1.2 s 8 BEUS (Q 1,Q 7 ) ns = 0.5 s 9 BEUS Z Pi ns = 0.5 s IF b OF b 10 2D BEUS ( RMSD, Z Pi ) ns = 1 s 11 SMwST ({Q}, Z Pi ) ns = 1 s 12 BEUS ({Q}, Z Pi ) ns = 1 s 13 Full Cycle BEUS ({Q}, Z Pi ) ns = 7.5 s GlpT Crystal Structure BEUS SMwST Total Simulation Time Full Cycle PHSM 18.7 s Nonequilibrium M. Moradi, G. Enkavi, and ET (2015) Nature Communica9on, 6: BLUE WATER

28 M. Moradi, G. Enkavi, and ET (2015) Nature Communica9on, 6: 8393.

29 Battling the Timescale - Case III Multiscale Simulations Membrane Budding/Fusion Combining multiple replica simulations and coarsegrained models to describe membrane fusion

30 Workflow for Multi-Scale Modeling Parametrically Defined Sine Function simulation box periodic image Initial Frame Final Frame z Δt y x Christopher Mayne, Tajkhorshid Lab

31 Workflow for Multi-Scale Modeling G40 G30 G20 G10 G1 Christopher Mayne, Tajkhorshid Lab

32 Applications of Computational Methodologies to Cell-Scale Structural Biology Using simulations as a structure-building tool The most detailed model of a chromatophore Computational model of a minimal cell envelope

33 Molecular Dynamics Flexible Fitting (MDFF) Electron Microscope (Ribosome-bound YidC) APS Synchrotron Match through MD cryo-em density map crystallographic structure Supercomputer [1] Trabuco et al. Structure (2008) 16: [2] Trabuco et al. Methods (2009) 49:

34 Molecular Dynamics Flexible Fitting (MDFF)

35 Nano-biotechnology Gold Nanoparticles as Delivery Vehicles Transmission Electron Micrograph Schematic model with no prediction power Yang, J. A.; Murphy, C. J. Langmuir 2012, 28, Experiment: Murphy Lab Modeling/Simulation: Tajkhorshid Lab

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