Alchemical free energy calculations in OpenMM
|
|
|
- Silvester Flowers
- 5 years ago
- Views:
Transcription
1 Alchemical free energy calculations in OpenMM Lee-Ping Wang Stanford Department of Chemistry OpenMM Workshop, Stanford University September 7, 2012 Special thanks to: John Chodera, Morgan Lawrenz
2 Outline Introduction to free energy calculations Applications: Solvation and binding free energies Methods: Reaction coordinate vs. alchemical methods Accuracy and benchmarking Part I: Running the alchemical simulations Alchemical intermediates Soft-core nonbonded force Restraints Part II: Analyzing the data Multistate Bennett acceptance ratio Part III: Hydration free energy exercise
3 Introduction to free energy calculations Free energy differences are central to many questions in biological and pharmaceutical chemistry. How does compound X partition between different environments? e.g. How well does Lipitor partition between octanol and water? How tightly does compound X bind protein Y? How selectively? e.g. How well does clomifene bind/discriminate ERα/ERβ? How do modifications in compound X modulate affinity and selectivity? e.g. Why does selectivity of imatinib differ from that of other kinase inhibitors? How do target mutations affect binding affinities? e.g. How do clinically isolated β-lactamase resistance mutations restore ability to hydrolyze β-lactam antibiotics?
4 Calculation of free energies Without specialized simulation methods, free energy calculations would be impractically slow. Observing binding events from a normal MD simulation: P + L PL unbound bound time For typical drug off-rates (10-4 s -1 ), trajectories would need to be impractically long (hours), requiring ~10 9 CPU-years to simulate.
5 Calculation of free energies Free energy differences are calculated efficiently by simulating a path between reactant and product. Potential of Mean Force: Track the free energy along a specified reaction coordinate Alchemical: Simulate along an unphysical pathway to get free energy differences between two physical endpoints
![05 kcal/mol [17] (Nicholls and Mobley et al.](/docs-images/87/95874917/images/6-3.jpg ", J Med Chem) solvent only small, neutral molecules fixed protonation states small")
![apolar ligands T4 lysozyme L99A 1.89±0.04 kcal/mol [13] (Mobley and Graves et al.](/docs-images/87/95874917/images/6-4.jpg ", JMB 2007) 0.6±0.2 kcal/mol [3] (Mobley and Graves et al.")
![1.42 kcal/mol [9] 0.94 kcal/mol [7] (Shirts et al.](/docs-images/87/95874917/images/6-6.jpg ", in preparation) small, rigid protein fixed protonation states larger drug-like")
6 Accuracy of free energy calculations The accuracy of a free energy calculation depends on the complexity of the problem.... hydration free energies of small neutral molecules 1.23±0.01 kcal/mol [502] (Mobley et al., in preparation) 1.33±0.05 kcal/mol [17] (Nicholls and Mobley et al., J Med Chem) solvent only small, neutral molecules fixed protonation states small apolar ligands T4 lysozyme L99A 1.89±0.04 kcal/mol [13] (Mobley and Graves et al., JMB 2007) 0.6±0.2 kcal/mol [3] (Mobley and Graves et al., JMB 2007) small, rigid protein small, neutral ligands fixed protonation states multiple sidechain orientations multiple ligand binding modes polar ligands FKBP kcal/mol [9] 0.94 kcal/mol [7] (Shirts et al., in preparation) small, rigid protein fixed protonation states larger drug-like ligands rotatable bond in ligands JNK3 kinase 6.3 kcal/mol [44] (Haque, Chodera, Shirts, Mobley, Pande) large protein multiple conformations large drug-like ligands, rotatable bonds in ligands multiple protonation states phosphorylation and activation peptide substrate? MgCl2 salt effects? retrospective RMS error [sample size] prospective RMS error [sample size] easy hard
7 Outline Introduction to free energy calculations Applications: Solvation and binding free energies Methods: Reaction coordinate vs. alchemical methods Accuracy and benchmarking Part I: Running the alchemical simulations Alchemical intermediates Soft-core nonbonded force Restraints Part II: Analyzing the data Multistate Bennett acceptance ratio Part III: Hydration free energy exercise
8 Alchemical free energy calculations Alchemical simulations progress through a series of intermediates between initial and final states. 8 simulations 16 simulations 5 simulations 8 simulations 16 simulations 1. Atomic charges on ligand are switched off 2. VdW interaction between ligand and water is switched off Harmonic restraints are switched on 3. VdW interaction between ligand and protein is switched on 4. Atomic charges on ligand are switched on 5. Harmonic restraints are removed Graphics from David Mobley
9 Alchemical free energy calculations The accuracy of alchemical simulations relies on the phase space overlap of neighboring intermediates. p(x) x Intermediate states are introduced to ensure a contiguous chain of good overlap. D. Wu and D. A. Kofke. "Phase-space overlap measures. I. Fail-safe bias detection...", J. Chem. Phys. 123: (2005).
10 Soft-core Lennard Jones potentials The Lennard-Jones repulsion is switched off gently, greatly improving the overlap between intermediates. Example: Using thermodynamic integration, the free energy difference is the area under the curve. Lennard-Jones is off at λ=1. Standard (linear) decoupling Soft core decoupling diverges near λ=1! T. A. Steinbrecher, D. L. Mobley, and D. A. Case. JCP 127: (2007) (More efficient forms known, but all based on same idea.)
11 Restraint potentials Restraint potentials eliminate convergence issues related to translational / rotational motion. Alchemical intermediates with weakened interactions may encounter convergence issues from the ligand diffusing out of the binding pocket or otherwise moving around. Harmonic restraint potentials keep the ligand in place; the free energy of restrained degrees of freedom are added back in analytically. Absolute B inding Free E nergies: A Quantitative Approach for Their Calculation Boresch, S.; Tettinger, F.; Leitgeb, M.; Karplus, M. J. Phys. Chem. B.; (Article); 2003; 107(35); DOI: /jp
12 Demonstration of alchemical intermediates Using enhanced sampling methods, multiple binding events and reorientation of the ligand are observed. [closeup] p-xylene bound to T4 lysozyme L99A
13 Outline Introduction to free energy calculations Applications: Solvation and binding free energies Methods: Reaction coordinate vs. alchemical methods Accuracy and benchmarking Part I: Running the alchemical simulations Alchemical intermediates Soft-core nonbonded force Restraints Part II: Analyzing the data Multistate Bennett acceptance ratio Part III: Hydration free energy exercise
14 Estimating free energy differences from data Not all estimators are created equal (in terms of bias and statistical efficiency). Thermodynamic integration quadrature error (bias) difficult to quantify Free energy perturbation / exponential reweighting Zwanzig RW. JCP 22:1420, Shirts MR and Pande VS. JCP 122:144107, suffers from large bias and variance Bennett acceptance ratio Bennett CH. J Comput Phys 22:245, Shirts MR, Bair E, Hooker G, and Pande VS. PRL 91:140601, only applicable to two states - can t we use all the data?
15 Multistate Bennett acceptance ratio (MBAR) The MBAR method provides statistically optimal estimates of free energy differences. Statistically optimal analysis of samples from multiple equilibrium states Michael R. Shirts (Department of Chemistry, Columbia University), John D. Chodera (Department of Chemistry, Stanford University)
16 Outline Introduction to free energy calculations Applications: Solvation and binding free energies Methods: Reaction coordinate vs. alchemical methods Accuracy and benchmarking Part I: Running the alchemical simulations Alchemical intermediates Soft-core nonbonded force Restraints Part II: Analyzing the data Multistate Bennett acceptance ratio Part III: Hydration free energy exercise
17 Hydration free energy exercise Hydration free energies are relatively simple to compute compared to binding free energies. gas Benefits: Test the accuracy of half of binding reaction (withdrawal from water) A simple, tractable system for studying accuracy of calculations Note: Run simulate-water.py now, it will take a few minutes water D. L. Mobley, E. Dumont, J. D. Chodera and K. A. Dill. J. Phys. Chem. B. 111: (2007) (DOI)
18 Some hydration free energy results Hydration free energies are often used for force field benchmarking. Slide by: David Rinaldo, Applications Scientist at Schrodinger
19 Choice of intermediates annihilation (remove interactions within solute) decoupling (keep interactions within solute) electrostatics potentially large G short correlation times requires vacuum recharging calculation smaller G longer correlation times due to bare charges no vacuum recharging calculation required 3 PME evaluations per timestep, tricky to use Lennard-Jones requires simulation in vacuum no vacuum simulation required eliminates potentially unphysical conformations Note that the literature is full of extremely confusing terminology, like double decoupling, which is actually annihilation... annihilation vs decoupling discussed in: M. R. Shirts, D. L. Mobley, and J. D. Chodera. "Alchemical free energy calculations: Ready for prime time?",annual Reports in Computational Chemistry 3:41-59 (2007).
20 Example calculation Perform a simple solvation free energy simulation. Example calculation 1: Hydration free energy of a water molecule Run simulate-water.py. What the script does: 1. Set up the OpenMM simulation, minimize the energy 2. Create several alchemical intermediates 3. Run a simulation for each alchemical intermediate 4. Post-process the simulation trajectories, evaluating the energy of each alchemical state over each trajectory 5. Save the data to disk Run analyze.py --ncfile output.nc 1. Run MBAR analysis on output data. 2. Get hydration free energy! ########################################################################### ### At K, the solvation free energy is: kcal/mol ### ###########################################################################
21 Example calculation How about a more interesting molecule? Example calculation 2: Hydration free energy of ethanol Copy simulate-water.py to ethanol.py 1. Change the number of alchemical atoms. 2. Load the ethanol force field and PDB files. 3. Use the Modeller class to create a solvated box (or use provided solvated box). Run analyze.py --ncfile output.nc 1. Run MBAR analysis on output data. 2. Get hydration free energy! Finished? Try these: 1. Experiment with the number of alchemical intermediates, solvent box size and the simulation length. How to optimize accuracy vs. efficiency? 2. Set up your own molecule of interest using AMBER tools.
Free energy calculations with alchemlyb
Free energy calculations with alchemlyb Oliver Beckstein Arizona State University SPIDAL Teleconference 2019-02-01 Binding free energy measure of how strong a protein P and a ligand X stick together key
Free energy simulations
Free energy simulations Marcus Elstner and Tomáš Kubař January 14, 2013 Motivation a physical quantity that is of most interest in chemistry? free energies Helmholtz F or Gibbs G holy grail of computational
YANK: Free energy calculations made useful.
YANK: Free energy calculations made useful. Kim Branson Vertex Pharmaceuticals John D. Chodera QB3-Berkeley Fellow Vijay Pande Stanford University http://www.choderalab.org SimBios talk - Stanford University
Free energy calculations
Free energy calculations Berk Hess May 5, 2017 Why do free energy calculations? The free energy G gives the population of states: ( ) P 1 G = exp, G = G 2 G 1 P 2 k B T Since we mostly simulate in the
Large Scale FEP on Congeneric Ligand Series Have Practical Free Energy Calculations arrived at Last?
Large Scale FEP on Congeneric Ligand Series Have Practical Free Energy Calculations arrived at Last? Thomas Steinbrecher, Teng Lin, Lingle Wang, Goran Krilov, Robert Abel, Woody Sherman, Richard Friesner
Chemical properties that affect binding of enzyme-inhibiting drugs to enzymes
Chemical properties that affect binding of enzyme-inhibiting drugs to enzymes Introduction The production of new drugs requires time for development and testing, and can result in large prohibitive costs
Chemical properties that affect binding of enzyme-inhibiting drugs to enzymes
Introduction Chemical properties that affect binding of enzyme-inhibiting drugs to enzymes The production of new drugs requires time for development and testing, and can result in large prohibitive costs
Multi-Ensemble Markov Models and TRAM. Fabian Paul 21-Feb-2018
Multi-Ensemble Markov Models and TRAM Fabian Paul 21-Feb-2018 Outline Free energies Simulation types Boltzmann reweighting Umbrella sampling multi-temperature simulation accelerated MD Analysis methods
ONETEP PB/SA: Application to G-Quadruplex DNA Stability. Danny Cole
ONETEP PB/SA: Application to G-Quadruplex DNA Stability Danny Cole Introduction Historical overview of structure and free energy calculation of complex molecules using molecular mechanics and continuum
Biomolecular modeling. Theoretical Chemistry, TU Braunschweig (Dated: December 10, 2010)
Biomolecular modeling Marcus Elstner and Tomáš Kubař Theoretical Chemistry, TU Braunschweig (Dated: December 10, 2010) IX. FREE ENERGY SIMULATIONS When searching for a physical quantity that is of most
Lecture 19: Free Energies in Modern Computational Statistical Thermodynamics: FEP and Related Methods
Statistical Thermodynamics Lecture 19: Free Energies in Modern Computational Statistical Thermodynamics: FEP and Related Methods Dr. Ronald M. Levy ronlevy@temple.edu Free energy calculations Free energy
Statistical Mechanics for Proteins
The Partition Function From Q all relevant thermodynamic properties can be obtained by differentiation of the free energy F: = kt q p E q pd d h T V Q ), ( exp 1! 1 ),, ( 3 3 3 ),, ( ln ),, ( T V Q kt
An introduction to Molecular Dynamics. EMBO, June 2016
An introduction to Molecular Dynamics EMBO, June 2016 What is MD? everything that living things do can be understood in terms of the jiggling and wiggling of atoms. The Feynman Lectures in Physics vol.
Biochemistry,530:,, Introduc5on,to,Structural,Biology, Autumn,Quarter,2015,
Biochemistry,530:,, Introduc5on,to,Structural,Biology, Autumn,Quarter,2015, Course,Informa5on, BIOC%530% GraduateAlevel,discussion,of,the,structure,,func5on,,and,chemistry,of,proteins,and, nucleic,acids,,control,of,enzyma5c,reac5ons.,please,see,the,course,syllabus,and,
Supporting Information Quantitative characterization of the binding and unbinding of millimolar drug fragments with molecular dynamics simulations
Supporting Information Quantitative characterization of the binding and unbinding of millimolar drug fragments with molecular dynamics simulations Simulation Details All binding simulations were run on
Supplementary information for cloud computing approaches for prediction of ligand binding poses and pathways
Supplementary information for cloud computing approaches for prediction of ligand binding poses and pathways Morgan Lawrenz 1, Diwakar Shukla 1,2 & Vijay S. Pande 1,2 1 Department of Chemistry, Stanford
Limitations of temperature replica exchange (T-REMD) for protein folding simulations
Limitations of temperature replica exchange (T-REMD) for protein folding simulations Jed W. Pitera, William C. Swope IBM Research pitera@us.ibm.com Anomalies in protein folding kinetic thermodynamic 322K
PROTEIN-PROTEIN DOCKING REFINEMENT USING RESTRAINT MOLECULAR DYNAMICS SIMULATIONS
TASKQUARTERLYvol.20,No4,2016,pp.353 360 PROTEIN-PROTEIN DOCKING REFINEMENT USING RESTRAINT MOLECULAR DYNAMICS SIMULATIONS MARTIN ZACHARIAS Physics Department T38, Technical University of Munich James-Franck-Str.
Using Bayesian Statistics to Predict Water Affinity and Behavior in Protein Binding Sites. J. Andrew Surface
Using Bayesian Statistics to Predict Water Affinity and Behavior in Protein Binding Sites Introduction J. Andrew Surface Hampden-Sydney College / Virginia Commonwealth University In the past several decades
Using the Fast Fourier Transform in Binding Free Energy Calculations
arxiv:1708.07045v1 [q-bio.bm] 27 Jul 2017 Using the Fast Fourier Transform in Binding Free Energy Calculations Trung Hai Nguyen, Huan-Xiang Zhou, David D. L. Minh August 24, 2017 Abstract According to
Characterizing Structural Transitions of Membrane Transport Proteins at Atomic Detail Mahmoud Moradi
Characterizing Structural Transitions of Membrane Transport Proteins at Atomic Detail Mahmoud Moradi NCSA Blue Waters Symposium for Petascale Science and Beyond Sunriver, Oregon May 11, 2015 Outline Introduction
DISCRETE TUTORIAL. Agustí Emperador. Institute for Research in Biomedicine, Barcelona APPLICATION OF DISCRETE TO FLEXIBLE PROTEIN-PROTEIN DOCKING:
DISCRETE TUTORIAL Agustí Emperador Institute for Research in Biomedicine, Barcelona APPLICATION OF DISCRETE TO FLEXIBLE PROTEIN-PROTEIN DOCKING: STRUCTURAL REFINEMENT OF DOCKING CONFORMATIONS Emperador
Structural Bioinformatics (C3210) Molecular Mechanics
Structural Bioinformatics (C3210) Molecular Mechanics How to Calculate Energies Calculation of molecular energies is of key importance in protein folding, molecular modelling etc. There are two main computational
MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors
MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors Thomas Steinbrecher Senior Application Scientist Typical Docking Workflow Databases
Molecular Interactions F14NMI. Lecture 4: worked answers to practice questions
Molecular Interactions F14NMI Lecture 4: worked answers to practice questions http://comp.chem.nottingham.ac.uk/teaching/f14nmi jonathan.hirst@nottingham.ac.uk (1) (a) Describe the Monte Carlo algorithm
Overview & Applications. T. Lezon Hands-on Workshop in Computational Biophysics Pittsburgh Supercomputing Center 04 June, 2015
Overview & Applications T. Lezon Hands-on Workshop in Computational Biophysics Pittsburgh Supercomputing Center 4 June, 215 Simulations still take time Bakan et al. Bioinformatics 211. Coarse-grained Elastic
Lecture 27. Transition States and Enzyme Catalysis
Lecture 27 Transition States and Enzyme Catalysis Reading for Today: Chapter 15 sections B and C Chapter 16 next two lectures 4/8/16 1 Pop Question 9 Binding data for your thesis protein (YTP), binding
MD Simulations of Proteins: Practical Hints and Pitfalls to Avoid
MD Simulations of Proteins: Practical ints and Pitfalls to Avoid Stefan Boresch stefan@mdy.univie.ac.at Department of Computational Biological Chemistry Faculty of Chemistry, University of Vienna Vienna
MD Simulation in Pose Refinement and Scoring Using AMBER Workflows
MD Simulation in Pose Refinement and Scoring Using AMBER Workflows Yuan Hu (On behalf of Merck D3R Team) D3R Grand Challenge 2 Webinar Department of Chemistry, Modeling & Informatics Merck Research Laboratories,
Basic Ingredients of Free Energy Calculations: A Review
Feature Article Basic Ingredients of Free Energy Calculations: A Review CLARA D. CHRIST, 1 ALAN E. MARK, 2 WILFRED F. van GUNSTEREN 1 1 Laboratory of Physical Chemistry, Swiss Federal Institute of Technology,
BUDE. A General Purpose Molecular Docking Program Using OpenCL. Richard B Sessions
BUDE A General Purpose Molecular Docking Program Using OpenCL Richard B Sessions 1 The molecular docking problem receptor ligand Proteins typically O(1000) atoms Ligands typically O(100) atoms predicted
10/26/2010. An Example of a Polar Reaction: Addition of H 2 O to Ethylene. to Ethylene
6.5 An Example of a Polar Reaction: Addition of H 2 O to Ethylene Addition of water to ethylene Typical polar process Acid catalyzed addition reaction (Electophilic addition reaction) Polar Reaction All
Exercise 2: Solvating the Structure Before you continue, follow these steps: Setting up Periodic Boundary Conditions
Exercise 2: Solvating the Structure HyperChem lets you place a molecular system in a periodic box of water molecules to simulate behavior in aqueous solution, as in a biological system. In this exercise,
MM-PBSA Validation Study. Trent E. Balius Department of Applied Mathematics and Statistics AMS
MM-PBSA Validation Study Trent. Balius Department of Applied Mathematics and Statistics AMS 535 11-26-2008 Overview MM-PBSA Introduction MD ensembles one snap-shots relaxed structures nrichment Computational
Structure Investigation of Fam20C, a Golgi Casein Kinase
Structure Investigation of Fam20C, a Golgi Casein Kinase Sharon Grubner National Taiwan University, Dr. Jung-Hsin Lin University of California San Diego, Dr. Rommie Amaro Abstract This research project
Free energy calculations
Free energy calculations Jochen Hub & David van der Spoel Overview Free energies and Probabilities Thermodynamic cycles (Free energy perturbation (FEP)) Thermodynamic integration (TI) (Jarzynski equality
From Friday s material
5.111 Lecture 35 35.1 Kinetics Topic: Catalysis Chapter 13 (Section 13.14-13.15) From Friday s material Le Chatelier's Principle - when a stress is applied to a system in equilibrium, the equilibrium tends
Large scale free energy calculations for blind predictions of protein ligand binding: the D3R Grand Challenge 2015
J Comput Aided Mol Des (2016) 30:743 751 DOI 10.1007/s10822-016-9952-x Large scale free energy calculations for blind predictions of protein ligand binding: the D3R Grand Challenge 2015 Nanjie Deng 1 William
Type II Kinase Inhibitors Show an Unexpected Inhibition Mode against Parkinson s Disease-Linked LRRK2 Mutant G2019S.
Type II Kinase Inhibitors Show an Unexpected Inhibition Mode against Parkinson s Disease-Linked LRRK2 Mutant G219S. Min Liu@&*, Samantha A. Bender%*, Gregory D Cuny@, Woody Sherman, Marcie Glicksman@ Soumya
Problems from Previous Class
1 Problems from Previous lass 1. What is K m? What are the units of K m? 2. What is V max? What are the units of V max? 3. Write down the Michaelis-Menten equation. 4. What order of reaction is the reaction
Towards fast and accurate binding affinity. prediction with pmemdgti: an efficient. implementation of GPU-accelerated. Thermodynamic Integration
Towards fast and accurate binding affinity prediction with pmemdgti: an efficient implementation of GPU-accelerated Thermodynamic Integration Tai-Sung Lee,, Yuan Hu, Brad Sherborne, Zhuyan Guo, and Darrin
Outline. The ensemble folding kinetics of protein G from an all-atom Monte Carlo simulation. Unfolded Folded. What is protein folding?
The ensemble folding kinetics of protein G from an all-atom Monte Carlo simulation By Jun Shimada and Eugine Shaknovich Bill Hawse Dr. Bahar Elisa Sandvik and Mehrdad Safavian Outline Background on protein
Improving Protein Function Prediction with Molecular Dynamics Simulations. Dariya Glazer Russ Altman
Improving Protein Function Prediction with Molecular Dynamics Simulations Dariya Glazer Russ Altman Motivation Sometimes the 3D structure doesn t score well for a known function. The experimental structure
Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation
This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source
Conformational Searching using MacroModel and ConfGen. John Shelley Schrödinger Fellow
Conformational Searching using MacroModel and ConfGen John Shelley Schrödinger Fellow Overview Types of conformational searching applications MacroModel s conformation generation procedure General features
Molecular Modeling Lecture 11 side chain modeling rotamers rotamer explorer buried cavities.
Molecular Modeling 218 Lecture 11 side chain modeling rotamers rotamer explorer buried cavities. Sidechain Rotamers Discrete approximation of the continuous space of backbone angles. Sidechain conformations
The Molecular Dynamics Method
The Molecular Dynamics Method Thermal motion of a lipid bilayer Water permeation through channels Selective sugar transport Potential Energy (hyper)surface What is Force? Energy U(x) F = d dx U(x) Conformation
Lecture 2 and 3: Review of forces (ctd.) and elementary statistical mechanics. Contributions to protein stability
Lecture 2 and 3: Review of forces (ctd.) and elementary statistical mechanics. Contributions to protein stability Part I. Review of forces Covalent bonds Non-covalent Interactions: Van der Waals Interactions
Structure based drug design and LIE models for GPCRs
Structure based drug design and LIE models for GPCRs Peter Kolb kolb@docking.org Shoichet Lab ACS 237 th National Meeting, March 24, 2009 p.1/26 [Acknowledgements] Brian Shoichet John Irwin Mike Keiser
Thermodynamic Integration with Enhanced Sampling (TIES)
Thermodynamic Integration with Enhanced Sampling (TIES) A. P. Bhati, S. Wan, D. W. Wright and P. V. Coveney agastya.bhati.14@ucl.ac.uk Centre for Computational Science Department of Chemistry University
Bioengineering 215. An Introduction to Molecular Dynamics for Biomolecules
Bioengineering 215 An Introduction to Molecular Dynamics for Biomolecules David Parker May 18, 2007 ntroduction A principal tool to study biological molecules is molecular dynamics simulations (MD). MD
Applications of Molecular Dynamics
June 4, 0 Molecular Modeling and Simulation Applications of Molecular Dynamics Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo Tohru
Specific Ion Solvtion in Ethylene Carbonate and Propylene Carbonate
Specific Ion Solvtion in Ethylene Carbonate and Propylene Carbonate A. Arslanargin, A. Powers, S. Rick, T. Pollard, T. Beck Univ Cincinnati Chemistry Support: NSF, OSC TSRC 2016 November 2, 2016 A. Arslanargin,
Molecular dynamics simulations of anti-aggregation effect of ibuprofen. Wenling E. Chang, Takako Takeda, E. Prabhu Raman, and Dmitri Klimov
Biophysical Journal, Volume 98 Supporting Material Molecular dynamics simulations of anti-aggregation effect of ibuprofen Wenling E. Chang, Takako Takeda, E. Prabhu Raman, and Dmitri Klimov Supplemental
4 Examples of enzymes
Catalysis 1 4 Examples of enzymes Adding water to a substrate: Serine proteases. Carbonic anhydrase. Restrictions Endonuclease. Transfer of a Phosphoryl group from ATP to a nucleotide. Nucleoside monophosphate
2013 W. H. Freeman and Company. 6 Enzymes
2013 W. H. Freeman and Company 6 Enzymes CHAPTER 6 Enzymes Key topics about enzyme function: Physiological significance of enzymes Origin of catalytic power of enzymes Chemical mechanisms of catalysis
High Throughput In-Silico Screening Against Flexible Protein Receptors
John von Neumann Institute for Computing High Throughput In-Silico Screening Against Flexible Protein Receptors H. Sánchez, B. Fischer, H. Merlitz, W. Wenzel published in From Computational Biophysics
How is molecular dynamics being used in life sciences? Davide Branduardi
How is molecular dynamics being used in life sciences? Davide Branduardi davide.branduardi@schrodinger.com Exploring molecular processes with MD Drug discovery and design Protein-protein interactions Protein-DNA
MMGBSA: Thermodynamics of Biomolecular Systems
MMGBSA: Thermodynamics of Biomolecular Systems The MMGBSA approach employs molecular mechanics, the generalized Born model and solvent accessibility method to elicit free energies from structural information
Structure-based maximal affinity model predicts small-molecule druggability
Structure-based maximal affinity model predicts small-molecule druggability Alan Cheng alan.cheng@amgen.com IMA Workshop (Jan 17, 2008) Druggability prediction Introduction Affinity model Some results
Hamiltonian Replica Exchange Molecular Dynamics Using Soft-Core Interactions to Enhance Conformational Sampling
John von Neumann Institute for Computing Hamiltonian Replica Exchange Molecular Dynamics Using Soft-Core Interactions to Enhance Conformational Sampling J. Hritz, Ch. Oostenbrink published in From Computational
All-atom Molecular Mechanics. Trent E. Balius AMS 535 / CHE /27/2010
All-atom Molecular Mechanics Trent E. Balius AMS 535 / CHE 535 09/27/2010 Outline Molecular models Molecular mechanics Force Fields Potential energy function functional form parameters and parameterization
Free energy calculations and the potential of mean force
Free energy calculations and the potential of mean force IMA Workshop on Classical and Quantum Approaches in Molecular Modeling Mark Tuckerman Dept. of Chemistry and Courant Institute of Mathematical Science
Biomolecules are dynamic no single structure is a perfect model
Molecular Dynamics Simulations of Biomolecules References: A. R. Leach Molecular Modeling Principles and Applications Prentice Hall, 2001. M. P. Allen and D. J. Tildesley "Computer Simulation of Liquids",
Variational Implicit Solvation of Biomolecules: From Theory to Numerical Computations
Variational Implicit Solvation of Biomolecules: From Theory to Numerical Computations Bo Li Department of Mathematics and Center for Theoretical Biological Physics UC San Diego CECAM Workshop: New Perspectives
Advanced Molecular Dynamics
Advanced Molecular Dynamics Introduction May 2, 2017 Who am I? I am an associate professor at Theoretical Physics Topics I work on: Algorithms for (parallel) molecular simulations including GPU acceleration
Computational Study of Gleevec and G6G Reveals Molecular Determinants of Kinase Inhibitor Selectivity
This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. pubs.acs.org/jacs Computational
BIOC : Homework 1 Due 10/10
Contact information: Name: Student # BIOC530 2012: Homework 1 Due 10/10 Department Email address The following problems are based on David Baker s lectures of forces and protein folding. When numerical
k θ (θ θ 0 ) 2 angles r i j r i j
1 Force fields 1.1 Introduction The term force field is slightly misleading, since it refers to the parameters of the potential used to calculate the forces (via gradient) in molecular dynamics simulations.
An Informal AMBER Small Molecule Force Field :
An Informal AMBER Small Molecule Force Field : parm@frosst Credit Christopher Bayly (1992-2010) initiated, contributed and lead the efforts Daniel McKay (1997-2010) and Jean-François Truchon (2002-2010)
C a h p a t p e t r e r 6 E z n y z m y e m s
Chapter 6 Enzymes 4. Examples of enzymatic reactions acid-base catalysis: give and take protons covalent catalysis: a transient covalent bond is formed between the enzyme and the substrate metal ion catalysis:
Objectives INTRODUCTION TO METABOLISM. Metabolism. Catabolic Pathways. Anabolic Pathways 3/6/2011. How to Read a Chemical Equation
Objectives INTRODUCTION TO METABOLISM. Chapter 8 Metabolism, Energy, and Life Explain the role of catabolic and anabolic pathways in cell metabolism Distinguish between kinetic and potential energy Distinguish
schematic diagram; EGF binding, dimerization, phosphorylation, Grb2 binding, etc.
Lecture 1: Noncovalent Biomolecular Interactions Bioengineering and Modeling of biological processes -e.g. tissue engineering, cancer, autoimmune disease Example: RTK signaling, e.g. EGFR Growth responses
Computational Modeling of Protein Kinase A and Comparison with Nuclear Magnetic Resonance Data
Computational Modeling of Protein Kinase A and Comparison with Nuclear Magnetic Resonance Data ABSTRACT Keyword Lei Shi 1 Advisor: Gianluigi Veglia 1,2 Department of Chemistry 1, & Biochemistry, Molecular
Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context
pubs.acs.org/jctc Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context Nadine Homeyer, Friederike Stoll, Alexander Hillisch, and Holger
CE 530 Molecular Simulation
1 CE 530 Molecular Simulation Lecture 14 Molecular Models David A. Kofke Department of Chemical Engineering SUNY Buffalo kofke@eng.buffalo.edu 2 Review Monte Carlo ensemble averaging, no dynamics easy
MARTINI simulation details
S1 Appendix MARTINI simulation details MARTINI simulation initialization and equilibration In this section, we describe the initialization of simulations from Main Text section Residue-based coarsegrained
Computational Studies of the Photoreceptor Rhodopsin. Scott E. Feller Wabash College
Computational Studies of the Photoreceptor Rhodopsin Scott E. Feller Wabash College Rhodopsin Photocycle Dark-adapted Rhodopsin hn Isomerize retinal Photorhodopsin ~200 fs Bathorhodopsin Meta-II ms timescale
Gromacs Workshop Spring CSC
Gromacs Workshop Spring 2007 @ CSC Erik Lindahl Center for Biomembrane Research Stockholm University, Sweden David van der Spoel Dept. Cell & Molecular Biology Uppsala University, Sweden Berk Hess Max-Planck-Institut
Supplementary Information
Supplementary Information Resveratrol Serves as a Protein-Substrate Interaction Stabilizer in Human SIRT1 Activation Xuben Hou,, David Rooklin, Hao Fang *,,, Yingkai Zhang Department of Medicinal Chemistry
Sampling the free energy surfaces of collective variables
Sampling the free energy surfaces of collective variables Jérôme Hénin Enhanced Sampling and Free-Energy Calculations Urbana, 12 September 2018 Please interrupt! struct bioinform phys chem theoretical
Structural Bioinformatics (C3210) Molecular Docking
Structural Bioinformatics (C3210) Molecular Docking Molecular Recognition, Molecular Docking Molecular recognition is the ability of biomolecules to recognize other biomolecules and selectively interact
Improved Solvation Models using Boundary Integral Equations
Improved Solvation Models using Boundary Integral Equations Matthew Knepley and Jaydeep Bardhan Computational and Applied Mathematics Rice University SIAM Conference on the Life Sciences Minneapolis, MN
Title Theory of solutions in the energy r of the molecular flexibility Author(s) Matubayasi, N; Nakahara, M Citation JOURNAL OF CHEMICAL PHYSICS (2003), 9702 Issue Date 2003-11-08 URL http://hdl.handle.net/2433/50354
Conformational sampling of macrocycles in solution and in the solid state
Conformational sampling of macrocycles in solution and in the solid state Paul Hawkins, Ph.D. Head of Scientific Solutions Stanislaw Wlodek, Ph.D. Senior Scientific Developer 6/6/2018 https://berkonomics.com/?p=2437
An Introduction to Metabolism
An Introduction to Metabolism Chapter 8 Objectives Distinguish between the following pairs of terms: catabolic and anabolic pathways; kinetic and potential energy; open and closed systems; exergonic and
Other Cells. Hormones. Viruses. Toxins. Cell. Bacteria
Other Cells Hormones Viruses Toxins Cell Bacteria ΔH < 0 reaction is exothermic, tells us nothing about the spontaneity of the reaction Δ H > 0 reaction is endothermic, tells us nothing about the spontaneity
Applications of the Spring. Force: molecules
Applications of the Spring Atoms interact via the coulomb force Force: molecules When atoms are far apart, they are attractive When atoms are too close, they are repulsive Atoms in a molecule display relative
2054, Chap. 8, page 1
2054, Chap. 8, page 1 I. Metabolism: Energetics, Enzymes, and Regulation (Chapter 8) A. Energetics and work 1. overview a. energy = ability to do work (1) chemical, transport, mechanical (2) ultimate source
Interpreting and evaluating biological NMR in the literature. Worksheet 1
Interpreting and evaluating biological NMR in the literature Worksheet 1 1D NMR spectra Application of RF pulses of specified lengths and frequencies can make certain nuclei detectable We can selectively
Protein-Ligand Docking Methods
Review Goal: Given a protein structure, predict its ligand bindings Protein-Ligand Docking Methods Applications: Function prediction Drug discovery etc. Thomas Funkhouser Princeton University S597A, Fall
Supporting Information
Supporting Information Constant ph molecular dynamics reveals ph-modulated binding of two small-molecule BACE1 inhibitors Christopher R. Ellis 1,, Cheng-Chieh Tsai 1,, Xinjun Hou 2, and Jana Shen 1, 1
Molecular Driving Forces
Molecular Driving Forces Statistical Thermodynamics in Chemistry and Biology SUBGfittingen 7 At 216 513 073 / / Ken A. Dill Sarina Bromberg With the assistance of Dirk Stigter on the Electrostatics chapters
Principles of Physical Biochemistry
Principles of Physical Biochemistry Kensal E. van Hold e W. Curtis Johnso n P. Shing Ho Preface x i PART 1 MACROMOLECULAR STRUCTURE AND DYNAMICS 1 1 Biological Macromolecules 2 1.1 General Principles
Why Proteins Fold. How Proteins Fold? e - ΔG/kT. Protein Folding, Nonbonding Forces, and Free Energy
Why Proteins Fold Proteins are the action superheroes of the body. As enzymes, they make reactions go a million times faster. As versatile transport vehicles, they carry oxygen and antibodies to fight
New approaches to scoring function design for protein-ligand binding affinities. Richard A. Friesner Columbia University
New approaches to scoring function design for protein-ligand binding affinities Richard A. Friesner Columbia University Overview Brief discussion of advantages of empirical scoring approaches Analysis
Molecular Modeling Study of Some Anthelmintic 2-phenyl Benzimidazole-1- Acetamides as β-tubulin Inhibitor
Sawant et al : Molecular Modeling Study of Some Anthelmintic 2-phenyl Benzimidazole-1-Acetamides as -tubulin Inhibitor 1269 International Journal of Drug Design and Discovery Volume 5 Issue 1 January March
Computing Relative Free Energies of Solvation Using Single Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange
J. Chem. Theory Comput. 2010, 6, 3427 3441 3427 Computing Relative Free Energies of Solvation Using Single Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange Ilja V. Khavrutskii*
A Brief Guide to All Atom/Coarse Grain Simulations 1.1
A Brief Guide to All Atom/Coarse Grain Simulations 1.1 Contents 1. Introduction 2. AACG simulations 2.1. Capabilities and limitations 2.2. AACG commands 2.3. Visualization and analysis 2.4. AACG simulation
Advanced in silico drug design
Advanced in silico drug design RNDr. Martin Lepšík, Ph.D. Lecture: Advanced scoring Palacky University, Olomouc 2016 1 Outline 1. Scoring Definition, Types 2. Physics-based Scoring: Master Equation Terms