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分子动力学软件Amber18说明书.pdf
Contents
Introduction and Installation
Introduction
Information flow in Amber
List of programs
Installation
Install from binary distribution
Uninstalling and cleaning
Python in Amber
Applying Updates
Building with cmake
Contacting the developers
Amber force fields
Molecular mechanics force fields
Proteins
Nucleic acids
Carbohydrates
Lipids
Solvents
Ions
Modified amino acids and nucleotides
Force fields related to semi-empirical QM
The GAL17 force field for water over platinum
Obsolete force field files
The Generalized Born/Surface Area Model
GB/SA input parameters
ALPB (Analytical Linearized Poisson-Boltzmann)
GBNSR6
GB equations available in gbnsr6
Numerical implementation of the R6 integral
Usage
PBSA
Introduction
Usage and keywords
Example inputs and demonstrations of functionalities
Visualization functions in pbsa
pbsa in sander and NAB
GPU accelerated pbsa
Reference Interaction Site Model
Introduction
Practical Considerations
Work Flow
rism1d
3D-RISM in NAB
rism3d.snglpnt
3D-RISM in sander
RISM File Formats
Empirical Valence Bond
Introduction
General usage description
Biased sampling
Quantization of nuclear degrees of freedom
Distributed Gaussian EVB
EVB input variables and interdependencies
sqm: Semi-empirical quantum chemistry
Available Hamiltonians
Dispersion and hydrogen bond correction
Usage
QM/MM calculations
Built-in semiempirical NDDO methods and SCC-DFTB
Interface for ab initio and DFT methods
Adaptive solvent QM/MM simulations
Adaptive buffered force-mixing QM/MM
SEBOMD: SemiEmpirical Born-Oppenheimer Molecular Dynamics
paramfit
Usage
The Job Control File
Multiple molecule fits
Fitting Forces
Examples
System preparation
Preparing PDB Files
Cleaning up Protein PDB Files for AMBER
Residue naming conventions
Chains, Residue Numbering, Missing Residues
pdb4amber
reduce
packmol-memgen
Building bilayer systems with AMBAT
LEaP
Introduction
Concepts
Running LEaP
Basic instructions for using LEaP to build molecules
Error Handling and Reporting
Commands
Building oligosaccharides, lipids and glycoproteins
Reading and modifying Amber parameter files
Understanding Amber parameter files
ParmEd
Antechamber and GAFF
Principal programs
A simple example for antechamber
Using the components.cif file from the PDB
Programs called by antechamber
Miscellaneous programs
New Development of Antechamber And GAFF
Metal Center Parameter Builder (MCPB)
Python Metal Site Modeling Toolbox (pyMSMT)
Setting up crystal simulations
UnitCell
PropPDB
AddToBox
ChBox
Running simulations
sander
Introduction
File usage
Example input files
Namelist Input Syntax
Overview of the information in the input file
General minimization and dynamics parameters
Potential function parameters
Varying conditions
File redirection commands
Getting debugging information
multisander (and multipmemd)
APBS as an alternate PB solver in Sander
Programmer's Corner: The sander API
pmemd
Introduction
Functionality
PMEMD-specific namelist variables
Slightly changed functionality
Parallel performance tuning and hints
GPU Accelerated PMEMD
Intel® Many Integrated Core Architecture
pmemd.gem
Atom and Residue Selections
Amber Masks
"Atom Expressions" in NAB Applications
GROUP Specification
Sampling configuration space
Self-Guided Langevin dynamics
Accelerated Molecular Dynamics
Gaussian Accelerated Molecular Dynamics
Targeted MD
Multiply-Targeted MD (MTMD)
Nudged elastic band calculations
Low-MODe (LMOD) methods
Free energies
Thermodynamic integration
Absolute Free Energies using EMIL
Linear Interaction Energies
Umbrella sampling
Replica Exchange Molecular Dynamics (REMD)
Adaptively Biased MD, Steered MD, Umbrella Sampling with REMD and String Method
Steered Molecular Dynamics (SMD) and the Jarzynski Relationship
Constant pH calculations
Background
Preparing a system for constant pH simulation
Running at constant pH
Analyzing constant pH simulations
Extending constant pH to additional titratable groups
pH Replica Exchange MD
cphstats
Constant Redox Potential calculations
Preparing a system for constant Redox Potential simulation
Running at constant Redox Potential
Analyzing constant Redox Potential simulations
Extending constant Redox Potential to additional titratable groups
Redox Potential Replica Exchange MD
cestats
Continuous constant pH molecular dynamics
Implementation notes
Usage description
Continuous constant pH MD with pH replica exchange
Obtaining parameters for a novel titratable group
NMR, X-ray, and cryo-EM/ET refinement
Distance, angle and torsional restraints
NOESY volume restraints
Chemical shift restraints
Pseudocontact shift restraints
Direct dipolar coupling restraints
Residual CSA or pseudo-CSA restraints
Preparing restraint files for Sander
Getting summaries of NMR violations
Time-averaged restraints
Multiple copies refinement using LES
Some sample input files
X-ray Crystallography Refinement using SANDER
EMAP restraints for rigid and flexible fitting into EM maps
LES
Preparing to use LES with Amber
Using the ADDLES program
More information on the ADDLES commands and options
Using the new topology/coordinate files with SANDER
Using LES with the Generalized Born solvation model
Case studies: Examples of application of LES
Quantum dynamics
Path-Integral Molecular Dynamics
Centroid Molecular Dynamics (CMD)
Ring Polymer Molecular Dynamics (RPMD)
Linearized semiclassical initial value representation
Reactive Dynamics
Isotope effects
mdgx
Input and Output
Installation
Special Algorithmic Features of mdgx
Customizable Virtual Site Support in mdgx
Implicitly Polarized Charge Development in mdgx
Restrained Electrostatic Potential Fitting in mdgx
Bonded Term Fitting in mdgx
Configuration Sampling
Thermodynamic Integration
Future Directions and Goals of the mdgx Project
Analysis of simulations
mdout_analyzer.py and ambpdb
ambpdb
cpptraj
Running Cpptraj
General Concepts
Variables and Control Structures
Data Sets and Data Files
Data File Options
Coordinates (COORDS) Data Set Commands
General Commands
Topology File Commands
Trajectory File Commands
Action Commands
Analysis Commands
Analysis Examples
pytraj
Introduction
Development
Documentation and examples
MMPBSA.py
Introduction
Preparing for an MM/PB(GB)SA calculation
Running MMPBSA.py
Python API
MM_PBSA
General instructions
Input explanations
FEW
Installation
Overview of workflow steps and minimal input
Common setup of molecular dynamics simulations
Workflow for automated MM-PBSA & MM-GBSA calculations (WAMM)
Linear interaction energy workflow (LIEW)
Thermodynamic integration workflow (TIW)
XtalAnalyze
XtalAnalyze.sh
XtalPlot.sh
md2map.sh
SAXS
Introduction and theory
Usage
NAB/sff and AmberLite
NAB and sff
A little history
A C interface to libsff
NAB overview
Fiber Diffraction Duplexes in NAB
Symmetry Functions
Symmetry server programs
libsff: Molecular mechanics and dynamics
Basic molecular mechanics routines
NetCDF read/write routines
Second derivatives and normal modes
Low-MODe (LMOD) optimization methods
The Generalized Born with Hierarchical Charge Partitioning (GB-HCP)
amberlite: Some AmberTools-Based Utilities
Introduction
Coordinates and Parameter-Topology Files
pytleap: Creating Coordinates and Parameter- Topology Files
Energy Checking Tool: ffgbsa
Energy Minimizer: minab
Molecular Dynamics "Lite": mdnab
MM(GB)(PB)/SA Analysis Tool: pymdpbsa
Examples and Test Cases
Bibliography
Index
Amber2018ReferenceManual(CoversAmber18andAmberTools18)
Amber 2018
Reference Manual
(Covers Amber18 and AmberTools18)
Principal contributors to the current codes:
David A. Case (Rutgers)
Ross C. Walker (UCSD, GSK)
Thomas E. Cheatham III (Utah)
Carlos Simmerling (Stony Brook)
Adrian Roitberg (Florida)
Kenneth M. Merz (Michigan State)
Ray Luo (UC Irvine)
Tom Darden (OpenEye)
Junmei Wang (Pitt)
Robert E. Duke (UNT)
Daniel R. Roe (Utah, NIH)
Scott LeGrand (Amazon)
Jason Swails (Lutron)
Andreas W. Götz (UCSD)
Jamie Smith (UCSD)
David Cerutti (Rutgers)
Scott R. Brozell (Rutgers)
Tyler Luchko (CSU Northridge)
Vinícius Wilian D. Cruzeiro (Florida)
Delaram Ghoreishi (Florida)
Gérald Monard (U. Lorraine)
Celeste Sagui (NCSU)
Feng Pan (NCSU)
G. Andrés Cisneros (UNT)
Yinglong Miao (Kansas)
Jana Shen (Maryland)
Robert Harris (Maryland)
Charles Lin (UCSD)
Daniel J. Mermelstein (UCSD)
Pengfei Li (UIUC, Yale)
Alexey Onufriev (Virginia Tech)
Saeed Izadi (Virginia Tech, Genentech)
Romain M. Wolf (Novartis)
Xiongwu Wu (NIH)
Holger Gohlke (Düsseldorf)
Stephan Schott-Verdugo (Düsseldorf)
Nadine Homeyer (Düsseldorf)
Ruxi Qi (UC Irvine)
Li Xiao (UC Irvine)
Haixin Wei (UC Irvine)
D’Artagnan Greene (UC Irvine)
Taisung Lee (Rutgers)
Darrin York (Rutgers)
Jian Liu (Peking Univ.)
Hai Nguyen (Stony Brook, Rutgers)
Igor Omelyan (NINT)
Andriy Kovalenko (NINT)
Mike Gilson (UCSD)
Ido Ben-Shalom (UCSD)
Crystal Nguyen (UCSD)
Romelia Salomon-Ferrer (UCSD)
Tom Kurtzman (CUNY)
Peter A. Kollman (UC San Francisco)
For more information, please visit http://ambermd.org/contributors.html
3
Acknowledgments
Research support from DARPA, NIH, ONR, DOE and NSF is gratefully acknowledged, along with support
from NVIDIA, Amazon and Exxact. Many people helped add features to various codes; these contributions are
described in the documentation for the individual programs; see also http://ambermd.org/contributors.html.
Recommended Citation:
• When citing Amber 2018 (comprised of AmberTools18 and Amber18) in the literature, the following citation
should be used:
D.A. Case, I.Y. Ben-Shalom, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, V.W.D. Cruzeiro, T.A. Darden,
R.E. Duke, D. Ghoreishi, M.K. Gilson, H. Gohlke, A.W. Goetz, D. Greene, R Harris, N. Homeyer, S. Izadi,
A. Kovalenko, T. Kurtzman, T.S. Lee, S. LeGrand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, D.J. Mermelstein,
K.M. Merz, Y. Miao, G. Monard, C. Nguyen, H. Nguyen, I. Omelyan, A. Onufriev, F. Pan, R. Qi, D.R. Roe,
A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen, C.L. Simmerling, J. Smith, R. Salomon-Ferrer, J. Swails,
R.C. Walker, J. Wang, H. Wei, R.M. Wolf, X. Wu, L. Xiao, D.M. York and P.A. Kollman (2018), AMBER
2018, University of California, San Francisco.
Peter Kollman died unexpectedly in May, 2001. We dedicate Amber to his memory.
Notes
• We thank Chris Bayly and Merck-Frosst, Canada for permission to include charge increments for the AM1-
BCC charge scheme.
• Some of the force field routines in NAB were adapted from similar routines in the MOIL program package:
R. Elber, A. Roitberg, C. Simmerling, R. Goldstein, H. Li, G. Verkhivker, C. Keasar, J. Zhang and A. Ulitsky,
"MOIL: A program for simulations of macromolecules" Comp. Phys. Commun. 91, 159-189 (1995).
Cover illustration: A pseudotrajectory of the homodecameric human glutamine synthetase (each subunit is colored
differently). The figure was prepared by Benedikt Frieg and Holger Gohlke, based on work reported by B. Frieg,
B. Görg, N. Homeyer, V. Keitel, D. Häussinger, and H. Gohlke, Molecular mechanisms of glutamine synthetase
mutations that lead to clinically relevant pathologies. PLoS Comput. Biol. 12 (2016) e1004693 and by B. Frieg, D.
Häussinger, and H. Gohlke, Towards restoring catalytic activity of glutamine synthetase with a clinically relevant
mutation. In: Proceedings of the NIC Symposium 2016, Jülich (2016).
4
Contents
Contents
I.
1.
2.
Introduction and Installation
Introduction
1.1.
1.2. List of programs .
Information flow in Amber .
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Install from binary distribution .
Installation
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2.2. Uninstalling and cleaning .
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2.3. Python in Amber
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2.4. Applying Updates .
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2.5. Building with cmake .
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2.6. Contacting the developers .
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II. Amber force fields
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3. Molecular mechanics force fields
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3.7. Modified amino acids and nucleotides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8. Force fields related to semi-empirical QM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.9. The GAL17 force field for water over platinum . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.10. Obsolete force field files
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4. The Generalized Born/Surface Area Model
4.1. GB/SA input parameters
4.2. ALPB (Analytical Linearized Poisson-Boltzmann)
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5. GBNSR6
5.1. GB equations available in gbnsr6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5.2. Numerical implementation of the R6 integral
5.3. Usage .
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6. PBSA
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6.2. Usage and keywords
6.3. Example inputs and demonstrations of functionalities . . . . . . . . . . . . . . . . . . . . . . . .
6.4. Visualization functions in pbsa .
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5
CONTENTS
6.5. pbsa in sander and NAB .
6.6. GPU accelerated pbsa .
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Introduction .
7. Reference Interaction Site Model
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7.7. 3D-RISM in sander .
7.8. RISM File Formats .
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8. Empirical Valence Bond
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8.1.
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8.2. General usage description .
8.3. Biased sampling .
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8.5. Distributed Gaussian EVB .
8.6. EVB input variables and interdependencies
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9. sqm: Semi-empirical quantum chemistry
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9.2. Dispersion and hydrogen bond correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
9.3. Usage .
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10. QM/MM calculations
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10.1. Built-in semiempirical NDDO methods and SCC-DFTB . . . . . . . . . . . . . . . . . . . . . . 148
10.2. Interface for ab initio and DFT methods . . . .
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10.3. Adaptive solvent QM/MM simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
10.4. Adaptive buffered force-mixing QM/MM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
10.5. SEBOMD: SemiEmpirical Born-Oppenheimer Molecular Dynamics . . . . . . . . . . . . . . . . 184
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11. paramfit
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11.1. Usage .
11.2. The Job Control File
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11.5. Examples .
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III. System preparation
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12. Preparing PDB Files
202
12.1. Cleaning up Protein PDB Files for AMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
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12.2. Residue naming conventions .
12.3. Chains, Residue Numbering, Missing Residues
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12.4. pdb4amber
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12.5. reduce .
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12.6. packmol-memgen .
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12.7. Building bilayer systems with AMBAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
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6
CONTENTS
13. LEaP
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13.1. Introduction .
13.2. Concepts
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13.3. Running LEaP .
13.4. Basic instructions for using LEaP to build molecules
13.5. Error Handling and Reporting .
13.6. Commands
13.7. Building oligosaccharides, lipids and glycoproteins . . . . . . . . . . . . . . . . . . . . . . . .
208
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
. 217
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
. 235
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14. Reading and modifying Amber parameter files
14.1. Understanding Amber parameter files
14.2. ParmEd .
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244
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
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280
15. Antechamber and GAFF
15.1. Principal programs
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
15.2. A simple example for antechamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
15.3. Using the components.cif file from the PDB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
15.4. Programs called by antechamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
15.5. Miscellaneous programs
. 295
15.6. New Development of Antechamber And GAFF . . . . . . . . . . . . . . . . . . . . . . . . . .
. 296
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15.7. Metal Center Parameter Builder (MCPB)
15.8. Python Metal Site Modeling Toolbox (pyMSMT)
. . . . . . . . . . . . . . . . . . . . . . . . .
. 297
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16. Setting up crystal simulations
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16.1. UnitCell .
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16.2. PropPDB .
16.3. AddToBox .
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16.4. ChBox .
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311
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
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IV. Running simulations
314
17. sander
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316
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.
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17.1. Introduction .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
.
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17.2. File usage .
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
17.3. Example input files .
.
17.4. Namelist Input Syntax .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
17.5. Overview of the information in the input file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
17.6. General minimization and dynamics parameters . . . . . . . . . . . . . . . . . . . . . . . . . .
. 320
.
17.7. Potential function parameters .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
.
17.8. Varying conditions
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
17.9. File redirection commands .
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
17.10.Getting debugging information .
17.11.multisander (and multipmemd) .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
17.12.APBS as an alternate PB solver in Sander
. 352
17.13.Programmer’s Corner: The sander API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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18. pmemd
375
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
18.1. Introduction .
.
18.2. Functionality .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
18.3. PMEMD-specific namelist variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
18.4. Slightly changed functionality .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
18.5. Parallel performance tuning and hints
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7
CONTENTS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
18.6. GPU Accelerated PMEMD .
18.7. Intel® Many Integrated Core Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386
18.8. pmemd.gem .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
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19. Atom and Residue Selections
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395
19.1. Amber Masks .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
19.2. "Atom Expressions" in NAB Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
19.3. GROUP Specification .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
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20. Sampling configuration space
402
20.1. Self-Guided Langevin dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405
20.2. Accelerated Molecular Dynamics .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408
20.3. Gaussian Accelerated Molecular Dynamics
.
20.4. Targeted MD .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412
.
20.5. Multiply-Targeted MD (MTMD) .
.
20.6. Nudged elastic band calculations .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
20.7. Low-MODe (LMOD) methods .
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
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21. Free energies
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421
21.1. Thermodynamic integration .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421
21.2. Absolute Free Energies using EMIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
21.3. Linear Interaction Energies .
21.4. Umbrella sampling .
.
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
21.5. Replica Exchange Molecular Dynamics (REMD)
. . . . . . . . . . . . . . . . . . . . . . . . . . 436
21.6. Adaptively Biased MD, Steered MD, Umbrella Sampling with REMD and String Method . . . . . 461
21.7. Steered Molecular Dynamics (SMD) and the Jarzynski Relationship . . . . . . . . . . . . . . . . 478
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22. Constant pH calculations
.
.
481
22.1. Background .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481
22.2. Preparing a system for constant pH simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481
22.3. Running at constant pH .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484
22.4. Analyzing constant pH simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487
22.5. Extending constant pH to additional titratable groups . . . . . . . . . . . . . . . . . . . . . . . . 487
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492
22.6. pH Replica Exchange MD .
22.7. cphstats .
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492
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23. Constant Redox Potential calculations
501
23.1. Preparing a system for constant Redox Potential simulation . . . . . . . . . . . . . . . . . . . . . 501
23.2. Running at constant Redox Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503
23.3. Analyzing constant Redox Potential simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 504
23.4. Extending constant Redox Potential to additional titratable groups
. . . . . . . . . . . . . . . . . 504
23.5. Redox Potential Replica Exchange MD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504
23.6. cestats .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505
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24. Continuous constant pH molecular dynamics
508
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508
24.1. Implementation notes .
24.2. Usage description .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509
.
24.3. Continuous constant pH MD with pH replica exchange . . . . . . . . . . . . . . . . . . . . . . . 513
24.4. Obtaining parameters for a novel titratable group . . . . . . . . . . . . . . . . . . . . . . . . . . 515
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8
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