Difference between revisions of "Best Practices"

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= Best Practices =
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This is intended as an overview of best practices for free energy calculations, with references, when possible.
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We provide an overview of some of what we believe are the best practices for free energy calculations, with references, wherever possible.  
 
  
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This document assumes you already have a basic idea of what these calculations are and what they do (if not, you can learn more from the [[Free Energy Fundamentals]] section), and that you already have a working knowledge of molecular simulations and basic terms like convergence and equilibration (if not, start with a textbook like Leach's [http://www.google.com/search?hl=en&lr=&client=safari&rls=en&q=Leach+Molecular+Modelling:+Principles+and+Applications&btnG=Search Molecular Modelling: Principles and Applications]{{cite|Leach2001}}). Simulation-package specific issues will not be addressed here.
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This document assumes you already have a basic idea of what these calculations are and what they do (if not, you can learn more from the [[Free Energy Fundamentals]] section and introductory-level reviews{{Cite|Michel2010}}, {{Cite|Christ2010}}, {{Cite|Shirts2013}}), and that you already have a working knowledge of molecular simulation, convergence and equilibration. If not, start with a textbook like Leach's [http://www.google.com/search?hl=en&lr=&client=safari&rls=en&q=Leach+Molecular+Modelling:+Principles+and+Applications&btnG=Search Molecular Modelling: Principles and Applications]{{cite|Leach2001}}). The discussion is simulation-package agnostic, focusing on the important ideas.
  
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Feel free to edit this document or the attached discussion pages. We hope this to be a place storing the community consensus on these issues. Do back up any edits with appropriate references.
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This document is open for editing. We hope this to be a place to build and store the community consensus on these issues. Do back up any edits with appropriate references. If you are not sure about changes, post questions on the discussion pages. 
  
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[[Previous Best Practices]]
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= Introduction =
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Free energies calculated from molecular simulation are appealing, because, in principle, they are rigorously correct, '''given a particular set of parameters and physical assumptions'''. Given this particular set of parameters and physical assumptionstarting point, there exists a single ''right'' answer for a free energy calculation. The goal is to obtain that free energy with as little statistical error as possible  -- and ''not'' necessarily to match experiment. Without convergence, agreement with experiment may be due only to fortuitous cancellation of error. Only after free energy estimates have converged can the underlying parameters and physical assumptions really be tested. 
  
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== Introduction ==
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Free energy is a function of state, so there are many possible choices of pathway for a thermodynamic cycle connecting the same two endpoints. Some pathways may be more efficient than others (sometimes by many orders of magnitude). Methods which are in principle correct may not always be practical or useful. A large part of the following discussion will discuss exactly how to construct useful and efficient pathways connecting the end states.
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Free energy calculations are appealing, in that, in principle, they allow calculation of rigorously correct free energies, '''given a particular set of parameters and physical assumptions'''. We believe the goal of these calculations, then, should be to obtain these correct free energies given the particular assumptions -- and ''not'' necessarily to match experiment. Only then can the underlying parameters and physical assumptions really be tested. In other words, given this particular set of parameters and physical assumption, sthere exists a single ''right'' answer for a free energy calculation, and our goal is to obtain that free energy with as little statistical error as possible. We will call these free energies "correct" free energies. "Correct", then, implies at the very least that the computed free energies have converged, and that there are no underlying methodological problems.
 
  
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It is also important to remember in what follows that free energy is a function of state, so there are many possible choices of pathway for a thermodynamic cycle connecting the same two endpoints. Some pathways may be more efficient than others (sometimes by many orders of magnitude) so methods which are in principle correct may not always be practical or useful.
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While free energy calculations may be done for many different situations, here we focus on best practices for binding and solvation free energy calculations. Solvation free energies provide a basic starting point for considering a number of the key issues, and are used frequently here because of this and because of their simplicity. Often, additional complications make it harder to track down problems when considering binding free energy calculations.
  
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Below, we discuss best practices in the context of two practical situations; calculating solvation free energies and calculating binding free energies. Solvation free energies provide a basic starting point for considering a number of the key issues.  For binding free energies, there are many more choices of pathway, which raises additional complications. In both cases, there are also different methods for computing the relevant free energy differences, which may differ in efficiency.
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= Guidelines for setting up your system files =
  
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== Guidelines for setting up your system files ==
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== Guideline 1: Automate when possible ==
  
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=== Guideline 1: Automate when possible ===
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If you manually set up your systems for simulation, we would urge you not to. It is extremely easy to make a mistake that affects the entire subsequent set of simulations. Additionally, if your mistake is a result of mistyping something or similar, you may end up with no record of what you did wrong. Instead, when possible, automate simulation preparation tasks so that they do not need to be done manually. Periodically validate the results of automated scripts, especially when you change them. Not only can scripts help mistakes, but when they do have bugs, the script itself provides a permanent record of exactly what you did. Version control your scripts (using SVN or Git) so you can go back check this record more easily.
  
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It is extremely easy to make a mistake that affects the entire simulation system.  When possible, automate simulation preparation tasks so that they do not need to be done manually. 
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= Guidelines for free energy calculation pathways =
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Validating the results of automated scripts is a very good idea too.
 
  
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== Guidelines for free energy calculation pathways ==
 
 
Alchemical free energies almost involve some insertion or deletion of atoms -- both in hydration free energy calculations, and in absolute and relative binding free energy calculations. By insertion and deletion, we mean decoupling or annihilation (see [[Decoupling and annihilation]] for definitions) of the interactions of the atoms in question. We believe that a basic list of rules and guidelines should be followed in any calculation that involves insertion or deletion:
 
Alchemical free energies almost involve some insertion or deletion of atoms -- both in hydration free energy calculations, and in absolute and relative binding free energy calculations. By insertion and deletion, we mean decoupling or annihilation (see [[Decoupling and annihilation]] for definitions) of the interactions of the atoms in question. We believe that a basic list of rules and guidelines should be followed in any calculation that involves insertion or deletion:
  
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===Guideline 1: Always use soft-core potentials while decoupling or annihilating Lennard-Jones interactions===
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==Guideline 1: Always use soft-core potentials while decoupling or annihilating Lennard-Jones interactions==
  
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When removing atoms from a system, the atoms should be removed gradually. Because of how potentials are defined, What gradual removal means can be somewhat subtle.  The soft-core potential formalism is a well-tested way to do this. [[Intermediate States | Read more about how to choose the potential to decouple or annihilate Lennard-Jones sites]].
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When removing atoms from a system, the atoms should be removed gradually.   Because of how potentials are defined, what gradual removal means can be somewhat subtle.  The soft-core potential formalism is a well-tested way to do this. [[Intermediate States | Read more about how to choose the potential to decouple or annihilate Lennard-Jones sites]].
  
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===Guideline 2: Never leave a partial atomic charge on an atom while its Lennard-Jones interactions are being removed===
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==Guideline 2: Never leave a partial atomic charge on an atom while its Lennard-Jones interactions are being removed==
  
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Partial atomic charge should never be allowed to remain on an atom while its Lennard-Jones interactions are being removed, because it can result in an instance of two bare charges. [[Constructing_a_Pathway_of_Intermediate_States#Avoid_turning_off_charges_and_Lennard-Jones_sites_at_the_same_time |  Read more about how to handle changes to both charges and Lennard-Jones sites]].
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Partial atomic charge should never be allowed to remain on an atom while its Lennard-Jones interactions are being removed, because it can result in extremely strong electrostatic interactions in the absence of steric interactions. [[Constructing_a_Pathway_of_Intermediate_States#Avoid_turning_off_charges_and_Lennard-Jones_sites_at_the_same_time |  Read more about how to handle changes to both charges and Lennard-Jones sites]].
  
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===Guideline 3: Use few insertions/deletions===
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==Guideline 3: Use few insertions/deletions==
  
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Electrostatics transformations are usually smooth functions of lambda , and require relatively few lambda values, while Lennard-Jones transformations – especially those involving insertion and deletions – can require substantially more lambda values (even when using soft core potentials) to obtain good phase-space overlap and therefore accurate free energy differences.{{cite|Shirts2005}}{{cite|Mobley2007}}{{cite|Mobley2007b}} Insertions and deletions of particles can be thought of as “difficult” transformations.{{cite|Jarzynski2006}}  
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Electrostatics transformations are usually smooth functions of lambda, and require relatively few lambda values, while Lennard-Jones transformations – especially those involving insertion and deletions – can require substantially more lambda values (even when using soft core potentials) to obtain good phase-space overlap and therefore accurate free energy differences.{{cite|Shirts2005}}{{cite|Mobley2007}}{{cite|Mobley2007b}} Insertions and deletions of particles can be thought of as “difficult” transformations.{{cite|Jarzynski2006}}  
  
 
Consequently, it is far more computationally efficient to modify existing particles (atoms) than to insert or delete new atoms.  Construct mutation pathways for relative free energy calculations that minimize the number of times atoms, especially large atoms, need to be inserted and deletet, since multiple choices of mutation pathways between a set of molecules are typically possible.
 
Consequently, it is far more computationally efficient to modify existing particles (atoms) than to insert or delete new atoms.  Construct mutation pathways for relative free energy calculations that minimize the number of times atoms, especially large atoms, need to be inserted and deletet, since multiple choices of mutation pathways between a set of molecules are typically possible.
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This guideline is unfortunately not useful for absolute free energy calculations, since these involve inserting or deleting entire molecules.
 
This guideline is unfortunately not useful for absolute free energy calculations, since these involve inserting or deleting entire molecules.
  
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===Guideline 4: Think about whether your intermediate states are likely to converge or not===
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==Guideline 4: Think about whether your intermediate states are likely to converge or not==
  
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Given that many choices of pathway are possible, it can often be helpful to think about whether a particular choice of pathway makes convergence easier or more difficult at each lambda.
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Many choices of pathway are possible. It can often be helpful to think about whether a particular choice of pathway makes convergence easier or more difficult at each lambda, thus reducing the amount of simulation time required.
  
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Imagine what happens to a ligand when it is partially noniteracting with the protein (say at <math>\lambda=0.5</math>during an absolute binding free energy simulation.  It will have significantly more freedom to move around the pocket than when fully interacting- but the kinetics may still be slow, since it will get trapped in free energy minima.  It then may take quite a while to sample these states.
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Visualize what happens to a ligand when it is partially noninteracting with the protein, especially as interactions become quite weak during an absolute binding free energy simulation.  It will have significantly more freedom to move around the pocket than when fully interacting- but the kinetics may still be slow, since it will get trapped in free energy minima.  It then may take quite a while to sample these states.
  
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One can introduce distance or orientation restraints between the ligand and the protein.  These reduce the free space of exploration of the ligand, thus increasing convergence.  In the noninteracting state, the interaction potential can be removed analytically. In the interacting state, the free energy of applying these constraints need to be included.  The free energy of applying constraints can be non-trivial, since the ligand will be pulled away from alternative minima, but convergence issues at other intermediates will be greatly reduced.
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One can introduce distance or orientation restraints between the ligand and the protein.  These reduce the space the ligand must explore, thus speeding convergence, at the expense of an additional restraining calculation.  In the noninteracting state, the interaction potential can often be removed analytically. In the interacting state, the free energy of applying these restraints needs to be included.  The free energy of applying restraints can be non-trivial, since the ligand will be pulled away from alternative minima, but convergence issues at other intermediates will be greatly reduced.
  
 
At the fully noninteracting state, the amount of configurational sampling the ligand undergoes is dictated by this choice. A ligand with a single reference distance restrained relative to the protein will need to sample a spherical shell in configuration space, and a ligand with all six relative degrees of freedom restrained would need to sample only a very small region of configuration space.  
 
At the fully noninteracting state, the amount of configurational sampling the ligand undergoes is dictated by this choice. A ligand with a single reference distance restrained relative to the protein will need to sample a spherical shell in configuration space, and a ligand with all six relative degrees of freedom restrained would need to sample only a very small region of configuration space.  
 
These two can take drastically different amounts of time, so in fact it can be much more efficient, at least in some cases, to use the additional restraints.{{cite|Mobley2006}}
 
These two can take drastically different amounts of time, so in fact it can be much more efficient, at least in some cases, to use the additional restraints.{{cite|Mobley2006}}
  
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=== Phase space overlap ===
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=Guidelines for carrying out free energy calculations=
  
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Phase space overlap is an additional consideration in performing free energy calculations. Essentially, all of the equilibrium approaches for estimating free energy differences rely on having simulations run at separate lambda values that are sufficiently closely-spaced that adjoining lambda values sample similar configurations. There are three basic ways to attempt to assess whether this requirement is met, listed in order of ascending sophistication:
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==Guideline 1: Equilibrium methods are easier to use for beginners than nonequilibrium or mixed-ensemble methods==
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# Trial and error: For a particular system, begin with very closely spaced lambda values, and then gradually increase separation until results begin to deteriorate.
 
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# Error analysis:
 
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## Using autocorrelation analysis{{cite|Chodera2006}}
 
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## If necessary, using block bootstrap approaches{{cite|Mobley2006}}
 
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# Phase space overlap measures{{cite|Wu2005}}
 
  
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Different types of transformations may have different overlap properties. For example, above, we noted that electrostatics transformations are usually smooth, which, more rigorously, means that relatively good phase space overlap is maintained even with fairly coarsely-spaced lambda values. Insertion and deletion of particles, on the other hand, requires more closely-spaced lambda values to ensure good overlap.
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There are a very large number of basic methods for performing free energy calculation: [[Simulation_Acceleration#Other_Methods|Slow-growth, fast-growth (Jarzynski-style)]], and equilibrium (or instantaneous growth) free energy methods. For advanced users, there are significant efficiency advantages with careful use of these methods.
  
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==Guidelines for carrying out free energy calculations==
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However, for beginners, and for standard simulations, we believe the evidence is in favor of equilibrium methods: Run simulations at fixed values of lambda, collect equilibrium data from these simulations, and calculate the free energies from these observations. There are far fewer things that can go wrong.  If it's a problem that really requires significantly more efficiency, it's probably worth doing a warmup problem for practice first!
  
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Here, we attempt to separate methods for analyzing the results of free energy calculations, from methods for performing free energy calculations. For example, thermodynamic integration and exponential averaging (among others) are methods for analyzing free energy calculations, while equilibrium, slow-growth, and fast-growth methods can be used for performing free energy calculations. In this section, we focus on methods for performing free energy calculations, and address analysis methods below.
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[[Simulating_States_of_Interest|Read more about running equilibrium methods at the states of interest]].
  
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===Guideline 1: Equilibrium methods are easier to use for beginners than nonequilibrium or mixed-ensemble methods===
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If you are interested it digging deeper, [[Simulation_Acceleration|read about some more advanced sampling methods]] that use exchanges between equilibrated sites to improve efficiency.
  
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There are several basic groups of methods for performing free energy calculations: Slow-growth, fast-growth, and equilibrium (or instantaneous growth) free energy methods. Slow-growth and equilibrium methods are more traditional, while fast-growth (non-equilibrium) methods have gained considerable recent interest. In slow-growth methods, the coupling parameter, <math>\lambda</math>, is slowly varied over the course of one or several simulations to take a system gradually from <math>\lambda=0</math> to <math>\lambda=1</math>; from this, the free energy difference between endpoints is estimated. In equilibrium methods, on the other hand, separate simulations are run at multiple different lambda values and information from the individual simulations is used to estimate free energy differences between adjoining lambda values. Fast-growth methods are based on the demonstration by Jarzynski in 1997{{cite|Jarzynski1997}} that the free energy difference associated with a particular equilibrium process can be computed by taking an appropriate average of the irreversible work done in performing many (potentially rapid) non-equilibrium trials of the same process. When applied to alchemical free energy calculations, this simply amounts to estimating free energy differences by performing many different rapid versions of a slow-growth trial – that is, rapidly changing lambda between two values (i.e. 0 and 1)  in many separate trials, and monitoring the work done each time.  Equilibrium free energy calculations can be thought of as "instantaneous growth" as they rely on estimating the free energy difference between neighboring <math>\lambda</math> values based only on instantaneous evaluations of potential energy differences between <math>\lambda</math> values (or by evaluation of and extrapolation of <math>dV/d\lambda</math> at a particular <math>\lambda</math> value).
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==Guideline 2: Use equilibrated data==
  
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Which method should be used for alchemical free energy calculations? At this point, we believe the evidence is in favor of equilibrium methods. Slow-growth methods have a number of well-documented problems, such as Hamiltonian lag and hysteresis (''add references''). Additionally, if a slow-growth calculation does not lead to a sufficiently precise free energy estimate, the only way to improve the free energy estimate is to repeat the calculation with a slower rate of change in lambda – the simulation cannot be extended to gain additional precision, meaning that significant data can be wasted. Fast-growth methods, while conceptually appealing, do not appear to offer substantial advantages over equilibrium methods.{{cite|Jarzynski2006}}{{cite|Oostenbrink2006}}
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For free energy methods to work, the simulations must sample from the correct Boltzmann distribution.  Make sure that your simulation is well-equilibrated, and that you are not in the transient region.  Simulations must separately be equilibrated at each lambda value.
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Equilibration at only <math>\lambda=0</math>, followed by sequential short simulations at other lambda values may not be sufficient.   For example in a study by Mobley et al.{{cite|Mobley2007c}} the protein remains kinetically trapped in its starting <math>\lambda=0</math> equilibrium conformational state in all calculations. If long equilibration is deemed necessary, equilibration periods should be equally long at each lambda value.
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==Guideline 3: Use sufficiently rigorous underlying simulation techniques==
  
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Sometimes simulation approximations that work for single end point simulations break down for free energy calculations. Make sure you are simulating.  Particularly watch out for simulation parameters that are a function of the intermediate state!  For example, a shifted potential will have a different shift constant at each intermediate.  If the ''same'' error is being made at each state, then it will partially cancel out of the free energy calculation. If a ''different'' error is made at each intermediate, it can drastically affect the answer.
  
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===Guideline 3: Use equilibrated data===
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[[Simulating_States_of_Interest#Choosing_Simulation_Parameters| Read more about properly setting simulation parameters]] in the context of free energy simulations.
  
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For free energy methods to work, the simulations must sample from the correct Boltzmann distribution.  Make sure that your simulation is well-equilibrated, and that you are not in the transient region.  Simulations must separately be equilibrated at each lambda value.  
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= Guidelines for analyzing simulations =
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See [[Draft Standards for Post-Calculation Health Reports]] for a discussion on the proposed standard practices for diagnosing typical simulation issues after an alchemical free energy calculation.
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== Guideline 1: Use [[Thermodynamic_Integration|TI]], [[Bennett_Acceptance_Ratio|BAR]], or [[Multistate_Bennett_Acceptance_Ratio|MBAR]] to calculate free energies ==
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These methods are by far the most robust.{{cite|Paliwal2011}}  BAR (the Bennett acceptance ratio) is almost as good as MBAR (the multistate Bennett's acceptance ration) in most standard situations. If a sufficiently large number of intermediate states are used, which is usually not significantly more than the number required for good BAR and MBAR estimates, TI (thermodynamic integration) gives good results.{{cite|Paliwal2011}} [[Analyzing_Simulation_Results|Read more here about simulation analysis]].
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== Guideline 2: Be careful and rigorous when calculating statistical uncertainties  ==
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Just taking the difference between two simulations is not the same thing as computing.  The easiest person to fool about the true statistical error in your calculation is yourself; you have more invested in a certain answer than your readers do! The "gold standard" is to run a sufficiently large number of copies and compute the standard error in the measurement. Sufficiently large is a bit subjective, but it's certainly more than two! However this can be inefficient. Many methods have built in error estimates, though these estimates usually assume uncorrelated data, so don't trust those errors unless you can show the data is uncorrelated.
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Read more about:
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* [[Simulation_Information_Gathering#Correlation| Calculating the degree of correlation in simulation data]].
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* [[Analyzing_Simulation_Results#Bootstrap_Sampling|Using bootstrap sampling to calculate uncertainties]].
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* [[Thermodynamic_Integration#Variance_of_TI|Some subtleties in propagating uncertainty in TI]].
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== Guideline 3: Correct for possible artifacts resulting from periodicity  ==
  
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Equilibration at only <math>\lambda=0</math>, followed by sequential short simulations at other lambda values may not be sufficient.   For example in a study by Mobley et al.{{cite|Mobley2007c}} the protein remains kinetically trapped in its starting <math>\lambda=0</math> equilibrium conformational state in all calculations. If long equilibration is deemed necessary, equilibration periods should be equally long at each lambda value.
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Most calculations will be performed using periodic boundary conditions. If you are calculating free energies that involve charged species, such as the binding of a charged ligand, the protonation of a protein side chain, or the solvation free energy of an ion, be aware that periodic boundary conditions can introduce large artifacts that can be corrected in the final data analysis. For more detail, see [[Charged binding calculations]].
  
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===Guideline 3: Monitor whether your free energy calculations are actually statistically converging===
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=Final Guideline: Verify, Verify, Verify! =
  
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# Trial and error: For a particular system, begin with very closely spaced lambda values, and then gradually increase separation until results begin to deteriorate.
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As you can tell there are a number of ways that the simulations could fail.  Try to determine as many ways as you can to make sure that the simulations are working correctly: for example:
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# Error analysis:
 
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## Using autocorrelation analysis{{cite|Chodera2006}}
 
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## If necessary, using block bootstrap approaches{{cite|Mobley2006}}
 
  
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===Guideline 2: Verify, Verify, Verify! ===
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* Do alternate thermodynamic cycles give the same result?
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* Are results sufficiently similar to previous results?
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* If the same calculation is repeated multiple times, is the difference between simulations consistent with the estimated uncertainties?
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* Are there any weird observations in the data as a results of errors in the scripts (like minuses being cut off?)
  
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<!--
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In view of these facts, our recommendation is to use equilibrium simulations at a set of separate <math>\lambda</math> values to estimate free energy differences. But how should these simulations be performed? Should a <math>\lambda=0</math> simulation be performed first, for example, and then the ending conformation used to seed a <math>\lambda=0.1</math> simulation? Or should they be independent? There is no reason in principle that simulations cannot be performed serially – but this leads to several potential pitfalls and disadvantages. First, if simulations are performed serially, and it is later concluded that some of the intermediate simulations were not long enough, the entire set of calculations performed after those intermediate simulations must be repeated. Second, results at one <math>\lambda</math> value become coupled to those at previous lambda values, potentially leading to hysteresis (''add references'') and difficulties in troubleshooting. Independent simulations, on the other hand, provide the great practical advantage that if, at the analysis stage, it appears that data is insufficient at some range of <math>\lambda</math> values, these particular simulations can simply be extended without requiring repetition of other component simulations. Additionally, with large computer clusters now becoming relatively common, independent simulations can often be performed in parallel, leading to significant real-time savings over running the simulations serially. -->
  
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In view of these facts, our recommendation is to use equilibrium simulations at a set of separate <math>\lambda</math> values to estimate free energy differences. But how should these simulations be performed? Should a <math>\lambda=0</math> simulation be performed first, for example, and then the ending conformation used to seed a <math>\lambda=0.1</math> simulation? Or should they be independent? There is no reason in principle that simulations cannot be performed serially – but this leads to several potential pitfalls and disadvantages. First, if simulations are performed serially, and it is later concluded that some of the intermediate simulations were not long enough, the entire set of calculations performed after those intermediate simulations must be repeated. Second, results at one <math>\lambda</math> value become coupled to those at previous lambda values, potentially leading to hysteresis (''add references'') and difficulties in troubleshooting. Independent simulations, on the other hand, provide the great practical advantage that if, at the analysis stage, it appears that data is insufficient at some range of <math>\lambda</math> values, these particular simulations can simply be extended without requiring repetition of other component simulations. Additionally, with large computer clusters now becoming relatively common, independent simulations can often be performed in parallel, leading to significant real-time savings over running the simulations serially.
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= Old materials =
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Our original version of this page is under [[Previous Best Practices]].
  
 
= References =
 
= References =
 
<references>
 
<references>
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{{Cite|Michel2010|Michel, J.; Essex, J. W. J Comp. Aided Mol. Des. 2010, 24:649.|http://www.citeulike.org/group/14929/article/9052428}}
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{{Cite|Christ2010|Christ, C. D.; Mark, A. E.; van Gunsteren, W. F. J. Comp. Chem. 2010, 31:1569|http://www.citeulike.org/group/14929/article/9052143}}
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{{Cite|Shirts2013|Shirts, M. R.; Mobley, D. L. Biomolecular Simulations, 2013, 24:271-311|http://www.citeulike.org/group/14929/article/12477253}}
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{{cite|Leach2001|Leach, A. Molecular Modelling: Principles and Applications (2nd Edition); Prentice Hall: 2 ed.; 2001.}}
 
{{cite|Leach2001|Leach, A. Molecular Modelling: Principles and Applications (2nd Edition); Prentice Hall: 2 ed.; 2001.}}
  
 
{{cite|Shirts2005|Shirts, M. R.;  Pande, V. S. J. Chem. Phys. 2005, 122, 134508.|http://www.citeulike.org/group/14929/article/9052562}}
 
{{cite|Shirts2005|Shirts, M. R.;  Pande, V. S. J. Chem. Phys. 2005, 122, 134508.|http://www.citeulike.org/group/14929/article/9052562}}
  
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{{cite|Mobley2007|Mobley, D. L.;  Dumont, Ã.;  Chodera, J. D.;  Dill, K. A. Journal of Physical Chemistry B 2007, 111.|http://www.citeulike.org/group/14929/article/9052441}}
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{{cite|Mobley2007|Mobley, D. L.;  Dumont, E.;  Chodera, J. D.;  Dill, K. A. Journal of Physical Chemistry B 2007, 111.|http://www.citeulike.org/group/14929/article/9052441}}
  
 
{{cite|Mobley2007b|Mobley, D. L.;  Graves, A. P.;  Chodera, J. D.;  McReynolds, A. C.;  Shoichet, B. K.;  Dill, K. A. Journal of Molecular Biology 2007, 371,.|http://www.citeulike.org/group/14929/article/9052443}}
 
{{cite|Mobley2007b|Mobley, D. L.;  Graves, A. P.;  Chodera, J. D.;  McReynolds, A. C.;  Shoichet, B. K.;  Dill, K. A. Journal of Molecular Biology 2007, 371,.|http://www.citeulike.org/group/14929/article/9052443}}
  
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{{cite|Jarzynski2006|Jarzynski, C. Phys. Rev. E 2006, 73, 046105.|http://www.citeulike.org/group/14929/article/9052297}}
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{{cite|Mobley2006|Mobley, D. L.;  Chodera, J. D.;  Dill, K. A. Journal of Chemical Physics 2006, 125, 084902.|http://www.citeulike.org/group/14929/article/9052440}}
  
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{{cite|Mobley2006|Mobley, D. L.;  Chodera, J. D.;  Dill, K. A. Journal of Chemical Physics 2006, 125, 084902.|http://www.citeulike.org/group/14929/article/9052440}}
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<!--{{cite|Chodera2006|Chodera, J. D.;  Swope, W. C.;  Pitera, J. W.;  Seok, C.;  Dill, K. A. Journal of Chemical Theory and Computation 2007, 3.|http://www.citeulike.org/group/14929/article/9052139}}-->
  
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{{cite|Chodera2006|Chodera, J. D.;  Swope, W. C.;  Pitera, J. W.;  Seok, C.;  Dill, K. A. Journal of Chemical Theory and Computation 2007, 3.|http://www.citeulike.org/group/14929/article/9052139}}
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<!--{{cite|Wu2005|Wu, D.;  Kofke, D. A. Journal of Chemical Physics 2005, 123, 054103.|http://www.citeulike.org/group/14929/article/9052669}}-->
  
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{{cite|Wu2005|Wu, D.;  Kofke, D. A. Journal of Chemical Physics 2005, 123, 054103.|http://www.citeulike.org/group/14929/article/9052669}}
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{{cite|Jarzynski2006|Jarzynski, C. Phys. Rev. E 2006, 73, 046105.|http://www.citeulike.org/group/14929/article/9052297}}
  
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{{cite|Jarzynski1997|Jarzynski, C. Physical Review Letters 1997, 78.|http://www.citeulike.org/group/14929/article/9052290}}
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Latest revision as of 10:41, 2 July 2014

This is intended as an overview of best practices for free energy calculations, with references, when possible.

This document assumes you already have a basic idea of what these calculations are and what they do (if not, you can learn more from the Free Energy Fundamentals section and introductory-level reviews[1], [2], [3]), and that you already have a working knowledge of molecular simulation, convergence and equilibration. If not, start with a textbook like Leach's Molecular Modelling: Principles and Applications[4]). The discussion is simulation-package agnostic, focusing on the important ideas.

This document is open for editing. We hope this to be a place to build and store the community consensus on these issues. Do back up any edits with appropriate references. If you are not sure about changes, post questions on the discussion pages.

Introduction

Free energies calculated from molecular simulation are appealing, because, in principle, they are rigorously correct, given a particular set of parameters and physical assumptions. Given this particular set of parameters and physical assumptionstarting point, there exists a single right answer for a free energy calculation. The goal is to obtain that free energy with as little statistical error as possible -- and not necessarily to match experiment. Without convergence, agreement with experiment may be due only to fortuitous cancellation of error. Only after free energy estimates have converged can the underlying parameters and physical assumptions really be tested.

Free energy is a function of state, so there are many possible choices of pathway for a thermodynamic cycle connecting the same two endpoints. Some pathways may be more efficient than others (sometimes by many orders of magnitude). Methods which are in principle correct may not always be practical or useful. A large part of the following discussion will discuss exactly how to construct useful and efficient pathways connecting the end states.

While free energy calculations may be done for many different situations, here we focus on best practices for binding and solvation free energy calculations. Solvation free energies provide a basic starting point for considering a number of the key issues, and are used frequently here because of this and because of their simplicity. Often, additional complications make it harder to track down problems when considering binding free energy calculations.

Guidelines for setting up your system files

Guideline 1: Automate when possible

If you manually set up your systems for simulation, we would urge you not to. It is extremely easy to make a mistake that affects the entire subsequent set of simulations. Additionally, if your mistake is a result of mistyping something or similar, you may end up with no record of what you did wrong. Instead, when possible, automate simulation preparation tasks so that they do not need to be done manually. Periodically validate the results of automated scripts, especially when you change them. Not only can scripts help mistakes, but when they do have bugs, the script itself provides a permanent record of exactly what you did. Version control your scripts (using SVN or Git) so you can go back check this record more easily.

Guidelines for free energy calculation pathways

Alchemical free energies almost involve some insertion or deletion of atoms -- both in hydration free energy calculations, and in absolute and relative binding free energy calculations. By insertion and deletion, we mean decoupling or annihilation (see Decoupling and annihilation for definitions) of the interactions of the atoms in question. We believe that a basic list of rules and guidelines should be followed in any calculation that involves insertion or deletion:

Guideline 1: Always use soft-core potentials while decoupling or annihilating Lennard-Jones interactions

When removing atoms from a system, the atoms should be removed gradually. Because of how potentials are defined, what gradual removal means can be somewhat subtle. The soft-core potential formalism is a well-tested way to do this. Read more about how to choose the potential to decouple or annihilate Lennard-Jones sites.

Guideline 2: Never leave a partial atomic charge on an atom while its Lennard-Jones interactions are being removed

Partial atomic charge should never be allowed to remain on an atom while its Lennard-Jones interactions are being removed, because it can result in extremely strong electrostatic interactions in the absence of steric interactions. Read more about how to handle changes to both charges and Lennard-Jones sites.

Guideline 3: Use few insertions/deletions

Electrostatics transformations are usually smooth functions of lambda, and require relatively few lambda values, while Lennard-Jones transformations – especially those involving insertion and deletions – can require substantially more lambda values (even when using soft core potentials) to obtain good phase-space overlap and therefore accurate free energy differences.[5][6][7] Insertions and deletions of particles can be thought of as “difficult” transformations.[8]

Consequently, it is far more computationally efficient to modify existing particles (atoms) than to insert or delete new atoms. Construct mutation pathways for relative free energy calculations that minimize the number of times atoms, especially large atoms, need to be inserted and deletet, since multiple choices of mutation pathways between a set of molecules are typically possible.

This guideline is unfortunately not useful for absolute free energy calculations, since these involve inserting or deleting entire molecules.

Guideline 4: Think about whether your intermediate states are likely to converge or not

Many choices of pathway are possible. It can often be helpful to think about whether a particular choice of pathway makes convergence easier or more difficult at each lambda, thus reducing the amount of simulation time required.

Visualize what happens to a ligand when it is partially noninteracting with the protein, especially as interactions become quite weak during an absolute binding free energy simulation. It will have significantly more freedom to move around the pocket than when fully interacting- but the kinetics may still be slow, since it will get trapped in free energy minima. It then may take quite a while to sample these states.

One can introduce distance or orientation restraints between the ligand and the protein. These reduce the space the ligand must explore, thus speeding convergence, at the expense of an additional restraining calculation. In the noninteracting state, the interaction potential can often be removed analytically. In the interacting state, the free energy of applying these restraints needs to be included. The free energy of applying restraints can be non-trivial, since the ligand will be pulled away from alternative minima, but convergence issues at other intermediates will be greatly reduced.

At the fully noninteracting state, the amount of configurational sampling the ligand undergoes is dictated by this choice. A ligand with a single reference distance restrained relative to the protein will need to sample a spherical shell in configuration space, and a ligand with all six relative degrees of freedom restrained would need to sample only a very small region of configuration space. These two can take drastically different amounts of time, so in fact it can be much more efficient, at least in some cases, to use the additional restraints.[9]

Guidelines for carrying out free energy calculations

Guideline 1: Equilibrium methods are easier to use for beginners than nonequilibrium or mixed-ensemble methods

There are a very large number of basic methods for performing free energy calculation: Slow-growth, fast-growth (Jarzynski-style), and equilibrium (or instantaneous growth) free energy methods. For advanced users, there are significant efficiency advantages with careful use of these methods.

However, for beginners, and for standard simulations, we believe the evidence is in favor of equilibrium methods: Run simulations at fixed values of lambda, collect equilibrium data from these simulations, and calculate the free energies from these observations. There are far fewer things that can go wrong. If it's a problem that really requires significantly more efficiency, it's probably worth doing a warmup problem for practice first!

Read more about running equilibrium methods at the states of interest.

If you are interested it digging deeper, read about some more advanced sampling methods that use exchanges between equilibrated sites to improve efficiency.

Guideline 2: Use equilibrated data

For free energy methods to work, the simulations must sample from the correct Boltzmann distribution. Make sure that your simulation is well-equilibrated, and that you are not in the transient region. Simulations must separately be equilibrated at each lambda value.

Equilibration at only [math]\displaystyle{ \lambda=0 }[/math], followed by sequential short simulations at other lambda values may not be sufficient. For example in a study by Mobley et al.[10] the protein remains kinetically trapped in its starting [math]\displaystyle{ \lambda=0 }[/math] equilibrium conformational state in all calculations. If long equilibration is deemed necessary, equilibration periods should be equally long at each lambda value.

Guideline 3: Use sufficiently rigorous underlying simulation techniques

Sometimes simulation approximations that work for single end point simulations break down for free energy calculations. Make sure you are simulating. Particularly watch out for simulation parameters that are a function of the intermediate state! For example, a shifted potential will have a different shift constant at each intermediate. If the same error is being made at each state, then it will partially cancel out of the free energy calculation. If a different error is made at each intermediate, it can drastically affect the answer.

Read more about properly setting simulation parameters in the context of free energy simulations.

Guidelines for analyzing simulations

See Draft Standards for Post-Calculation Health Reports for a discussion on the proposed standard practices for diagnosing typical simulation issues after an alchemical free energy calculation.

Guideline 1: Use TI, BAR, or MBAR to calculate free energies

These methods are by far the most robust.[11] BAR (the Bennett acceptance ratio) is almost as good as MBAR (the multistate Bennett's acceptance ration) in most standard situations. If a sufficiently large number of intermediate states are used, which is usually not significantly more than the number required for good BAR and MBAR estimates, TI (thermodynamic integration) gives good results.[11] Read more here about simulation analysis.

Guideline 2: Be careful and rigorous when calculating statistical uncertainties

Just taking the difference between two simulations is not the same thing as computing. The easiest person to fool about the true statistical error in your calculation is yourself; you have more invested in a certain answer than your readers do! The "gold standard" is to run a sufficiently large number of copies and compute the standard error in the measurement. Sufficiently large is a bit subjective, but it's certainly more than two! However this can be inefficient. Many methods have built in error estimates, though these estimates usually assume uncorrelated data, so don't trust those errors unless you can show the data is uncorrelated.

Read more about:

Guideline 3: Correct for possible artifacts resulting from periodicity

Most calculations will be performed using periodic boundary conditions. If you are calculating free energies that involve charged species, such as the binding of a charged ligand, the protonation of a protein side chain, or the solvation free energy of an ion, be aware that periodic boundary conditions can introduce large artifacts that can be corrected in the final data analysis. For more detail, see Charged binding calculations.

Final Guideline: Verify, Verify, Verify!

As you can tell there are a number of ways that the simulations could fail. Try to determine as many ways as you can to make sure that the simulations are working correctly: for example:

  • Do alternate thermodynamic cycles give the same result?
  • Are results sufficiently similar to previous results?
  • If the same calculation is repeated multiple times, is the difference between simulations consistent with the estimated uncertainties?
  • Are there any weird observations in the data as a results of errors in the scripts (like minuses being cut off?)


Old materials

Our original version of this page is under Previous Best Practices.

References

  1. ↑ Michel, J.; Essex, J. W. J Comp. Aided Mol. Des. 2010, 24:649. - Find at Cite-U-Like
  2. ↑ Christ, C. D.; Mark, A. E.; van Gunsteren, W. F. J. Comp. Chem. 2010, 31:1569 - Find at Cite-U-Like
  3. ↑ Shirts, M. R.; Mobley, D. L. Biomolecular Simulations, 2013, 24:271-311 - Find at Cite-U-Like
  4. ↑ Leach, A. Molecular Modelling: Principles and Applications (2nd Edition); Prentice Hall: 2 ed.; 2001.
  5. ↑ Shirts, M. R.; Pande, V. S. J. Chem. Phys. 2005, 122, 134508. - Find at Cite-U-Like
  6. ↑ Mobley, D. L.; Dumont, E.; Chodera, J. D.; Dill, K. A. Journal of Physical Chemistry B 2007, 111. - Find at Cite-U-Like
  7. ↑ Mobley, D. L.; Graves, A. P.; Chodera, J. D.; McReynolds, A. C.; Shoichet, B. K.; Dill, K. A. Journal of Molecular Biology 2007, 371,. - Find at Cite-U-Like
  8. ↑ Jarzynski, C. Phys. Rev. E 2006, 73, 046105. - Find at Cite-U-Like
  9. ↑ Mobley, D. L.; Chodera, J. D.; Dill, K. A. Journal of Chemical Physics 2006, 125, 084902. - Find at Cite-U-Like
  10. ↑ Mobley, D. L.; Chodera, J. D.; Dill, K. A. Journal of Chemical Theory and Computation 2007, 3. - Find at Cite-U-Like
  11. ↑ 11.0 11.1 Paliwal, H.; Shirts M.R.; Journal of Chemical Theory and Computation 2011, 7, 4115-4134, - Find at Cite-U-Like
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