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This module introduces the concept of free energy and potential fields in the context of protein conformation spaces and motion planning. It then provides examples of applications of motion planning techniques to problems from structural computational biology.

    Topics in this module

  • Free Energy and Potential Functions
    • Free Energy
    • Potential Functions
  • Applications of Protein Motion Planners
    • Kinetics of Protein Folding
    • Protein-Ligand Docking Pathways and Kinetics

As we suggested in Robotic Motion Planning and Protein Motion , the main difference between modeling a macroscopic robot arm and a protein chain is that the protein is subject to forces resulting from differences in free energy between its states. The protein's conformation space does not consist only of colliding and non-colliding structures, but of structures on a continuum of free energy values. In this module, we will provide a very brief overview of free energy as it relates to protein structures, and then give some examples of how path planning techniques have been applied to solving problems in structural biology.

Free energy and potential functions

Free energy

In other modules, we have introduced the concept of a native conformation for any given protein, that is, the conformation of the protein that is observed, or expected to be observed, under physiological conditions of temperature, pH, and ion balance. What distinguishes this structure from other structures is that it has the minimum free energy of all accessible conformations. There are several different definitions of free energy depending on how the system is defined (for example, whether it is allowed to change in temperature, volume, and/or pressure). One common definition, applicable when temperature and volume are constant, is the Helmholtz Free Energy:

Helmholtz free energy
The quantity U is the internal energy of the system, both kinetic and potential, although for our purposes, we will usually think of changes in U as resulting from changes in potential energy. T is the absolute temperature of the system, and S is the entropy of the system, which is very difficult to predict computationally. Entropy is a measure of the number of accessible states to a molecule in a given state, and corresponds to a notion of disorder. In general, the probability of observing a particular state of a system (such as a protein in solution) increases exponentially as the free energy decreases , in accordance with the Boltzmann distribution:
Boltzmann-like distribution.
E is a particular free energy, kB is the Boltmann constant, and T is the absolute temperature.

In practice, because entropy is very difficult to approximate computationally, potential energy is often used instead of free energy in molecular simulations and docking procedures. When the process is driven by potential energy, this is a reasonable approximation. Some processes are entropically driven, and results are usually poor when trying to model these processes using only potential energy.

Potential functions

Potential functions are functions used to evaluate the feasibility of a particular structure of a molecule. Ideally, this would be done with quantum mechanics, in which case the energy function could report the true energy of a particular structure. In practice, quantum mechanical analysis of molecules the size of proteins is wildly intractable. As a compromise, biophysicists have developed artificial functions based on classical physics to approximate the true energy of molecular systems. Sometimes called potential functions or molecular force fields , these functions generally accept as input a molecular conformation, in the form of Cartesian coordinates for all atoms, and output an energy value. These energy values are generally only meaningful in relative terms: They provide information on what conformations of the molecule are more or less probable than others. The lower the energy value, then the more likely the conformation is to be observed. Most molecular potential functions have the form:

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Source:  OpenStax, Geometric methods in structural computational biology. OpenStax CNX. Jun 11, 2007 Download for free at http://cnx.org/content/col10344/1.6
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