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Molecular Mechanics

By far, the most popular application of the empirical potential energy function is to find the geometry of a molecule (or an assemblage of molecules) which corresponds to a minimum potential energy. The process of finding a minimum of an empirical potential energy function is called frequently Molecular Mechanics (MM). This process produces a motionless molecule of idealized geometry. In reality, the molecules undergo thermal motions and constantly change their geometry. Molecular Dynamics, (MD), and Monte Carlo, (MC) are based on principles of statistical mechanics and allow calculation of thermodynamic properties of molecular systems by simulating these thermal fluctuations of geometry and corresponding energies. These methods will be described briefly later in this chapter. For a review of molecular mechanics consult Burkert & Allinger, (1982).

The potential energy function refers to a ground electronic state of the molecule and does not explicitly incorporate electronic structure. Hence, it cannot be used to study electronic spectra or covalent bond formation/breaking. These phenomena are studied with quantum methods. There are methods which combine a quantum description for the small portion of the molecule (e.g., groups of substrate and enzyme interacting in the active center) with a molecular mechanics description for the rest of the molecule (see e.g., Field et al., 1990).

The minimization of the potential energy function (i.e., geometry optimization) involves a search for the minimum of a function, and, to be efficient, requires calculations of derivatives of a function (in this case, the potential energy) versus independent variables (in our case, coordinates). Most programs use cartesian coordinates as independent variables, however, in some cases, internal coordinates may be used. The derivatives of potential energy are denoted as:


where tex2html_wrap_inline2478 is the gradient (i.e., first derivative) of the potential energy tex2html_wrap_inline2308 with respect to a cartesian coordinate tex2html_wrap_inline2482 of an atomgif, and tex2html_wrap_inline2488 is the second derivative of the energy with respect to the cartesian coordinates. In most modern programs these derivatives are calculated analytically, i.e., the appropriate mathematical formulae for corresponding terms are incorporated into the program. Some older codes compute derivatives numerically by approximating the slope of an energy function (or its gradient in the case of second derivatives) from finite differences. The table of all possible second derivatives versus cartesian coordinates of atoms has 3N rows and 3N columns and is called a Hessian matrix. The derivatives are used not only in function minimization but also yield forces acting on atoms (from energy gradients) and normal modes of vibration (from the Hessian matrix). There are three major approaches to finding a minimum of a function of many variables:

In general, the minimization methods are iterative. They require on input some initial estimate for the position of the minimum, and provide a better estimate for the minimum as a result. This corrected estimate is used as an input into the next cycle (i.e., iteration) and the process is continued until there is no significant improvement in the position of the minimum.

Figure 6.23: Local and global minima for a function of one variable and an example of a sequence of solutions: tex2html_wrap_inline1894 for a descent series minimization algorithm.

Most search methods and minimization methods using derivatives are the descent series methods, i.e., each iteration results in a solution which corresponds to a lower (or equal) value for the energy function:


As a consequence, these methods can only find the minimum closest to the starting estimate and will never cross to a minimum (however deep) if it is separated from the starting estimate by a maximum (however small). This situation is schematically illustrated in Fig. 6.23 where arrows indicate the direction of geometry optimization depending upon the starting point. Fig. 6.23 is only a cartoon to illustrate the behavior of descent series minimization methods. Geometry optimization of real molecular systems (e.g. a protein with a drug molecule bound) involves simultaneous optimization of 3N cartesian coordinates, i.e, sometimes many thousands of variables. There is no general way of finding a global minimum (i.e., the minimum corresponding to the lowest possible value of the function). A different initial geometry will usually lead to a different final minimum. Only on very simple molecules will the single geometry optimization yield the global minimum on the first trial. It cannot be overemphasized that the result of a single minimization is usually a local minimum, not a global one. To find a global minimum (or at least, to be more confident about it) you have to perform many minimizations and use different initial coordinates for each run. There is another practical difficulty with contemporary molecular mechanics programs. Most of them optimize the geometry in atomic cartesian coordinates. To illustrate the problem let us join Alice on her journey to the other side of the mirror. The appearance of the potential energy function in cartesian space is dramatically different from the one given by internal coordinates. If we could see a cartesian representation of a potential energy surface in, let us say, fifty dimensionsgif, the picture would look like the cortex of a human brain -- lots of narrow tortuous valleys of similar depth. This is because low energy paths for individual atoms are very narrow due to the presence of hard bond stretching and angle bending terms. The low energy paths correspond only to the rotation of groups or large portions of the molecule as a result of varying torsional angles. In other words, the low energy paths for atoms are strongly correlated in cartesian space and atoms can move easily only along narrow grooves. On the other hand, the outlook of the potential energy surface is very different in the space of internal coordinates. The surface looks like a valley surrounded by high mountains. The high peaks correspond to stretching and bending terms, and close van der Waals contacts, while the bottom of the valley represents the torsional degrees of freedom. If you happen to start at the mountain tops in the internal coordinate space, the minimizer sees the bottom of the valley clearly from above. If you are in the valley, you also see where the mountains are. In cartesian space, the minimizer walks along the bottom of a narrow winding channel which is frequently a dead-end.

In more scientific terms, all contemporary function minimization procedures operate efficienty for functions close to quadratic (i.e., for two dimensions a parabola, for three dimensions a boat like shape, for n dimensions: tex2html_wrap_inline2502 ) and are inefficient for functions of other shapes. Assuming that bond lengths and valence angles are already optimized and the goal of minimization is to find the minimum corresponding to optimal values of torsional angles, cartesian space is probably the worst possible. Look at equations 6.16, 6.27 and 6.9, which relate the torsional energy to cartesian coordinates. The dependance of the torsional energy on cartesian coordinates is as far from a quadratic shape as it can be! Moreover, it has several minima. There are also additional advantages to using minimization in internal coordinate space. In this space there is a clear separation of variables into the hard ones (i.e., those whose small changes produce large changes in the function value) and soft ones (i.e., those whose changes do not affect the function value substantially). During function optimization in internal coordinates, the minimizer first optimizes the hard variables and in the subsequent iterations ``cleans up'' the details by optimizing the soft variables. In cartesian coordinate space all variables are of the same type, i.e., hard or soft depending on preference. Changing a cartesian coordinate of an atom results in simultaneous change of bond lengths, valence angles and torsonal angles associated with this atom, and distances to non-bonded atoms. For this reason, the minimizer is only involved with details, and therefore in the last stages of the optimization all variables are as important as at the beginning. The reason why most programs optimize geometry in cartesian space is that it is not easy to derive and manage expressions for derivatives of potential energy function in internal coordinates. Note that the derivative of a van der Waals energy term between two atoms versus the cartesian coordinates of some third atom is zero. This derivative depends only on the cartesian coordinates of the two atoms involved. The situation is dramatically different in internal coordinate space. Since the distance between two atoms depends on all bond lengths, bond angles and torsional angles on the path between two atoms, the derivative of the van der Waals term for the two atoms will depend on all these variables. There are matrix methods of calculating derivatives of distances and angles as contributions from individual internal coordinates, and the final derivative is calculated from the chain rule using these parameters. But as you can see, the problem is far from trivial. To avoid evaluation of derivatives, some programs use search techniques like SIMPLEX when optimization is requested in internal coordinate space. Since SIMPLEX is inefficient for large numbers of variables, usually only torsional angles are optimized by this technique. If the number of variables is too large, then a block technique is used where only a portion of all independent variables is optimized at a given stage. Another reason why internal coordinates are not used routinely for geometry optimization is that modern molecular mechanics programs are usually a portion of a molecular mechanics/dynamics package. As will be shown later in this chapter, molecular dynamics in coordinates other than cartesian is a difficult problem, since the terms containing generalized momenta and generalized coordinates in the Hamiltonian cannot be separated. Molecular mechanics and dynamics share large portions of the actual code, and hence there is a preference by software authors to use the space which is common to both methods.

The potential energy of the molecule calculated from a well balanced force field represents a strain in the molecule. Augmented with bond/group equivalents and statistical mechanical corrections, it can be used to estimate the heat of formation of a compound (i.e., its molar enthalpy of formation, tex2html_wrap_inline2504 ). The quantity tex2html_wrap_inline2504 can be used to compare the relative stability of different compounds. In most cases however, you should assume that the calculated potential energy incorporates some arbitrary component which depends upon the types of atoms and covalent bonds in the molecule, i.e., comparison of the energies calculated for molecules with different numbers and/or types of atoms and bonds cannot be rigorous. For this reason, potential energy will in most cases reliably assess the difference in energy between conformers, but will fail if you attempt to calculate the energetics of incorporating a new functional group into the molecule. Molecular mechanics can also provide the interaction energy, tex2html_wrap_inline2508 , of two molecules A and B as:


where tex2html_wrap_inline2514 , tex2html_wrap_inline2516 , and tex2html_wrap_inline2518 are potential energies of the optimized complex, the optimized molecule A, and the optimized molecule B; respectively. Note that the type and number of atoms and covalent bonds in the complex AB is equal to their sum in isolated molecules A and B, and the arbitrary ``energy zero'' should cancel out in this case. For this reason, the difference between interaction energies calculated for different complexes, (i.e., tex2html_wrap_inline2530 ) is the preferred method over direct comparison of the energies of different complexes (i.e., tex2html_wrap_inline2532 ).

Potential energy functions can also be used to estimate contributions from intramolecular vibrations to the vibrational free energy and vibrational entropy. These quantities, and contributions from translation and rotation of the molecule as a whole, vary with temperature and are main contributors to the thermodynamic functions such as enthalpy, free energy, specific heat, etc., (see, e.g., Hill, 1960). A well known approach is to use the frequencies, tex2html_wrap_inline2534 , corresponding to normal modes within harmonic approximation (i.e, to derive them from a mass scaled Hessian matrix at energy minimum). The expressions for relating classical vibrational contributions to Helmholtz free energy, tex2html_wrap_inline2536 , internal energy, tex2html_wrap_inline2538 , heat capacity at constant volume tex2html_wrap_inline2540 , and entropy tex2html_wrap_inline2542 of the nonlinear molecule, are derived in many standard textbooks for statistical mechanics (see e.g., McQuarrie, 1976):


where R, T, and h are the gas constant, the absolute temperature, and Planck's constant respectively; and N denotes the number of atoms in the molecule. Frequently, these values are augmented with a correction to account for vibrations at 0 tex2html_wrap_inline1876 K, which is of quantum origin, by adding a zero-point energy (ZPE) to the free energy tex2html_wrap_inline2536 and the internal energy tex2html_wrap_inline2538 :


The harmonic approximation is quite accurate for isolated molecules. However, for complexes of two or more molecules or systems containing the solvent, the harmonic approximation breaks down, because the motion of the individual molecules with respect to each other is diffusional, i.e., not constrained by a skeleton of rigid covalent bonds. The potential of this motion is different than the simple parabola of a harmonic oscillator and contains multiple minima and maxima. In this case, molecular dynamics or Monte Carlo approaches are more reliable for estimation of thermodynamic functions.

next up previous
Next: Molecular Dynamics and Monte Up: Molecular Modeling Previous: Empirical Potential Energy Function

Computational Chemistry
Wed Dec 4 17:47:07 EST 1996