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Presented by Mr. Pradeep V. Kore M. Pharm. ( II Sem ) Guided by Mr. M. Pharm. Department of Pharmaceutical Chemistry JSPM’s Charak College of Pharmacy & Research, Gat No. 720(1/2), Wagholi, Pune-Nagar road, Wagholi-412 207. 1 1 CONTENTS: 5/9/2012 2 INTRODUCTION In computational chemistry energy minimization (also called energy optimization or geometry optimization) methods are used to compute the equilibrium configuration of molecules and solids. Energy minimizatiom methods can precisely locate minimum energy confirmation by mathematically “homing in” on the energy function minima (one at a time). The goal of energy minimization is to find a route (consisting of variation of the intramolecular degrees of freedom) from an initial confirmation to nearest minimum energy confirmation using the smallest number of calculations possible. 5/9/2012 3 In molecular modeling we are interested in minimum points on the energy surface. Minimum energy arrangments of the atoms corresponds to stable states of the system: Any movement away from a minimum gives a configuration with higher energy. There may be a very large number of minima on energy surface. The minimum with very lowest energy is known as the Global Energy Minimum. 5/9/2012 4 Fig: One dimentional energy surface 5/9/2012 5 What can energy minimization do? It can repair distorted geometries by moving atoms to release internal constraints, as shown below: In this example, the CZ of a phenylanalnine ring was artificially stretched out, which lead to bonds much too long. Fig: Phenylanalnine 5/9/2012 6 By first invoking an energy computation, the C-terminal Oxygen (OXT) is added to the residue. Note that the residue must also be protonated, and in this case an N-terminal blocking group (HHT) is added. Then the energy computation can be done: The direction in which atoms should be displaced in order to reach a lowe energy state are shown by dotted lines. A minimal deplacement appears in dark blue, while a big deplacement appear in red (blue-green-red gradient). 5/9/2012 7 After an energy minimization (200 cycles of Steepest Descent), the geometry is repaired, and all the force vectors are dark blue, which means a minimum has been reached. Fig: Repaired geometry structure 5/9/2012 8 Energy minimising procedures 1) Conformational energy searching 2) Energy minimisation 3) Minimisation algorithms 5/9/2012 9 Conformational energy searching Energy is a function of the degrees of freedom in a molecule: bonds, angles, dihedrals. Conformational energy searching is used to find all of the energetically preferred conformations of a molecule. This is mathematically equivalent to locating all of the minima of the energy function of the molecule. The possible conformations for a molecule lie on an n-dim. Lattice, with n being the number of degrees of freedom. 5/9/2012 10 Systematic energy sampling is thus technically impossible for almost all molecules in question, due to the high large number of required sampling points. Need for methods to speed up energy minima localisation. 5/9/2012 11 Energy minimization: 5/9/2012 12 Minimisation algorithms are designed to head down-hill towards the nearest minimum. Remote minima are not detected, because this would require some period of up-hill movement. Minimisation algorithms monitor the energy surface along a series of incremental steps to determine a down-hill direction. 5/9/2012 13 •The local shape of the energy surface around a given conformation en route to a minimum is often assumed to be quadratic so as to simplify the mathematics. •An energy minimum can be characterised by a small change in energy between steps and/or by a zero gradient of the energy function 5/9/2012 14 Approximation of the quadratic energy function Approximation of the quadratic energy function is given by aTaylor series: f(x)= f(P) – bx + 1/2Ax2 P- is the current point x -an arbitrary point on the energy surface b- is the gradient at P, A- is the Hessian matrix (the second partial derivatives) at P. A and b can be viewed as parameters that fit the idealised quadratic form to the actual energy surface. 5/9/2012 15 Minimisation algorithms Simplex algorithm - Not a gradient minimization method. - Used mainly for very crude, high energy starting structures. Steepest descent minimiser - Follows the gradient of the energy function (b) at each step. This results in successive steps that are always mutually perpendicular, which can lead to backtracking. - Works best when the gradient is large (far from a minimum). - Tends to have poor convergence because the gradient becomes smaller as a minimum is approached. 5/9/2012 16 Conjugate gradient and Powell minimiser Remembers the gradients calculated from previous steps to help reduce backtracking. Generally finds a minimum in fewer steps than Steepest Descent. May encounter problems when the initial conformation is far from a minimum. 5/9/2012 17 Newton-Raphson and BFGS minimiser - Predicts the location of a minimum, and heads in that direction. - Calculates (Newton-Raphson) or approximates (BFGS) the second derivatives in A. - Storage of the A term can require substantial amounts of computer memory - May find a minimum in fewer steps than the gradient-only methods. - May encounter serious problems when the initial conformation is far from a minimum. 5/9/2012 18 Types of minima: f(x) strong local minimum weak local minimum strong global minimum strong local minimum feasible region x which of the minima is found depends on the starting point such minima often occur in real applications 5/9/2012 19