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# minimiz (c44b2)

Energy Manipulations: Minimization and Dynamics

One can minimize the energy by adjusting the coordinates

of all the atoms in order to reduce its value. Several minimization

algorithms are provided. They include:

Steepest Descent (SD)

Conjugate Gradient (CONJ)

Adopted Basis Newton-Raphson (ABNR)

Newton-Raphson (NRAP)

Powell (POWE)

Truncated Newton Method (TNPACK)

* Syntax | Syntax of the energy manipulation commands

and a table of keywords

* Description | Description of the various keyword functions

* Discussion | Discussion of the various methods

One can minimize the energy by adjusting the coordinates

of all the atoms in order to reduce its value. Several minimization

algorithms are provided. They include:

Steepest Descent (SD)

Conjugate Gradient (CONJ)

Adopted Basis Newton-Raphson (ABNR)

Newton-Raphson (NRAP)

Powell (POWE)

Truncated Newton Method (TNPACK)

* Syntax | Syntax of the energy manipulation commands

and a table of keywords

* Description | Description of the various keyword functions

* Discussion | Discussion of the various methods

Top

Syntax for Energy Manipulation Commands

[SYNTAX MINImize]

MINI { SD steepd-spec } [ nonbond-spec ] [ hbond-spec ] -

{ CONJ conj-spec } [ INBFrq 0 ] [ IHBFrq 0 ] [NOUPdate]

{ ABNR abnr-spec }

{ NRAP nrap-spec }

{ POWEll powell_spec }

{ TN tnpack-spec }

[STEP real] [GRADient] [NUMErical]

[ frequency-spec ] [ tolerance-spec ] [ io-spec ] }

[ CHEQ [CGMD int] [CGIN] [CGFC] [PBEQ] [QPOL [IPOL int] ] ]

hbond-spec::=

nonbond-spec::=

frequency-spec::= [NSTEP int] [IHBFrq int] [INBFrq int] [NPRInt int]

tolerance-spec::= [TOLENR real] [TOLGRD real] [TOLITR int] [TOLSTP real]

io-spec::= [DEBUG] [IUNCrd int [NSAVC int] ] [IUNXyz [NSAVX int] [MXYZ int] ]

conj-spec::= [NCGCyc int] [PCUT real] [PRTMin int]

[LATTice] [NOCOordinates]

powell-spec::= [LATTice] [NOCOordinates]

steepd-spce::= [NOENergy ] [LATTice] [NOCOordinates]

abnr-spec::= [EIGRng real] [MINDim int] [STPLim real] -

[STRIct real] [ MASS ] [PSTRct real]

[LATTice] [NOCOordinates] [FMEM real]

nrap-spec::= [TFREq real]

tnpack-spec::= [NCGCyc int]

[PREC or NOPR] [USER or OURH] [REST or QUAT] [NOSC or SCHE]

[DEFS or SEAR] [LOWP or HIGP] [IORD or NOOR] [PERM or NOPM]

Syntax for Energy Manipulation Commands

[SYNTAX MINImize]

MINI { SD steepd-spec } [ nonbond-spec ] [ hbond-spec ] -

{ CONJ conj-spec } [ INBFrq 0 ] [ IHBFrq 0 ] [NOUPdate]

{ ABNR abnr-spec }

{ NRAP nrap-spec }

{ POWEll powell_spec }

{ TN tnpack-spec }

[STEP real] [GRADient] [NUMErical]

[ frequency-spec ] [ tolerance-spec ] [ io-spec ] }

[ CHEQ [CGMD int] [CGIN] [CGFC] [PBEQ] [QPOL [IPOL int] ] ]

hbond-spec::=

**»**hbondsnonbond-spec::=

**»**nbondsfrequency-spec::= [NSTEP int] [IHBFrq int] [INBFrq int] [NPRInt int]

tolerance-spec::= [TOLENR real] [TOLGRD real] [TOLITR int] [TOLSTP real]

io-spec::= [DEBUG] [IUNCrd int [NSAVC int] ] [IUNXyz [NSAVX int] [MXYZ int] ]

conj-spec::= [NCGCyc int] [PCUT real] [PRTMin int]

[LATTice] [NOCOordinates]

powell-spec::= [LATTice] [NOCOordinates]

steepd-spce::= [NOENergy ] [LATTice] [NOCOordinates]

abnr-spec::= [EIGRng real] [MINDim int] [STPLim real] -

[STRIct real] [ MASS ] [PSTRct real]

[LATTice] [NOCOordinates] [FMEM real]

nrap-spec::= [TFREq real]

tnpack-spec::= [NCGCyc int]

[PREC or NOPR] [USER or OURH] [REST or QUAT] [NOSC or SCHE]

[DEFS or SEAR] [LOWP or HIGP] [IORD or NOOR] [PERM or NOPM]

Top

Options common to minimization and dynamics

The following table describes the keywords which apply to all

minimization methods.

Keyword Default Purpose

------- ------- --------------------------------------------------------

NSTEP 100 The number of steps to be taken. This

is the number of cycles of minimization, not the number

of energy evaluations.

INBFRQ 50 The frequency of regenerating the non-bonded list.

The list is regenerated if the current step number

modulo INBFRQ is zero and if INBFRQ is non-zero.

Specifying zero prevents the non-bonded list from being

regenerated at all.

IHBFRQ 50 The frequency of regenerating the hydrogen bond list.

Analogous to INBFRQ

non-bond- The specifications for generating the non-bonded list.

-spec See

hbond- The specifications for generating the hydrogen bond list.

-spec See

NPRINT 1 The frequency with which energies are printed during

the course of dynamics or minimization.

GRADient Minimize the magnitude of the gradient of the energy

instead of the energy.

NUMErical Forces will be determined by finite differences

IUNCrd -1 Unit to write out a "trajectory" file for the minimization

NSAVC 1 Frequency for writing out frames (only with IUNCrd)

DEBUg Extra print for debug purposes

In the table which follows, keywords enclosed in square brackets

means that one can choose one in the set. Such enclosed keywords do not

expect a value after them. All other keywords are used for specifying

values, syntax::. The method column shows which method the

keyword affects.

Keyword Default Method Purpose

-------- -------- ------- -------------------------------------------------

[ CONJ ] CONJ Do conjugate gradient minimization.

[ SD ] Do steepest descent minimization.

[ NRAP ] Do Newton-Raphson minimization.

[ ABNR ] [MASS] Do Adopted Basis Newton-Raphson minimization,

[ TN ] Do Truncated-Newton minimization.

with mass weighted forces if specified.

STEP .02 ALL Initial step size for the minimization algorithms.

except TN Reasonable values for the various methods are best

determined by trial and error.

LATTice ABNR With the CRYSTAL facility, also optimize the unit

cell box size and/or shape.

NOCOords ABNR With the CRYSTAL facility, only optimize the unit

cell. This leaves coordinates unchanged, but allows

the box size and/or shape to change.

PRTMIN 1 CONJ A flag indicating how much to print during

minimization.

If less than 2, the energy is printed only once

each cycle. A setting of 2 shows the energy for

each evaluation plus variables used in the method.

NCGCYC 100 CONJ The number of conjugate gradient cycles executed

before the algorithm restarts.

PCUT .9999 CONJ If the cosine of the angle between the old and new

P vector is greater than PCUT, the algorithm will be

restarted. This prevents the algorithm from plodding

down the same path repeatedly. If PRTMIN is less

than 2, one effect of the restart is that the step

size will go to its initial value. If this happens

many times, you've converged.

EIGRNG .0005 ABNR The smallest eigenvalue (relative to the largest)

that will be considered nonsingular.

MINDIM 5 ABNR The dimension of the basis set stored.

STPLIM 1.0 ABNR The maximum Newton Raphson step that will

be allowed.

STRICT 0.1 ABNR The strictness of descent. The energy of a new step

must be less than the previous best energy + STRICT

for the new step to be accepted.

MASS false ABNR Use unweighted forces by default or mass weighted

if specified. Mass weights converge more slowly but

allow association with normal mode frequencies.

TFREQ 1.0 NRAP The smallest eigenvalue that is considered to be

non-negative (i.e. do cubic fitting on all

eigenvalues smaller than this).

TOLENR 0.0 ABNR A tolerance applied to the change in total energy

change during a cycle of minimization (NCYCLE steps).

If the energy change is less than or equal to

TOLENR, the minimization routine will exit.

TOLGRD 0.0 ABNR A tolerance applied to the average gradient during

a cycle of minimization. If the average gradient

is less than or equal to TOLGRD, the routine

will exit.

1.0 TN A parameter which determines the desired accuracy

of the computed solution. The following four

convergence tests are checked:

(T1) f(x_{k-1})-f(x_k) < tolgrd (1+|f(x_k)|)

(T2) ||x_{k-1} - x_k|| < sqrt(tolgrd) (1+||x_k||)

(T3) ||g(x_k)|| < tolgrd^(1/3) (1+ ||f(x_k)||)/100

(T4) ||g(x_k)|| < eg (1+ ||f(x_k)||)

If TOLGRD is equal to 0. in the input file, TOLGRD

is set to 10^(-8) in the calculation. If it is equal

to 1., it is set to 10^(-12).

eg is the square root of machine precision.

The routine will exit when either (T1,T2, and T3)

are satisfied or (T4). (T4) is a useful test at

the first Newton iteration or for comparison with

other methods (see TNPACK paper).

TOLITR 100 ABNR The maximum number of energy evaluations allowed

CONJ for a single step of minimization.

TOLSTP 0.0 ABNR A tolerance applied to the average step size during

a cycle of minimization. If the average step size

is less than or equal to TOLSTP, the routine

will exit.

FMEM 0.0 ABNR Memory factor. It is used to compute average

gradient and step size according to the formula :

AVERAGE_VALUE = FMEM * AVERAGE_VALUE

+ (1-FMEM) * CURRENT_VALUE.

FMEM=0 means no memory (i.e current value is used)

and FMEM=1 means infinitely long memory (i.e.

initial value will be used all the time).

NOUP false ALL Turns off the list updates.

PREC NOPR TN selects preconditioning (PREC) or no preconditioning

or (NOPR).

NOPR

ANAL ANAL TN selects option for Hd multiplication:

or ANAL for analytic version,

FDIF FDIF for the finite-difference formula.

REST REST TN specifies choice of PCG truncation test:

or residual (REST) or quadratic (QUAT).

QUAT

SCOF SCOF TN specifies whether the scheduling subroutine is used

or (SCON for on, SCOF for off). The subroutine turns

SCON on preconditioning (if chosen) when the gradient is

smaller than some tolerance, and uses steepest

descent steps beforehand.

SRON SROF TN specifies whether the optimal search-vector subrou-

or tine is turned on (SRON) or off (SROF). This subrou-

SROF tine considers more than one possible descent

directions at a Newton iteration and chooses the

one that leads to greatest energy reduction.

Additional energy + gradient evaluations are

required.

IORD NOOR TN specifies whether a reordering of M will be performed

or to minimize fill-in (IORD) or not (NOOR). This might

NOOR be useful if M is very large and sparse. The

reordering is done only once, but the savings are

reflected in each-inner loop iteration where a

linear system involving M is solved.

PERM NOPM TN determines if the permutation array for reordering

or M is known when the current TNPACK call is made

NOPM (PERM - known, NOPM - unknown).

NOEN FALSE SD only use the information of force to minimize

a system. implemented for the case of minimizing

a reaction path using the eudged elastic band method.

SADD 0 NRAP sets the order of saddle point you want to find.

SADD=1 will search in the opposite direction of

the most negative eigenvector (i.e. uphill) until

a stationary point is located (i.e. transition state

at SADD=1).

Note that the following commands are equivalent:

ANAL = USER , FDIF = OURH , SCOF = NOSC, SCON = SCHE, SRON = DEFS, SROF = SEAR

Options common to minimization and dynamics

The following table describes the keywords which apply to all

minimization methods.

Keyword Default Purpose

------- ------- --------------------------------------------------------

NSTEP 100 The number of steps to be taken. This

is the number of cycles of minimization, not the number

of energy evaluations.

INBFRQ 50 The frequency of regenerating the non-bonded list.

The list is regenerated if the current step number

modulo INBFRQ is zero and if INBFRQ is non-zero.

Specifying zero prevents the non-bonded list from being

regenerated at all.

IHBFRQ 50 The frequency of regenerating the hydrogen bond list.

Analogous to INBFRQ

non-bond- The specifications for generating the non-bonded list.

-spec See

**»**nbondshbond- The specifications for generating the hydrogen bond list.

-spec See

**»**hbondsNPRINT 1 The frequency with which energies are printed during

the course of dynamics or minimization.

GRADient Minimize the magnitude of the gradient of the energy

instead of the energy.

NUMErical Forces will be determined by finite differences

IUNCrd -1 Unit to write out a "trajectory" file for the minimization

NSAVC 1 Frequency for writing out frames (only with IUNCrd)

DEBUg Extra print for debug purposes

In the table which follows, keywords enclosed in square brackets

means that one can choose one in the set. Such enclosed keywords do not

expect a value after them. All other keywords are used for specifying

values, syntax::. The method column shows which method the

keyword affects.

Keyword Default Method Purpose

-------- -------- ------- -------------------------------------------------

[ CONJ ] CONJ Do conjugate gradient minimization.

[ SD ] Do steepest descent minimization.

[ NRAP ] Do Newton-Raphson minimization.

[ ABNR ] [MASS] Do Adopted Basis Newton-Raphson minimization,

[ TN ] Do Truncated-Newton minimization.

with mass weighted forces if specified.

STEP .02 ALL Initial step size for the minimization algorithms.

except TN Reasonable values for the various methods are best

determined by trial and error.

LATTice ABNR With the CRYSTAL facility, also optimize the unit

cell box size and/or shape.

NOCOords ABNR With the CRYSTAL facility, only optimize the unit

cell. This leaves coordinates unchanged, but allows

the box size and/or shape to change.

PRTMIN 1 CONJ A flag indicating how much to print during

minimization.

If less than 2, the energy is printed only once

each cycle. A setting of 2 shows the energy for

each evaluation plus variables used in the method.

NCGCYC 100 CONJ The number of conjugate gradient cycles executed

before the algorithm restarts.

PCUT .9999 CONJ If the cosine of the angle between the old and new

P vector is greater than PCUT, the algorithm will be

restarted. This prevents the algorithm from plodding

down the same path repeatedly. If PRTMIN is less

than 2, one effect of the restart is that the step

size will go to its initial value. If this happens

many times, you've converged.

EIGRNG .0005 ABNR The smallest eigenvalue (relative to the largest)

that will be considered nonsingular.

MINDIM 5 ABNR The dimension of the basis set stored.

STPLIM 1.0 ABNR The maximum Newton Raphson step that will

be allowed.

STRICT 0.1 ABNR The strictness of descent. The energy of a new step

must be less than the previous best energy + STRICT

for the new step to be accepted.

MASS false ABNR Use unweighted forces by default or mass weighted

if specified. Mass weights converge more slowly but

allow association with normal mode frequencies.

TFREQ 1.0 NRAP The smallest eigenvalue that is considered to be

non-negative (i.e. do cubic fitting on all

eigenvalues smaller than this).

TOLENR 0.0 ABNR A tolerance applied to the change in total energy

change during a cycle of minimization (NCYCLE steps).

If the energy change is less than or equal to

TOLENR, the minimization routine will exit.

TOLGRD 0.0 ABNR A tolerance applied to the average gradient during

a cycle of minimization. If the average gradient

is less than or equal to TOLGRD, the routine

will exit.

1.0 TN A parameter which determines the desired accuracy

of the computed solution. The following four

convergence tests are checked:

(T1) f(x_{k-1})-f(x_k) < tolgrd (1+|f(x_k)|)

(T2) ||x_{k-1} - x_k|| < sqrt(tolgrd) (1+||x_k||)

(T3) ||g(x_k)|| < tolgrd^(1/3) (1+ ||f(x_k)||)/100

(T4) ||g(x_k)|| < eg (1+ ||f(x_k)||)

If TOLGRD is equal to 0. in the input file, TOLGRD

is set to 10^(-8) in the calculation. If it is equal

to 1., it is set to 10^(-12).

eg is the square root of machine precision.

The routine will exit when either (T1,T2, and T3)

are satisfied or (T4). (T4) is a useful test at

the first Newton iteration or for comparison with

other methods (see TNPACK paper).

TOLITR 100 ABNR The maximum number of energy evaluations allowed

CONJ for a single step of minimization.

TOLSTP 0.0 ABNR A tolerance applied to the average step size during

a cycle of minimization. If the average step size

is less than or equal to TOLSTP, the routine

will exit.

FMEM 0.0 ABNR Memory factor. It is used to compute average

gradient and step size according to the formula :

AVERAGE_VALUE = FMEM * AVERAGE_VALUE

+ (1-FMEM) * CURRENT_VALUE.

FMEM=0 means no memory (i.e current value is used)

and FMEM=1 means infinitely long memory (i.e.

initial value will be used all the time).

NOUP false ALL Turns off the list updates.

PREC NOPR TN selects preconditioning (PREC) or no preconditioning

or (NOPR).

NOPR

ANAL ANAL TN selects option for Hd multiplication:

or ANAL for analytic version,

FDIF FDIF for the finite-difference formula.

REST REST TN specifies choice of PCG truncation test:

or residual (REST) or quadratic (QUAT).

QUAT

SCOF SCOF TN specifies whether the scheduling subroutine is used

or (SCON for on, SCOF for off). The subroutine turns

SCON on preconditioning (if chosen) when the gradient is

smaller than some tolerance, and uses steepest

descent steps beforehand.

SRON SROF TN specifies whether the optimal search-vector subrou-

or tine is turned on (SRON) or off (SROF). This subrou-

SROF tine considers more than one possible descent

directions at a Newton iteration and chooses the

one that leads to greatest energy reduction.

Additional energy + gradient evaluations are

required.

IORD NOOR TN specifies whether a reordering of M will be performed

or to minimize fill-in (IORD) or not (NOOR). This might

NOOR be useful if M is very large and sparse. The

reordering is done only once, but the savings are

reflected in each-inner loop iteration where a

linear system involving M is solved.

PERM NOPM TN determines if the permutation array for reordering

or M is known when the current TNPACK call is made

NOPM (PERM - known, NOPM - unknown).

NOEN FALSE SD only use the information of force to minimize

a system. implemented for the case of minimizing

a reaction path using the eudged elastic band method.

SADD 0 NRAP sets the order of saddle point you want to find.

SADD=1 will search in the opposite direction of

the most negative eigenvector (i.e. uphill) until

a stationary point is located (i.e. transition state

at SADD=1).

Note that the following commands are equivalent:

ANAL = USER , FDIF = OURH , SCOF = NOSC, SCON = SCHE, SRON = DEFS, SROF = SEAR

Top

Discussion of the various minimization methods

There are several different algorithms for minimizing the energy

of the system. They all involve calculating the derivative of the

potential energy, and possibly the second derivative, and using that

information to adjust the coordinates in order to find a lower energy.

In the descriptions of the algorithms below, a capitalized keyword at

the beginning of each paragraph is the key word used to invoke that

method. After the descriptions is a listing of all keywords associated

with minimization.

The simplest minimization algorithm is steepest descent (SD).

In each step of this iterative procedure, we adjust the coordinates in

the negative direction of the gradient. It has one adjustable parameter,

the step size, which determines how far to shift the coordinates at each

step. The step size is adjusted depending on whether a step results in a

lower energy. I.e., if the energy drops, we increase the step size by

20% to accelerate the convergence. If the energy rises, we overshot a

minimum, so the step size is halved. Steepest descent does not converge

in general, but it will rapidly improve a very poor conformation.

A second method is the conjugate gradient technique (CONJ) which has

better convergence characteristics (R. Fletcher & C.M. Reeves, The Computer

Journal 7:149 (1964)). The method is iterative and makes use of the previous

history of minimization steps as well as the current gradient to determine the

next step. It can be shown that the method converges to the minimum energy in

N steps for a quadratic energy surface where N is the number of degrees of

freedom in the energy. Since several terms in the potential are quadratic, it

requires less evaluations of the energy and gradient to achieve the same

reduction in energy in comparison to steepest descent. Its main drawback is

that with very poor conformations, it is more likely to generate numerical

overflows than steepest descent. The algorithm used in CHARMM has a slightly

better interpolation scheme and automatic step size selection.

A third method is the conjugate gradient powell method minimizer

(POWE) (A. Brunger). Its efficiency is much improved over the Fletcher

and Reeves method. The use of the POWELL minimizer is encouraged whenever

ABNR is not possible. The POWELL minimizer has no INBFRQ or IHBFRQ

feature. The use of CHARMM loops to mimic this important feature is

encouraged. The CHARMM loop facilities allow harmonic constrained

minimizations with periodic updates. In case of bad contacts or

unlikely conformations the SHAKE method might give an error when the

displacements become to large. Using a harmonic constraint setup

with periodic updates solves this problem.

A fourth method involves solving the Newton-Raphson minimization

equations iteratively (NRAP). This procedure requires the computation of

the derivative of the gradient which is a matrix of size O(n**2). The

procedure here is to find a point where the gradient will be zero

(hopefully a minimum in energy) assuming that the potential is

quadratic. The Newton-Raphson equations can be solved by a number of

means, but the method adopted for CHARMM involves diagonalizing the

second derivative matrix and then finding the optimum step size along

each eigenvector. When there are one or more negative eigenvalues, a

blind application of the equations will find a saddle point in the

potential. To overcome this problem, a single additional energy and

gradient determination is performed along the eigenvector displacement

for each small or negative eigenvalue. From this additional data, the

energy function is approximated by a cubic potential and the step size

that minimizes this function is adopted. The advantages of this method

are that the geometry cannot remain at a saddle point, as sometimes

occurs with the previous procedures, and that the procedure converges

rapidly when the potential is nearly quadratic (or cubic). The major

disadvantage is that this procedure can require excessive storage

requirements, O(n**2), and computation time, O(n**3), for large

molecules. Thus we are currently restricted to systems with about 200

atoms or less for this method. IMAGES and SHAKE are currently unavailable

with this method.

The fifth method available is an adopted basis Newton-Raphson

method (ABNR) (D. J. States). This routine performs energy minimization

using a Newton-Raphson algorithm applied to a subspace of the coordinate

vector spanned by the displacement coordinates of the last (mindim)

positions. The second derivative matrix is constructed numerically from

the change in the gradient vectors, and is inverted by an eigenvector

analysis allowing the routine to recognize and avoid saddle points in

the energy surface. At each step the residual gradient vector is

calculated and used to add a steepest descent step onto the

Newton-Raphson step, incorporating new direction into the basis set.

This method is the best for most circumstances.

SHAKE is currently unavailable with this method.

The sixth method is the truncated-Newton (TN) minimization

package TNPACK, developed by T. Schlick and A. Fogelson. TNPACK is

based on the preconditioned linear conjugate-gradient technique for

solving the Newton equations. The structure of the problem ---

sparsity of the Hessian --- is exploited for preconditioning.

Thorough experience with the new version of TNPACK in CHARMM has been

described in the paper: Journal of Computational Chemistry: 15,

532--552, 1994. Through comparisons among the minimization algorithms

available in CHARMM, we find that TNPACK compares favorably to ABNR in

terms of CPU time when curvature information is calculated by a

finite-difference of gradients (the "numeric" option of TNPACK).

With the analytic option, TNPACK can converge more rapidly than ABNR

for small and medium systems (up to 400 atoms) as well as large

molecules that have reasonably good starting conformations; for large

systems that are poorly relaxed (i.e., the initial Brookhaven Protein

Data Bank structures are poor approximations to the minimum), TNPACK

performs similarly to ABNR. SHAKE is currently unavailable with this

method.

Barriers and Minima

-------------------

The GRADient option causes the minimizers to find a zero of the

target function (grad(V))^2. The square of the gradient replaces the

energy in the minimizers. Depending on the initial condition (initial

set of coordinates), the search can either be terminated in a minimum

or in a saddle point of the potential energy function (a barrier). If

the second derivative of the initial condition is negative BARI will

look for a saddle point; if it is positive it will stop at a minimum.

The second derivative matrix is employed to calculate first derivatives

of the target function. As a result it is much slower compared to

ABNR and NRAP in reaching a minimum. For minimum energy calculations:

DO NOT USE THE GRADient OPTION.

The SADD keyword turns on special code that follows positive

eigenvectors thus searching for a saddle point. Care must be taken

when choosing the starting structure for this code (i.e. you should

not start the search from a true minima as the code can get confused

about which eigenvector to follow). The best suggestion is to

slightly perturb your structure in the direction you believe that

transition state (or higher order saddle point) of interest to be.

Discussion of the various minimization methods

There are several different algorithms for minimizing the energy

of the system. They all involve calculating the derivative of the

potential energy, and possibly the second derivative, and using that

information to adjust the coordinates in order to find a lower energy.

In the descriptions of the algorithms below, a capitalized keyword at

the beginning of each paragraph is the key word used to invoke that

method. After the descriptions is a listing of all keywords associated

with minimization.

The simplest minimization algorithm is steepest descent (SD).

In each step of this iterative procedure, we adjust the coordinates in

the negative direction of the gradient. It has one adjustable parameter,

the step size, which determines how far to shift the coordinates at each

step. The step size is adjusted depending on whether a step results in a

lower energy. I.e., if the energy drops, we increase the step size by

20% to accelerate the convergence. If the energy rises, we overshot a

minimum, so the step size is halved. Steepest descent does not converge

in general, but it will rapidly improve a very poor conformation.

A second method is the conjugate gradient technique (CONJ) which has

better convergence characteristics (R. Fletcher & C.M. Reeves, The Computer

Journal 7:149 (1964)). The method is iterative and makes use of the previous

history of minimization steps as well as the current gradient to determine the

next step. It can be shown that the method converges to the minimum energy in

N steps for a quadratic energy surface where N is the number of degrees of

freedom in the energy. Since several terms in the potential are quadratic, it

requires less evaluations of the energy and gradient to achieve the same

reduction in energy in comparison to steepest descent. Its main drawback is

that with very poor conformations, it is more likely to generate numerical

overflows than steepest descent. The algorithm used in CHARMM has a slightly

better interpolation scheme and automatic step size selection.

A third method is the conjugate gradient powell method minimizer

(POWE) (A. Brunger). Its efficiency is much improved over the Fletcher

and Reeves method. The use of the POWELL minimizer is encouraged whenever

ABNR is not possible. The POWELL minimizer has no INBFRQ or IHBFRQ

feature. The use of CHARMM loops to mimic this important feature is

encouraged. The CHARMM loop facilities allow harmonic constrained

minimizations with periodic updates. In case of bad contacts or

unlikely conformations the SHAKE method might give an error when the

displacements become to large. Using a harmonic constraint setup

with periodic updates solves this problem.

A fourth method involves solving the Newton-Raphson minimization

equations iteratively (NRAP). This procedure requires the computation of

the derivative of the gradient which is a matrix of size O(n**2). The

procedure here is to find a point where the gradient will be zero

(hopefully a minimum in energy) assuming that the potential is

quadratic. The Newton-Raphson equations can be solved by a number of

means, but the method adopted for CHARMM involves diagonalizing the

second derivative matrix and then finding the optimum step size along

each eigenvector. When there are one or more negative eigenvalues, a

blind application of the equations will find a saddle point in the

potential. To overcome this problem, a single additional energy and

gradient determination is performed along the eigenvector displacement

for each small or negative eigenvalue. From this additional data, the

energy function is approximated by a cubic potential and the step size

that minimizes this function is adopted. The advantages of this method

are that the geometry cannot remain at a saddle point, as sometimes

occurs with the previous procedures, and that the procedure converges

rapidly when the potential is nearly quadratic (or cubic). The major

disadvantage is that this procedure can require excessive storage

requirements, O(n**2), and computation time, O(n**3), for large

molecules. Thus we are currently restricted to systems with about 200

atoms or less for this method. IMAGES and SHAKE are currently unavailable

with this method.

The fifth method available is an adopted basis Newton-Raphson

method (ABNR) (D. J. States). This routine performs energy minimization

using a Newton-Raphson algorithm applied to a subspace of the coordinate

vector spanned by the displacement coordinates of the last (mindim)

positions. The second derivative matrix is constructed numerically from

the change in the gradient vectors, and is inverted by an eigenvector

analysis allowing the routine to recognize and avoid saddle points in

the energy surface. At each step the residual gradient vector is

calculated and used to add a steepest descent step onto the

Newton-Raphson step, incorporating new direction into the basis set.

This method is the best for most circumstances.

SHAKE is currently unavailable with this method.

The sixth method is the truncated-Newton (TN) minimization

package TNPACK, developed by T. Schlick and A. Fogelson. TNPACK is

based on the preconditioned linear conjugate-gradient technique for

solving the Newton equations. The structure of the problem ---

sparsity of the Hessian --- is exploited for preconditioning.

Thorough experience with the new version of TNPACK in CHARMM has been

described in the paper: Journal of Computational Chemistry: 15,

532--552, 1994. Through comparisons among the minimization algorithms

available in CHARMM, we find that TNPACK compares favorably to ABNR in

terms of CPU time when curvature information is calculated by a

finite-difference of gradients (the "numeric" option of TNPACK).

With the analytic option, TNPACK can converge more rapidly than ABNR

for small and medium systems (up to 400 atoms) as well as large

molecules that have reasonably good starting conformations; for large

systems that are poorly relaxed (i.e., the initial Brookhaven Protein

Data Bank structures are poor approximations to the minimum), TNPACK

performs similarly to ABNR. SHAKE is currently unavailable with this

method.

Barriers and Minima

-------------------

The GRADient option causes the minimizers to find a zero of the

target function (grad(V))^2. The square of the gradient replaces the

energy in the minimizers. Depending on the initial condition (initial

set of coordinates), the search can either be terminated in a minimum

or in a saddle point of the potential energy function (a barrier). If

the second derivative of the initial condition is negative BARI will

look for a saddle point; if it is positive it will stop at a minimum.

The second derivative matrix is employed to calculate first derivatives

of the target function. As a result it is much slower compared to

ABNR and NRAP in reaching a minimum. For minimum energy calculations:

DO NOT USE THE GRADient OPTION.

The SADD keyword turns on special code that follows positive

eigenvectors thus searching for a saddle point. Care must be taken

when choosing the starting structure for this code (i.e. you should

not start the search from a true minima as the code can get confused

about which eigenvector to follow). The best suggestion is to

slightly perturb your structure in the direction you believe that

transition state (or higher order saddle point) of interest to be.