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	Lisa Balbes' Guide to Rational (Computer-Aided) Drug Design
			February 1992


	Definitions are indicated by an = .
	  (see also J. Med. Chem. (1985) _28(9)_, 1133-1139)
	Program names are in all capital letters.


1.  Determine ligand(s) (=small molecule with known biological effect) 
	or receptor site (=place where the ligand binds and acts) of interest.
2.  Build model of active site of the receptor.
	a.  Model protein then focus on active site  
	b.  build pharmacophore (=geometric description of the minimal
		requirements of the active site, including both shape 
		and charge complementarity to the ligands.)
3.  Develop inhibitor structures that fit active site model 
	and dock into the active site model.
4.  Quantitate interaction, to predict activity of new compounds.
5.  Synthesize compounds; evaluate their activity in the system of interest.
6.  Refine (2), repeat (3) - (5).
7.  Find effective, safe, synthesizable compound.  Patent.  Sell. $$$$$$$. 
	Either a) start again at (1) or b) retire to Hawaii.

Implicit Assumptions:

1.  Receptor shape does not change upon binding or for different ligands.
2.  All active analogs are interacting at the same site.

********** step 2a ******************************
Homology Modeling = Knowledge Based Modeling = use homologous 
	proteins (where 3D structure of one is known) to predict 
	structure of protein of interest (whose 3D structure is unknown,
	but sequence is known)
	Steps - find homologous sequences (using sequence alignment
	methods), use residue substitution followed by refinement 
	(minimization or molecular dynamics) to predict structure 
	of unknown protein

COMPOSER by Blundell and co-workers
	Eur. J. Biochem. (1988) _172_, 513-520. Blundell et al.
	Nature (1987) _326_ 347-352.  Review Article.
	Protein Engineering (1987) _1(5)_ 377-384. Sutcliffe,...Blundell
	Protein Engineering (1987) _1(5)_ 385-392. Sutcliffe,Hayes,Blundell

Not to be confused with:

Protein Folding = predict 3d structure from amino acid sequence.
	(predict both backbone conformation & side chain packing)
	Current Opinion in Structural Biology (1991) _1_, 224-229. Review.
	Biochemistry (1988) _27(1)_ 7167-7174.  Review, uses of NMR
	J. Mol. Biol. (1991) _217_, 373-388.  C. Lee & S. Subbiah
	Scientific American (January 1991) 54-63.

Protein Engineering = modifying residues of protein to change its specificity
	_or_ trying to find sequence that will fold into a specific 3D shape.
	Current Opinion in Structural Biology (1991) _1_, 617-623. Review.
	J. Mol. Biol. (1991) _220_, 495 - 506. Wilson, Mace and Agard.
	Nature (1991) _352_, 448-451.  Lee and Levitt. Accurate prediction
		of stability/activity of protein mutants.

********** step 2 *******************************
GRID method of Peter J. Goodford
	J. Med. Chem (1985) 28,849-857. Original paper, method described.
	J. Mol. Graphics (1989) 7,103-108.
	J. Med. Chem. (1989) 32, 1083-1094.

Assuming you have the structure of the receptor of interest, this will
	define the regions favorable for ligand binding.

Interaction of small probe with protein of known structure is computed
	at positions throughout and around macromolecule, resulting in 
	grid array of energy values.  Contour surfaces at appropriate
	energy values, when displayed over protein structure, can locate
	potential binding pockets.  (contour at negative energy = 
	region of attraction between probe and macromolecule).
Potential = Lennard-Jones, electrostatic and H-bonding terms
	Probe = water, methyl, amine N, carbonyl O, hydroxyl
	Input is a pdb file
	Output is a list of #s, display part must be user-written.
$12,500 US commercial, free to academic (as of 1989).
	Molecular Discovery Limited, West Way House, 
	Elms Parade, Oxford OX2 9LL, England
	Telephone +44-993-830385

********** step 3 *******************************
Only way to know active conformation for sure is to:
	1.  See it in an xtal structure bound to the receptor
		(but is the crystal conf the same as the solution conf?)
	2.  Find a rigid analogue with no conf. flexibility and high activity
		(but are you sure it's binding where you think it is?)
********** step 3 *******************************
Distance Geometry
Developed by Crippen and co-workers.  Good summary in J. Med. Chem (1986)
	_29(6)_, 899-906.  Original work in refs 14,15,16 of that paper.

For a given set of N points, and distance constraints (min and max
	distance between some Ni and Nj's), generate 3D coordinates
	for all N's such that all constraints are met.  Structures 
	are generated from random initial point, so end up with 
	"monte carlo sampling of conformational space with 
	distance constraints".

QCPE program # 590, DGEOM by Jeff Blaney

Extension:  include all atoms of all molecules in one large distance
	bounds matrix.  Acc. Chem. Research (1987) _20_, 322-329.

********** step 3 *******************************
3D Database Searching
search database of 3D molecule structures for any that fit active site model
	Reviews in Comp. Chem,  vol. 1, 213-263.  Martin, Bures, Willett.
	Emerging Technologies and New Direction in Drug Abuse Research,
		Ed. R. Rapaka, NIDA Monograph, Row Scientific, Rockville
		Maryland, 1991. p 62 - 77. R. S. Pearlman

CONCORD - rule based program, converts 2D structure to low energy 3D structure
	Pearlman, Chemical Design Automation News (1987) _2(1)_, 1,5-7.
	Available from Tripos Assoc. 1-800-323-2960
COBRA - converts 2D structure to "all" 3D low energy confs
	J. Chem. Inf. Comput. Sci.(1990) _30_, 316. Leach, Dolata, Prout.

Software to Search 3D Databases:
	ALADDIN - Daylight Chemical Information Systems (714-476-0451).
	MACCS-3D - Molecular Design Ltd. (201-540-9090 Darlene Ortiz)
	SYBYL/3DB - Tripos (1-800-323-2960)
	ChemDBS-3D - Chemical Design Ltd.
	3DSEARCH program - Sheridan (@Lederle, not being distributed)

********** step 3 (docking) **********************
	Irwin D. Kuntz, Jr.    J. Mol. Biol. (1982) 161, 269-288.

Finds potential docking sites on proteins of known structure by
	starting with solvent accessible surface, and filling
	cavities with overlapping spheres to make binding pockets.  
	Ligands of known structure (found by searching database*) are
	then automatically docked into this "site".
* must also have Cambridge database or same format database

Potential is 2 terms - hard sphere repulsions and hydrogen bonding only.
Both molecules assumed to be rigid.

Extensions - 
	Divide ligand into small pieces, dock separately, then rejoin.
		J. Med. Chem (1986) 29, 2149-2153.
	Evaluate for goodness of fit, keep best to examine further.
		J. Med. Chem. (1988) 31, 722-729.
	Add second step that examines electrostatic and hydrogen bonding
		properties of receptor site, displays them and suggests
		possible structural modifications to the ligand.
		Probing Bioactive Mechanisms, ACS Symposium Series #413,
			Chapter 4, DesJarlais, Seibel and Kuntz, ACS, 1989.

********** steps 3, 4 ******************************
GROW method of Moon and Howe (proprietary program)

Proteins: Structure, Function and Genetics (1991) _11_ 314-328.

Intrinsic activity = resulting from steric/electronic factors, ignoring
	distribution, metabolism and delivery to active site

GROW - Program to generate peptides of a specific length to fit a 
		pre-defined cavity (active site)

	user defines active _site_ of interest, and places acetyl group
	as _seed_ for peptide inhibitor growth.  Templates of amino
	acids from library are attached, goodness of fit evaluated,
	and top scoring structures are retained as templates for the
	next round of attachments.  Growth is N to C, C to N, or 
	alternating (user defined), so each level is one residue longer.
	Score = - [ E    + E   + E      + E    (template) + E    (recep) ]
		     vdw    es    conf     solv              solv

        Library of amino acid fragments generated by conformational
                search and _partial_ optimization.  Low energy, non-
                identical structures retained (100 - 5000 per residue).

Final structures evaluated visually and by energy criteria - 

	E        = E(complex) - E(unbound receptor) - E(unbound peptide)
	making sure to solvate unbound receptor and unbound peptide.

************* step 4 *************************
Free Energy Perturbation
	Ann. Rev. Biophys. Biophys. Chem. (1989) _18_, 431-492.  Review
	J. Am. Chem. Soc. (1989) _111_, 8050-8508.  Thr => Val, calc & exptl
	J. Med. Chem. (1989) _32_, 2542-2547.  Selective Elimin of Interactions
	J. Med. Chem. (1991) _34_, 2654-2659. Application to HIV inhibitors
	J. Comp. Chem. (1991) 12(2), 271-175.  Required length of simulation.
	J. Chem. Phys. (1991) 94(6) 4532-4545. Problems, assumptions.

Allows calculation of difference in delta(G) of 2 similar structures,
	by slowly changing one molecule into another.  Growth must
	be slow enough that system is "always" at equilibrium.

	Molecule A, condition 1 <=====>   Molecule B, condition 1
		   /\				     /\
		   ||				     ||
		  c||				    d||
		   ||				     ||
		   \/             b                  \/
	Molecule A, condition 2 <=====>   Molecule B, condition 2
 	delta(G) for a and b can be calculated, c and d can usually be 
		measured experimentally.  b - a = d - c = delta(delta(G))
		can be used to check accuracy of calculations.
	Results are more accurate when changes are electrostatic, not
	Can be difficult to determine whether errors are from fault
		in methodology, or from badly parameterized force field

	Simulations of > 100 ps (or even 200 ps) are needed for precise 
		free energy values
		Averaging results from shorter runs are not accurate

************* step 4 *************************
Min/MD cycling
	Friedrich Rippmann and N. Michael Green, personal communication

	Alternate MD (400 K, 0.2 ps) with MIN (200 steps), ~20 cycles
	Calculate interaction energy (I) at end of each cycle
	Average I's
	Linear correlation with delta(G)

Quantitative Structure Activity Relationship (QSAR)
Acc. Chem. Res. (1986) _19_, 392-400.
Acc. Chem. Res. (1969) _2_, 232.
Quant. Struct. Activity Relat. (1988) _7_ 18-25.
J. Med. Chem. (1991) _34_, 2824-2836.  Neural Nets in QSAR

Measure observables for series of compounds, then use various 
	mathematical techniques to derive equation where activity 
	is a function of all(some) of these observables.  This equation
	is then used to predict the activity of novel compounds.
	No 3D information is involved - just exptl parameters

Biological Property (activity) = f(observable, measurable properties)
	where the function is almost always a sum of terms of the form

 *** QSAR Terms 

COmparative Molecular Field Analysis (CoMFA) - extension to QSAR to 
	include 3D information

	Biomedical Technology (Jan or Feb 1992) 80-84, Meyer and McMillan.
	J. Med. Chem. (1991) _34_, 2338-2343.
	J. Am. Chem. Soc. (1988) _110_, 5959-5967.

Align series of compounds, then calculate steric and electrostatic field
	for each compound at each point on a grid surrounding the molecule.
	Use these field points to arrive at an equation as described above - 
		most will disappear, but you will be left with a small
		set of important regions.  These will tell you exactly
		where to add/remove substituents/charges to increase activity.
Partial Least Squares (PLS) - a mathematical method for solving for one 
	equation from a multitude of unknowns.  
	less likely than conventional regression to produce chance correlation
	if all "signal" concentrated in a few columns, PLS may overlook it
Crossvalidation - a mathematical method used to determine whether the 
	equation is generalizable to other sets of molecules
Bootstrapping - a mathematical method used to generate confidence limits 
	for each term of the equation. Normal QSAR assumes that the 
	variables are drawn from normal, independent distributions.
	Bootstrapping does not, assumes that the only thing you know
	about the variable distribution is the values you actually have.
	Select random rows from table, generate best model for that data.  
	Repeat several times, saving each model, then combine all of 
	them to generate a final model.  
	(With replacement - some compounds may be used more than once
		in a single analysis)

Things that indicate closer examination of the data is needed:
	Dramatic decline from "normal" r^2 to a crossvalidated r^2
	A high ( >> 0.05) std deviation for a bootstrapped r^2

Active Analog Approach (marketing rights owned by Tripos Associates)

Recognition at active site, not biological potency, is factor to consider.
Want to deduce minimal recognition requirements to understand how a
	diverse set of chemical structures can activate the same receptor.

Technique used is manipulation of orientation maps.  Must define the
	essential groups for activity, and all possible conformations 
	of each active compound (thus possible orientations of essential
	groups relative to each other).

Orientation map = each point represents one possible arrangement 
	of essential groups, thus one possible pharmacophore.

Intersection of OMaps for all active compounds = only possible pharmacophores

Receptor essential volume = volume not available to drugs for binding = 
	Volume of inactive compounds - volume of inactive compounds
	(volume inactives use, that actives don't)

G. R. Marshall, C. Dave Barry, H. E. Bosshard, R. A. Dammkoehler,
	D. A. Dunn, "Computer Assisted Drug Design", ACS, 1979, ACS 
	Symposium Series #112, E. C. Olson and R. E. Christofferson, editors.
Simulated Annealing

General (mathematical) review in Science (1983) 220, 671-680.

Method to find min or max of function that depends on many variables.

Run a long dynamics simulation, while gradually lowering the
	temperature.  Energetically excited molecule should then
	cool into a favorable energy well which corresponds to
	a local energy minimum in conformational space.  

Typically do many runs, and observe where final structures are clustering.

Similar to FEP in that catastrophic changes in system are avoided (you are
	essentially always at equilibrium).  Therefore error is minimized.

Molecular Dynamics (MD)
	Angew. Chem. Int. Ed. Engl. (1990) _29_ 992-1023.
Solving the equations of motion for all atoms in a system as a function
	of time, thus creating a picture of the system as it evolves in time.
	Longer simulations (~100s of ps) and with explicit water give
	better results.

Temperature dependence of MD Simulations, J. Mol. Biol. (1990) _215_, 
	430-455.  Loncharich & Brooks
Effect of Solvation (water) on MD simulations.
	Chemics Scripta (1989) _29A_, 197-203, Michael Levitt.
	Chemical Physics (991) _158_ 383-394. Brooks, Steinbach, Loncharich

Conformational Search
	Move specified bonds x degrees, generate all possible conformations

	Types of conformation Search:
	Systematic: on a rotatable bond by systematically incrementing 
		a torsion angle through a range (typically 0 to 359 degrees).  
		The value of the increment step size) determines the 
		fine/coarse-ness of the search.
	Constrained: pre-process above results, throwing away high energy, 
		sterically dis-allowed, etc. conformations
	Torsion Driver: same as systematic, but each conformation is minimized.

	J. Computer-Aided Molecular Design (1989) _3_, 3-21.  

General Reviews:

Topics in Stereochemistry (199?) _20_, 1-85 Ripka and Blaney
Methods in Enzymology (1991) _203_, 587-613.  Martin
Dynamics of Proteins and Nucleic acids, McCammon and Harvey, 1987, 
	Cambridge University Press.
Interaction Energies: their role in drug design, Pettitt and Karplus,
	Topics in Molecular Pharmacology, (1986) _3_ 76-113.
J. Med. Chem. (1990) _33_, 883-894.
Ann. Rev. Pharmacol. Toxicol. (1987) _27_, 193-213.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% standard disclaimer %%%%
 Lisa M. Balbes, Ph.D.     		       phone: 919-541-6563
 Research Triangle Institute, PO Box 12194     vmail: 919-541-6767, xt 6563    
 Research Triangle Park,  NC 27709-2194        email: 
- This came directly from a computer and should not be doubted or disbelieved.-
Modified: Mon Feb 3 17:00:00 1992 GMT
Page accessed 47139 times since Sat Apr 17 21:15:47 1999 GMT