U.S. patent application number 10/411206 was filed with the patent office on 2004-10-14 for computer-based model for identification and characterization for non-competitive inhibitors of nicotinic acetylcholine receptors and related ligand-gated ion channel receptors.
Invention is credited to Collins, Jack R., Jozwiak, Krzysztof, Ravichandran, Sarangan, Wainer, Irving W..
Application Number | 20040204862 10/411206 |
Document ID | / |
Family ID | 33130931 |
Filed Date | 2004-10-14 |
United States Patent
Application |
20040204862 |
Kind Code |
A1 |
Wainer, Irving W. ; et
al. |
October 14, 2004 |
Computer-based model for identification and characterization for
non-competitive inhibitors of nicotinic acetylcholine receptors and
related ligand-gated ion channel receptors
Abstract
A computer readable medium holding data of a molecular model of
a ligand-gated ion channel receptor and/or a computer system for
modeling said receptor are provided by the instant invention. The
molecular model can be used to design novel compounds having
activity as non-competitive inhibitors of the ion channel. A
preferred embodiment of the invention relates to nicotinic
acetylcholine receptors. Compounds having activity as
non-competitive inhibitors of ligand-gated ion channel receptors
and methods for inhibiting the receptor and treating diseases or
disorders mediated by function of the receptor are also
disclosed.
Inventors: |
Wainer, Irving W.;
(Washington, DC) ; Jozwiak, Krzysztof; (Abingdon,
MD) ; Ravichandran, Sarangan; (Frederick, MD)
; Collins, Jack R.; (Frederick, MD) |
Correspondence
Address: |
Birch, Stewart, Kolasch & Birch, LLP
8110 Gatehouse Rd, Suite 500 East
P.O. Box 747
Falls Church
VA
22040-0747
US
|
Family ID: |
33130931 |
Appl. No.: |
10/411206 |
Filed: |
April 11, 2003 |
Current U.S.
Class: |
702/19 ;
514/1 |
Current CPC
Class: |
A61P 25/24 20180101;
A61P 25/28 20180101; A61P 25/34 20180101; G16B 15/00 20190201; C07K
14/70571 20130101; A61K 31/43 20130101; A61K 38/00 20130101; C07K
1/00 20130101; G16B 15/30 20190201; A61P 25/18 20180101; G16B 20/00
20190201; A61K 31/00 20130101 |
Class at
Publication: |
702/019 ;
514/001 |
International
Class: |
A61K 031/00; G06F
019/00; G01N 033/48; G01N 033/50 |
Claims
What is claimed is:
1. A computer system comprising: i) a memory storing positional
data of the atomic coordinates of the transmembrane portion of at
least one subunit of a ligand-gated neurotransmitter receptor
protein; and ii) a processor generating a molecular model having a
three dimensional shape representative of the pore portion of the
ligand-gated neurotransmitter receptor based the positional
data.
2. The computer system of claim 1, wherein the memory stores data
of the atomic coordinates of at least an .alpha.3 chain of a
nicotinic acetylcholine receptor and a .beta.4 chain of a nicotinic
acetylcholine receptor.
3. The computer system of claim 1, wherein the memory stores data
of the atomic coordinates of at least one polypeptide having an
amino acid sequence selected from the group consisting of SEQ ID
NOS: 1 through 15.
4. The computer system of claim 1, wherein the processor generates
a molecular model of the pore portion of a ligand-gated
neurotransmitter receptor having a subunit stoichiometry ranging
from (.alpha.).sub.5(.beta.).sub.0 to
(.alpha.).sub.2(.beta.).sub.3, or
(.alpha.).sub.2.beta..delta..gamma..
5. The computer system of claim 4, in which the stoichiometry is
(.alpha.).sub.2(.beta.).sub.3.
6. The computer system of claim 1, wherein the data comprises the
atomic coordinates of a portion of the transmembrane portion of the
subunit consisting of the amino acid sequence of residues 8 to 19
of at least one of SEQ ID NOS: 1-15.
7. The computer system of claim 1, further comprising a removable,
computer readable medium in which the data are stored.
8. A method for screening a compound for activity as a
non-competitive inhibitor of a ligand-gated neurotransmitter
receptor comprising; identifying as a compound having activity as a
non-competitive inhibitor as one having a .DELTA.G less than -6
kcal/mol by docking a model of the compound in a model of the
three-dimensional shape of the pore portion of the ligand-gated
neurotransmitter receptor.
9. The method of claim 8, in which the docking step is performed
using a computer program implementing a genetic algorithm.
10. The method of claim 8, further comprising assaying the binding
of the compound to the receptor and confirming as a non-competitive
inhibitor one having a k' of greater than 8, the assaying step
being performed either before or after the docking step.
11. The method of claim 10, further comprising modifying the
structure of the compound confirmed as a non-competitive inhibitor
and repeating the method using the compound having the modified
structure.
12. A method for making a non-competitive inhibitor of a
ligand-gated neurotransmitter receptor comprising: A method for
screening a compound for activity as a non-competitive inhibitor of
a ligand-gated neurotransmitter receptor comprising: i) identifying
as a compound having activity as a non-competitive inhibitor as one
having a .DELTA.G less than -6 kcal/mol by docking a model of the
compound in a model of the three-dimensional shape of the pore
portion of the ligand-gated neurotransmitter receptor; ii) testing
the compound for binding to the ligand-gated neurotransmitter
receptor and/or inhibiting the ion-channel activity thereof; iii)
obtaining as a non-competitive inhibitor of the ligand-gated
neurotransmitter receptor the compound that binds to the
ligand-gated neurotransmitter receptor with a k' value greater than
8 and being displaced in chromatographic experiments by
mecamylamine and/or inhibits the ion channel activity of the
ligand-gated neurotransmitter receptor in nicotine stimulated
.sup.86Rb.sup.+ efflux with IC.sub.50 lower than 100 .mu.M.
13. The method of claim 10, in which the ligand-gated
neurotransmitter receptor is a neuronal receptor.
14. The method of claim 12, in which the ligand-gated
neurotransmitter receptor is a muscle receptor.
15. A compound comprising: a bulky hydrophobic moiety and primary,
secondary or tertiary amino group located 5 to 10 .ANG. from said
hydrophobic moiety, said compound having activity of inhibiting the
ion-channel activity of a ligand-gated neurotransmitter receptor in
an assay of nicotine stimulated .sup.86Rb.sup.+ efflux with an
IC.sub.50 lower than 50 .mu.M.
16. The compound of claim 15, wherein the bulky hydrophobic moiety
is selected from the group consisting of a phenyl ring, a napthyl
ring, a cyclopentyl ring, a cyclohexyl ring, a fused ring.
17. The compound of claim 16, wherein the hydrophobic moiety is a
fused ring selected from the group consisting of bicyclo [2.2.1]
heptane, bicyclo [2.2.2] octane, morphinan and dibenzo [1.41]
diazepine.
18. The compound of claim 15, that does not compete with the
neurotransmitter ligand of a receptor for binding of a
neurotransmitter ligand binding site of the receptor located on the
external surface at the interface of two subunits in a pocket
approximately 30-35 .ANG. from the transmembrane portion of the
subunits.
19. The compound of claim 15, wherein said receptor is a neuronal
nicotinic acetylcholine receptor.
20. The compound of claim 15, wherein the receptor is a muscular
nicotinic acetylcholine receptor.
21. A compound comprising a bulky hydrophobic moiety and a primary,
secondary or tertiary amino group placed 5 to 10 .ANG. from said
hydrophobic moiety, said compound having activity of binding to the
non-competitive inhibitor site of a ligand-gated neurotransmitter
receptor with a .DELTA.G of at least -6 kcal/mol.
22. The compound of claim 21, that does not compete with the
neurotransmitter ligand of the receptor for binding to the
neurotransmitter ligand binding site of the receptor.
23. The compound of claim 21, wherein said receptor is a neuronal
nicotinic acetylcholine receptor.
24. The compound of claim 21, wherein the receptor is a muscular
nicotinic acetylcholine receptor.
25. A kit comprising a computer readable medium having stored
therein positional data of the atomic coordinates of the
transmembrane portion of at least one subunit of a ligand-gated
neurotransmitter receptor protein; and a composition comprising the
ligand-gated neurotransmitter receptor protein.
26. The kit of claim 25, in which the composition comprising the
receptor protein is an affinity chromatography medium.
27. The kit of claim 26, in which the receptor is a nicotinic
acetylcholine receptor.
28. A computer readable medium having stored therein positional
data of the atomic coordinates of the transmembrane portion of at
least one subunit of a ligand-gated neurotransmitter receptor
protein.
29. The computer readable medium of claim 28, wherein the data
comprise the atomic coordinates of at least an .alpha.3 chain of a
nicotinic acetylcholine receptor and a .beta.4 chain of a nicotinic
acetylcholine receptor.
30. The computer readable medium of claim 28, wherein the data
comprise the atomic coordinates of at least one polypeptide having
an amino acid sequence selected from the group consisting of SEQ ID
NOS: 1 through 15.
31. The computer readable medium of claim 28, wherein the data
comprise a molecular model of the pore portion of a ligand-gated
neurotransmitter receptor having a subunit stoichiometry ranging
from (.alpha.).sub.5(.beta.).sub.0 to
(.alpha.).sub.2(.beta.).sub.3, or
(.alpha.).sub.2.beta..delta..gamma..
32. The computer readable medium of claim 31, in which the
stoichiometry is (.alpha.).sub.2(.beta.).sub.3.
33. A method for non-competitively inhibiting a ligand-gated ion
channel receptor comprising contacting the ligand-gated ion channel
receptor with a compound of claim 15, except for bupropion,
ketamine, laudanosine, mecamylamine, methadone, MK-801,
phenylcylclidine, ethidium, and dextromethorphan.
34. A method for treating Tourette's syndrome, or a cognitive
disorder, pain, anxiety, depression, neurodegeneration or an
addiction caused by an overactive ligand-gated ion channel
receptor, comprising administering to a subject an amount of a
compound of claim 15 effective to inhibit ion flux through said
ligand-gated ion channel.
35. The method of claim 34, that is a method of treating smoking
addiction and the receptor is a neuronal nicotinic acetylcholine
receptor.
36. A method for evaluating cardiovascular toxicity or GI spasming
or diarrheal side effects of a compound comprising testing a
compound for activity in the method of claim 8, wherein the
receptor is a muscular nicotinic acetylcholine receptor subtype.
Description
[0001] The present application includes an appended Sequence
Listing of 15 amino acid sequences and Appendices 1 to 3 providing
computer programming scripts and parameter files.
FIELD OF THE INVENTION
[0002] The present invention relates to a computer system for
generating molecular models of ligand-gated ion channels and in
particular, molecular models of the inner lumen of a ligand-gated
ion channel and associated binding pockets. The present invention
further relates to a computer system simulating interaction of the
computer-based model of the ligand-gated channel and
non-competitive inhibitor compounds for identification and
characterization of non-competitive inhibitors and to inhibitors
compounds so discovered. The present invention also relates to
methods for treating various disorders related to ligand-gated ion
channel receptor function.
BACKGROUND OF THE INVENTION
[0003] Ligand-gated ion channels are currently a popular target for
drug discovery in the pharmaceutical industry. The Ligand-Gated Ion
Channel (LGIC) superfamily is separated into the nicotinic receptor
superfamily (muscular and neuronal nicotinic, GABA-A and C, glycine
and 5-HT3 receptors), the excitatory amino acid superfamily
(glutamate, aspartate and kainate receptors) and the ATP purinergic
ligand-gated ion channels. These families only differ in the number
of transmembrane domains found in each subunit (nAChRs have 5
transmembrane domains), excitatory amino acid receptors have 4
transmembrane domains and ATP purinergic LGICs have 3 transmembrane
domains).
[0004] The nicotinic acetylcholine receptor (nAChR) is presently
the best characterized member of the ligand-gated ion channel
superfamily. The nicotinic receptors are of great therapeutic
importance. The subunits assemble combinatorily to form a variety
of pentameric transmembrane protein subtypes.
[0005] Neuronal nicotinic acetylcholine receptors (nAChRs) are the
class of ligand-gated ion channels of the central and peripheral
nervous system that regulate synaptic activity. The basic structure
of the nAChR is shown in FIG. 1. nAChR consists of five
transmembrane subunits 1, 2, 3, 4, 5 oriented around a central pore
6 permeable to cations. Cations flow through the pore is regulated
by ligand binding. The subunits in nAChR are typically a subunits
and .beta. subunits.
[0006] At present, 12 different homologous subunits have been
identified in neuronal nAChRs, 9 .alpha. subunits
(.alpha.2-.alpha.10) and 3 .beta. subunits (.alpha.2-.alpha.4). The
major difference between .alpha. and .beta. subunits is the
presence and location of the disulfide bond formed by two adjacent
cysteines in the .alpha. systems, the absence of this feature
distinguishes non-.alpha. subunits. This disulfide bond located on
the extracellular domain plays an important role in
neurotransmitter binding as well as the mechanism of channel
opening. These subunits combine to form multiple nAChR subtypes and
predominant stoichiometry is (.alpha.).sub.2(.beta.).sub.3, however
pentamers containing only .alpha. subunit are also known e.g.,
(.alpha.7).sub.5. In case of muscular nAChR the stoichiometry is
more complicated, the muscular nAChR receptor is predominantly
described as (.alpha.).sub.2.beta..delta..gamma..
[0007] The nAChRs are very complex systems with dozens of potential
different binding domains for different classes of compounds of
both endo- and exogenous origin (Arias H. R., (1997) Topology of
ligand binding sites on the nicotinic acetylcholine receptor. Brain
Res. Rev. 25: 133-91). Two primary cholinergic binding sites are
located on the extracellular side 7 (approximately 30-35 .ANG.
above the membrane) in the pocket at the interface between the
.alpha. and .beta. subunits. The nAChR contains several other
classes of binding sites at which non-competitive inhibitors (NCIs)
bind (Arias H. R. (1998) Binding sites for exogenous and endogenous
non-competitive inhibitors of the nicotinic acetylcholine receptor.
Biochim. Biophys. Act. 1376: 173-220). One, so-called "luminal high
affinity" NCI binding domain is located on the surface of the
internal lumen forming the ion channel. This site is a highly polar
and negatively charged domain, which primarily plays the role as a
cation selector. In general, an NCI compound does not compete with
the neurotransmitter ligand of the receptor for binding to the
neurotransmitter ligand binding site of the receptor located on the
external surface both .alpha. subunits in a pocket approximately
30-35 .ANG. from the transmembrane portion of the subunit (that is,
above the surface membrane when the receptor is expressed on in a
cell), as described by Arias [Arias, H. R. (2000) Localization of
agonist and competitive antagonist binding sites on nicotinic
acetylcholine receptors Neurochem. Int 36, 595-645].
[0008] Such drugs as mecamylamine, ketamine, bupropion or
barbiturates bind in the narrowest region of the channel on the
cell membrane level. Inhibitors acting there are mainly amines. It
is believed that the ligands, bind into this region and sterically
plug the channel, blocking the flux of ions.
[0009] "Non-luminal" sites are the population of 10-30 binding
sites located mostly at the lipid-protein interface for which an
allosteric mechanism of non-competitive inhibition was proposed.
Agents of different origin (steroids, fatty acids, alcohols, local
anesthetics etc.) can bind to those sites and modulate nAChR
activity.
[0010] Other classes of ligand-gated ion channels include GABA
(Johnston G. A. (2002) Medicinal chemistry and molecular
pharmacology of GABA(C) receptors. Curr Top Med Chem 2, 903-13),
5HT3 (D. C. Reeves, S. C. Lummis, (2002) The molecular basis of the
structure and function of the 5-HT3 receptor: a model ligand-gated
ion channel (review). Mol. Membr. Biol. 19, 11-26), AMPA (T. B.
Stensbol, U Madsen, P. Krogsgaard-Larsen, (2002) The AMPA receptor
binding site: focus on agonists and competitive antagonists. Curr.
Pharm. Des. 8, 857-72) and NMDA (K. A. Macritchie, A. H. Young,
(2001) Emerging targets for the treatment of depressive disorder.
Expert Opin. Ther. Targets 5, 601-612) receptors, etc. Although the
molecular structure of these receptors differ significantly, it is
believed that the luminal domains are homologous to the luminal
domain of nAChRs. There are five (or occasionally four)
transmembrane helices forming the wall of the channel with "rings"
of polar amino-acids exposed on the pre-forming surface and the
same non-competitive inhibition phenomenon can be observed.
[0011] In summary, the luminal high affinity NCI binding domain is
located on the surface of the internal lumen forming the ion
channel. Drugs of different origin bind in this region and
sterically plug the channel blocking the flux of ions.
[0012] Non-competitive inhibition of the nAChR can be responsible
for severe adverse drug effects. On the other hand, designing
ligands that specifically interact with this site can be part of
the development of new treatments of Alzheimer's and Parkinson's
diseases, for example by identifying compounds likely to exhibit
side effects through non-competitive inhibition of a LGIC.
Furthermore, the compounds identified as NCIs by the present method
are likely to find use in treating Tourette's syndrome and
cognitive disorders, pain [see, Lloyd, G. K. and Williams, M.
(2000) J. Pharmacol. Exper. Ther. 292, 461-467.], anxiety,
depression, neurodegeneration and addictions caused by an
overactive LGIC receptor, especially diseases in which nicotine
agonist activity against a neuronal nAChR is part of the etiology
(e.g. smoking addiction). The invention can also be used to
evaluate cardiovascular toxicity of a compound mediated by
non-competitive inhibition of a LGIC receptor, e.g. arrythmia and
GI spasming or diarrheal side effects of a compound caused by
inhibition of a muscle nAChR.
[0013] Classical methods of NCI identification are time consuming
and not effective in rapid screening of chemical libraries of drug
candidates.
[0014] Several different molecular models of the nAChR
transmembrane domain have been reported (Capener C E, Kim H J,
Arinaminpathy Y, Sansom M S (2002) Ion channels: structural
bioinformatics and modelling. Hum Mol Genet 11:2425-33). However,
none of those models were used to investigate interaction with
channel blockers. A computer based model for in silico simulations
of NCI interactions with the luminal domain of LGICs is needed to
better understand the phenomenon of the receptor's inhibition by
NCIs.
[0015] Furthermore, in drug discovery, the potential adverse
effects of drug candidates are of great importance. In depth
understanding of mechanistic interaction of luminal NCIs with
different subtypes of LGICs, especially of nAChRs, is required to
remove potential unwanted side effects at this site. In this
respect, a rapid screening technology that would identify NCIs of
LGICs, and especially of nAChRs would be greatly desired.
[0016] The functional determination and characterization of a NCI
of an LGIC is very complex and time consuming. One approach is
affinity chromatography based on immobilized receptor protein. This
is a versatile tool for investigation of intermolecular
interactions of a receptor with its ligands. The chemometric
approach of affinity chromatography can be employed for
determination of reliable relative affinities of ligands as well as
kinetic characterization, which otherwise would be inaccessible,
for large set of compounds (Kaliszan R., Wainer I. W. (1997)
Combination of Biochromatography and Chemometrics: A Potential New
Research Strategy in Molecular Pharmacology and Drug Design. In
Chromatographic Separations Based on Molecular Recognition. K.
Jinno, editors Wiley-VCH).
[0017] Methods using nAChR and other receptors immobilized on a
chromatographic support have been elaborated (U.S. Pat. Nos.
6,387,268, 6,139,735, provisional application No. 60/337,172). It
was shown that the obtained stationary phases worked as selective
binding materials for competitive cholinergic ligands and can be
used for high throughput screening of various competitive agonists
and antagonists (R. Moaddel, I. W. Wainer, (2003) Immobilized
nicotinic receptor stationary phases: going with the flow in
high-throughput screening and pharmacological studies J Pharm
Biomed Anal. 30, 1715-24). The usefulness of such columns based on
immobilized nAChR for investigations and modeling of NCI affinity
has also been demonstrated. Using a novel non-linear chromatography
approach off and on kinetics of ligand interaction with the
receptor can be determined. (K. Jozwiak, J. Haginaka et al., (2002)
Displacement and nonlinear chromatographic techniques in the
investigation of interaction of noncompetitive inhibitors with an
immobilized .alpha.3.beta.4 nicotinic acetylcholine receptor liquid
chromatographic stationary phase. Anal Chem 74: 4618-4624).
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The features of the invention may be better understood by
reference to the drawings described below.
[0019] FIG. 1 shows a structure of a neuronal nicotinic
acetylcholine receptor (nAChR).
[0020] FIG. 2 is a schematic representation of a computer system
useful in the practice of the invention.
[0021] FIG. 3 is a model of luminal domain of .alpha.3.beta.4
subtype of nAChR illustrating its electrostatic potential of the
inner surface of the channel. The Figure particularly shows the
electronegative potential of the cation selector region of the
channel.
[0022] FIG. 4 shows a luminal domain model having five helices
forming the wall of the ion channel.
[0023] FIG. 5 shows a luminal domain model (in wireframe rendering)
in perpendicular view. Specific residue rings depicted in different
colors: going down--red--E, first orange--T, second orange S,
grey--S/A, blue--V/F, green LL, last red--E/K, respectively).
[0024] FIGS. 6-8 are of example binding complexes. FIG. 6 shows the
mecamylamine luminal domain of .alpha.3.beta.4. FIG. 7 shows the
MK-801 luminal domain of .alpha.3.beta.4.
[0025] FIG. 8 shows a two cluster interaction of the ligand PCP
with .alpha.3.beta.4. Generally NCIs bind into the small pocked
formed on the apolar domain (Phenylalanine/Valine rings). Tested
structures primarily entered hydrophobic pocket formed between
.alpha.3 and .beta.4 strains and subsequently interacted with
protein side chains forming hydrogen bonds. Ligands most likely
form two separate clusters on two symmetrical active sites.
Estimated free energies of docking are in the range of experimental
IC.sub.50 of tested inhibitors.
[0026] FIG. 9 shows example compounds tested on
.alpha..sub.3.beta..sub.4 of nAChR column. Among the tested drugs
are aliphatic amines like mcm, amt, mtn and such compound like bup,
ket and mk-801. Also, some examples of more complicated structures
include clo, pcp mtd vera. Further, the structures of two
enantiomer dmt and lmt. Finally, there is a structure for ethidium:
the only compound permanently ionized and binds to its specific
site.
[0027] FIG. 10 shows a correlation of log k' (chromatographic) with
log (1/k.sub.i) (docking simulation).
[0028] FIG. 11 shows the enantioselectivity of
dextromethorphan/levomethor- phan pair determined in
chromatographic experiments. Dextromethorphan had longer retention
time and the profile was more asymmetric.
[0029] FIG. 12 shows a comparison of
dextromethorphan/levomethorphan complexes obtained in a docking
simulation: dextromethorphan--grey wireframe structure,
levomethorhan--red wireframe structure. Both systems interact
initially with hydrophobic pocket on the border between .alpha.3
and .beta.4 strains this binding determines positions of amine
group different for dextromethorphan (blue) than levomethorphan. In
case of dextromethorphan amine group can easily form secondary
interaction hydrogen bonds with neighboring polar residues (orange
balls), while levomethorphan is more likely to form such
interaction. This make a difference in stabilities of two complexes
by ca. 0.3 kcal/mol determined by both docking and chromatographic
analysis (FIG. 11).
DETAILED DESCRIPTION OF THE INVENTION
[0030] The present invention lies in part in a computer system that
generates molecular models of ligand-gated neuronal receptors and a
method of using the same. The computer system generates a
computer-based model of the inner lumen of a ligand-gated ion
channel having binding pockets for non-competitive inhibitors. The
computer system simulates interaction of structures from chemical
libraries or of any desired compound with the generated
computer-based model of the ligand-gated ion channel. The
simulation can serve to predict and describe the pharmacological
importance of the interaction. Thus, the invention constitutes a
system for drug discovery and for screening of a drug candidate for
unexpected side effects and toxicities.
[0031] In an embodiment of the present invention, as shown in FIG.
2, the computer system comprises a memory, e.g. disk 105, storing
positional data of the atomic coordinates of the transmembrane
portion of at least one subunit of a ligand-gated neurotransmitter
receptor protein, and a processor 101 generating a molecular model
having a three dimensional shape representative of the pore portion
of the ligand-gated neurotransmitter receptor based on positional
data. During execution of the process for generating the molecular
model, it is understood that the positional data would be stored
in, for example, RAM 102, or other memory readily accessible by the
processor 101.
1 TABLE 1 Residue No 1' 2' 3' 4' 5' 6' 7' 8' 9' 10 11 12 13 14 15
16 17 18 19 20 21 22 23 delta.sup.1) E K M S T A I S V L L A G A V
F L L L T S G R gamma.sup.2) Q K C T L S I S V L L A Q T I F L F L
I A Q K alpha E K M T L S I S V L L S L T V F L L V I V E L
1.sup.3) alpha 3.sup.3) E K V T L C I S V L L S L T V F L L V I T E
T alpha 4.sup.5) E K I T L C I S V L L S L T V F L L L I T E I
alpha 5.sup.8) E K I C L C T S V L V S L T V F L L V I E E I alpha
6.sup.8) E K V T L C I S V L L S L T V F L L V I T E T alpha
7.sup.6) E K I S L G I T V L L S L T V F M L L V A E I alpha
9.sup.8) E K V S L G V T I L L A M T V F Q L M V A E I alpha E K V
S L G V T V L L A L T V F Q L I L A E S 10.sup.7) beta 1.sup.3) E K
M G L S I F A L L T L T V F L L L L A D K beta 2.sup.4) E K M T L C
I S V L L A L T V F L L L I S K I beta 3.sup.8) E K L S L S T S V L
V S L T V F L L V I E E I beta 4.sup.3) E K M T L C I S V L L A L T
F F L L L I S K I epsilon.sup.8) Q K C T V S I N V L L A Q T V F L
F F L I A Q .sup.1)S. J. Opella, F. M. Marassi, et al., Nat. Struc.
Biol., 1999, 6, 374-379. .sup.2)Hucho, F.; Tsetlin, V. I.; Machold,
J. Eur. J. Biochem. 1996, 239, 539-55. .sup.3)J. C. Webster, M. M.
Francis, et al., Brit. J. Pharmacol., 1999, 127, 1337-1348.
.sup.4)M. W. Francis, R. W. Pazquez, et al., Mol. Pharmacol. 2000,
58, 109-119. .sup.5)O. K. Steinlein, A Magnusson, et al., Hum. Mol.
Genet., 1997, 6, 943-947. .sup.6)E Bertacini, JR Trudell, Protein
Eng. 2002, 15, 443-453. .sup.7)A B Elgoyhen, D E Vetter et al.,
Proc Natl Acad Sci USA 2001, 98, 3501-6. .sup.8)ENTREZ protein
databank at the US National Library of Medicine
[0032] The memory, in particular, stores data of the atomic
coordinates of at least an .alpha. chain and a .beta. chain of a
nicotinic acetylcholine receptor. The data of the atomic
coordinates can include atomic coordinates of at least one
polypeptide having an amino acid sequence selected from the group
consisting of the polypeptides shown in Table 1 (SEQ ID NOS: 1-15).
The data of the atomic coordinates should include atomic
coordinates of the portion of the transmembrane portion of the
subunit consisting of the amino acid sequence of residues 8 to 19
of SEQ. ID NOS: 1-15.
[0033] The processor 101 can generate a molecular model of the pore
portion of a ligand-gated neurotransmitter receptor having a
subunit stoichiometry ranging from (.alpha.).sub.5(.beta.).sub.0 to
(.alpha.).sub.0(.beta.).sub.5. For example, the subunit
stoichiometry can include(.alpha.).sub.2(.beta.).sub.3 useful for
modeling the neuronal nAChR regulating cardiovascular and GI
actions.
[0034] Modeling Step:
[0035] In generating a molecular model and simulating its
interaction, the computer system of the present invention first
generates a molecular model of the receptor channel based on a
template structure determined in an NMR investigation of synthetic
channel model (Opella S. J., Marassi F. M., Gesell J. J., Valente
A. P., Kim Y., Oblatt-Montal M., Montal M., (1999) Structures of
the M2 channel-lining segments from nicotinic acetylcholine and
NMDA receptors by NMR spectroscopy. Nat. Struct. Biol. 6:374-9).
Using this model, the molecular structures of all of the neuronal
subtypes of nAChR can be built. All subtypes of nAChR share several
common structural arrangements in the luminal domain, which makes
it possible to build the model of a particular subtype using a
homology modeling approach.
[0036] Once a molecular model is generated, the model is refined. A
preferred software package for refining the molecular model is the
AMBER molecular modeling package, e.g. AMBER version 7, (D. A.
Pearlman, D. A. Case, J. W. Caldwell, W. S. Ross, T. E. Cheatham
III, S. De Bolt, D. M. Ferguson, G. L. Seibel and P. A. Kollman,
(1995) AMBER, a package of computer programs for applying molecular
mechanics, normal mode analysis, molecular dynamics and free energy
calculations to simulate the structural and energetic properties of
molecules. Comp. Phys. Comm. 91, 1-41). The AMBER package contains
a set of molecular mechanical force fields for the simulation of
biomolecules and a package of molecular simulation programs. In
particular, the model is preferably refined using the "SANDER"
program (for Simulated Annealing with NMR-Derived Energy
Restraints) was used. SANDER is the main program used for molecular
dynamics simulations. SANDER allows for NMR refinement based on
NOE-derived distance restraints, torsion angle restraints, and
penalty functions based on chemical shifts and NOESY volumes.
[0037] Once the model has been refined using the SANDER program of
AMBER, the final model is evaluated. A preferred software package
for evaluating the final model is the PROCHECK package, e.g.
version 3.5.4 (Laskowski R A, MacArthur M W, Moss D S &
Thornton J M, (1993) PROCHECK: a program to check the
stereochemical quality of protein structures. J. Appl. Cryst., 26,
283-291). PROCHECK checks the stereochemical quality of a protein
structure, producing a number of PostScript plots analyzing its
overall and residue-by-residue geometry.
[0038] In order to construct subtype-specific molecular models, the
primary structures of the particular subtypes are required.
Different subtypes can be found in different region of the human
brain and are responsible for specific functions. Subtype-specific
models of the nAChR luminal domain can be utilized in designing
subtype-specific NCIs.
[0039] The procedure of building the luminal model can be easily
adopted to constrain models of luminal domain of other subtypes of
the nAChR and with some modification to constrain lumen models of
other classes of ligand-gated ion channels. The procedure is
basically explained in the modeling step of Example 1. The model of
the .alpha.3.beta.4-nAChR can serve as the template to constrain
other neuronal and muscular subtypes: since those subtypes are very
homologous (see Table 1). Only a few residues need to be modified
in order to obtain new subtype. The new model after residue
modification must be subjected to energy minimization by AMBER
procedures described previously and finally should be evaluated
using PROCHECK. Elaborated docking procedures can be applied to
those models and the entire approach can be used in detailed
molecular characterization of the luminal domain of specific
subtypes of nAChR and moreover, subtype specific interaction with
different classes of NCIs.
[0040] More complicated procedures must be applied if one want to
obtain model of the domain formed by other classes of ligand-gated
ion channels (GABA, NMDA, 5HT3 etc). First, amino-acid sequence
alignment modeling is performed. An example and detailed
description of such analysis can be found in the paper by
Bertaccini and Trudel [E. Bertaccini and J. R. Trudell, (2002)
Predicting the transmembrane secondary structure of ligand-gated
ion channels Protein Eng. 15, 443-453]. Thus, homologous parts of
the ion channel can be found and a new model of transmembrane
domain LGIC can be made. For some LGICs, the transmembrane domain
is formed by four transmembrane helices instead of five as in the
case of nAChR. In such case one of the helices must be removed and
remaining four need to be properly repositioned in order to form
channel structure. Then the model can be relaxed and refined in
AMBER procedures and finally evaluated in PROCHECK. In case of such
distinct models the docking procedures need be parameterized by
initial studies as described in the simulation step of Example 1
the invention and the values of the size of the grid box, the
dielectric constant, the ga_num_evals must be optimized, since the
size, and environment of the channel would have been changed
significantly.
[0041] Using the modeling method of the invention, it has been
discovered that there are NCI binding sites at the interface
between .alpha. and .beta. helices of LGICs, especially of
nicotinic AchRs. Among modeled candidate NCIs, the compound enters
into a small hydrophobic pocket formed by residues 12, 15 and 18 of
the transmembrane domains of the receptor subunits (e.g. SEQ ID
NOS: 1-15, Table 1). A hydrophobic group of the NCI compound will
interact with this portion of the NCI binding site. A polar group
(e.g. an amino group) of a putative NCI can interact by hydrogen
bonding with surrounding polar residues (e.g. residues 12 and 14 of
SEQ ID NOS: 1-15).
[0042] Simulation step:
[0043] After generating the molecular model, the final molecular
model is used as a target protein for docking simulation for
compounds that may be potential inhibitors. A preferred software
package for docking simulation is the AutoDock package, e.g.
version 3.5. AutoDock allows docking of a flexible ligand into a
rigid structure of the target protein using genetic algorithms as
the search method.
[0044] A particular genetic algorithm included in the AutoDock
package is the Lamarckian genetic algorithm. The Lamarckian genetic
algorithm was preferably used with local search in order to improve
efficiency. The Lamarckian genetic algorithm works in a reverse
order compared to typical genetic algorithms. In particular, new
traits in an organism develop because of a need created by the
environment and these acquired characteristics are transmitted to
its offspring. In AutoDock the ligand's atomic coordinates
represent a genotype and fitness is represented by interaction free
energy with the proteins. Genotypes are found through interations
of the local search and then the atomic coordinates are translated
into the ligand's state coordinates, as the phenotype. In other
words, in AutoDock local search is used to update the fitness
associated with an individual in the genetic algorithm
selection.
[0045] The Lamarckian genetic algorithm uses as input a grid data
set produced by the AutoGrid module. The AutoGrid module is used to
create 3-dimensional maps over the host protein using several atom
specific and electronic probes at each grid point.
[0046] Results of these simulations allow the classification of
tested compounds in terms of free energy of binding, which leads to
the identification of ligands that may be potent inhibitors. The
same approach can be used to design new compounds with high binding
properties to a specific subtype of the nAChR. A compound that is
identified as a non-competitive inhibitor of a LGIC is one having a
.DELTA.G less than -6 kcal/mol, preferably less than -7 kcal/mol,
still more preferably one having a .DELTA.G less than -10
kcal/mol.
[0047] The ligand structures used in docking simulations are
preferably made using the HyperChem package (of HyperCube Inc.,
Gainsville, Fla.). In particular, it is preferred that the AM1
semiempirical method implemented in HyperChem be used to minimize
the system energy and to calculate atomic charges in final
structures (J. J. P. Stewart, Semiempirical molecular orbital
methods, in: K. B. Lipkowitz, D. B. Boyd (Eds.), Reviews in
Computational Chemistry, vol. 1, VCH, New York, 1990, pp.
45-81).
[0048] The in silico approach described above can be supported by
examining the NCI-nAChR interaction by affinity chromatography
(Jozwiak K, Haginaka J, Moaddel R and Wainer I W (2002)
Displacement and nonlinear chromatographic techniques in the
investigation of interaction of noncompetitive inhibitors with an
immobilized nicotinic acetylcholine receptor liquid chromatographic
stationary phase. Anal Chem 74: 4618-4624), preferably in an
iterative fashion. Chromatographic affinity screening can provide
experimental data that is then employed for proper parameterization
of the computer-based molecular simulation. Alternatively, the
results of computer-based simulation can be related and evaluated
by further chromatographic and functional experiments.
[0049] Until recently, the screening of drug candidates for
activity as NCIs was not a standard procedure in the drug
development process. However, the present invention will permit
pharmaceutical companies to rapidly screen their potential drugs
for NCI properties. In addition, the luminal domain of nAChR can be
used as a target in drug discovery programs, which represents a new
therapeutic approach to the treatment of diseases such as
Alzheimer's and Parkinson's diseases and for treatment of drug and
tobacco dependency, which are related to LGIC functions,especially
to nAChR functions.
[0050] The nAChR, for example, was found to contain two cholinergic
agonist binding sites located at the interface between the .alpha.
and .beta. subunits and on the extracellular N-terminal of the
.alpha. subunits. These sites are key targets for drug discovery in
a variety of diseases, including Alzheimer's disease
(.alpha..sub.4.beta..sub.2), Parkinson's disease
(.alpha..sub.3.beta..sub.2), cardiovascular and GI actions
(.alpha..sub.3.beta..sub.4), anxiety and depression
(.alpha..sub.4.beta..sub.4), short term memory (.alpha.7) and
auditory function and development (.alpha.9).
[0051] Candidate NCI compounds discovered by the computational
modeling method of the invention can be confirmed by in vitro
experimental methods. Two preferred methods are by binding
experiments or by functional assays. Either of these methods may
employ the target LGIC, a population of LGICs representing the
target receptor and receptors that the compound should preferably
not inhibit (to avoid side effects), or a population of LGICs
representing a group of target receptors (with or without a group
representing LGICs that the compound should preferably not
inhibit). The LGICs for the in vitro functional assays can be
present either as expression products in cells, a partially
purified proteins, e.g. membrane preparations made as known in the
art, or as isolated proteins. If isolated proteins are used in
binding experiments, the proteins are preferably immobilized.
[0052] A preferred binding assay is a displacement assay performed
as described by Jozwiak et al. [Jozwiak K, Haginaka J, Moaddel R
and Wainer I W (2002) Displacement and Nonlinear chromatographic
techniques in the investigation of interaction of noncompetitive
inhibitors with an immobilized .alpha.3.beta.4 nicotinic
acetylcholine receptor liquid chromatographic stationary phase.
Anal Chem 74: 4618-4624.] Using this assay, a compound is
identified as a non-competitive inhibitor of the ligand-gated
neurotransmitter receptor is one that specifically binds to the
ligand-gated neurotransmitter receptor with a k' value greater than
8, preferably with a k' value greater than 9 or even more
preferably a k' value greater than 10.
[0053] Specificity of NCI binding to particular LGICs can be shown
by displacement of compounds that are selective to the pore portion
of the desired LGIC. Specificity of the binding to a nicotinic AChR
and homologous receptors can be shown by displacement by
mecamylamine. Displacement of mecamylamine at a concentration of 10
.mu.M indicates good specific binding, ability to displace
mecamylamine at a concentration of 40 .mu.M indicates strong
specific binding. Preferably it is possible to displace
mecamylamine at a concentration of 100 .mu.M. Thus, a compound that
is a preferred NCI of a nicotinic AChR is one that exhibits a k' of
greater than 8 in a chromotagraphic binding experiment and can be
displaced by mecamylamine at a concentration of 10 to 100
.mu.M.
[0054] Preferred functional ion channel activity assays are
described by Hernandez et al. [Hernandez S C, Bertolino M, Xiao Y,
Pringle K E, Caruso F S and Kellar K J (2000) Dextromethorphan and
its metabolite dextrorphan block .alpha.3.beta.4 neuronal nicotinic
receptors. J Pharmacol Exp Ther 293: 962-967] and by Jozwiak et al.
[K. Jozwiak, S C Hernandez, K J Kellar, I W Wainer (2003) The
Enantioselective Interactions of Dextromethorphan and
Levomethorphan with the .alpha.3.beta.4-Nicotinic Acetylcholine
Receptor: Comparison of Chromatographic and Functional Data
submitted to J Pharmacol Exp Ther]. In these assays a compound is
identified as a NCI that inhibits the ion channel activity of the
ligand-gated neurotransmitter receptor in nicotine stimulated
.sup.86Rb.sup.+ efflux with an IC.sub.50 lower than 50 .mu.M. A
more preferred NCI compound is one that inhibits ion efflux with an
IC.sub.50 lower than 5 .mu.M. Even more preferable compounds are
those that inhibit ion efflux with an IC.sub.50 lower than 500 nM.
One of skill in the art will recognize that compounds that are
effective at even lower concentrations are still more preferable,
and IC.sub.50 of 50 nM, or even 5 nM might be observed.
[0055] In some instances as described above, it might be preferred
to have a NCI that is selective for a particular LGIC. By
"selective" is meant that the NCI inhibits the target LGIC with an
IC.sub.50 that is at least 5-fold higher than the IC.sub.50 of the
one or more LGICs that it is desired not to inhibit. The degree of
selectivity is preferably 10-fold, more preferably 20- to 50-fold,
and still more preferably 100- to 500-fold or more.
[0056] On the other hand, the binding assays or functional assays
also can be used to provide initial data that can be used to
constrain the in silico modeling method desribed above.
Alternatively, the in silico modeling and the in vitro assays can
be run iteratively to converge upon NCI compounds that have desired
properties.
[0057] A structure-activity relation for a NCI of a LGIC has been
derived using the above-described methods. Thus, a compound having
a bulky hydrophobic moiety (e.g., a phenyl or napthyl ring system,
cyclopentyl or cyclohexyl ring system, a fused ring system
including but not limited to bicyclo [2.2.1] heptane, bicyclo
[2.2.2] octane, morphinan and dibenzo [1.4] diazepine) and a
primary, secondary or tertiary amino group in proximity to (i.e,
approximately 5 to 10 .ANG. from, preferably from 5 to 8 .ANG.
from, more preferably less than 7 .ANG. from) said hydrophobic
moiety. The amino group can be directly bonded to the bulky
hydrophobic moiety or can be linked by a spacer moiety, such as,
but not limited to, a short hydrocarbon chain. The amino group can
be substituted (--NR.sub.1R.sub.2, where R.sub.1 and R.sub.2 are
the same or different and are selected from the group consisting of
H, C.sub.1-C.sub.3 alkyl, C.sub.1-C.sub.4 alkoxy, dialkyl keto).
The substituent is preferably one that retains a hydrogen-bonding
potential; a preferred substituent is a keto-group, for example a
dialkyl keto group, especially CH.sub.2(C.dbd.O)CH.sub.3. Another
preferred substituent is a hydroxyl or alkoxyl (--CH.sub.2OH)
group, e.g. a C.sub.1-C.sub.4 normal or branched alkoxyl group.
Preferred substituted amino groups are a dialkyl keto amino group
(e.g., HNCH.sub.2(C.dbd.O)CH.sub.3), a hydroxyl amino group or a
methoxy amino group. An example of such a compound is
3-methoxy-17-propane-2-one 9 .alpha., 13.alpha., 14.alpha.
morphinan.
[0058] No compound listed in Table 2 is considered a compound per
se of the invention. Methods of the invention for non-competitively
inhibiting a LGIC, especially a nicotinic AChR, or for treatment of
a disease mediated by overactivity of a nicotinic AChR, exclude the
use of bupropion, ketamine, laudanosine, mecamylamine, methadone,
MK-801, phenylcylclidine, ethidium, and dextromethorphan.
[0059] Methods for synthesis of compounds of the invention are
considered within the skill of the ordinary synthetic chemist.
Preferred NCI compounds have the above structural features and
exhibit activity of inhibiting the ion-channel activity of a
ligand-gated neurotransmitter receptor in nicotine stimulated
.sup.86Rb.sup.+ efflux with an IC.sub.50 lower than 100 .mu.M or
other activities as set forth in detail above.
[0060] Dosage of compounds used for treatment of a subject can be
easily determined by the ordinarily-skilled pharmacologist using
known pharmacokinetic and pharmacodynamic assays and calculations
from IC.sub.50 data obtained by the inventive method. Formulation
and administration of compounds useful for treatment is also
well-known in the art. For example, many of the compounds listed in
Table 2 have been administered therapeutically and it is expected
that compounds of the invention can be similarly formulated and
administered.
EXAMPLE 1
Modeling of the Lumen of a .alpha.3.beta.4 nAChR and Docking of a
Putative NCI
[0061] The molecular model of a .delta.-M2--nAChR transmembrane
channel determined by frozen state NMR was used as the template for
further modification (atomic coordinates were found in Protein Data
Bank--PDB id: 1EQ8). This model represents a channel that mimics
the transmembrane arrangement of known LGICs (Opella S. J., Marassi
F. M., Gesell J. J., Valente A. P., Kim Y., Oblatt-Montal M.,
Montal M., (1999) Structures of the M2 channel-lining segments from
nicotinic acetylcholine and NMDA receptors by NMR spectroscopy.
Nat. Struct. Biol. 6:374-9). The model channel consisted of 5
uniform polypeptides oriented around a central pore. The amino-acid
sequence of this polypeptide is analogous to the sequence of
transmembrane M2 segment of .delta. subunit of nAChR found in
Torpedo californica. Table 1 presents the primary structure of this
.delta.-M2-segment.
[0062] In the .delta.-M2--nAChR transmembrane channel, the spatial
arrangement of polypeptide helices conserves five-fold symmetry,
with certain residues exposed to the center of the pore. These
residues (predominantly polar) form an explicit surface of the
channel. This is consistent with the concept of the presence of
amino acid rings distributed along the pore and is a common
property found in all subtypes of nAChR and also other ligand-gated
ion channels [Changeux J. P., Galzi J. L., Devillers-Thiery A.,
Bertrand D., (1992) The functional architecture of the
acetylcholine nicotinic receptor explored by affinity labelling and
site-directed mutagenesis. Q. Rev. Biophys. 25: 395-432].
[0063] With respect to the spatial arrangement of five helices in
the luminal domain, distribution of certain amino-acid rings along
the channel is a common property of all subtypes of nAChR. Since
primary sequences across different subtypes are predominantly
homologous as presented in Table 1, and essential (exposed)
residues are highly conserved, a subtype specific model of the
luminal domain can be built using homology modeling techniques.
[0064] Based on the sequence comparison presented in Table 1, the
initial model was modified by exchange of .delta. helix residues
into .alpha.3 and .beta.4 using the SYBYL 6.8 molecular modeling
system (Tripos Inc., 1699 South Hanley Road, St. Louis, Mo., 63144,
USA). Therefore, the channel containing .alpha.3, .beta.4,
.alpha.3, .beta.4 and .beta.4 helices, respectively, was
constrained.
[0065] The model was further refined by energy minimization using
the SanderClassic module of AMBER 6.0 software. Both termini of
each helix were blocked in a standard AMBER procedure: acetyl
beginning groups (ACE) and N-methylamine ending group (NME) groups
were attached, respectively, to each helix. The AMBER '94 force
field (Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R.,
Merz, Jr. K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T.,
Caldwell, J. W., Kollman, P. A., (1995) J. Am. Chem. Soc. 117,
5179-5197) parameters were used for energy minimization with the
convergence criterion of the root-mean-square of the gradient to be
less than 1.0E-4 kcal/mole .ANG.. Each minimization run was started
with the steepest descent followed by the conjugate gradient
method. A distance-dependent dielectric function was used to
evaluate the electrostatic energy. The energy minimization run was
carried-out in stages by relaxing i) only hydrogen atoms, ii)
hydrogen+side-chain atoms, or iii) all atoms except alpha-carbons.
Finally, a restrained minimization was also performed on the
alpha-carbons of all the chains/residues of the model. This was to
relax the structure but keep it near the initial position of the
known template structure (PDB accession no. 1EQ8). Respective
scripts used to run model refining with AMBER are presented in
Apendix 1.
[0066] Using PROCHECK to evaluate the model it was found that the
whole luminal domain is constrained fully by .alpha.-helix
secondary structure. Along the lumen model seven rings of residues
exposed to the center of the channel can be found; three polar
residues (E, T and S) and then three apolar residues (L, V/F and
LL) and the last polar residue (E/K).
[0067] It is believed that apolar rings in the middle of the
structure form the actual "gate" of the channel and play a role in
conformational change of the receptor from a closed to an open
state. Polar residues on both sides of the "gate" participate in
the cation selective function of the receptor. An important
structural parameter found in the obtained model is the change in
position from valine in the .alpha.3 sequence to phenylalanine in
the .beta.4 sequence (see residue 15 in Table 1). This provides the
formation of small pockets between .alpha.3 and .beta.4 subunits,
found during the simulation of NCI-.alpha.3.beta.4-nAChR
interactions. The developed model of .alpha.3.beta.4-nAChR luminal
domain can be used as a template to constrain homologous systems of
other nicotinic receptors, especially neuronal nicotinic receptors,
and other ligand-gated ion channels.
[0068] The resulting atomic coordinates represent the final model.
FIG. 3 illustrates the electrostatic potential of the inner surface
of the ion channel, and especially the electronegative potential of
the cation selector of the channel. FIG. 4 shows an example of the
resulting luminal domain model having five helicies forming the
wall of the ion channel.
[0069] FIG. 5 shows a luminal domain model in perpendicular view
with residue rings.
[0070] In order to perform docking simulations, the AutoGrid module
was first used to create 3-dimensional maps over the host protein
using several atom specific and electronic probes at each grid
point. An example parameterization file for the AutoGrid module
used in this example can be found in Appendix 2. The optimal size
of constrained grid maps was a 22.5.times.22.5.times.45 .ANG. box
(i.e., a grid of 60.times.60.times.120 points, each separated by
0.375 .ANG.). This allowed exploration of the whole internal space
of the lumen domain but prevented ligands from being bound on the
external side. The grid-box size can be altered in the 3.sup.rd
dimension (along the lumen) in order to explore interaction with a
particular segment of the lumen or to calculate the interaction
profile along the model.
[0071] An important parameter to properly explore electronic
interaction in ligand receptor complexes is the dielectric constant
value (d) used to calculate the electronic grid map. During the
initial evaluation tests, the standard distant-dependent dielectric
constant did not produce proper results: the electrostatic
interaction were almost zero. The simulation did not discriminate
between neutral and protonated ligands.
2TABLE 2 .DELTA.G (kcal/mol) of best docked conformation obtained
in different dielectric environment. Dist. Diel. const. dependent
40 30 20 15 10 5 1 MCM -6.23 -6.22 -6.22 -6.23 -6.26 -6.43 -6.76
-19.52 MCM+ -6.60 -7.56 -8.13 -9.29 -11.01 -14.47 -27.25 -138.2 DMT
-8.65 -8.66 -8.68 -8.71 -8.73 -8.81 -8.99 DMT+ -8.74 -9.46 -9.77
-10.39 -11.87 -15.72 -28.00 LMT -8.31 -8.33 -8.34 -8.38 -8.40 -8.49
-8.70 LMT+ -8.95 -9.53 -9.81 -10.59 -12.28 -15.69 -27.85
[0072] A detailed test of several d values was carried out using
three pairs of ligands and the results are presented in Table 2.
Table 2 shows an unexpected diminished difference between neutral
and protonated systems when distant-dependent d was used;
differences gradually increase with decreasing d. Simultaneously
the increase in electrostatic impact in the ligand receptor
interaction was noticed when a low dielectric value was used.
However, a very low value (d.ltoreq.10) produced unrealistic
.DELTA.G values. Finally, as a mater of compromising these two
effects, d=15 was chosen for final calculations as the value
producing suitable electronic properties of the ligand-receptor
complex in the transmembrane ion channel system. This approach is
in agreement with values of the dielectric constant in
transmembrane pores obtained by theoretical calculations (Cheng et
al., (1998) Eur. Biophys J., 27105-112 and Gutman et al., (1992)
Biochim. Biophys Acta 1109: 141-148) where it was found that the
actual dielectric constant in transmembrane channels remains low
and ranges from 25 to 5 depending on the structure. Thus, in the
case of the NCI-nAChR docking simulations d value can vary from 10
to 20.
[0073] The resulting ligand 3D structure was loaded into the
AutoDock system and was iteratively sampled over previously created
grid-maps in order to find optimal positions and the lowest energy
of interaction. An example parameterization file for the AutoDock
module used in this example can be found in Appendix 3.
[0074] The Lamarckian genetic algorithm with local search was used
from the AutoDock package. Atomic coordinate files of ligands were
transformed into format suitable to AutoDock using the HIN2PDBQ
script (Johansson M. (2002) Some computational chemistry related
python conversion scripts. See Web site
helsinki.fi/%7Empjohans/python/).
[0075] The ligand structures used in the docking simulations were
made using the HyperChem software package. Further, the AM1
semiempirical method implemented in HyperChem was used to minimize
the system energy and to calculate atomic charges in final
structures.
[0076] An initial simulation was performed in order to optimize the
docking settings. Since previously described docking space seemed
to be large in the model of .alpha.3.beta.4-nAChR active site
(22,781.25 .ANG..sup.3) it was important to optimize the maximum
number of energy evaluations (ga_num_evals) required in each search
run. It was found that too low a value of ga_num_evals could result
in finishing the simulation too quickly, and the global minimum of
the complex conformation may not be found. A set of test
simulations on several ligands including conformationally flexible
and rigid systems was performed. It was found that a ga_num_evals
value of at least 5 million is required to assure obtaining a
statistically significant number of lowest energy complexes. In the
case of bigger ligand molecules with more than 2 rotatable bonds,
the optimal value should be at least 50 million. Higher values are
acceptable; however higher values may dramatically increase the
time of each simulation.
[0077] The optimal number of docking search runs was found to be
50. Again the number of docking search runs can be higher, but
would take more time for simulation and have no effect on the final
result.
[0078] The AutoDock 3.5 implemented a free-energy scoring function
that is based on a linear regression analysis, the AMBER force
field, and a large set of diverse protein-ligand complexes with
known inhibition constants (e.g. see Web site at
scripps.edu/pub/olson-web/doc/autodock/). This function was
employed to estimate the free energy change of the NCI-nAChR
complex and eventually lead to an estimated inhibition constant of
a particular ligand. Docking simulations allow quantitative
classification of the stability of the NCI-nAChR complexes formed
by tested ligands in terms of free energy of binding, which
eventually lead to the identification of ligands exerting potent
inhibitory properties. It was found that molecular systems forming
the complex with .DELTA.G value lower than -6.0 kcal/mol should be
considered as potential NCIs. Lower .DELTA.G values represent more
potent NCI compounds. Preferred NCI compounds exhibit a .DELTA.G
value lower than -7.0 kcal/mol; more preferred compounds exhibit a
.DELTA.G value lower than -10.0 kcal/mol.
[0079] Detailed exploration of the spatial arrangement of
ligand-receptor conformations leads to building a pharmacophore
model of .alpha. subtype specific NCI-nAChR. Simulations on the
.alpha.3.beta.4 model showed that NCIs bind predominantly into the
channel on the apolar domain (F/V ring). Tested structures
primarily entered a small hydrophobic pocket formed between
.alpha.3 and .alpha.34 subunits and subsequently interacted with
protein side chains forming hydrogen bonds. It is expected that
this is a type of interaction that would not be found in those
receptor subtypes that lack the bulky phenylalanine residue in this
position. Since there are two quasi-symmetrical pockets between
.alpha.3 and .beta.4 helices in the model, ligands most likely form
two separate clusters on these two symmetrical sites (FIG. 8) at
which the energy of interaction does not significantly differ.
Estimated free energies of docking are in the range of experimental
IC.sub.50 of tested inhibitors and also can be related to
experimental affinity chromatography results. The model can be
applied to a variety of compounds and is useful for in silico
designing of new drugs with particularly high non-competitive
inhibitory activity.
EXAMPLE 2
Chromatographic Assay of NCI Activity
[0080] Chromatographic studies based on immobilized nAChRs were
performed to characterize ligand binding for broad groups of
compounds. In order to further understand the mechanistic action of
NCIs on the molecular level, the model of the transmembrane domain
of the .alpha.3.beta.4 nAChR was built and used for computer
simulations of docking inhibitors into the receptor. The entire
approach allowed the classification of NCIs in terms of their
functional effectiveness.
[0081] FIG. 9 presents compounds tested on an .alpha.3.beta.4 nAChR
column. The chemicals can be divided into several subgroups. The
first group contains drugs from different origin, which are well
known as non-competitive inhibitors of nAChRs. The second group is
of the dextromethorphan family, levomethorphan, dextromethorphan
and its analogues, and the final group is verapamil, its congeners,
and metabolites. In order to properly assess the influence of
non-specific retention, five other chemicals (acetanilide,
acetaminophen, 2,4-dinitrobenzoic acid, 3,4-dimethoxybenzoic acid
and phenylbutazone) were tested as negative controls. The affinity
of ligands was investigated by non-linear chromatography on an
.alpha.3.beta.4, nicotinic receptor affinity column.
[0082] 10.sup.6 Cells from the KX.alpha.3.beta.4R2 cell line were
suspended in Tris-HCl [50 mM, pH 7.4] (buffer A), homogenized for
30 sec, and centrifuged at 35,000.times.g for 10 min at 4.degree.
C. The pellet was resuspended in 2% cholate in buffer A and stirred
for 2 h. The mixture was centrifuged at 35,000.times.g for 30 min,
and the supernatant containing .alpha.3.beta.4 nAChR-cholate
solution was collected. 200 mg of the IAM stationary phase was
added to the .alpha.3.beta.4 nAChR-cholate solution. Subsequently
the solution was stirred for 1 h. The suspension was dialyzed
against 2.times.1L buffer A for 24 h at 4.degree. C. The IAM liquid
chromatographic support containing the .alpha.3.beta.4-nAChR was
packed into a HR5/2 glass column to form a chromatographic bed of
20 mm.times.5 mm i.d. The .alpha.3.beta.4-nAChR column was then
placed in the chromatographic system and used. The non-linear
chromatography approach was used to determine kinetics of the
NCI-nAChR interaction in affinity chromatography studies. The
mathematical model assumes limited (and a relatively low) number of
active sites on the column. Slow association and dissociation of
the drug-protein complex are the main cause of band broadening and
asymmetry of the peak profile. The chromatographic peak profiles
were analyzed using PeakFit v4.11 for Windows Software (SPSS Inc.,
Chicago, Ill.). The mathematical approach used was the non-linear
chromatography (NLC) model derived from Impulse Input Solution
[Wade J L, Bergold A F and Carr P W (1987) Theoretical description
of nonlinear chromatography, with applications to psychochemical
measurements in affinity chromatography and implications for
preparative-scale separations. Anal Chem 59:1286-1295.] and
described by Equation 1 (PeakFit User's Manual, p. 8-25): 1 y = a 0
a 3 [ 1 - exp ( - a 3 a 2 ) ] [ a 1 x I 1 ( 2 a 1 x a 2 ) exp ( - x
- a 1 a 2 ) 1 - T ( a 1 a 2 , x a 2 ) [ 1 - exp ( - a 3 a 2 ) ] ]
Eqn . 1
[0083] where:
[0084] y--intensity of signal,
[0085] x--reduced retention time, 2 T ( u , v ) = exp ( - v ) 0 u
exp ( - t ) I 0 ( 2 vt ) t
[0086] I.sub.0( ) and I.sub.1( ) are Modified Bessel functions
[0087] a.sub.0--area parameter,
[0088] a.sub.1--center parameter, reveal to true thermodynamic
capacity factor,
[0089] a.sub.2--width parameter,
[0090] a.sub.3--distortion parameter.
[0091] Experimental chromatograms obtained by single injection of
ligand into the chromatographic column with immobilized receptor
were processed with PeakFit v4.11 software. After standard linear
baseline subtraction, each peak profile was fitted to the NLC
function. The set of NLC parameters (a.sub.0, a.sub.1, a.sub.2 and
a.sub.3) was collected for each profile and used for the
calculation of descriptors of the kinetic interactions with the
immobilized nAChR, dissociation rate constant (k.sub.off);
equilibrium constant (K.sub.a); association rate constant
(k.sub.on) real thermodynamic capacity factor (k'), according to
the following equations:
k'=a.sub.1 Eqn. 2
[0092] 3 k off = 1 a 2 t 0 Eqn . 3 K = a 3 C 0 Eqn . 4
k.sub.on=k.sub.offK Eqn. 5
[0093] where: t.sub.0 is the dead time of a column (time needed by
non-retained substance to reach the detector); C.sub.0 is a
concentration of solute injected multiplied by a width of the
injection pulse (as a fraction of column dead volume).
[0094] Thus, by analyzing the ligand in an immobilized receptor
system four descriptors can be collected: retention (k'),
association rate constant (k.sub.on), dissociation rate constant
(k.sub.off) and equilibrium constant (logK). It was found that
ligands which are non-competitive inhibitors have k' greater than
8, k.sub.on greater than 10.times.10.sup.-6 M.sup.-1s.sup.-1
(preferred inhibitors have k.sub.on of greater than
15.times.10.sup.-6 M.sup.-1s.sup.-1 especially potent inhibitors
have k.sub.on greater than 30.times.10.sup.-6 M.sup.-1s.sup.-1),
k.sub.off smaller than 15 s.sup.-1 (preferably lower than 2
s.sup.-1) and logK greater than 5.9 (preferably greater than
6.5).
[0095] The k.sub.on value obtained in chromatographic experiments
is the one which is closely correlated with IC.sub.50 values from
functional in vitro or in vivo experiments. In the docking
simulation, it is preferred that .DELTA.G be lower than -6 kcal/mol
(preferably less than -7 kcal/mol, most preferably less than -10
kcal/mol). In functional nicotine stimulated Rb+ efflux
experiments, the IC.sub.50 value is preferrably lower than 100
.mu.M (preferred inhibitors exhibit an IC.sub.50<10 .mu.M).
3 k.sub.on [*10.sup.-6] k.sub.off log K k'.sub.(NLC)
[M.sup.-1s.sup.-1] [s.sup.-1] [M.sup.-1] tested drugs amantadine
8.98 30.8 6.73 6.66 bupropion 12.97 28.7 5.14 6.75 chlorpromazine
-- -- -- -- clozapine 155.17 24.8 0.55 7.65 dilthiazem 43.53 26.8
1.60 7.22 ketamine 8.25 38.4 8.50 6.65 laudanosine 22.87 25.0 2.18
7.06 mecamylamine 10.89 40.1 5.96 6.83 memantine 16.71 18.8 3.45
6.74 methadone 44.45 15.9 1.37 7.06 methamphetamine 8.38 29.1 6.81
6.63 MK-801 19.10 27.1 3.48 6.89 phenylcyclidine 24.06 23.2 2.69
6.94 quinacrine -- -- -- -- ethidium 191.82 35.9 0.18 8.30
dextromethorphan 61.30 23.7 1.01 7.37 levomethorphan 35.81 18.6
1.55 7.08 dextrorphan 26.79 20.7 2.30 6.95 3MM 56.47 18.8 1.00 7.28
3OM 26.45 14.3 1.97 6.86 verapamil-R 96.86 31.0 0.68 7.66
verapamil-S 96.32 30.6 0.66 7.66 nor-verapamil-R 97.99 16.0 0.58
7.44 nor-verapamil-S 97.86 15.6 0.61 7.40 galapamil 75.93 20.0 0.74
7.43 D-617 22.22 15.0 2.72 6.74 D-620 17.72 11.6 3.43 6.53 PR-22
99.29 16.0 0.53 7.48 PR-25 19.42 10.6 2.52 6.63 control compounds
acetaminophen 5.30 8.4 17.17 5.69 acetanilide 5.95 8.2 25.54 5.51
dimethoxybenzoic ac 4.46 9.8 18.21 5.73 dinitrobenzoic acid 7.77
9.1 12.12 5.87 phenylbutazone 6.29 8.7 22.22 5.59
[0096] Values of logK and k' presented in Table 3 can be regarded
as a measure of relative affinity of tested NCI compounds for the
nicotinic AChR. Among tested compounds, ethidium, clozapine,
verapamil and some of its congeners (PR-22, nor-verapamil and
galapamil) have the highest affinities towards the .alpha.3.beta.4
nicotinic receptor column as reflected by both logK and k'. Both
verapamil and nor-verapamil were tested for enantioselectivity of
binding towards nicotinic affinity column but chromatographic
experiments as well as NLC data did not exhibit noticeable
differences between enantiomers. Interestingly, dextromethorphan
exhibited markedly increased affinity compared to the optical
enantiomer levomethorphan.
[0097] The NLC approach allows estimating the kinetic rates of the
complex formation and dissociation, k.sub.on and k.sub.off,
respectively. The well-known and potent NCIs mecamylamine,
ketamine, ethidium and bupropion had high association constant
rates. Ketamine, methamphetamine, amantadine and mecamylamine
dissociated markedly quicker than other tested ligands. The lowest
dissociation constant rates exhibit ethitium, clozapine and
verapamil congeners.
4 Equation r F n log k' = 5.328(.+-.0.745) +
0.00633(.+-.0.000715)Volume + 0.519(.+-.0.0740)E.sub.HOMO - -0.165
.961 63.021 26 (.+-.0.0317)Hbond.sub.acceptors -
0.2087(.+-.0.0538)N.sub.order log k.sub.on = 4.152(.+-.0.595) +
1.474(.+-.0.483)RASA + 2.383(.+-.0.499)XY.sub.fract + +0.117 .802
9.441 26 (.+-.0.033)N.sub.order +
0.0486(.+-.0.0161)Hbond.sub.acceptors log k.sub.off =
-3.440(.+-.0.653) - 0.00654(.+-.0.000635)Volume -
0.507(.+-.0.0657)E.sub.HOMO + +0.168 .969 80.326 26
(.+-.0.0281)Hbond.sub.acceptors + 0.2308(.+-.0.0478)N.sub.order log
K = 9.830(.+-.0.752) + 0.00321(.+-.0.000652)TASA +
0.3982(.+-.0.0794)E.sub.HOMO - -0.057 .908 34.654 26
0.057(.+-.0.022)X.sub.length
[0098] Examples of complexes resulting from simulations are
provided in FIGS. 6-8. FIG. 6 shows the mecamylamine luminal domain
of .alpha.3.beta.4. FIG. 7 shows the MK-801 luminal domain of
.alpha.3.beta.4. FIG. 8 shows a two cluster interaction of the
ligand PCP with .alpha.3.beta.4.
[0099] Quantitative results of simulated docking affinities were
related to experimental results from chromatographic studies. Using
AutoDock's scoring function, estimated inhibition constant were
calculated. These values exhibited very good correlations with
affinity data from NLC calculations (FIG. 10). This correlation can
be illustrated by equation:
log k'=0.418(.+-.10.037) log(1/K.sub.i)-0.89(0.19)
r=0.930 F=127.7 n=22
[0100]
5 Descriptor DM LM functional IC.sub.50 [.mu.M] 10.10(.+-.1.10)
10.90(.+-.1.08) in vivo % recovery after 49.83(.+-.5.16)
79.00(.+-.3.50) 7 min. washout % recovery after 82.09(.+-.3.64)
94.09(.+-.4.43) 4 h. washout chromatographic k' 61.30(.+-.0.27)
35.81(.+-.0.15) (NLC and k.sub.on [.mu.M.sup.-1sec.sup.-1]
23.66(.+-.0.61) 18.61(.+-.0.38) van't Hoff) k.sub.off [sec.sup.-1]
1.01(.+-.0.01) 1.549(.+-.0.002) k.sub.a [.mu.M.sup.-1]
23.40(.+-.0.36) 12.01(.+-.0.23) logK.sub.a 7.37 7.08
.DELTA.H.degree. [kcal mol.sup.-1] -6.92(.+-.0.19) -6.59(.+-.0.18)
.DELTA.S.degree. [cal mol.sup.-1T.sup.-1] -15.70(.+-.0.7)
-15.20(0.6) .DELTA.G.degree. [kcal mol.sup.-1] -2.33(.+-.0.4)
-2.04(.+-.0.4) docking .DELTA.G [kcal mol.sup.-1] -8.73 -8.40
E.sub.docked [kcal mol.sup.-1] -8.84 -8.52 K.sub.i [M] 3.98 *
10.sup.-07 6.91 * 10.sup.-07 logK.sub.i -6.40 -6.16
[0101] Enantiomers have identical physiochemical properties and,
therefore, all possible non-specific interactions between the
enantiomers of a chiral NCI and an immobilized nAChR stationary
phase should be equivalent. Any differences in the chromatographic
retention between the enantiomers will be due to specific binding
interactions with the active site of the protein. FIG. 11 shows
chromatographic tracks of dextromethorphan (DM) and its
enantiomer--levomethorphan (LM). The pair of enantiomers was
further investigated by chromatographic, docking and functional
studies (Table 5). It was learned from the chromatographic
experiments that the drug dextromethorphan (DM) exert higher
affinity on .alpha.3.beta.4-nAChR than its enantiomer
levomethorphan (LM) and the difference in .DELTA.G of the complexes
was 0.3 kcal/mol. These data were valuable in evaluating parameter
selection during initial tests of the docking simulations to
optimally choose the channel dielectric constant or evaluate the
usefulness of the scoring function for calculating estimated
.DELTA.G implemented in AutoDock. The docking simulations give
insights into chiral recognition on the molecular level (FIG. 12).
Furthermore, the estimated inhibition constant obtained during the
simulations is very well correlated with equilibrium measures
obtained in affinity chromatographic experiments.
[0102] The invention may be embodied in other specific forms
without departing from the spirit or essential characteristics
thereof. The foregoing embodiments are therefore to be considered
in all respects illustrative rather than limiting on the invention
described herein. Scope of the invention is thus indicated by the
appended claims rather than by the foregoing description, and all
changes that come within the meaning and range of equivalency of
the claims are intended to be embraced therein.
[0103] All patent and literature references cited herein are hereby
incorporated by reference in their entirety and for all purposes,
including the following references:
[0104] 1. Wainer I W, Zhang Y, Xiao Y, Kellar K J (1999) Liquid
chromatographic studies with immobilized neuronal nicotinic
acetylcholine receptor stationary phases: effects of receptor
subtypes, pH and ionic strength on drug-receptor interactions. J
Chromatogr B Biomed Sci Appl 724:65-72.
[0105] 2. Zhang Y, Xiao Y, Kellar K J, Wainer I W (1998)
Immobilized nicotinic receptor stationary phase for on-line liquid
chromatographic determination of drug-receptor affinities. Anal
Biochem 264:22-5.
[0106] 3. Barrantes F J. (2002) Lipid matters: nicotinic
acetylcholine receptor-lipid interactions (Review). Mol Membr Biol
19:277-84.
[0107] 4. Morris G M, Goodsell D S, Halliday R S, et al. (1998)
Automated docking using a Lamarckian genetic algorithm and
empirical binding free energy function. 19:1639-62.
6APPENDIX 1 AMBER Scripts for stepwise refining the model All Runs
were made in AMBER 6.0. The computer used was SGI Octane. SGI
Octane information is given below:
----------------------------------------- 1 195 MHZ IP30 Processor
CPU: MIPS R10000 Processor Chip Revision: 2.7 FPU: MIPS R10010
Floating Point Chip Revision: 0.0 Main memory size: 1536 Mbytes
Xbow ASIC: Revision 1.3 Instruction cache size: 32 Kbytes Data
cache size: 32 Kbytes Secondary unified instruction/data cache
size: 1 Mbyte Integral SCSI controller 0: Version QL1040B (rev. 2),
single ended Disk drive: unit 1 on SCSI controller 0 Disk drive:
unit 2 on SCSI controller 0 Integral SCSI controller 1: Version
QL1040B (rev. 2), single ended IOC3 serial port: tty1 IOC3 serial
port: tty2 IOC3 parallel port: p1p1 Graphics board: SI Integral
Fast Ethernet: ef0, version 1, pci 2 Iris Audio Processor: version
RAD revision 12.0, number 1
------------------------------------------- ----------------------
The Amber runs were made on a potassium channel receptor model that
was built using the template structure of PDB entry 1EQ8 on Sybyl
6.8. Amber 6.0 was used to refine the structure that was built in
sybyl 6.8. Scripts used to do the energy minimization are attached
below: #++++++++++++++++++++++++++++++++++++++++++++++++++ SCRIPT 1
#------------------------------------------------------- #Script
for running Sander_Classic in AMBER 6.0 #Relaxing only Hydrogen
atoms ----Ravi (May 23, 2002) # # #ALL Hs are relaxed IBELLY
OPTION, .backslash.epsilon(r)
#----------------------------------------------------- &cntrl
timlim=36000., imin=1, nmropt=0, ntx=1, irest=0, ntrx=1, ntxo=1,
ntpr=10, ntwr=0, ntwx=50, ntwv=0, ntwe=50, ntwxm=0, ntwvm=0,
ntwem=0, ioutfm=0, ntwprt=0, ntf=1, ntb=0, idiel=0, dielc=4.0,
cut=9.0, ntnb=1, nsnb=25, ntid=0, scnb=2.0,scee=1.2, cut2nd=0.0
ichdna=0, isftrp=0, rwell=0.0, ipol=0, ibelly=1, ntr=0,
maxcyc=5000, ncyc=550, ntmin=1, dx0=0.01, dxm=0.05, &end GROUP
NUMBER 1 FIND * H * * * H1 * * * HC * * * HP * * * HO * * * HA * *
* HS * * SEARCH RES 1 125 END END
#+++++++++++++++++++++++++++++++++- +++++++++++++++++++ SCRIPT 2
#------------------------------- ------------------------- #Script
for running Sander_Classic in AMBER 6.0 #Relaxing Hydrogen +
Side-Chains ----Ravi (May 23, 2002) # #
#------------------------------------------------ -------- #
Channel ALL H+SC are moving # &cntrl timlim=36000., imin=1,
nmropt=0, ntx=1, irest=0, ntrx=1, ntxo=1, ntpr=5, ntwr=0, ntwx=50,
ntwv=0, ntwe=50, ntwxm=0, ntwvm=0, ntwem=0, ioutfm=0, ntwprt=0,
ntf=1, ntb=0, idiel=0, dielc=4.0, cut=9.0, ntnb=1, nsnb=25, ntid=0,
scnb=2.0,scee=1.2, cut2nd=0.0 ichdna=0, isftrp=0, rwell=0.0,
ipol=0, ibelly=1, ntr=0, maxcyc=5000, ncyc=250, ntmin=1, dx0=0.01,
dxm=0.05, &end GROUP NUMBER 1 FIND * CT 3 * * CA B* * CA S * *
OH S * * SH S * * S S* * C B* * N3 3 * * * E * SEARCH RES 1 125 END
END #++++++++++++++++++++++++++++++++++++++++++++++++++++ SCRIPT 3:
#------------------------------------------------------- #Script
for running Sander_Classic in AMBER 6.0 #Relaxing everything except
alpha-Carbons #----Ravi (May 23, 2002) # #
#------------------------------------------------------- # Except
Alpha C, all other atoms move # &cntrl timlim=36000., imin=1,
nmropt=0, ntx=1, irest=0, ntrx=1, ntxo=1, ntpr=5, ntwr=0, ntwx=50,
ntwv=0, ntwe=50, ntwxm=0, ntwvm=0, ntwem=0, ioutfm=0, ntwprt=0,
ntf=1, ntb=0, idiel=0, dielc=4.0, cut=9.0, ntnb=1, nsnb=25, ntid=0,
scnb=2.0,scee=1.2, cut2nd=0.0 ichdna=0, isftrp=0, rwell=0.0,
ipol=0, ibelly=1, ntr=0, maxcyc=5000, ncyc=250, ntmin=1, dx0=0.01,
dxm=0.05, &end GROUP NUMBER 1 FIND * * 3 * * * B * * * S * * *
E * N N M * C C M * CH3 CT M * * HC M * SEARCH RES 1 125 END END
#++++++++++++++++++++++++++++++++++++++++++++++++++++ SCRIPT 4:
#------------------------------------------------------- #Script
for running Sander_Classic in AMBER 6.0 #Restrained minimization of
the alpha-Carbons of the channel #----Ravi (May 23, 2002) # #
#-------------------------------------- ------------------ #
Restrained minimization of the alpha-Carbons # &cntrl
timlim=36000., imin=1, nmropt=0, ntx=1, irest=0, ntrx=1, ntxo=1,
ntpr=5, ntwr=0, ntwx=50, ntwv=0, ntwe=50, ntwxm=0, ntwvm=0,
ntwem=0, ioutfm=0, ntwprt=0, ntf=1, ntb=0, idiel=0, dielc=4.0,
cut=9.0, ntnb=1, nsnb=25, ntid=0, scnb=2.0,scee=1.2, cut2nd=0.0
ichdna=0, isftrp=0, rwell=0.0, ipol=0, ibelly=0, ntr=1,
maxcyc=2000, ncyc=250, ntmin=1, dx0=0.01, dxm=0.05, &end GROUP
NUMBER 1 10.0 FIND CA * * * SEARCH RES 1 125 END END
#++++++++++++++++++++++++++++++++++++++++++++++- ++++++
[0108]
7 receptor M3.pdbqs # macromolecule model gridfld M3.maps.fld #
grid_data_file npts 60 60 120 # num.grid points in xyz spacing
0.375 # spacing(A) gridcenter 0.009 0.026 -0.172 # xyz-coordinates
or auto types CANOH # atom type names smooth 0.5 # store minimum
energy w/in rad(A) map M3.C.map # atom-specific affinity map
nbp_r_eps 4.00 0.0222750 12 6 # C-C lj nbp_r_eps 3.75 0.0230026 12
6 # C-N lj nbp_r_eps 3.60 0.0257202 12 6 # C-O lj nbp_r_eps 4.00
0.0257202 12 6 # C-S lj nbp_r_eps 3.00 0.0081378 12 6 # C-H lj
nbp_r_eps 3.00 0.0081378 12 6 # C-H lj nbp_r_eps 3.00 0.0081378 12
6 # C-H lj sol_par 12.77 0.6844 # C atomic fragmental volume,
solvation parameters constant 0.000 # C grid map constant energy
map M3.A.map # atom-specific affinity map nbp_r_eps 4.00 0.0222750
12 6 # A-C lj nbp_r_eps 3.75 0.0230026 12 6 # A-N lj nbp_r_eps 3.60
0.0257202 12 6 # A-O lj nbp_r_eps 4.00 0.0257202 12 6 # A-S lj
nbp_r_eps 3.00 0.0081378 12 6 # A-H lj nbp_r_eps 3.00 0.0081378 12
6 # A-H lj nbp_r_eps 3.00 0.0081378 12 6 # A-H lj sol_par 10.80
0.1027 # A atomic fragmental volume, solvation parameters constant
0.000 # A grid map constant energy map M3.N.map # atom-specific
affinity map nbp_r_eps 3.75 0.0230026 12 6 # N-C lj nbp_r_eps 3.50
0.0237600 12 6 # N-N lj nbp_r_eps 3.35 0.0265667 12 6 # N-O lj
nbp_r_eps 3.75 0.0265667 12 6 # N-S lj nbp_r_eps 1.90 0.3280000 12
10 # N-N hb nbp_r_eps 1.90 0.3280000 12 10 # N-H hb nbp_r_eps 1.90
0.3280000 12 10 # N-H hb sol_par 0.00 0.0000 # N atomic fragmental
volume, solvation parameters constant 0.000 # N grid map constant
energy map M3.O.map # atom-specific affinity map nbp_r_eps 3.60
0.0257202 12 6 # O-C lj nbp_r_eps 3.35 0.0265667 12 6 # O-N lj
nbp_r_eps 3.20 0.0297000 12 6 # O-O lj nbp_r_eps 3.60 0.0297000 12
6 # O-S lj nbp_r_eps 1.90 0.3280000 12 10 # O-H hb nbp_r_eps 1.90
0.3280000 12 10 # O-H hb nbp_r_eps 1.90 0.3280000 12 10 # O-H hb
sol_par 0.00 0.0000 # O atomic fragmental volume, solvation
parameters constant 0.236 # O grid map constant energy map M3.H.map
# atom-specific affinity map nbp_r_eps 3.00 0.0081378 12 6 # H-C lj
nbp_r_eps 1.90 0.3280000 12 10 # H-N hb nbp_r_eps 1.90 0.3280000 12
10 # H-O hb nbp_r_eps 3.00 0.0093852 12 6 # H-S lj nbp_r_eps 2.00
0.0029700 12 6 # H-H lj nbp_r_eps 2.00 0.0029700 12 6 # H-H lj
nbp_r_eps 2.00 0.0029700 12 6 # H-H lj sol_par 0.00 0.0000 # H
atomic fragmental volume, solvation parameters constant 0.118 # H
grid map constant energy elecmap M3.e.map # electrostatic potential
map dielectric 15.0 # <0, distance-dep.diel;>0, constant fmap
M3.f.map # floating point potential gridmap
[0109]
8 seed pid time # seeds for random generator types CANOH # atom
type names fld M3.maps.fld # grid_data_file map M3.C.map #
atom-specific affinity map map M3.A.map # atom-specific affinity
map map M3.N.map # atom-specific affinity map map M3.O.map #
atom-specific affinity map map M3.H.map # atom-specific affinity
map map M3.e.map # electrostatics map move DMT.out.pdbq # small
molecule about 0.088 0.126 0.069 # small molecule center tran0
random # initial coordinates/A or random quat0 random # initial
quaternion ndihe 1 # number of active torsions dihe0 random #
initial dihedrals (relative) or random tstep 2.0 # translation
step/A qstep 50.0 # quaternion step/deg dstep 50.0 # torsion
step/deg torsdof 1 0.3113 # torsional degrees of freedom and
coeffiecent intnbp_r_eps 4.00 0.0222750 12 6 # C-C lj intnbp_r_eps
4.00 0.0222750 12 6 # C-A lj intnbp_r_eps 3.75 0.0230026 12 6 # C-N
lj intnbp_r_eps 3.60 0.0257202 12 6 # C-O lj intnbp_r_eps 3.00
0.0081378 12 6 # C-H lj intnbp_r_eps 4.00 0.0222750 12 6 # A-A lj
intnbp_r_eps 3.75 0.0230026 12 6 # A-N lj intnbp_r_eps 3.60
0.0257202 12 6 # A-O lj intnbp_r_eps 3.00 0.0081378 12 6 # A-H lj
intnbp_r_eps 3.50 0.0237600 12 6 # N-N lj intnbp_r_eps 3.35
0.0265667 12 6 # N-O lj intnbp_r_eps 2.75 0.0084051 12 6 # N-H lj
intnbp_r_eps 3.20 0.0297000 12 6 # O-O lj intnbp_r_eps 2.60
0.0093852 12 6 # O-H lj intnbp_r_eps 2.00 0.0029700 12 6 # H-H lj
outlev 1 # diagnostic output level rmstol 0.5 # cluster_tolerance/A
extnrg 1000.0 # external grid energy e0max 0.0 10000 # max initial
ernergy; max number of retries ga_pop_size 50 # number of
individuals in population ga_num_evals 5000000 # maximum number of
energy evaluations ga_num_generations 27000 # maximum number of
generations ga_elitism 1 # number of top individuals to survive to
next generation ga_mutation_rate 0.02 # rate of gene mutation
ga_crossover_rate 0.8 # rate of crossover ga_window_size 10 #
ga_cauchy_alpha 0.0 # Alpha parameter of Cauchy distribution
ga_cauchy_beta 1.0 # Beta parameter Cauchy distribution set_ga #
set the above parameters for GA or LGA sw_max_its 300 # iterations
of Solis & Wets local search sw_max_succ 4 # consecutive
successes before changing rho sw_max_fail 4 # consecutive failures
before changing rho sw_rho 1.0 # size of local search space to
sample sw_lb_rho 0.01 # lower bound on rho ls_search_freq 0.06 #
probability of performing local search on individual set_psw1 # set
the above pseudo-Solis & Wets parameters ga_run 50 # do this
many hybrid GA-LS runs analysis # perform a ranked cluster
analysis
[0110]
Sequence CWU 1
1
15 1 23 PRT Unknown Table 1 Delta Sequence - Transmembrane domain
of ligand gated ion channel subunit 1 Glu Lys Met Ser Thr Ala Ile
Ser Val Leu Leu Ala Gly Ala Val Phe 1 5 10 15 Leu Leu Leu Thr Ser
Gly Arg 20 2 23 PRT Unknown Table 1 Gamma Sequence - Transmembrane
domain of ligand gated ion channel subunit 2 Gln Lys Cys Thr Leu
Ser Ile Ser Val Leu Leu Ala Gln Thr Ile Phe 1 5 10 15 Leu Phe Leu
Ile Ala Gln Lys 20 3 23 PRT Unknown Table 1 Alpha 1 Sequence -
Transmembrane domain of ligand gated ion channel subunit 3 Glu Lys
Met Thr Leu Ser Ile Ser Val Leu Leu Ser Leu Thr Val Phe 1 5 10 15
Leu Leu Val Ile Val Glu Leu 20 4 23 PRT Unknown Table 1 Alpha 3
Sequence - Transmembrane domain of ligand gated ion channel subunit
4 Glu Lys Val Thr Leu Cys Ile Ser Val Leu Leu Ser Leu Thr Val Phe 1
5 10 15 Leu Leu Val Ile Thr Glu Thr 20 5 23 PRT Unknown Table 1
Alpha 4 Sequence - Transmembrane domain of ligand gated ion channel
subunit 5 Glu Lys Ile Thr Leu Cys Ile Ser Val Leu Leu Ser Leu Thr
Val Phe 1 5 10 15 Leu Leu Leu Ile Thr Glu Ile 20 6 23 PRT Unknown
Table 1 Alpha 5 Sequence - Transmembrane domain of ligand gated ion
channel subunit 6 Glu Lys Ile Cys Leu Cys Thr Ser Val Leu Val Ser
Leu Thr Val Phe 1 5 10 15 Leu Leu Val Ile Glu Glu Ile 20 7 23 PRT
Unknown Table 1 Alpha 6 Sequence - Transmembrane domain of ligand
gated ion channel subunit 7 Glu Lys Val Thr Leu Cys Ile Ser Val Leu
Leu Ser Leu Thr Val Phe 1 5 10 15 Leu Leu Val Ile Thr Glu Thr 20 8
23 PRT Unknown Table 1 Alpha 7 Sequence - Transmembrane domain of
ligand gated ion channel subunit 8 Glu Lys Ile Ser Leu Gly Ile Thr
Val Leu Leu Ser Leu Thr Val Phe 1 5 10 15 Met Leu Leu Val Ala Glu
Ile 20 9 23 PRT Unknown Table 1 Alpha 9 Sequence - Transmembrane
domain of ligand gated ion channel subunit 9 Glu Lys Val Ser Leu
Gly Val Thr Ile Leu Leu Ala Met Thr Val Phe 1 5 10 15 Gln Leu Met
Val Ala Glu Ile 20 10 23 PRT Unknown Table 1 Alpha 10 Sequence -
Transmembrane domain of ligand gated ion channel subunit 10 Glu Lys
Val Ser Leu Gly Val Thr Val Leu Leu Ala Leu Thr Val Phe 1 5 10 15
Gln Leu Ile Leu Ala Glu Ser 20 11 23 PRT Unknown Table 1 Beta 1
Sequence - Transmembrane domain of ligand gated ion channel subunit
11 Glu Lys Met Gly Leu Ser Ile Phe Ala Leu Leu Thr Leu Thr Val Phe
1 5 10 15 Leu Leu Leu Leu Ala Asp Lys 20 12 23 PRT Unknown Table 1
Beta 2 Sequence - Transmembrane domain of ligand gated ion channel
subunit 12 Glu Lys Met Thr Leu Cys Ile Ser Val Leu Leu Ala Leu Thr
Val Phe 1 5 10 15 Leu Leu Leu Ile Ser Lys Ile 20 13 23 PRT Unknown
Table 1 Beta 3 Sequence - Transmembrane domain of ligand gated ion
channel subunit 13 Glu Lys Leu Ser Leu Ser Thr Ser Val Leu Val Ser
Leu Thr Val Phe 1 5 10 15 Leu Leu Val Ile Glu Glu Ile 20 14 23 PRT
Unknown Table 1 Beta 4 Sequence - Transmembrane domain of ligand
gated ion channel subunit 14 Glu Lys Met Thr Leu Cys Ile Ser Val
Leu Leu Ala Leu Thr Phe Phe 1 5 10 15 Leu Leu Leu Ile Ser Lys Ile
20 15 23 PRT Unknown Table 1 Epsilon Sequence - Transmembrane
domain of ligand gated ion channel subunit 15 Gln Lys Cys Thr Val
Ser Ile Asn Val Leu Leu Ala Gln Thr Val Phe 1 5 10 15 Leu Phe Phe
Leu Ile Ala Gln 20
* * * * *