WO1996030849B1 - Consensus configurational bias monte carlo method and system for pharmacophore structure determination - Google Patents
Consensus configurational bias monte carlo method and system for pharmacophore structure determinationInfo
- Publication number
- WO1996030849B1 WO1996030849B1 PCT/US1996/004229 US9604229W WO9630849B1 WO 1996030849 B1 WO1996030849 B1 WO 1996030849B1 US 9604229 W US9604229 W US 9604229W WO 9630849 B1 WO9630849 B1 WO 9630849B1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- compound
- compounds
- conformational
- pharmacophore
- structures
- Prior art date
Links
- 238000000342 Monte Carlo simulation Methods 0.000 title claims abstract 6
- 238000005481 NMR spectroscopy Methods 0.000 claims abstract 10
- 239000003814 drug Substances 0.000 claims abstract 9
- 229940079593 drugs Drugs 0.000 claims abstract 9
- 150000002611 lead compounds Chemical class 0.000 claims abstract 9
- 102000004196 processed proteins & peptides Human genes 0.000 claims abstract 6
- 108090000765 processed proteins & peptides Proteins 0.000 claims abstract 6
- 238000004351 rotational echo double resonance Methods 0.000 claims abstract 4
- 150000001875 compounds Chemical class 0.000 claims 146
- 230000004075 alteration Effects 0.000 claims 24
- 230000002153 concerted Effects 0.000 claims 16
- 210000004940 Nucleus Anatomy 0.000 claims 14
- 239000000126 substance Substances 0.000 claims 12
- 230000003993 interaction Effects 0.000 claims 11
- 239000000758 substrate Substances 0.000 claims 11
- 125000004429 atoms Chemical group 0.000 claims 9
- 239000000203 mixture Substances 0.000 claims 9
- 238000005259 measurement Methods 0.000 claims 7
- 102000037240 fusion proteins Human genes 0.000 claims 6
- 108020001507 fusion proteins Proteins 0.000 claims 6
- 230000005284 excitation Effects 0.000 claims 4
- 230000001939 inductive effect Effects 0.000 claims 4
- 230000004913 activation Effects 0.000 claims 2
- 239000011324 bead Substances 0.000 claims 2
- 230000001934 delay Effects 0.000 claims 2
- -1 divinylbenzyl Chemical group 0.000 claims 2
- 230000000694 effects Effects 0.000 claims 2
- 239000011521 glass Substances 0.000 claims 2
- 239000012071 phase Substances 0.000 claims 2
- 229920005990 polystyrene resin Polymers 0.000 claims 2
- 239000011148 porous material Substances 0.000 claims 2
- 229920005989 resin Polymers 0.000 claims 2
- 239000011347 resin Substances 0.000 claims 2
- 238000009987 spinning Methods 0.000 claims 2
- 210000002729 Polyribosomes Anatomy 0.000 claims 1
- LEVWYRKDKASIDU-IMJSIDKUSA-N cystine group Chemical group C([C@@H](C(=O)O)N)SSC[C@@H](C(=O)O)N LEVWYRKDKASIDU-IMJSIDKUSA-N 0.000 claims 1
- 238000002592 echocardiography Methods 0.000 claims 1
- 230000002068 genetic Effects 0.000 claims 1
- 239000007791 liquid phase Substances 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 claims 1
- 238000000034 method Methods 0.000 claims 1
- 230000004048 modification Effects 0.000 claims 1
- 238000006011 modification reaction Methods 0.000 claims 1
- 239000012299 nitrogen atmosphere Substances 0.000 claims 1
- 238000002823 phage display Methods 0.000 claims 1
- 239000007787 solid Substances 0.000 claims 1
- 239000007790 solid phase Substances 0.000 claims 1
- 230000002194 synthesizing Effects 0.000 claims 1
- 230000002103 transcriptional Effects 0.000 claims 1
- 238000009509 drug development Methods 0.000 abstract 1
Abstract
In a specific embodiment, this invention comprises a method for selecting highly targeted lead compounds for design of a drug that binds to a target molecule. The method comprises screening a diversity library against the target molecule of interest to pick the selectively binding members. Next the structure of the selected members is examined and a candidate pharmacophore responsible for the binding to the target molecule is determined. Next, preferably by REDOR nuclear magnetic resonance, several highly accurate interatomic distances are determined in certain of the selected members which are related to the candidate pharmacophore. A highly accurate consensus, configurational bias, Monte Carlo method determination of the structure of the candidate pharmacophore is made using the structure of the selected members and incorporating as constraints the shared selected members and incorporating as constraints the shared candidate phamacophore and the several measured distances. This determination is adapted to efficiently examine only relatively low energy configurations while respecting any structural constraints present in the organic diversity library. If the diversity library contains short peptides, the determination respects the known degrees of freedom of peptides as well as any internal constraints, such as those imposed by disulfide bridges. Finally, the highly accurate pharmacophore so determined is used to select lead organics for drug development targeted at the initial target molecule.
Claims
1. A method of determining a consensus pharmacophore structure comprising the steps of:
(a) identifying from one or more diversity libraries a plurality of compounds that bind to a target molecule,
(b) measuring one or more distances in one or more of the compounds, and
(c) determining a consensus pharmacophore structure for the compounds.
2. The method of claim 1 wherein said compounds are peptides, peptide derivatives, or peptide analogs.
3. The method of claim 2 wherein said compounds are peptides containing one or more cystines.
4. The method of claim 3 wherein the peptides comprise the sequence CX£C (SEQ ID NO:l).
5. The method of claim 1 further comprising a step of selecting a plurality of candidate pharmacophores based on chemical structures of said compounds, the selected plurality of candidate pharmacophores being used in step (c) to determine the consensus pharmacophore structure.
6. The method of claim 5 wherein said selecting is further according to rules of ho ology that determine that two candidate pharmacophores are homologous if they have chemically similar side chains.
7. The method of claim 1 which further comprises after said identifying step, a screening step involving a genetic selection technique.
8. The method of claim 1 wherein the step of measuring distance comprises making solid- phase nuclear magnetic
-342- resonance measurements on selected nuclei in a nuclear magnetic resonance spectrometer upon a sample comprising one of the compounds.
5 9. The method of claim 8 wherein the step of measuring distances further comprises making rotational echo double resonance nuclear magnetic resonance measurements of internuclear dipole-dipole interaction strength between selected nuclei in the compound in the sample. 10
10. The method of claim 8 wherein the sample further comprises a substrate having a surface to which the compound is attached.
15 11. The method of claim 8 wherein the sample is cooled below room temperature.
12. The method of claim 8 wherein the compound is bound to the target molecule.
20
13. The method of claim 10 wherein a plurality of the compound is attached to the surface at a surface density such that the inter-nuclear dipole-dipole interactions between different molecules is less than 10% of the
25 inter-nuclear dipole-dipole interaction within one molecule.
14. The method of claim 10 wherein the substrate has pores of sufficient size to permit the target to diffuse and
30 bind to the compound in the sample.
15. The method of claim 9 wherein rotational echo double resonance nuclear magnetic resonance measurements can be made on the compound bound to the target or hydrated or
35 in a dry nitrogen atmosphere.
-343-
16. The method of claim 10 wherein the compound is a peptide, and a plurality of the peptide is attached to the substrate surface, which has a purity of the peptide of at least 95% and wherein the surface density of the
5 peptide is no more than one peptide per 100 A2 of substrate surface.
17. The method of claim 10 wherein the substrate is selected from the group consisting of p-MethylBenzhydrilamine
10 resin, divinylbenzyl polystyrene resin, and glass beads.
18. The method of claim 8 wherein the selected nuclei are selected from the group consisting of 13C, 15N, 19F, and 31P.
15
19. The method of claim 9 wherein the nuclear magnetic resonance spectrometer comprises magnetic excitation means, a sample rotor, and free induction decay observing means, and the step of making rotational echo 0 double resonance nuclear magnetic resonance measurements further comprises the steps of:
(a) spinning the sample in the sample rotor,
(b) initially exciting magnetically the selected nuclei to be observed,
25 (c) providing subsequently one π spin flip magnetic excitation during each rotor period to each of the selected nuclei, the pulses to the different nuclei having fixed phase delays,
(d) observing the free induction decay signal as a 30 function of the number of rotor periods; and
(e) finding the dipole-dipole strength between the selected nuclei, whereby the internuclear distance between the selected nuclei can be obtained.
35 20. The method of claim 1 wherein the step of measuring distances comprises making liquid phase nuclear magnetic resonance measurements.
21. A method of determining a consensus pharmacophore structure comprising the steps of:
(a) identifying from one or more diversity libraries a plurality of compounds that bind to a target molecule,
(b) determining a consensus pharmacophore structure for the compounds.
22. A method of determining a consensus pharmacophore structure comprising the steps of:
(a) measuring one or more distances in one or more compounds that bind to a target molecule, and
(b) determining a consensus pharmacophore structure for the compounds that is constrained by said distances.
23. The method of claim 21 or 22 further comprising a step of selecting a plurality of candidate pharmacophores based on chemical structures of said compounds, the selected plurality of candidate pharmacophores being used in step (b) to determine the consensus pharmacophore structure.
24. The method of claim 21 or 22 wherein the compounds have limited conformational degrees of freedom at the temperature of interest, and wherein the step of determining a consensus pharmacophore structure for each compound further comprises, performing a consensus configurational bias Monte Carlo method, said Monte Carlo method comprising the steps of:
(a) generating a proposed structure for a compound identified from said one or more diversity libraries by making conformational alterations consistent with the conformational degrees of freedom, the alterations being made to a representation of the compound's current chemical and conformational structure to generate a proposed
-345-
AMENDED SHEET (ARTICLE 19] representation, the proposed structure being generated with a bias toward more acceptable configurations of lower energy, whereby the method is made more efficient, (b) accepting and storing the proposed structure according to a probability depending on an energy determined for the proposed structure, and (c) repeating these steps until sufficient structures have been stored for each compound to permit statistically significant determination of an equilibrium structure for each compound.
25. A method of determining one or more lead compounds for use as a drug that binds to a target molecule comprising the steps of:
(a) identifying from one or more diversity libraries a plurality of compounds that bind to a target molecule;
(b) determining a consensus pharmacophore structure for the compounds; and
(c) determining one or more lead compounds for use as a drug which share a pharmacophore specification with the determined consensus pharmacophore structure.
26. A method of determining one or more lead compounds for use as a drug that binds to a target molecule comprising the steps of:
(a) measuring one or more distances in one or more compounds that bind to a target molecule; (b) determining a consensus pharmacophore structure for the compounds that is constrained by said distances; and (c) determining one or more lead compounds for use as a drug which share a pharmacophore specification with the determined consensus pharmacophore structure.
-346-
AMENDED SHEET (ARTICLE 13)
27. The method according to claim 25 or 26 wherein said step of determining one or more lead compounds comprises modifying a compound identified as binding to the target molecule, said modification being done outside of the pharmacophore structure, to render the compound more attractive for use as a drug.
28. The method of claim 1 wherein the compounds have limited conformational degrees of freedom at a temperature of interest, and wherein the step of determining a consensus pharmacophore structure for the compounds further comprises performing a consensus configurational bias Monte Carlo method, said Monte Carlo method comprising the steps of: (a) generating a proposed structure for a compound identified from said one or more diversity libraries by making conformational alterations consistent with the conformational degrees of freedom, the alterations being made to a representation of the compound's current chemical and conformational structure to generate a proposed representation, the proposed structure being generated with a bias toward more acceptable configurations of lower energy, (b) accepting and storing the proposed structure according to a probability depending on an energy determined for the proposed structure, and (c) repeating these steps until sufficient structures have been stored for each compound to permit statistically significant determination of an equilibrium structure for each compound.
29. The method of claim 28 wherein the limited conformational degrees of freedom comprise torsional rotations about mutual bonds between otherwise rigid subunits of the compound, each rigid unit's representation comprising its interconnections and
-347- atomic composition, each atom's representation comprising its type and position, the torsional rotations respecting any conformational constraints present.
30. The method of claim 28 wherein the compound is a peptide, peptide derivative, or peptide analog.
31. The method of claim 28 wherein the conformational alterations comprise constrained, concerted torsional rotations or removal of a side chain and regrowth of the side chain with a new torsional conformation.
32. The method of claim 31 wherein the constrained, concerted torsional rotations are constrained so that no more than four rigid units are spatially displaced.
33. The method of claim 28 wherein determining the energy for the proposed structure of one compound comprises including one or more constraint terms which represent knowledge of measured structure for the compound.
34. The method of claim 33 wherein the constraint terms comprise a weighted sum of squares of differences of the actual and measured structures.
35. The method of claim 28 wherein the energy is determined for the proposed structure of one compound by a method comprising including consensus terms which represent knowledge that the identified compounds all bind to the same target, the compounds being otherwise treated independently by the method.
36. The method of claim 35 wherein the consensus terms are a weighted sum of squares of differences in the atomic positions of a candidate pharmacophore from the average values of these positions in all the compounds.
-348-
A ENDE0 SHEET (ARTICLE 19)
37. The method of claim 35 wherein the step of determining the consensus pharmacophore structure comprises determining a candidate pharmacophore for which the consensus terms are relatively small compared to the total energy.
38. The method of claim 35 wherein the step of determining the consensus pharmacophore structure comprises determining a candidate pharmacophore for which the consensus terms are minimum compared to other selected regions.
39. The method of claim 28 wherein the equilibrium structure is determined by a method comprising averaging selected generated and accepted structures for each compound.
40. The method of claim 39 wherein the averaging of structures comprises clustering selected generated and accepted structures into sets of similar structures and averaging these sets for each member.
41. A method of identifying a compound that binds to a target molecule comprising the following steps in the order stated: (a) contacting compounds of a phage display or polysome-based diversity library with a target molecule; (b) identifying one or more compounds in the library that bind to the target molecule; (c) contacting one or more first fusion proteins, each first fusion protein comprising an identified compound, with a second fusion protein comprising the target molecule or a binding portion thereof, in which binding of the first fusion protein to the second fusion protein results in an increase in activity or activation of a transcriptional promoter or an origin of replication; and
-349- (d) identifying one or more of the compounds that when present in said first fusion protein result in said increase in activity or activation.
42. A method of making solid state nuclear magnetic resonance measurements comprising measuring internuclear dipole-dipole interaction strengths between selected nuclei in a compound, said compound being covalently attached to the surface of a substrate.
43. The method of claim 42 which further comprises before said measuring step the step of synthesizing a plurality of said compound on the surface of the substrate.
44. The method of claim 43 wherein said plurality of the compound is at least 95% pure.
45. The method of claim 42 wherein a plurality of said compound is attached to the substrate surface, with at least 10 A spacing between molecules of the compound.
46. The method of claim 42 wherein the substrate has pores of sufficient size to permit a molecule to diffuse and bind to the compound.
47. The method of claim 42 wherein the substrate has a surface density of the compound such that the inter¬ nuclear dipole-dipole interactions between different molecules of the compound is less than 10% of the inter- nuclear dipole-dipole interaction within one molecule of the compound.
48. The method of claim 42 wherein the compound is a peptide, peptide derivative, or peptide analog.
49. The method of claim 42 wherein the substrate is selected from the group consisting of p-MethylBenzhydrilamine
-350- resin, divinylbenzyl polystyrene resin, and a glass bead.
50. The method of claim 42 wherein said measuring step comprises using a nuclear magnetic resonance spectrometer, said spectrometer comprising magnetic excitation means, a sample rotor, and free induction decay observing means; and said measurement of internuclear dipole-dipole interaction is done by a method comprising the steps of:
(a) spinning the sample in the sample rotor;
(b) initially exciting magnetically the selected nuclei to be observed;
(c) providing subsequently one or more π spin flip magnetic excitations during each rotor period to one or both of the selected nuclei, wherein pulses to the different nuclei have fixed phase delays;
(d) observing a free induction decay signal as a function of the number of rotor periods; and (e) determining the dipole-dipole strength between the selected nuclei, whereby the internuclear distance between the selected nuclei can be obtained.
51. A method of configurational bias Monte Carlo determination of the structure of a compound having limited conformational degrees of freedom at a temperature of interest, the method comprising the steps of:
(a) generating a proposed structure for the compound by making conformational alterations consistent with the conformational degrees of freedom, the alterations being made to a representation of the compound's current chemical and conformational structure to generate a proposed representation, said proposed structure being generated with a bias toward more acceptable configurations of lower energy;
-351- (b) accepting and storing the proposed structure according to a probability depending on an energy determined for the proposed structure; and
(c) repeating these steps until sufficient structures 5 have been stored to permit statistically significant determination of an equilibrium structure.
52. The method of claim 51 wherein the conformational
10 degrees of freedom comprise torsional rotations about mutual bonds between otherwise rigid subunits of the compound, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, the
15 torsional rotations respecting any conformational constraints present.
53. The method of claim 51 wherein the compound is a peptide, peptide derivative, or peptide analog.
20
54. The method of claim 51 wherein the conformational alterations comprise constrained, concerted torsional rotations.
25 55. The method of claim 54 wherein the constrained, concerted torsional rotations are constrained so that no more than four rigid units are spatially displaced.
56. The method of claim 51 wherein the conformational 30 alterations comprise removal of a side chain and regrowth of the side chain with a new torsional conformation.
57. The method of claim 51 wherein the energy is determined 35 for the proposed structure by a method comprising including constraint terms which represent knowledge of measured structure for the compound.
-352-
58. The method of claim 57 wherein the constraint terms comprise a weighted sum of squares of differences of the actual and measured structures.
5 59. The method of claim 51 applied to a plurality of compounds of limited conformational degrees of freedom all of which bind to the same target molecule wherein the method further comprises a step of selecting a plurality of candidate pharmacophores based on chemical 10 structures of said compounds.
60. The method of claim 51 wherein the energy is determined for the proposed structure of one of the plurality of compounds by a method comprising including consensus
15 terms which represent knowledge that the compounds all bind to the same target molecule.
61. The method of claim 61 wherein the consensus terms are a weighted sum of squares of differences in the atomic
20 positions of a candidate pharmacophore of said one of the plurality of compounds from the average values of these positions in all the compounds.
62. The method of claim 61 which further comprises a step of 25 determining a consensus pharmacophore structure by determining a candidate pharmacophore for which the consensus terms are minimum compared to other candidate pharmacophores.
30 63. The method of claim 60 which further comprises a step of determining a consensus pharmacophore structure by determining a candidate pharmacophore for which the consensus terms are relatively small compared to the total energy.
35
64. The method of claim 62 or 63 which further comprises a step of determining one or more lead compounds for use
-353- as a drug which share a pharmacophore specification with the determined consensus pharmacophore structure.
65. The method of claim 51 wherein the equilibrium structure is determined by a method comprising averaging selected generated and accepted structures.
66. The method of claim 66 wherein the averaging of structures comprises clustering selected generated and accepted structures into sets of similar structures and averaging these sets.
67. An apparatus for configurational bias Monte Carlo determination of the structure of a compound having limited conformational degrees of freedom at a temperature of interest, the apparatus comprising:
(a) memory means for storing
(i) data structures representing the compound's chemical and conformational structure consistently with the compound's degrees of freedom, said data structures capable of representing substantially continuous changes in said compound's conformational structure; (ii) similar data structures representing the compound's proposed structure and prior structures, and (iii) parameters representing atomic interactions, and
(b) processor means for executing programs for (i) generating a proposed structure by making conformational alterations consistent with the conformational degrees of freedom and with a bias toward more acceptable configurations of lower energy, (ii) accepting and storing the proposed structure according to a probability depending on an
-354- energy determined for the proposed structure, and (iii) repeating these steps until sufficient structures have been stored to permit 5 statistically significant determination of an equilibrium structure.
68. The apparatus of claim 67 wherein the conformational degrees of freedom comprise torsional rotations about
10 mutual bonds between otherwise rigid subunits of the compound, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, the torsional rotations respecting any conformational
15 constraints present.
69. The apparatus of claim 67 wherein the compound is a peptide, peptide derivative, or peptide analog.
20 70. The apparatus of claim 67 wherein the memory, processor, and control means are configured from a workstation type digital computer comprising RAM memory, disk memory, processor, and input and display devices.
25 71. The apparatus of claim 67 wherein the conformational alterations made by the processor means further comprise constrained, concerted torsional rotations or removal of a side chain and regrowth of the side chain with a new torsional conformation.
30
72. The apparatus of claim 71 wherein the constrained, concerted torsional rotations are constrained so that no more than four rigid units are spatially displaced.
35 73. The apparatus of claim 67 wherein the processor means determines an energy for the proposed structure by a method comprising including constraint terms which
, -355- represent knowledge of measured structure for the compound.
74. The apparatus of claim 73 wherein the constraint terms
5 comprise a weighted sum of squares of differences of the actual and measured structures.
75. The apparatus of claim 67 applied to a plurality of compounds of limited conformational degrees of freedom
10 all of which bind to the same target molecule, and wherein the processor means further comprises programs for selecting a plurality of candidate pharmacophores based chemical structures of said compounds.
15 76. The apparatus of claim 67 wherein the processor means determines an energy for the proposed structure of any one compound by a method comprising including consensus terms which represent knowledge that the compounds all bind to the same target molecule.
20
77. The apparatus of claim 76 wherein the consensus terms are a weighted sum of squares of differences in the atomic positions of a candidate pharmacophore of said one compound from the average values of these positions
25 in all the compounds.
78. The apparatus of claim 76 wherein the processor means further comprises programs for determining a consensus pharmacophore structure by determining a candidate
30 pharmacophore for which the consensus terms are minimum compared to other candidate pharmacophores.
79. The apparatus of claim 76 wherein the processor means further comprises programs for determining a consensus
35 pharmacophore structure by determining a candidate pharmacophore for which the consensus terms are relatively small compared to the total energy.
-356-
80. The apparatus of claim 78 or 79 wherein the processor means further comprises programs for determining one or more lead compounds for use as a drug that share a pharmacophore specification with the consensus pharmacophore structure.
81. The apparatus of claim 67 wherein the processor means determines an equilibrium structure by a method comprising averaging selected generated and accepted structures.
82. The apparatus of claim 81 wherein the averaging of structures further comprises clustering selected generated and accepted structures into sets of similar structures and averaging these sets.
83. In a digital computer, apparatus for configurational bias Monte Carlo determination of the structure of at least one compound having limited conformational degrees of freedom at a temperature of interest, said apparatus comprising:
(a) first memory means for storing data structures representing the compound's chemical and conformational structure consistently with the compound's degrees of freedom, said data structures capable of representing substantially continuous changes in said compound's conformational structure;
(b) second memory means for storing similar data structures representing the compound's proposed structure,
(c) third memory means for storing similar data structures representing the compound's prior structures, (d) first processor means for generating a proposed structure by making conformational alterations consistent with the conformational degrees of
-357- freedom and with a bias toward conformations of lower energy,
(e) second processor means for accepting and storing the proposed structure according to a probability depending on an energy determined for the proposed structure, and
(f) third processor means for controlling and repeating the generation and acceptance until sufficient structures have been stored to permit statistically significant determination of an equilibrium structure.
84. The digital computer apparatus of claim 83 wherein the conformational degrees of freedom comprise torsional rotations about mutual bonds between otherwise rigid subunits of the compound, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, the torsional rotations respecting any conformational constraints present.
85. The digital computer apparatus of claim 83 wherein the compound is a peptide, peptide derivative, or peptide analog.
86. The digital computer apparatus of claim 83 wherein the digital computer is a workstation type digital computer comprising RAM memory, disk memory, processor, and input and display devices.
87. The digital computer apparatus of claim 83 wherein the conformational alterations generated by the first processor means comprise constrained, concerted torsional rotations or removal of a side chain and regrowth of the side chain with a new torsional conformation.
-358-
88. The digital computer apparatus of claim 87 wherein the constrained, concerted torsional rotations are constrained so that no more than four rigid units are spatially displaced.
89. The digital computer apparatus of claim 83 wherein the second processor means determines an energy for the proposed structure by a method comprising including constraint terms which represent knowledge of measured structure for the compound.
90. The digital computer apparatus of claim 89 wherein the constraint terms comprise a weighted sum of squares of differences of the actual and measured structures.
91. The digital computer apparatus of claim 83 in which said at least one compound is a plurality of compounds of limited conformational degrees of freedom all of which bind to the same target and wherein data are stored in said first memory means representing the chemical and conformational structure of said plurality of compounds and wherein the apparatus further comprises additional processor means for selecting a plurality of candidate pharmacophores based on chemical structures of said compounds.
92. The digital computer apparatus of claim 83 wherein the second processor means determines an energy for the proposed structure of one of said plurality of compounds by a method comprising including consensus terms which represent knowledge that the compounds all bind to the same target molecule.
93. The digital computer apparatus of claim 91 wherein the consensus terms are a weighted sum of squares of differences in the atomic positions of a candidate pharmacophore of said one of the plurality of compounds
-359- from the average values of these positions in all the compounds.
94. The digital computer apparatus of claim 92 wherein the apparatus further comprises processor means for determining a consensus pharmacophore structure by determining a candidate pharmacophore for which the consensus terms are relatively small compared to the total energy.
95. The digital computer apparatus of claim 92 wherein the apparatus further comprises processor means for determining a consensus pharmacophore structure by determining a candidate pharmacophore for which the consensus terms are minimum compared to other candidate pharmacophores.
96. The digital computer apparatus of claims 94 or 95 wherein the apparatus further comprises processor means for determining one or more lead compounds for use as a drug that share a pharmacophore specification with the consensus pharmacophore structure.
97. The digital computer apparatus of claim 83 wherein the third processor means determines an equilibrium structure by a method comprising averaging selected generated and accepted structures.
98. The digital computer apparatus of claim 97 wherein the averaging of structures comprises clustering selected generated and accepted structures into sets of similar structures and averaging these sets.
99. In a digital computer, apparatus for configurational bias Monte Carlo determination of the structure of a plurality of compounds having limited conformational
-360- degrees of freedom, each compound having a backbone and side chains, said apparatus comprising:
(a) first memory means for storing data structures representing each compound's chemical and conformational structure consistently with that compound's degrees of freedom and constraints, said data structures capable of representing substantially continuous changes in each compound's conformational structure; (b) second memory means for storing similar data structures representing a proposed structure for one or more of the compounds,
(c) third memory means for storing similar data structures representing prior structures of the plurality of compounds,
(d) first processor means for generating a proposed structure of a randomly selected compound by making conformational alterations consistent with the conformational degrees of freedom, the conformational alterations being randomly distributed between alterations that alter the structure of a randomly selected side chain of the selected compound and alterations that alter the structure of a randomly selected region of the backbone of the selected compound, the proposed structure being stored in the second memory means, the proposed structure being generated with a bias toward more acceptable structures of lower energy, whereby the method is made more efficient, (e) second processor means for accepting a proposed structure according to a probability depending on an energy determined for the proposed structure, the energy including terms representing physical interactions and terms representing heuristic information about the compound's structure, the heuristic information comprising knowledge about measured distances in one or more compounds of said
-361- plurality and about the plurality of the compounds binding to a same target molecule, (f) third processor means for controlling and repeating these steps until sufficient structures have been generated and accepted to permit statistically significant determination of an equilibrium structure.
100. The digital computer of claim 99 wherein the conformational degrees of freedom comprise torsional rotations about mutual bonds between otherwise rigid subunits of the compound, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, the torsional rotations respecting any conformational constraints present.
101. The digital computer of claim 99 wherein the compound is a peptide, peptide derivative, or peptide analog.
102. A method of configurational bias Monte Carlo determination of the structure of a compound selected from the group consisting of a peptide, peptide derivative, and peptide analog, the method comprising the steps of:
(a) representing the conformation of the compound by interconnected rigid units capable of torsional rotation about common bonds, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position,
(b) generating a proposed structure by making conformational alterations consistent with the compound's structure, the proposed structure being generated with a bias toward more acceptable configurations of lower energy;
-362- (c) accepting a proposed structure according to a probability depending on an energy determined for the proposed structure, and
(d) repeating these steps until sufficient structures have been generated and accepted to permit statistically significant determination of an equilibrium structure.
103. An apparatus for configurational bias Monte Carlo determination of the structure of a compound selected from the group consisting of a peptide, peptide derivative, and peptide analog, the apparatus comprising:
(a) memory means for storing (i) data structures representing the compound's conformation as interconnected rigid units capable of torsional rotation about common bonds, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, said data structures capable of representing substantially continuous changes in each compound's conformational; (ii) similar data structures representing the compound's proposed structure and prior structures, and (iii) parameters representing atomic interactions, and (b) processor means for executing programs for
(i) generating a proposed structure by making conformational alterations consistent with the compound's structure and with a bias toward more acceptable configurations of lower energy,
-363- (ii) accepting a proposed structure according to a probability depending on an energy determined for the proposed structure, and (iii) repeating these steps until sufficient structures have been generated and accepted to permit statistically significant determination of an equilibrium structure.
104. In a digital computer, apparatus for configurational bias Monte Carlo determination of the structure of a compound selected from the group consisting of a peptide, peptide derivative, and peptide analog, said apparatus comprising:
(a) first memory means for storing data structures representing the compound's structure as interconnected rigid units capable of torsional rotation about common bonds, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, said data structures capable of representing substantially continuous changes in said compound's structure;
(b) second memory means for storing similar data structures representing the compound's proposed structure,
(c) third memory means for storing similar data structures representing the compound's prior structures,
(d) first processor means for generating a proposed structure by making conformational alterations consistent with the compound's structure and constraints and with a bias toward conformations of lower energy,
(e) second processor means for accepting a proposed structure according to a probability depending on an energy determined for the proposed structure, and
-364- (f) third processor means for controlling and repeating these steps until sufficient structures have been generated and accepted to permit statistically significant determination of an equilibrium structure.
105. In a digital computer, apparatus for configurational bias Monte Carlo determination of the structure of a plurality of compounds selected from the group consisting of peptides, peptide derivatives, and peptide analogs, each compound having a backbone and side chains, said apparatus comprising:
(a) first memory means for storing data structures representing each compound's structure as interconnected rigid units capable of torsional rotation about common bonds, each rigid unit's representation comprising its interconnections and atomic composition, each atom's representation comprising its type and position, said data structures capable of representing substantially continuous changes in conformational structure;
(b) second memory means for storing similar data structures representing a proposed structure for one or more of the compounds, (c) third memory means for storing similar data structures representing prior structures of the plurality of the compounds, (d) first processor means for generating a proposed structure of a randomly selected compound by making conformational alterations consistent with the compound's structure, the conformational alterations being randomly distributed between alterations that alter the structure of a randomly selected side chain of the selected compound and alterations that alter the structure of a randomly selected region of the backbone of the selected compound, the proposed structure being stored in
-365- the second memory means, the proposed structure being generated with a bias toward more acceptable structures of lower energy,
(e) second processor means for accepting a proposed structure according to a probability depending on an energy determined for the proposed structure, the energy including terms representing physical interactions and terms representing heuristic information about the compound's structure, the heuristic information comprising knowledge about measured distances in one or more compounds of said plurality and about the plurality of the compounds binding to a same target molecule,
(f) third processor means for controlling and repeating these steps until sufficient structures have been generated and accepted to permit statistically significant determination of an equilibrium structure.
106. The method of claim 42 wherein the nuclear magnetic resonance is rotational echo double resonance.
107. The method of claim 1 wherein the diversity libraries are structurally constrained organic diversity libraries.
108. The method of claim 29 wherein said conformational constraints further comprise internally linked backbone structure constraints preserved by concerted rotation.
109. The method of claim 52 wherein said conformational constraints further comprise internally linked backbone structure constraints preserved by concerted rotation.
110. The apparatus of claim 68 wherein said conformational constraints further comprise internally linked backbone structure constraints preserved by concerted rotation.
-366-
111. The digital computer apparatus of claim 84 wherein said conformational constraints further comprise internally linked backbone structure constraints preserved by concerted rotation.
112. The digital computer of claim 100 wherein said conformational constraints further comprise internally linked backbone structure constraints preserved by concerted rotation.
113. The method of claim 102 wherein said step of generating a proposed structure further comprises concerted rotation which preserves internally linked backbone structure constraints.
114. The apparatus of claim 103 wherein said step of generating a proposed structure further comprises concerted rotation which preserves internally linked backbone structure constraints.
115. The digital computer of claim 104 or 105 wherein said step of generating a proposed structure further comprises concerted rotation which preserves internally linked backbone structure constraints.
-367-
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| AU53246/96A AU5324696A (en) | 1995-03-31 | 1996-03-27 | Consensus configurational bias monte carlo method and system for pharmacophore structure determination |
| EP96909882A EP0826182A1 (en) | 1995-03-31 | 1996-03-27 | Consensus configurational bias monte carlo method and system for pharmacophore structure determination |
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| US08/418,992 US6341256B1 (en) | 1995-03-31 | 1995-03-31 | Consensus configurational bias Monte Carlo method and system for pharmacophore structure determination |
| US418,992 | 1995-03-31 |
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| EP (1) | EP0826182A1 (en) |
| AU (1) | AU5324696A (en) |
| CA (1) | CA2216994A1 (en) |
| WO (1) | WO1996030849A1 (en) |
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| US20050112607A1 (en) * | 1999-01-23 | 2005-05-26 | Bamdad Cynthia C. | Rapid and sensitive detection of protein aggregation |
| ES2327382T3 (en) * | 1999-07-20 | 2009-10-29 | Morphosys Ag | METHODS FOR SUBMITTING (POLI) PEPTIDES / PROTEINS IN PARTICLES OF BACTERIOPHAGES THROUGH DISULFIDE LINKS. |
| US6677160B1 (en) | 1999-09-29 | 2004-01-13 | Pharmacia & Upjohn Company | Methods for creating a compound library and identifying lead chemical templates and ligands for target molecules |
| US6764858B2 (en) | 1999-09-29 | 2004-07-20 | Pharmacia & Upjohn Company | Methods for creating a compound library |
| US7099502B2 (en) * | 1999-10-12 | 2006-08-29 | Biodiscovery, Inc. | System and method for automatically processing microarrays |
| US20020098524A1 (en) * | 2000-04-14 | 2002-07-25 | Murray Christopher J. | Methods for selective targeting |
| AU2002234164A1 (en) * | 2000-11-02 | 2002-05-15 | Protein Mechanics, Inc. | Method for residual form in molecular modeling |
| US20030032065A1 (en) * | 2001-03-12 | 2003-02-13 | Vince Hilser | Ensemble-based strategy for the design of protein pharmaceuticals |
| WO2003002724A2 (en) * | 2001-03-12 | 2003-01-09 | Affinium Pharmaceuticals, Inc. | Proteins, druggable regions of proteins and target analysis for chemistry of therapeutics |
| WO2003038442A2 (en) * | 2001-10-29 | 2003-05-08 | Vertex Pharmaceuticals Incorporated | Processes for producing optimized pharmacophores |
| US20030139907A1 (en) * | 2002-01-24 | 2003-07-24 | Mccarthy Robert J | System, Method, and Product for Nanoscale Modeling, Analysis, Simulation, and Synthesis (NMASS) |
| US20040015299A1 (en) * | 2002-02-27 | 2004-01-22 | Protein Mechanics, Inc. | Clustering conformational variants of molecules and methods of use thereof |
| US20030171873A1 (en) * | 2002-03-05 | 2003-09-11 | Bruce Hoff | Method and apparatus for grouping proteomic and genomic samples |
| US20030216867A1 (en) * | 2002-03-26 | 2003-11-20 | Campbell Phil G. | Methods and systems for molecular modeling |
| US20050181464A1 (en) * | 2002-04-04 | 2005-08-18 | Affinium Pharmaceuticals, Inc. | Novel purified polypeptides from bacteria |
| US20060089808A1 (en) * | 2002-07-01 | 2006-04-27 | Agrafiotis Dimitris K | Conformational sampling by self-organization |
| US7512496B2 (en) * | 2002-09-25 | 2009-03-31 | Soheil Shams | Apparatus, method, and computer program product for determining confidence measures and combined confidence measures for assessing the quality of microarrays |
| US20040267456A1 (en) * | 2003-06-27 | 2004-12-30 | Stephan Brunner | Method and computer program product for drug discovery using weighted grand canonical metropolis Monte Carlo sampling |
| US7415361B2 (en) * | 2003-12-09 | 2008-08-19 | Locus Pharmaceuticals, Inc. | Methods and systems for analyzing and determining ligand-residue interaction |
| US20050177318A1 (en) * | 2004-02-10 | 2005-08-11 | National Institute Of Statistical Sciences | Methods, systems and computer program products for identifying pharmacophores in molecules using inferred conformations and inferred feature importance |
| US20050222776A1 (en) * | 2004-03-31 | 2005-10-06 | Locus Pharmaceuticals, Inc. | Method for fragment preparation |
| RU2010102859A (en) * | 2007-08-21 | 2011-09-27 | Морфосис Аг (De) | IMPROVED METHODS FOR THE FORMATION OF DISULPHIDE LINKS |
| GB0816100D0 (en) * | 2008-09-04 | 2008-10-15 | Univ Bristol | Molecular structure determination from nmr spectroscopy |
| US9568574B2 (en) * | 2012-03-12 | 2017-02-14 | Bruker Biospin Corporation | Pulse sequence for homonuclear J-decoupling during NMR data acquisition |
| CN102930152B (en) * | 2012-10-26 | 2016-08-03 | 中国科学院上海药物研究所 | A kind of ligand molecular of simulating reacts with target receptor and calculates the method and system of thermodynamics and dynamics parameter predicting this reaction |
| US20160131603A1 (en) * | 2013-06-18 | 2016-05-12 | The George Washington University a Congressionally Chartered Not-for-Profit Corporation | Methods of predicting of chemical properties from spectroscopic data |
| DE102014202649B4 (en) * | 2014-02-13 | 2015-12-10 | Siemens Aktiengesellschaft | Silent MR imaging through a variable number of pulse sequence sections between two pre-pulses |
| US10426424B2 (en) | 2017-11-21 | 2019-10-01 | General Electric Company | System and method for generating and performing imaging protocol simulations |
| CN112461881B (en) * | 2020-10-16 | 2022-02-18 | 北京大学 | Solid nuclear magnetic resonance method for detecting weak-stability base pairs in RNA |
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| US5225533A (en) | 1988-05-02 | 1993-07-06 | The Regents Of The University Of California | General method for producing and selecting peptides with specific properties |
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| US5252743A (en) | 1989-11-13 | 1993-10-12 | Affymax Technologies N.V. | Spatially-addressable immobilization of anti-ligands on surfaces |
| ZA9010460B (en) * | 1989-12-29 | 1992-11-25 | Univ Technologies Int | Methods for modelling tertiary structures of biologically active ligands including agonists and antagonists thereto and novel synthetic antagonists based on angiotensin |
| US5747334A (en) | 1990-02-15 | 1998-05-05 | The University Of North Carolina At Chapel Hill | Random peptide library |
| US5265030A (en) * | 1990-04-24 | 1993-11-23 | Scripps Clinic And Research Foundation | System and method for determining three-dimensional structures of proteins |
| US5331573A (en) | 1990-12-14 | 1994-07-19 | Balaji Vitukudi N | Method of design of compounds that mimic conformational features of selected peptides |
| US5270170A (en) | 1991-10-16 | 1993-12-14 | Affymax Technologies N.V. | Peptide library and screening method |
| US5348867A (en) | 1991-11-15 | 1994-09-20 | George Georgiou | Expression of proteins on bacterial surface |
| US5241470A (en) | 1992-01-21 | 1993-08-31 | The Board Of Trustees Of The Leland Stanford University | Prediction of protein side-chain conformation by packing optimization |
| WO1993017032A1 (en) | 1992-02-24 | 1993-09-02 | The Trustees Of The University Of Pennsylvania | Techniques and intermediates for preparing non-peptide peptidomimetics |
| US5573905A (en) | 1992-03-30 | 1996-11-12 | The Scripps Research Institute | Encoded combinatorial chemical libraries |
| US5434796A (en) * | 1993-06-30 | 1995-07-18 | Daylight Chemical Information Systems, Inc. | Method and apparatus for designing molecules with desired properties by evolving successive populations |
| US5740072A (en) * | 1994-10-07 | 1998-04-14 | The Trustees Of Columbia Universuty In The City Of New York | Rapidly convergent method for boltzmann-weighted ensemble generation in free energy simulations |
| US5680319A (en) * | 1995-05-25 | 1997-10-21 | The Johns Hopkins University School Of Medicine | Hierarchical protein folding prediction |
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