WO2009048460A1 - Procédés et systèmes pour une simulation compétitive grand canonique de fragments moléculaires - Google Patents

Procédés et systèmes pour une simulation compétitive grand canonique de fragments moléculaires Download PDF

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WO2009048460A1
WO2009048460A1 PCT/US2007/080816 US2007080816W WO2009048460A1 WO 2009048460 A1 WO2009048460 A1 WO 2009048460A1 US 2007080816 W US2007080816 W US 2007080816W WO 2009048460 A1 WO2009048460 A1 WO 2009048460A1
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molecular
instance
fragment
selecting
molecular fragment
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PCT/US2007/080816
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Matthew Clark
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Locus Pharmaceuticals, Inc.
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Priority to PCT/US2007/080816 priority Critical patent/WO2009048460A1/fr
Publication of WO2009048460A1 publication Critical patent/WO2009048460A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/30—Drug targeting using structural data; Docking or binding prediction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G16B5/20—Probabilistic models
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50—Mutagenesis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

Definitions

  • TITLE METHODS AND SYSTEMS FOR GRAND CANONICAL COMPETITIVE SIMULATION OF MOLECULAR FRAGMENTS
  • the present disclosure generally relates to methods of identifying binding sites on proteins, methods for identifying classes of compounds suitable for binding with a protein and methods for determining more strongly binding compounds for particular binding sites.
  • binding sites can be identified that might be significant to a biological process, such as an enzyme active site or a site for interacting with another macromolecule or with itself.
  • a biological process such as an enzyme active site or a site for interacting with another macromolecule or with itself.
  • Computational efforts have focused on sampling the surface of a molecule to find good fits with known binding agents. Such methods are dependent on knowledge of the structure of good binding agents and the function of the protein.
  • Another method of identifying binding sites for molecules from a three- dimensional structural solution of a macromolecule is disclosed in U.S. Patent No. 6,735,530 to Guarnieri, which is incorporated herein by reference in its entirety.
  • the structural solution for the macromolecule can be derived from crystallography, spectroscopic analyses such as Nuclear Magnetic Resonance (NMR), computational derivations or any other method of determining such structures.
  • the method can narrow the number of potential binding sites and identify the organic fragments that effectively interact with the binding site(s).
  • the data obtained for the organic fragments can further identify the orientations of the fragments useful in a candidate. Binding sites can be ranked according for a particular fragment based on the generated data. Accordingly, sites having the potential to more strongly bind a fragment can be quantitatively determined.
  • the role of a solvent may be considered.
  • the role of a solvent is particularly relevant. The entropy and enthalpy for creating a cavity in the solvent, filling it with a ligand, and re-ordering the solvent about the ligand has been explored experimentally and theoretically.
  • Explicit water models whether modeled as populated periodic boxes or droplet models, bathe the protein in bulk water.
  • the bulk water does not permit the ligand to freely move about different poses and conformations. As such, sampling is inhibited.
  • Binding measurements only provide the sum of all effects (i.e., the total binding free energy).
  • MM/PBSA molecular mechanics/Poisson-Boltzma ⁇ n solvent-accessible surface area
  • the main limitation of the free energy perturbation methods is that they carry out free energy computations for a pre-selected pose in a pre-selected binding site of the protein. While the dynamics-based sampling regimen allows the ligand to move about within the energy barriers defined by the simulation temperature, the ligand cannot cross these barriers to explore different poses. The possible conformations to explore increase dramatically with the number of rotatable bonds in the molecule. Accordingly, fully sampling the conformations of a flexible molecule is very computationally intensive.
  • a computer-implemented method of analyzing a macromoiecule for potential binding sites may include selecting a plurality of molecular species, selecting a free energy value, selecting an operation from a molecular fragment insertion operation, a molecular fragment deletion operation and a molecular fragment movement operation, performing the selected operation on a computer representation of an instance of a molecular fragment at one of a plurality of binding sites based on a grand canonical ensemble probability density function associated with the selected operation, determining whether to store information pertaining to the plurality of binding sites, repeating the operation selecting. performing and determining steps a plurality of times, and outputting a plurality of occupation probabilities based on the stored information.
  • the occupation probabilities may include, for each molecular species, a probability that an instance of a molecular fragment of the molecular species resides at a binding site,
  • a system for analyzing a macromoiecufe for potential binding sites may include a processor, and a processor-readable storage medium in communication with the processor.
  • the processor-readable storage medium may include one or more programming instructions for performing a method of analyzing a macromolecule for potential binding sites.
  • the method may include selecting a plurality of molecular species, selecting a free energy value, selecting an operation from a molecular fragment insertion operation, a molecular fragment deletion operation and a molecular fragment movement operation, performing the selected operation on a computer representation of an instance of a molecular fragment at one of a plurality of binding sites based on a grand canonical ensemble probability density function associated with the selected operation, determining whether to store information pertaining to the plurality of binding sites, repeating the operation selecting, performing and determining steps a plurality of times, and outputting a plurality of occupation probabilities based on the stored information.
  • the occupation probabilities may include, for each molecular species, a probability that an instance of a molecular fragment of the molecular species resides at a binding site.
  • a method of analyzing a macromolecule for potential binding sites may include selecting an operation from a plurality of operations, performing the operation on a computer representation of an instance of a molecular fragment of a molecular species at a binding site with a probability based on an associated grand canonical ensemble probability density function, repeating the selecting and performing steps a plurality of times for instances of molecular fragments of a plurality of molecular species at a plurality of binding sites, and storing, in a storage medium, at least one occupancy probability pertaining at least to a ⁇ keiihooct that an instance of a molecular fragment of a molecular species resides at a binding site.
  • FIG. 1 depicts a flow diagram for an exemplary method of competitively simulating molecular fragments from a plurality of molecular species to determine binding sites on or near a macromolecule according to an embodiment.
  • FIG. 2 depicts a flow diagram for an exemplary method of determining whether to insert a molecular fragment at a sampling site of a macromolecule according to an embodiment.
  • FlG. 3 depicts a flow diagram for an exemplary method of determining whether to remove a molecular fragment from a sampling site of a macromolecule according to an embodiment.
  • FIG. 4 depicts a flow diagram for an exemplary method of determining whether to reposition a molecular fragment at a sampling site of a macromolecule according to an embodiment.
  • FIG. 5 is a block diagram of an exemplary system that may be used to contain or implement the program instructions according to an embodiment. I. DETAILED DESCRIPTION
  • a "macromolecule” refers to a molecule or collection of molecules.
  • the term typically refers to proteins, ribonucleic acids, structures formed of both nucleic acid and protein, carbohydrates, structures formed of two or more of the aforementioned, and the like, it can also refer to structures formed with any other molecule or molecules including lipids.
  • Macromolecules are used in the method described herein with reference to maps of their tertiary structure. Such maps are typically generated by X-ray diffraction studies, which have generated maps for thousands of macromolecules. However, maps can be produced by other methods such as computational methods or computational methods supplemented by other data such as NMR data.
  • ORFs Organic fragments
  • ORFs are molecules that can be used to model one or more modes of interaction with a maeromolecule, such as the interactions of carbonyls, hydroxyls, amides, hydrocarbons and the like,
  • a "molecular fragment” is a molecule selected from one of a plurality of molecular species defined for a particular simulation.
  • Exemplary molecular fragments may include ORFs and/or water.
  • a "sampling site” is a molecular binding site for a maeromolecule at which a molecular fragment of a particular molecular species can, as a practical matter, reside.
  • Sampling sites are determined by defining a volume that contains the protein in Cartesian space and randomly selecting points within the volume. The selection of points may be biased to be close to the protein or to specified regions within the volume. The probability of accepting a particular sampling site may be adjusted based on this bias.
  • a grand canonical ensemble simulation may be performed utilizing a Monte Carlo algorithm.
  • a Monte Carlo algorithm is a numerical computational algorithm that simulates the behavior of a physical system, such as a molecular model. Monte Carlo algorithms are stochastic in that they depend upon the use of random (or pseudorandom) numbers. In particular, the Monte Carlo algorithm used in the simulations described herein may determine whether a molecular fragment is inserted, deleted or moved. The probability of selecting an insertion, deletion or modification of a selected fragment may be based on the free energy of the molecular fragment.
  • the simulation is free from assumptions about the starting pose or nature of the binding site.
  • Free energy perturbation methods may compute the binding free energy of a pre-selected pose.
  • the grand canonical ensemble simulation samples and computes the free energy of molecular fragments without any assumptions other than proximity to the macromolecule. This makes the method well adapted to locating novel binding sites and poses.
  • the accuracy of the free energy computation is increased because the sampling includes the entire protein and a large number of configurations as compared to perturbation calculations on a limited number of poses.
  • a single simulation provides the data to determine the binding free energies of any pose near the surface of the macromolecule.
  • the binding free energies are a direct outcome of the simulation, and little processing is required to determine the free energies.
  • the chemical potential for the simulation is assigned, the ensemble of poses generated by the Monte Carlo Markov chain is for the free energy that the chemical potential represents. No other integration methods are necessary to extract the free energy of the molecular fragments.
  • the free energy is determined by observing the chemical potential at which the binding mode no longer appears. The lowest chemical potential at which the binding mode was populated is taken as the free energy of that mode.
  • a Monte Carlo simulation for a system with a variety of different molecular species may be performed.
  • the simulation may include tracking a separate N, a number of ORFs or water molecules, for each molecular species.
  • Each Monte Carlo step may operate on a single molecular fragment of a selected molecular species.
  • grand canonical ensemble simulations may include a changing number of molecular fragments (ORFs or water) of each molecular species in the system during the simulation.
  • ORFs or water molecular fragments
  • T is the absolute temperature
  • ⁇ is the chemical potential
  • k is the Boltzmann constant
  • the simulation may competitively simulate molecular fragments from a plurality of molecular species to determine binding sites for the molecular fragments using grand canonical ensemble probability density
  • FIG. 1 An exemplar ⁇ ' flow diagram for such a method is depicted in FIG. 1 .
  • a free energy value may be selected 105.
  • the free energy is commonly specified as the excess chemical potential
  • the free energy may be represented as the unitless property B defined by the excess chemical potential.
  • B is related to the concentration of molecular fragments in the system, as shown below, 5, rather than the chemical potential, may be annealed.
  • V therefore determined by multiplying — by a reference concentration to make the unitless value
  • a plurality of simulations may be performed. Each simulation may be performed at a selected free energy value. As the excess chemical potential decreases, retention of a fragment at a given sampling site indicates a high relative binding affinity.
  • the schedule of simulations may be conducted at a plurality of B values ranging from, for example and without limitation, about 10 to about -25. Other values of B may also be used within the scope of this disclosure.
  • the simulation may select 110 an operation to perform.
  • the simulation may select 110 between, for example, a molecular fragment insertion operation 115, a molecular fragment deletion operation 120 or a molecular fragment movement operation 125.
  • An operation may be selected 110 randomly, pseudorandomly or based on a sequential order. Alternately, operation selection may be weighted based on a weighting value assigned to each operation. For example, a molecular fragment insertion operation 115 may be selected 110 with a 70% probability; a molecular fragment deletion operation 120 may be selected with a 25% probability: and a molecular fragment movement operation 125 may be selected with a 5% probability.
  • two or more operations may have equal or substantially equal probabilities of being selected 110.
  • Other methods of selecting 110 an operation may also be performed within the scope of this disclosure.
  • Selection of a particular operation does not require that a molecular fragment actually be, for example, inserted, deleted or moved. Rather, the simulation may perform the selected operation probabilistically based on a probability function defined for the particular operation.
  • the embodiments disclosed below describe probability functions based on B for various operations. However, alternate free energy measures, such as the excess chemical potential, may be used to determine different probability functions for such operations within the scope of this disclosure.
  • FIG. 2 depicts a flow diagram for an exemplary method of determining whether to insert a molecular fragment at a sampling site of a macromolecule according to an embodiment.
  • a molecular fragment insertion operation 115 is selected 1 10
  • a molecular species may be selected 205 from a plurality of molecular species.
  • the plurality of molecular species may include one or more OWs and/or water.
  • the molecular species may be selected 205 from the plurality of molecular species based on a random selection process, a pseudorandom selection process or a process using a weighting factor for each molecular species.
  • ORFs considered may be representative of chemical features that have proven useful in the design of pharmaceuticals or other bioactive chemicals. Exampies of useful ORFs may include, without limitation, those displayed in Table 1.
  • sampling site may be selected 210 randomly from the volume in Cartesian space containing the protein.
  • sampling sites may be determined by identifying one or more binding sites for the macromoiecule that can receive the selected molecular fragment having the selected molecular species.
  • the sampling site may be randomly (including pseudorandomly) selected 210 from the sampling sites for the selected molecular species.
  • sampling sites may be assigned weighted values and selected 210 based on the assigned weighted values. For example, and without limitation, if a first sampiing site were assigned a weighted value of 1 and a second sampling site were assigned a weighted value of 2, the first sampling site and the second sampling site may be selected 210 with probability 1/3 and 2 ⁇ . respectively.
  • ⁇ 0046 j ⁇ n instance of a computer representation of a molecular fragment having the selected molecular species may be inserted 215 at the selected sampling site with a determined probability.
  • the determined probability for inserting the molecular fragment may be based on the value of B and a change in the binding energy that results from insertion of the molecular fragment at the sampling site, AE.
  • an attempt to insert a molecular fragment at a sampling site may be accepted based on a grand canonical ensemble probability density function with the following probability: where N is the number of molecular fragments of the selected type, and V is the volume of the Cartesian space (which is constant during the simulation).
  • the effect of the chemical potential may be introduced into the acceptance expression via the B parameter.
  • the presence of the J' and ⁇ r factors may follow from the relation between the canonical and grand canonical partition functions (see equation (I )).
  • the V and JV factors may also be given a probabilistic interpretation, where the insertion site may be chosen with probability I /V 1 and the molecular fragment to be inserted may be chosen with probability ⁇ /N.
  • the algorithm may model the insertion 215 of the selected fragment.
  • the probability of the insertion 215 may then be determined from the grand canonical ensemble probability density function, and the selected molecular fragment may be represented as resident at the site by a random number generating protocol weighted to the probability value.
  • FIG. 3 depicts a flow diagram for an exemplary method of determining whether to remove a molecular fragment from a sampling site of a macromolecuJe according to an embodiment.
  • a molecular fragment deletion operation 120 is selected 110
  • an instance of a computer representation of a molecular fragment at a sampling site of a macromolecule may be selected 305.
  • the instance of the molecular fragment may be randomly (including pseudorandomly) selected 305 from instances of molecular fragments at sampling sites.
  • the instance of the molecular fragment may be selected 305 based on a weighting factor applied to at least one instance.
  • a molecular species for the selected instance may be determined 310 from a plurality of molecular species.
  • the plurality of molecular species may include one or more ORFs and water.
  • the types of ORFs for a particular simulation may be representative of chemical features that have proven useful in the design of pharmaceuticals or other bioactive chemicals. Examples of useful ORFs may include, without limitation, those displayed in Table 1 above.
  • the instance of the molecular fragment may be removed 315 from the selected sampling site of the macromolecule with a determined probability.
  • the determined probability for removing 315 the instance of the molecular fragment may be based on the value of B and a change in the binding energy that results from removal of the molecular fragment from the sampling site, AE.
  • an attempt to remove 315 a molecular fragment from a sampling site may be accepted based on a grand canonical ensemble probability density function with the following probability:
  • FIG. 4 depicts a flow diagram for an exemplary method of determining whether to reposition a molecular fragment at a sampling site of a macromolecule according to an embodiment.
  • a molecular fragment movement operation 125 is selected 110
  • an instance of a computer representation of a molecular fragment at a sampling site of a macromolecule may be selected 405.
  • the instance of the molecular fragment may be randomly (including pseudorandomly) selected 405 from instances of molecular fragments at sampling sites.
  • the instance of the molecular fragment may be selected 405 based on a weighting factor applied to at least one instance.
  • the molecular fragment may be moved 410.
  • the molecular fragment may be translated 410.
  • the molecular fragment may be rotated 410. In an embodiment, the amount
  • a force bias canonical probability density function may be used to translate and/or rotate 410 the molecular fragment by a small amount (e.g., up to
  • a change in binding energy may be determined 415 based on the movement of the molecular fragment.
  • a determination as to whether to accept or reject the resulting pose may be made 420 based on the change in energy, ⁇ £, as a result of the change attempted. Accordingly, an attempt to move 410 a molecular fragment from a sampling site may be accepted based on a grand canonical ensemble probability density function with the following probability:
  • Such position shifting may be thought of as effecting a "shaking" of the molecular fragment to identify its favored positioning.
  • this shaking is applied, the outcome of a simulation may reflect higher probability orientations.
  • the temperature (which is kept constant during the simulation) enters the acceptance formula as a scaling factor of the energy change.
  • a determination may ⁇ be made 130 as to whether to perform an unbiased sampling at each sampling site after equilibrium is achieved.
  • a plurality of operations such as about 2x10 6 operations, may be performed for a simulation. Of these operations, the majority, such as approximately 1.5x10 6 operations, may be used to equilibrate the simulation so that the number of molecular fragments for each molecular species is substantially constant during the course of the simulation.
  • an unbiased sampling may be performed 135 after every 20000 operations are attempted. An occupation probability of a molecular fragment at a binding site may be determined based on these samplings.
  • An occupation probability for a molecular species at a binding site is the likelihood that a molecular fragment of a particular molecular species resides at the binding site.
  • the occupation probability may be determined by determining the ratio of the number of samples for which an instance of a molecular fragment is located at the binding site to the total number of samples.
  • the information pertaining to the occupation probabilities may include information describing the binding sites at which molecular fragments for at least one molecular species are located, the occupation probabilities for the molecular fragments for the at least one molecular species, the orientation and position of the molecular fragments at the binding sites, and the like.
  • the list may be stored 145 in a processor-readable storage medium. In an embodiment, the list or a portion thereof may be provided or otherwise outputted 150 to a user in a printed form, via a display, in an electronic or physical media, or the like.
  • the potential energy, E may be computed using the AMBER molecular mechanics force field, which is known to those of ordinary skill in the art.
  • the AMBER force field is comprised of a two-part non-bonded interaction including the Lennard- Jones dispersion potential and an electrostatic potential:
  • the total interaction energy between two molecular fragments is the sum of the pairwise energies for each of the atomic interactions.
  • the electrostatic and Lennard-Jones potentials are implemented using the protocols for the AMBER force field. Additional "mixing" rules may be used for the non-bonded terms to avoid spurious interactions among small molecular fragments,
  • [0060J In an embodiment in which two types of molecular fragments, such as an ORF and water, are used, five possible interactions exist among the ORF, the water and the protein in the system.
  • the fragment-protein and water-protein interactions are computed using the AMBER force field without modification.
  • the remaining interactions do not include interaction with the protein.
  • the fragment-water and water- water interactions are computed using the standard AMBER force field.
  • the mixing rule is that the epsilon value for the inter-molecular interaction is zero for the r " ⁇ term when both of the atoms in the interaction are ORFs. This permits the simulation to reproduce protein binding interactions mediated by water. Furthermore, it allows water to create hydrogen bonded networks around polar protein residues that reflect the high solvation enthalpy and entropy of those regions.
  • FIG. 5 is a block diagram of an exemplary system that may be used to contain or implement the program instructions according to an embodiment.
  • a bus 528 serves as the main information highway interconnecting the other illustrated components of the hardware.
  • CPU 502 is the central processing unit of the system, performing calculations and logic operations required to execute a program.
  • Read only memory (ROM) 518 and random access memory (RAM) 520 constitute exemplary memory devices or storage media.
  • a disk controller 504 interfaces with one or more optional disk drives to the system bus 528.
  • These disk drives may include, for example, external or internal DVD drives 510, CD ROM drives 506 or hard drives 508. As indicated previously, these various disk drives and disk controllers are optional devices.
  • Program instructions may be stored in the ROM 518 and/or the RAM 520.
  • program instructions may be stored on a computer readable storage medium, such as a compact disk, a digital disk, a memory or any other tangible recording medium.
  • An optional display interface 522 may permit information from the bus 528 to be displayed on the display 524 in audio, graphic or alphanumeric format. Communication with external devices may occur using various communication ports 526,
  • the hardware may also include an interface 512 which allows for receipt of data from input devices such as a keyboard 514 or other input device 516 such as a mouse, remote control, pointer and/or joystick.
  • ⁇ n embedded system may optionally be used to perform one, some or all of the operations described herein.
  • a multiprocessor system may optionally be used to perform one, some or all of the operations described herein.

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Abstract

L'invention porte sur des procédés et des systèmes pour analyser une macromolécule pour des sites de liaison potentiels. De multiples espèces moléculaires et une valeur d'énergie libre peuvent être sélectionnées. Une opération pour un fragment moléculaire de l'une des espèces moléculaires peut être sélectionnée parmi des opérations d'insertion, d'effacement et de mouvement. L'opération sélectionnée peut être exécutée sur une représentation informatique d'une instance d'un fragment moléculaire au niveau d'un site d'une pluralité de sites de liaison sur la base d'une fonction de densité de probabilité d'ensemble grand canonique associée à l'opération sélectionnée. Des informations peuvent être stockées concernant la pluralité de sites de liaison. Les opérations de sélection, d'exécution et de stockage d'informations d'opération peuvent être effectuées de multiples fois. De multiples probabilités d'occupation peuvent être fournies sur la base des informations stockées. Les probabilités d'occupation peuvent comprendre, pour chaque espèce moléculaire, une probabilité qu'une instance d'un fragment moléculaire de l'espèce moléculaire réside au niveau d'un site de liaison.
PCT/US2007/080816 2007-10-09 2007-10-09 Procédés et systèmes pour une simulation compétitive grand canonique de fragments moléculaires WO2009048460A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5854992A (en) * 1996-09-26 1998-12-29 President And Fellows Of Harvard College System and method for structure-based drug design that includes accurate prediction of binding free energy
US6735530B1 (en) * 1998-09-23 2004-05-11 Sarnoff Corporation Computational protein probing to identify binding sites
US6797482B2 (en) * 1994-05-10 2004-09-28 Exsar Corporation Methods for the high-resolution identification of solvent-accessible amide hydrogens in protein binding sites

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6797482B2 (en) * 1994-05-10 2004-09-28 Exsar Corporation Methods for the high-resolution identification of solvent-accessible amide hydrogens in protein binding sites
US5854992A (en) * 1996-09-26 1998-12-29 President And Fellows Of Harvard College System and method for structure-based drug design that includes accurate prediction of binding free energy
US6735530B1 (en) * 1998-09-23 2004-05-11 Sarnoff Corporation Computational protein probing to identify binding sites

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