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WO2003038672A1 - Technique d'analyse, systeme d'analyse et programme d'analyse - Google Patents

Technique d'analyse, systeme d'analyse et programme d'analyse Download PDF

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Publication number
WO2003038672A1
WO2003038672A1 PCT/JP2002/011401 JP0211401W WO03038672A1 WO 2003038672 A1 WO2003038672 A1 WO 2003038672A1 JP 0211401 W JP0211401 W JP 0211401W WO 03038672 A1 WO03038672 A1 WO 03038672A1
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WO
WIPO (PCT)
Prior art keywords
binding
screening
homology
free energy
model
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Application number
PCT/JP2002/011401
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English (en)
Japanese (ja)
Inventor
Kazuto Yamazaki
Masaharu Kanaoka
Original Assignee
Sumitomo Pharmaceuticals Company, Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Sumitomo Pharmaceuticals Company, Limited filed Critical Sumitomo Pharmaceuticals Company, Limited
Priority to JP2003540864A priority Critical patent/JP4377691B2/ja
Publication of WO2003038672A1 publication Critical patent/WO2003038672A1/fr

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Classifications

    • 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
    • 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

Definitions

  • the present invention relates to a screening method, a screening system, and a screening program for performing virtual screening from a virtual library upon drug discovery.
  • the lead compounds used here have been mainly those in which plants, microorganisms, natural products derived from higher organisms, etc., including hormones and transmitters, have been discovered by screening techniques.
  • the molecular design techniques for realizing the above-mentioned virtual screening can be broadly classified into Ligand Based Drug Design (hereinafter referred to as LBDD) based on known structure-activity relationship information, and Structure-Base Drug Design using the tertiary structure information of the target protein.
  • LBDD Ligand Based Drug Design
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure-Base Drug Design using the tertiary structure information of the target protein.
  • SBDD Structure
  • the purpose is to estimate the binding state between the target protein and the ligand and its pharmacological activity value on a computer, and to expect high-precision activity value prediction without the need for foresight information of the structure-activity relationship. It has the advantage that it can be done.
  • LBDD overlapping analysis and quantitative structure-activity relationship
  • QSAR quantitative structure-activity relationship
  • a receptor structure (binding model) is essential, and the prediction accuracy depends on the accuracy of the structure.
  • accumulation of structure-activity relationship information does not improve prediction accuracy. For this reason, as mentioned above, the general level of SBDD technology has not reached the point where it can be applied to concrete drug discovery research, and the current state of virtual screening is LBDD.
  • the present invention has been made in view of such a point, and an object of the present invention is to provide a screening method and a screening system having extremely high prediction accuracy and versatility showing a high rate of correct answers for various target systems. And a screening program.
  • the screening method of the present invention includes the following components: a plurality of candidate overlay models are prepared by performing a conformational overlay analysis based on a compound that is considered to have pharmacological activity;
  • a compound satisfying a predetermined condition is selected from the compounds used for the superimposition, and a plurality of binding models with a target protein are created;
  • the correlation coefficient between the binding free energy and the pharmacological activity and the homology score of the physicochemical parameters for the obtained plurality of binding models are calculated.
  • the present invention is characterized in that screening is performed by organically linking the two. Further, the screening method of the present invention includes the following constitution:
  • the screening method of the present invention includes the following constitutions: a plurality of candidate overlay models are created by performing a conformational overlay analysis based on a compound which is considered to have pharmacological activity; A compound that satisfies the specified conditions is selected from the compounds used in the above, multiple binding models with the target protein are created, the correlation coefficient between the binding free energy and the pharmacological activity of the obtained binding model, and physical chemistry Calculate the homology score of the target parameter and select multiple binding models
  • the screening system includes a database unit storing a compound and a conformation, and a superposition analysis for creating a plurality of superimposition candidate models by performing a superposition analysis of a conformation based on the compound stored in the database unit.
  • a binding analysis unit a binding unit that selects a compound that satisfies predetermined conditions from among the compounds used for superimposition, and creates a binding model with a target protein, and a binding free energy and pharmacological activity of the obtained binding model.
  • the virtual analysis shows the results of the total analysis, the homology map showing the homology of the physicochemical parameters of the binding model, and the binding free energy function optimized for the target protein based on the known structure-activity relationship information.
  • a more preferable result can be obtained if an analysis search unit that generates an index for the analysis is provided or the analysis search unit updates the index based on the analysis search result.
  • a screening program according to the present invention is a screening program for causing a computer to execute the following steps.
  • Another screening program according to the present invention is a screening program for causing a computer to execute the following steps.
  • FIG. 1 is a flowchart showing an example of the embodiment of the present invention.
  • FIG. 2 is a block diagram of the above.
  • FIG. 3 is an explanatory diagram of the above.
  • FIG. 4 is an explanatory diagram of the construction of the initial connection model according to the first embodiment.
  • Fig. 5 is an explanatory diagram of the application of the index.
  • FIG. 6 is a flowchart showing the detailed operation of the above.
  • FIG. 7 is a flowchart showing the detailed operation of the above. BEST MODE FOR CARRYING OUT THE INVENTION
  • a screen system according to an embodiment of the present invention will be described below in detail with reference to FIGS.
  • Figure 2 shows a block diagram of the screening system implemented as computer software. It consists of a database section 1, a conformation analysis section 2, an overlay analysis section 3, a molecular simulation section 5, a calculation section 6 for calculating binding free energy and optimizing parameters, an analysis search section 7, and the like.
  • the database unit 1 stores structural formulas, conformations, physicochemical parameters, and the like.
  • the conformation analysis unit 2 uses force field parameters such as bandits 2, ⁇ 3, MMFF, AMBER, CHARMm, and 0PLS. Search for the most stable and metastable conformation by conducting a molecular simulation (preferably taking into account the water-solvent effect such as GB-SA or S-GB). 3 is homology between compounds in any combination of physicochemical parameters A comprehensive search is performed using the gender as an index to enumerate candidate overlay models.
  • the statistical analysis unit 4 performs linear regression analysis, principal component analysis, CART method, multiple regression analysis, principal component analysis, K-Nearest Neighbors method, neural network, discriminant analysis, decision tree, support vector machine, Bayesian network
  • the molecular simulation section 5 performs molecular simulations by systematic search, molecular mechanics method, Monte Carlo method, molecular dynamics method, molecular orbital method, etc.
  • a program will be constructed with modules such as a homology analysis section, a statistical analysis section, and a molecular simulation section for physicochemical parameters for superposition, creation of a homology map, homology search, and the like.
  • modules such as a homology analysis section, a statistical analysis section, and a molecular simulation section for physicochemical parameters for superposition, creation of a homology map, homology search, and the like.
  • these individual modules already exist as commercially available application software, but in the present invention, by using a program to be described later, an advanced prediction function that has not been obtained by organically linking them can be obtained. It demonstrates.
  • each unit need not be independent.
  • the function of each part is completed by combining the parts with some functions, or the parts are combined
  • a combination of some of the above may be used to have all the functions of each part.
  • the binding state between a specific target protein and a drug is searched by combining the above-mentioned SBDD and LBDD, and for each of the compounds to be evaluated, "complementarity with the target protein" and "known” Simultaneously considers multiple drugs and simultaneously satisfies the ⁇ homology of physicochemical parameters '' and the ⁇ correlation between binding free energy and pharmacological activity '' Find a solution.
  • screening is performed in the following steps: constructing an initial connection model, modifying the connection model, optimizing evaluation indices, virtual screening, and screening experiments, as shown in Fig. 1.
  • the first step is to create an initial binding model.Including those with relatively weak pharmacological activities, multiple active compounds are often obtained in the early stages of research. To build a joint model. At this time, the estimation of the binding model usually evaluates whether or not the compound of interest is complementary to the target protein.However, in this case, overlay analysis of multiple molecules is performed in advance, and And then estimate the binding model of multiple molecules simultaneously.
  • the conformations Al, A2, A3, B1, B2, B3, C1 based on the known active compounds A, B, C ... obtained from known information and HTS, etc. , C2, C3... are analyzed first, and a plurality of candidate models A1B3C3, A3B1C1... are created. Then, among the compounds used for the overlay, The most rigid and large compound is selected and consistently binds to the target protein using the conformations that make up the candidate overlay model. Create a model that can be If the binding position and orientation of a molecule are determined, the position and orientation of another molecule will be uniquely determined based on the superposition model. Then, another compound is arranged on the obtained bond model based on the superimposition model, and the free energy of the complex is minimized on the basis of atomic coordinates by, for example, a simplex method.
  • the thus obtained binding model of a plurality of compounds is corrected.
  • a binding model of a plurality of active compounds is constructed using the correlation between the binding free energy and the pharmacological activity value as an index, it is estimated that the binding free energy calculated from the binding model and the pharmacological activity value have a linear relationship. Therefore, models that are far from the true connected state deviate greatly from this linear relationship, and such models are removed from the connected model (see Fig. 6). It is easy to identify unfavorable binding models and reduce the noise for subsequent predictions.
  • the physicochemical parameters include pharmacophore descriptor, WHIM descriptor, substituent length, substituent width, molecular refraction MR, Hammet substituent constant, Swain-Lupton electronic effect parameter, Dissociation constant, partial electron charge, hydrophobic constant of Hansch, hydrophobic constant of substituent, partition coefficient logP, hydrophobicity index measured by HPLC, calculated value of logP CL0GP, number of hydrogen bond acceptor, number of hydrogen bond donor The number, the total number of possible hydrogen bonds, and a dummy variable indicating whether hydrogen bonding is possible can be used.
  • the above three evaluation indices are used in virtual screening, and as shown in Fig. 5, the statistical model obtained from the above (a) was first used to quickly identify the presence or absence of activity on the target protein. Perform screening based on the (QS AR model) (a in Fig. 3). In this step, which can be positioned as a pre-screening for the subsequent detailed search, the LBDD technology (such as CART analysis using various structural descriptors) that can perform screening independent of the coordinate system can be used as it is. In the statistical model, high-speed processing is possible because the evaluation is performed only with the physicochemical parameters of each candidate compound.
  • a “pharmacore map” is used in which the number of compounds for each lattice point is tabulated for the presence of binding factors and atoms.
  • the compound to be evaluated is translated and rotated in this grid to identify the position where the similarity with the Pharmacore Map is optimal. If possible, a water molecule is placed between the compound to be evaluated and the target protein thus determined, if possible. It will be placed according to the hydrogen binding factor on the Pharmacore Map.
  • the bonding model is obtained by this operation, the free energy of the complex is minimized on the basis of atomic coordinates by, for example, the simplex method using the initial model as the initial coordinates.
  • the bond free energy is calculated after optimizing the bond mode based on atomic coordinates. To optimize the coordinates, minimize the energy after fixing the polymer side.
  • the evaluation function of the binding free energy includes, for example,
  • a linear binding format of multiple energy terms is used, and parameters optimized for the target protein of interest and the group of compounds to be searched are used.
  • Some of the compounds determined to be promising in the virtual screening are The pharmacological evaluation is actually performed, and based on the obtained pharmacological activity information, the binding model is modified and the above three indices used for virtual screening are updated. That is, compounds that were predicted to be active but were actually inactive are removed from the binding model.
  • the increase in the reliable binding model and the pharmacological activity data makes it possible to improve the accuracy of each f target.
  • the binding model is corrected by replacing the relevant compound and target protein in the model with the measured values, and the complex Minimize energy.
  • the superposition candidate model is extracted again, and the optimal solution is searched using the actually measured joint coordinates. At this time, the above three indicators are updated.
  • drug discovery research involves searching for peripheral substituents on a specific skeleton and searching for the skeleton itself.
  • the interaction between a compound and a protein is revealed. Because it is evaluated, it can be applied with versatility whatever the focus of the search. Moreover, not only can various experiment data be taken in flexibly, but also the prediction accuracy can be gradually improved by taking this data. In addition, optimization of indices specialized for specific compound groups and virtual screen It can respond to ding.
  • a binding model for a related protein for which a three-dimensional structure has not been obtained is constructed by homology modeling, and replaced with the protein of the previous binding model. Since a compound for which a binding model has been obtained already exists in the in-house library, the pharmacological activity against this related protein can be easily evaluated. Then, based on the obtained structure-activity relationship information, “the binding free energy and pharmacological activity By optimizing the vicinity of the active site of the related protein using “correlation” as an evaluation function, coordinate data with sufficient accuracy for subsequent virtual screening can be obtained.
  • FIG. 3 schematically shows the flow of screening by the system according to the present invention, wherein the arrows indicate the flow of structural data or activity data, and the white arrows indicate the flow of compound data.
  • a program that performs various statistical analyzes such as linear regression analysis and the CART method, and performs a database search based on the obtained statistical model, as well as a binding free program that includes conformational analysis and water molecules Energy calculation A program that can perform multi-molecule superposition analysis and a database search based on the results, a data model based on a statistical model obtained by calculating physicochemical parameters and performing a specific statistical analysis.
  • programs for performing base searches programs for estimating the binding mode and strength of specific macromolecules, and programs for optimizing the number of binding free energies and the like.
  • the method of the present invention is executed by adding the following program to these.
  • a program that imputes information on binding factors, etc. a program that sums up the imputed information for multiple molecules, and a homology between the imputed information between multiple molecules or the summed information of specific molecules
  • a program that identifies the position, orientation and conformation at which the above-mentioned homology index is favorable for any molecule or a plurality of molecules, and coordinate data for any protein is assigned to each grid point in the grid, and the A program for evaluating the interaction strength, a program for identifying the position, orientation, and conformation at which the above-mentioned interaction strength is favorable for an arbitrary molecule or a plurality of molecules, a homology index and an interaction intensity for an arbitrary molecule or a plurality of molecules Is a program that identifies the position,
  • the complementarity between a compound and a target protein and the homology of binding ability characteristics between a plurality of compounds are simultaneously considered, and the correlation between measured and predicted values of pharmacological activity of a plurality of compounds is also considered. Not only can this be taken into account, so that the prediction accuracy can be greatly improved, and optimal usage can be achieved according to the purpose, such as skeleton search and peripheral substituent search.
  • virtual screening at a practical level can be performed based on the known information on the drug and structure of the related protein.
  • the evaluation method for each layer can improve the accuracy sequentially by incorporating data (pharmacological activity, structure, etc.) accumulated with the progress of research.

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Abstract

La présente invention concerne une technique d'analyse applicable d'une façon générale qui présente une précision de présomption extrêmement élevée et un rapport élevé de réponses correctes. Une pluralité de modèles candidat superposés sont construits par superposition d'analyses de conformation fondées sur des composés qui semblent avoir une activité pharmaceutique. Parmi les composés utilisés dans cette superposition, on sélectionne ceux qui remplissent les critères définis et on construit une pluralité de modèles de liaison à une protéine cible. A l'aide des modèles de liaison ainsi obtenus, on calcule le coefficient de corrélation d'énergie de liaison libre avec les résultats d'activité pharmacologique et avec l'homologie des paramètres physico-chimiques. Dans le cas d'une recherche d'un état de liaison d'une protéine cible spécifique à un médicament, on prend simultanément en compte une pluralité de médicaments de façon à en déterminer un qui correspond à la corrélation d'énergie de liaison libre avec l'activité pharmacologique et avec l'homologie des paramètres physico-chimiques en même temps.
PCT/JP2002/011401 2001-10-31 2002-10-31 Technique d'analyse, systeme d'analyse et programme d'analyse WO2003038672A1 (fr)

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EP2782033A1 (fr) 2013-03-22 2014-09-24 Fujitsu Limited Procédé et dispositif de calcul d'énergie libre de liaison, programme et procédé de criblage d'un composé
JP2014182423A (ja) * 2013-03-18 2014-09-29 Fujitsu Ltd 薬剤候補化合物の設計方法、設計装置、及び合成方法、プログラム、並びに記録媒体
KR20160064291A (ko) * 2014-11-27 2016-06-08 이화여자대학교 산학협력단 약물 가상 탐색 방법과 집중 탐색 라이브러리 구축 방법 및 이를 위한 시스템
JP2017091180A (ja) * 2015-11-09 2017-05-25 富士通株式会社 結合自由エネルギー計算の前処理方法、結合自由エネルギーの算出方法、及び装置、並びにプログラム
CN113066525A (zh) * 2021-03-30 2021-07-02 中山大学 一种基于集成学习与混合神经网络的多靶标药物筛选方法
CN114974409A (zh) * 2022-05-31 2022-08-30 浙江大学 一种基于零样本学习的针对新发现靶点的药物虚拟筛选系统
US11621054B2 (en) 2019-02-08 2023-04-04 Fujitsu Limited Method and apparatus for preprocessing of binding free energy calculation, and binding free energy calculation method

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JP7011144B2 (ja) * 2016-12-05 2022-02-10 富士通株式会社 結合自由エネルギーの算出方法、及び算出装置、並びにプログラム
KR101991725B1 (ko) * 2017-07-06 2019-06-21 부경대학교 산학협력단 정량적 구조-성능 관계식의 수치적 반전과 분자동역학 전산모사를 통한 표적신약의 스크리닝 방법

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Cited By (11)

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Publication number Priority date Publication date Assignee Title
JP2014182423A (ja) * 2013-03-18 2014-09-29 Fujitsu Ltd 薬剤候補化合物の設計方法、設計装置、及び合成方法、プログラム、並びに記録媒体
EP2782033A1 (fr) 2013-03-22 2014-09-24 Fujitsu Limited Procédé et dispositif de calcul d'énergie libre de liaison, programme et procédé de criblage d'un composé
KR20160064291A (ko) * 2014-11-27 2016-06-08 이화여자대학교 산학협력단 약물 가상 탐색 방법과 집중 탐색 라이브러리 구축 방법 및 이를 위한 시스템
WO2016085262A3 (fr) * 2014-11-27 2016-07-14 이화여자대학교 산학협력단 Procédé d'analyse de médicament virtuel, procédé de création de bibliothèque d'analyse intensive, et système associé
KR101684742B1 (ko) 2014-11-27 2016-12-09 이화여자대학교 산학협력단 약물 가상 탐색 방법과 집중 탐색 라이브러리 구축 방법 및 이를 위한 시스템
EP3225989A4 (fr) * 2014-11-27 2018-08-15 Ewha University-Industry Collaboration Foundation Procédé d'analyse de médicament virtuel, procédé de création de bibliothèque d'analyse intensive, et système associé
US10418129B2 (en) 2014-11-27 2019-09-17 EWHA University—Industry Collaboration Foundation Method and system for drug virtual screening and construction of focused screening library
JP2017091180A (ja) * 2015-11-09 2017-05-25 富士通株式会社 結合自由エネルギー計算の前処理方法、結合自由エネルギーの算出方法、及び装置、並びにプログラム
US11621054B2 (en) 2019-02-08 2023-04-04 Fujitsu Limited Method and apparatus for preprocessing of binding free energy calculation, and binding free energy calculation method
CN113066525A (zh) * 2021-03-30 2021-07-02 中山大学 一种基于集成学习与混合神经网络的多靶标药物筛选方法
CN114974409A (zh) * 2022-05-31 2022-08-30 浙江大学 一种基于零样本学习的针对新发现靶点的药物虚拟筛选系统

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