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Škuta C, Cortés-Ciriano I, Dehaen W, Kříž P, van Westen GJP, Tetko IV, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J Cheminform 2020; 12:39. [PMID: 33431038 PMCID: PMC7260783 DOI: 10.1186/s13321-020-00443-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/16/2020] [Indexed: 02/11/2023] Open
Abstract
An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.![]()
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Affiliation(s)
- C Škuta
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic
| | - I Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - W Dehaen
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic.,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - P Kříž
- Department of Mathematics, Faculty of Chemical Engineering, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - G J P van Westen
- Computational Drug Discovery, Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - I V Tetko
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH) and BIGCHEM GmbH, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - A Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - D Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic. .,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
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Fukunishi Y, Yamasaki S, Yasumatsu I, Takeuchi K, Kurosawa T, Nakamura H. Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction. Mol Inform 2017; 36:1600013. [PMID: 28001004 PMCID: PMC5297997 DOI: 10.1002/minf.201600013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 04/01/2016] [Indexed: 01/26/2023]
Abstract
In order to improve docking score correction, we developed several structure-based quantitative structure activity relationship (QSAR) models by protein-drug docking simulations and applied these models to public affinity data. The prediction models used descriptor-based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. The binding free energy that corresponded to the docking score was approximated by a weighted average of docking scores for multiple proteins, and we tried linear, weighted linear and polynomial regression models considering the compound similarities. In addition, we tried a combination of these regression models for individual data sets such as IC50 , Ki , and %inhibition values. The cross-validation results showed that the weighted linear model was more accurate than the simple linear regression model. Thus, the QSAR approaches based on the affinity data of public databases should improve docking scores.
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Affiliation(s)
- Yoshifumi Fukunishi
- Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan
| | - Satoshi Yamasaki
- Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan
| | - Isao Yasumatsu
- Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan
- Daiichi Sankyo RD Novare Co., Ltd., 1-16-13, Kita-Kasai, Edogawa-ku, Tokyo, 134-8630, Japan
| | - Koh Takeuchi
- Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan
| | - Takashi Kurosawa
- Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Koto-ku, Tokyo, 135-0064, Japan
- Hitachi Solutions East Japan, 12-1 Ekimaehoncho, Kawasaki-ku, Kanagawa, 210-0007, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
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Pressor mechanism evaluation for phytochemical compounds using in silico compound–protein interaction prediction. Regul Toxicol Pharmacol 2013; 67:115-24. [DOI: 10.1016/j.yrtph.2013.07.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Revised: 07/20/2013] [Accepted: 07/22/2013] [Indexed: 01/30/2023]
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Peragovics Á, Simon Z, Tombor L, Jelinek B, Hári P, Czobor P, Málnási-Csizmadia A. Virtual affinity fingerprints for target fishing: a new application of Drug Profile Matching. J Chem Inf Model 2012; 53:103-13. [PMID: 23215025 DOI: 10.1021/ci3004489] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca. 1200 FDA-approved small-molecule drugs against a set of nontarget proteins and creates bioactivity predictions based on this pattern. The effectiveness of this approach was previously demonstrated for therapeutic effect prediction of drug molecules. In the current work, we investigated the applicability of DPM for target fishing, i.e. for the prediction of biological targets for compounds. Predictions were made for 77 targets, and their accuracy was measured by Receiver Operating Characteristic (ROC) analysis. Robustness was tested by a rigorous 10-fold cross-validation procedure. This procedure identified targets (N = 45) with high reliability based on DPM performance. These 45 categories were used in a subsequent study which aimed at predicting the off-target profiles of currently approved FDA drugs. In this data set, 79% of the known drug-target interactions were correctly predicted by DPM, and additionally 1074 new drug-target interactions were suggested. We focused our further investigation on the suggested interactions of antipsychotic molecules and confirmed several interactions by a review of the literature.
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Affiliation(s)
- Ágnes Peragovics
- Department of Biochemistry, Institute of Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary
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Peragovics Á, Simon Z, Brandhuber I, Jelinek B, Hári P, Hetényi C, Czobor P, Málnási-Csizmadia A. Contribution of 2D and 3D Structural Features of Drug Molecules in the Prediction of Drug Profile Matching. J Chem Inf Model 2012; 52:1733-44. [PMID: 22697495 DOI: 10.1021/ci3001056] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ágnes Peragovics
- Department of Biochemistry,
Institute of Biology, Eötvös Loránd University, Pázmány Péter sétány
1/C, H-1117 Budapest, Hungary
- Drugmotif Ltd., Szent Erzsébet
krt. 14, H-2112 Veresegyház, Hungary
| | - Zoltán Simon
- Drugmotif Ltd., Szent Erzsébet
krt. 14, H-2112 Veresegyház, Hungary
| | | | - Balázs Jelinek
- Department of Biochemistry,
Institute of Biology, Eötvös Loránd University, Pázmány Péter sétány
1/C, H-1117 Budapest, Hungary
- Drugmotif Ltd., Szent Erzsébet
krt. 14, H-2112 Veresegyház, Hungary
| | - Péter Hári
- Drugmotif Ltd., Szent Erzsébet
krt. 14, H-2112 Veresegyház, Hungary
| | - Csaba Hetényi
- HAS-ELTE Molecular Biophysics Research Group, Pázmány Péter sétány.
1/C, H-1117 Budapest, Hungary
| | - Pál Czobor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6, H-1083 Budapest,
Hungary
| | - András Málnási-Csizmadia
- Department of Biochemistry,
Institute of Biology, Eötvös Loránd University, Pázmány Péter sétány
1/C, H-1117 Budapest, Hungary
- HAS-ELTE Molecular Biophysics Research Group, Pázmány Péter sétány.
1/C, H-1117 Budapest, Hungary
- Drugmotif Ltd., Szent Erzsébet
krt. 14, H-2112 Veresegyház, Hungary
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Fukunishi Y, Mizukoshi Y, Takeuchi K, Shimada I, Takahashi H, Nakamura H. Protein–ligand docking guided by ligand pharmacophore-mapping experiment by NMR. J Mol Graph Model 2011; 31:20-7. [DOI: 10.1016/j.jmgm.2011.08.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2011] [Revised: 08/03/2011] [Accepted: 08/05/2011] [Indexed: 12/01/2022]
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Fukunishi Y, Nakamura H. Definition of Drug-Likeness for Compound Affinity. J Chem Inf Model 2011; 51:1012-6. [DOI: 10.1021/ci200035q] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yoshifumi Fukunishi
- Biomedicinal Information Research Center (BIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan
- Pharmaceutical Innovation Value Chain, BioGrid Center Kansai, 1-4-2 Shinsenri-Higashimachi, Toyonaka, Osaka 560-0082, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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Abstract
IMPORTANCE OF THE FIELD Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient. AREAS COVERED IN THIS REVIEW This review highlights the recent progress of the post-processing of protein-compound complexes after docking. WHAT THE READER WILL GAIN These methods utilize ensembles of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the ensemble of docking poses. TAKE HOME MESSAGE The protein-compound docking program provides an arbitral rather than a canonical ensemble of docking poses. When the ensemble of docking poses satisfies the canonical ensemble, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined ensembles of docking poses.
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Affiliation(s)
- Yoshifumi Fukunishi
- Biomedicinal Information Research Center (BIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135 0064, Japan.
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Veselovsky A, Sobolev B, Zharkova M, Archakov A. Computer-based substrate specifity prediction for cytochrome P450. ACTA ACUST UNITED AC 2010; 56:90-100. [DOI: 10.18097/pbmc20105601090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Cytochrome P450 is important class of enzymes metabolizing numerous drugs. The composition and activity of these enzymes are determined the drug distribution in organism, its pharmacological and toxic effect. Thus the prediction of the behaviour of compounds in organism is essential for discovery and development of new drugs in the early stages of this process. The different isoforms of cytochrome P450 can oxidized wide range of chemical compounds and their substrate specifity do not correlate with their taxonomical classification. The main methods of cytochrome P450 substrate specifity prediction is reviewed. These methods divided based on primary informations that used: prediction based on amino acid sequences, ligand-based (pharmacophore and QSAR models) and structure-based (molecular docking, affinity prediction) methods. The common problem of cytochrome P450 substrate prediction and advantage and disadvantages of these methods are discussed.
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A similarity search using molecular topological graphs. J Biomed Biotechnol 2009; 2009:231780. [PMID: 20037730 PMCID: PMC2796334 DOI: 10.1155/2009/231780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2009] [Revised: 07/26/2009] [Accepted: 09/19/2009] [Indexed: 11/22/2022] Open
Abstract
A molecular similarity measure has been developed using molecular topological graphs and atomic partial charges. Two kinds of topological graphs were used. One is the ordinary adjacency matrix and the other is a matrix which represents the minimum path length between two atoms of the molecule. The ordinary adjacency matrix is suitable to compare the local structures of molecules such as functional groups, and the other matrix is suitable to compare the global structures of molecules. The combination of these two matrices gave a similarity measure. This method was applied to in silico drug screening, and the results showed that it was effective as a similarity measure.
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FUKUNISHI Y, SUGIHARA Y, MIKAMI Y, SAKAI K, KUSUDO H, NAKAMURA H. Advanced in-silico drug screening to achieve high hit ratio. ACTA ACUST UNITED AC 2009. [DOI: 10.5571/syntheng.2.64] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Fukunishi Y, Nakamura H. A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method. J Mol Graph Model 2008; 27:628-36. [PMID: 19046907 DOI: 10.1016/j.jmgm.2008.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2008] [Revised: 10/13/2008] [Accepted: 10/15/2008] [Indexed: 11/28/2022]
Abstract
We developed a new molecular dynamics simulation method for molecular overlapping (alignment) and ligand-based in-silico drug screening based on molecular similarity. The molecular system consists of the query compound and the other compound(s) selected from a compound library. The newly introduced intermolecular interaction between compounds is proportional to the molecular overlap instead of the van der Waals and Coulomb interactions between atoms of different molecules. This method was able to achieve both conformer generation of molecules and molecular overlapping (alignment) at the same time. After an energy minimization and following short-time MD simulation, the molecules in the system were overlapped with each other and the similarity between compounds was measured by the volume of the overlap. We applied this MD simulation method to ligand-based in-silico drug screening and found that it worked well for several targets.
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Affiliation(s)
- Yoshifumi Fukunishi
- Biomedicinal Information Research Center (BIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi, Koto-ku, Tokyo, Japan.
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Stjernschantz E, Vermeulen NPE, Oostenbrink C. Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450. Expert Opin Drug Metab Toxicol 2008; 4:513-27. [PMID: 18484912 DOI: 10.1517/17425255.4.5.513] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s). OBJECTIVE This review discusses the recent advances of these methods and the implications that are specific for P450s. METHODS Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand-P450 interactions. CONCLUSION P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.
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Affiliation(s)
- Eva Stjernschantz
- Vrije Universiteit Amsterdam, Leiden/Amsterdam Centre for Drug Research, Division of Molecular Toxicology, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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Omagari K, Mitomo D, Kubota S, Nakamura H, Fukunishi Y. A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening. Adv Appl Bioinform Chem 2008; 1:19-28. [PMID: 21918604 PMCID: PMC3169939 DOI: 10.2147/aabc.s3767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
We examined the procedures to combine two different in silico drug-screening results to achieve a high hit ratio. When the 3D structure of the target protein and some active compounds are known, both structure-based and ligand-based in silico screening methods can be applied. In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method. To combine the predicted compound’s sets by these two screening methods, we examined the product of the sets (consensus set) and the sum of the sets. As a result, the consensus set achieved a higher hit ratio than the sum of the sets and than either individual predicted set. In addition, the current combination was shown to be robust enough for the structural diversities both in different crystal structure and in snapshot structures during molecular dynamics simulations.
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Affiliation(s)
- Katsumi Omagari
- Japan Biological Informatics Consortium (JBiC), Koto-ku, Tokyo, Japan
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