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Dalle Vedove A, Cazzanelli G, Batiste L, Marchand JR, Spiliotopoulos D, Corsi J, D’Agostino VG, Caflisch A, Lolli G. Identification of a BAZ2A-Bromodomain Hit Compound by Fragment Growing. ACS Med Chem Lett 2022; 13:1434-1443. [PMID: 36105334 PMCID: PMC9465710 DOI: 10.1021/acsmedchemlett.2c00173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BAZ2A is an epigenetic regulator affecting transcription of ribosomal RNA. It is overexpressed in aggressive and recurrent prostate cancer, promoting cellular migration. Its bromodomain is characterized by a shallow and difficult-to-drug pocket. Here, we describe a structure-based fragment-growing campaign for the identification of ligands of the BAZ2A bromodomain. By combining docking, competition binding assays, and protein crystallography, we have extensively explored the interactions of the ligands with the rim of the binding pocket, and in particular ionic interactions with the side chain of Glu1820, which is unique to BAZ2A. We present 23 high-resolution crystal structures of the holo BAZ2A bromodomain and analyze common bromodomain/ligand motifs and favorable intraligand interactions. Binding of some of the compounds is enantiospecific, with affinity in the low micromolar range. The most potent ligand has an equilibrium dissociation constant of 7 μM and a good selectivity over the paralog BAZ2B bromodomain.
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Affiliation(s)
- Andrea Dalle Vedove
- Department
of Cellular, Computational and Integrative Biology - CIBIO, University of Trento, via Sommarive 9, 38123 Povo - Trento, Italy
| | - Giulia Cazzanelli
- Department
of Cellular, Computational and Integrative Biology - CIBIO, University of Trento, via Sommarive 9, 38123 Povo - Trento, Italy
| | - Laurent Batiste
- Department
of Biochemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Jean-Rémy Marchand
- Department
of Biochemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Dimitrios Spiliotopoulos
- Department
of Biochemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Jessica Corsi
- Department
of Cellular, Computational and Integrative Biology - CIBIO, University of Trento, via Sommarive 9, 38123 Povo - Trento, Italy
| | - Vito Giuseppe D’Agostino
- Department
of Cellular, Computational and Integrative Biology - CIBIO, University of Trento, via Sommarive 9, 38123 Povo - Trento, Italy
| | - Amedeo Caflisch
- Department
of Biochemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Graziano Lolli
- Department
of Cellular, Computational and Integrative Biology - CIBIO, University of Trento, via Sommarive 9, 38123 Povo - Trento, Italy
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2
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Fragment-to-lead tailored in silico design. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 40:44-57. [PMID: 34916022 DOI: 10.1016/j.ddtec.2021.08.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 06/25/2021] [Accepted: 08/11/2021] [Indexed: 02/07/2023]
Abstract
Fragment-based drug discovery (FBDD) emerged as a disruptive technology and became established during the last two decades. Its rationality and low entry costs make it appealing, and the numerous examples of approved drugs discovered through FBDD validate the approach. However, FBDD still faces numerous challenges. Perhaps the most important one is the transformation of the initial fragment hits into viable leads. Fragment-to-lead (F2L) optimization is resource-intensive and is therefore limited in the possibilities that can be actively pursued. In silico strategies play an important role in F2L, as they can perform a deeper exploration of chemical space, prioritize molecules with high probabilities of being active and generate non-obvious ideas. Here we provide a critical overview of current in silico strategies in F2L optimization and highlight their remarkable impact. While very effective, most solutions are target- or fragment- specific. We propose that fully integrated in silico strategies, capable of automatically and systematically exploring the fast-growing available chemical space can have a significant impact on accelerating the release of fragment originated drugs.
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3
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Dalle Vedove A, Cazzanelli G, Corsi J, Sedykh M, D’Agostino VG, Caflisch A, Lolli G. Identification of a BAZ2A Bromodomain Hit Compound by Fragment Joining. ACS BIO & MED CHEM AU 2021; 1:5-10. [PMID: 36147311 PMCID: PMC9484724 DOI: 10.1021/acsbiomedchemau.1c00016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Andrea Dalle Vedove
- Department of Cellular, Computational and Integrative Biology - CIBio, University of Trento, via Sommarive 9, 38123 Trento, Italy
| | - Giulia Cazzanelli
- Department of Cellular, Computational and Integrative Biology - CIBio, University of Trento, via Sommarive 9, 38123 Trento, Italy
| | - Jessica Corsi
- Department of Cellular, Computational and Integrative Biology - CIBio, University of Trento, via Sommarive 9, 38123 Trento, Italy
| | - Maria Sedykh
- Department of Biochemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Vito Giuseppe D’Agostino
- Department of Cellular, Computational and Integrative Biology - CIBio, University of Trento, via Sommarive 9, 38123 Trento, Italy
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Graziano Lolli
- Department of Cellular, Computational and Integrative Biology - CIBio, University of Trento, via Sommarive 9, 38123 Trento, Italy
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4
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CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J Comput Aided Mol Des 2021; 35:737-750. [PMID: 34050420 DOI: 10.1007/s10822-021-00390-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.
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5
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Marchand JR, Knehans T, Caflisch A, Vitalis A. An ABSINTH-Based Protocol for Predicting Binding Affinities between Proteins and Small Molecules. J Chem Inf Model 2020; 60:5188-5202. [PMID: 32897071 DOI: 10.1021/acs.jcim.0c00558] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The core task in computational drug discovery is to accurately predict binding free energies in receptor-ligand systems for large libraries of putative binders. Here, the ABSINTH implicit solvent model and force field are extended to describe small, organic molecules and their interactions with proteins. We show that an automatic pipeline based on partitioning arbitrary molecules into substructures corresponding to model compounds with known free energies of solvation can be combined with the CHARMM general force field into a method that is successful at the two important challenges a scoring function faces in virtual screening work flows: it ranks known binders with correlation values rivaling that of comparable state-of-the-art methods and it enriches true binders in a set of decoys. Our protocol introduces innovative modifications to common virtual screening workflows, notably the use of explicit ions as competitors and the integration over multiple protein and ligand species differing in their protonation states. We demonstrate the value of modifications to both the protocol and ABSINTH itself. We conclude by discussing the limitations of high-throughput implicit methods such as the one proposed here.
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Affiliation(s)
- Jean-Rémy Marchand
- Department of Biochemistry, University of Zürich, CH 8057 Zürich, Switzerland
| | - Tim Knehans
- Department of Biochemistry, University of Zürich, CH 8057 Zürich, Switzerland
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zürich, CH 8057 Zürich, Switzerland
| | - Andreas Vitalis
- Department of Biochemistry, University of Zürich, CH 8057 Zürich, Switzerland
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6
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Prieto-Martínez FD, Medina-Franco JL. Current advances on the development of BET inhibitors: insights from computational methods. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 122:127-180. [PMID: 32951810 DOI: 10.1016/bs.apcsb.2020.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.
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Affiliation(s)
- Fernando D Prieto-Martínez
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
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7
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Goossens K, Wroblowski B, Langini C, van Vlijmen H, Caflisch A, De Winter H. Assessment of the Fragment Docking Program SEED. J Chem Inf Model 2020; 60:4881-4893. [PMID: 32820916 DOI: 10.1021/acs.jcim.0c00556] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The fragment docking program solvation energy for exhaustive docking (SEED) is evaluated on 15 different protein targets, with a focus on enrichment and the hit rate. It is shown that SEED allows for consistent computational enrichment of fragment libraries, independent of the effective hit rate. Depending on the actual target protein, true positive rates ranging up to 27% are observed at a cutoff value corresponding to the experimental hit rate. The impact of variations in docking protocols and energy filters is discussed in detail. Remaining issues, limitations, and use cases of SEED are also discussed. Our results show that fragment library selection or enhancement for a particular target is likely to benefit from docking with SEED, suggesting that SEED is a useful resource for fragment screening campaigns. A workflow is presented for the use of the program in virtual screening, including filtering and postprocessing to optimize hit rates.
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Affiliation(s)
- Kenneth Goossens
- Department of Pharmaceutical Sciences, Laboratory of Medicinal Chemistry, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
| | | | - Cassiano Langini
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Herman van Vlijmen
- Janssen Research and Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Hans De Winter
- Department of Pharmaceutical Sciences, Laboratory of Medicinal Chemistry, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
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8
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Cozza G, Zonta F, Dalle Vedove A, Venerando A, Dall'Acqua S, Battistutta R, Ruzzene M, Lolli G. Biochemical and cellular mechanism of protein kinase CK2 inhibition by deceptive curcumin. FEBS J 2019; 287:1850-1864. [DOI: 10.1111/febs.15111] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 08/01/2019] [Accepted: 10/26/2019] [Indexed: 12/13/2022]
Affiliation(s)
- Giorgio Cozza
- Department of Molecular Medicine University of Padua Padua Italy
| | - Francesca Zonta
- Department of Biomedical Sciences CNR Institute of Neuroscience University of Padua Padua Italy
| | - Andrea Dalle Vedove
- Department of Cellular, Computational and Integrative Biology – CIBIO University of Trento Trento Italy
| | - Andrea Venerando
- Department of Comparative Biomedicine and Food Science University of Padua Legnaro Italy
| | - Stefano Dall'Acqua
- Department of Pharmaceutical and Pharmacological Sciences University of Padua Padua Italy
| | - Roberto Battistutta
- Department of Chemical Sciences University of Padua Padua Italy
- Institute of Biomolecular Chemistry National Research Council (CNR) Padua Italy
| | - Maria Ruzzene
- Department of Biomedical Sciences CNR Institute of Neuroscience University of Padua Padua Italy
| | - Graziano Lolli
- Department of Cellular, Computational and Integrative Biology – CIBIO University of Trento Trento Italy
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9
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Wiedmer L, Schärer C, Spiliotopoulos D, Hürzeler M, Śledź P, Caflisch A. Ligand retargeting by binding site analogy. Eur J Med Chem 2019; 175:107-113. [DOI: 10.1016/j.ejmech.2019.04.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 04/11/2019] [Accepted: 04/13/2019] [Indexed: 12/27/2022]
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10
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Jacquemard C, Drwal MN, Desaphy J, Kellenberger E. Binding mode information improves fragment docking. J Cheminform 2019; 11:24. [PMID: 30903304 PMCID: PMC6431075 DOI: 10.1186/s13321-019-0346-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 03/13/2019] [Indexed: 12/11/2022] Open
Abstract
Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.![]()
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Affiliation(s)
- Célien Jacquemard
- Laboratoire d'innovation thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Malgorzata N Drwal
- Laboratoire d'innovation thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Jérémy Desaphy
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Esther Kellenberger
- Laboratoire d'innovation thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400, Illkirch, France.
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11
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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12
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From bench to bedside, via desktop. Recent advances in the application of cutting-edge in silico tools in the research of drugs targeting bromodomain modules. Biochem Pharmacol 2018; 159:40-51. [PMID: 30414936 DOI: 10.1016/j.bcp.2018.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 11/07/2018] [Indexed: 11/22/2022]
Abstract
The discipline of drug discovery has greatly benefited by computational tools and in silico algorithms aiming at rationalization of many related processes, from the stage of early hit identification to the preclinical phases of drug candidate validation. The various methodologies referred to as molecular modeling tools span a broad spectrum of applications, from straightforward approaches such as virtual screening of compound libraries to more advanced techniques involving the precise estimation of free energy upon binding of the candidate drug to its macromolecular target. To this end, we report an overview of specific studies where implementation of such sophisticated modeling algorithms has shown to be indispensable for addressing challenging systems and biological questions otherwise difficult to answer. We focus our attention on the emerging field of bromodomain inhibitors. Bromodomains are small modules involved in epigenetic signaling and currently comprise high-priority targets for developing both drug candidates and chemical probes for basic biomedical research. We attempt a critical presentation of selected cases utilizing cutting-edge in silico methodologies, with our main emphasis being on absolute or relative free energy simulations, on implementation of quantum-mechanics level calculations and on characterization of solvent thermodynamics. We discuss the advantages and strengths as well as the drawbacks and weaknesses of computational tools utilized in those works and we attempt to comment on specific issues related to their integration into the regular medicinal chemistry practice. Our conclusion is that while such methods indeed represent highly promising resources for further advancing the discipline, their application is not always trivial.
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13
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Esposito C, Wiedmer L, Caflisch A. In Silico Identification of JMJD3 Demethylase Inhibitors. J Chem Inf Model 2018; 58:2151-2163. [DOI: 10.1021/acs.jcim.8b00539] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- C. Esposito
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - L. Wiedmer
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - A. Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
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14
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Rachman MM, Barril X, Hubbard RE. Predicting how drug molecules bind to their protein targets. Curr Opin Pharmacol 2018; 42:34-39. [PMID: 30041063 DOI: 10.1016/j.coph.2018.07.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 07/01/2018] [Indexed: 01/27/2023]
Abstract
There have been substantial advances in the application of molecular modelling and simulation to drug discovery in recent years, as massive increases in computer power are coupled with continued development in the underlying methods and understanding of how to apply them. Here, we survey recent advances in one particular area-predicting how a known ligand binds to a particular protein. We focus on the four contributing classes of calculation: predicting where a binding site is on a protein; characterizing where chemical functional groups will bind to that site; molecular docking to generate a binding mode for a ligand and dynamics simulations to refine that pose and allow for protein conformation change. Examples of successful application are provided for each class.
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Affiliation(s)
- Moira M Rachman
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII, 27-31, 08028 Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Roderick E Hubbard
- YSBL, University of York, Heslington, York YO10 5DD, UK; Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
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15
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Marchand JR, Caflisch A. In silico fragment-based drug design with SEED. Eur J Med Chem 2018; 156:907-917. [PMID: 30064119 DOI: 10.1016/j.ejmech.2018.07.042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/11/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022]
Abstract
We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 103-104 fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-102 fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.
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Affiliation(s)
- Jean-Rémy Marchand
- Department of Biochemistry, University of Zürich, CH-8057, Zürich, Switzerland
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zürich, CH-8057, Zürich, Switzerland.
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16
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Dalle Vedove A, Spiliotopoulos D, D'Agostino VG, Marchand JR, Unzue A, Nevado C, Lolli G, Caflisch A. Structural Analysis of Small-Molecule Binding to the BAZ2A and BAZ2B Bromodomains. ChemMedChem 2018; 13:1479-1487. [DOI: 10.1002/cmdc.201800234] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 05/14/2018] [Indexed: 12/12/2022]
Affiliation(s)
- Andrea Dalle Vedove
- Centre for Integrative Biology; University of Trento; via Sommarive 9 38123 Povo-Trento Italy
| | - Dimitrios Spiliotopoulos
- Department of Biochemistry; University of Zürich; Winterthurerstrasse 190 8057 Zürich Switzerland
| | - Vito G. D'Agostino
- Centre for Integrative Biology; University of Trento; via Sommarive 9 38123 Povo-Trento Italy
| | - Jean-Rémy Marchand
- Department of Biochemistry; University of Zürich; Winterthurerstrasse 190 8057 Zürich Switzerland
| | - Andrea Unzue
- Department of Chemistry; University of Zürich; Winterthurerstrasse 190 8057 Zürich Switzerland
| | - Cristina Nevado
- Department of Chemistry; University of Zürich; Winterthurerstrasse 190 8057 Zürich Switzerland
| | - Graziano Lolli
- Centre for Integrative Biology; University of Trento; via Sommarive 9 38123 Povo-Trento Italy
| | - Amedeo Caflisch
- Department of Biochemistry; University of Zürich; Winterthurerstrasse 190 8057 Zürich Switzerland
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17
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Zhu J, Zhou C, Caflisch A. Structure-based discovery of selective BRPF1 bromodomain inhibitors. Eur J Med Chem 2018; 155:337-352. [PMID: 29902720 DOI: 10.1016/j.ejmech.2018.05.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 04/05/2018] [Accepted: 05/23/2018] [Indexed: 11/19/2022]
Abstract
Bromodomain and plant homeodomain (PHD) finger containing protein 1 (BRPF1) is a member of subfamily IV of the human bromodomains. Experimental evidence suggests that BRPF1 is involved in leukemia. In a previous high-throughput docking campaign we identified several chemotypes targeting the BRPF1 bromodomain. Here, pharmacophore searches using the binding modes of two of these chemotypes resulted in two new series of ligands of the BRPF1 bromodomain. The 2,3-dioxo-quinoxaline 21 exhibits a 2-μM affinity for the BRPF1 bromodomain in two different competition binding assays, and more than 100-fold selectivity for BRPF1 against other members of subfamily IV and representatives of other subfamilies. Cellular activity is confirmed by a viability assay in a leukemia cell line. Isothermal titration calorimetry measurements reveal enthalpy-driven binding for compounds 21, 26 (KD = 3 μM), and the 2,4-dimethyl-oxazole derivative 42 (KD = 10 μM). Multiple molecular dynamics simulations and a dozen co-crystal structures at high resolution provide useful information for further optimization of affinity for the BRPF1 bromodomain.
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Affiliation(s)
- Jian Zhu
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Chunxian Zhou
- Department of Pathology, Shanghai University of Traditional Chinese Medicine, Cailun Road 1200, Pudong District, Shanghai, China
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
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Śledź P, Caflisch A. Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol 2017; 48:93-102. [PMID: 29149726 DOI: 10.1016/j.sbi.2017.10.010] [Citation(s) in RCA: 312] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/05/2017] [Accepted: 10/09/2017] [Indexed: 01/24/2023]
Abstract
Recent years have witnessed rapid developments of computer-aided drug design methods, which have reached accuracy that allows their routine practical applications in drug discovery campaigns. Protein structure-based methods are useful for the prediction of binding modes of small molecules and their relative affinity. The high-throughput docking of up to 106 small molecules followed by scoring based on implicit-solvent force field can robustly identify micromolar binders using a rigid protein target. Molecular dynamics with explicit solvent is a low-throughput technique for the characterization of flexible binding sites and accurate evaluation of binding pathways, kinetics, and thermodynamics. In this review we highlight recent advancements in applications of ligand docking tools and molecular dynamics simulations to ligand identification and optimization.
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Affiliation(s)
- Paweł Śledź
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zürich, Switzerland.
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zürich, Switzerland.
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W Young D. Using Fragment Based Drug Discovery to Target Epigenetic Regulators in Cancer. ACTA ACUST UNITED AC 2017. [DOI: 10.15406/mojbb.2017.04.00062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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