1
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Eguida M, Bret G, Sindt F, Li F, Chau I, Ackloo S, Arrowsmith C, Bolotokova A, Ghiabi P, Gibson E, Halabelian L, Houliston S, Harding RJ, Hutchinson A, Loppnau P, Perveen S, Seitova A, Zeng H, Schapira M, Rognan D. Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2. J Chem Inf Model 2024; 64:5344-5355. [PMID: 38916159 DOI: 10.1021/acs.jcim.4c00601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - Guillaume Bret
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - François Sindt
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - Fengling Li
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Irene Chau
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Suzanne Ackloo
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Cheryl Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Albina Bolotokova
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Pegah Ghiabi
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Elisa Gibson
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Levon Halabelian
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Scott Houliston
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Rachel J Harding
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Ashley Hutchinson
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Peter Loppnau
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Sumera Perveen
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Almagul Seitova
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Hong Zeng
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Didier Rognan
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
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2
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Diedrich K, Ehrt C, Graef J, Poppinga M, Ritter N, Rarey M. User-centric design of a 3D search interface for protein-ligand complexes. J Comput Aided Mol Des 2024; 38:23. [PMID: 38814371 PMCID: PMC11139749 DOI: 10.1007/s10822-024-00563-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 05/17/2024] [Indexed: 05/31/2024]
Abstract
In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine's new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine's 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server ( https://proteins.plus ).
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Affiliation(s)
- Konrad Diedrich
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | - Joel Graef
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | - Martin Poppinga
- Universität Hamburg, Department of Informatics, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany
| | - Norbert Ritter
- Universität Hamburg, Department of Informatics, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany.
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3
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Angles R, Arenas-Salinas M, García R, Ingram B. An optimized relational database for querying structural patterns in proteins. Database (Oxford) 2024; 2024:baad093. [PMID: 38236197 PMCID: PMC10939390 DOI: 10.1093/database/baad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/16/2023] [Accepted: 12/19/2023] [Indexed: 01/19/2024]
Abstract
A database is an essential component in almost any software system, and its creation involves more than just data modeling and schema design. It also includes query optimization and tuning. This paper focuses on a web system called GSP4PDB, which is used for searching structural patterns in proteins. The system utilizes a normalized relational database, which has proven to be inefficient even for simple queries. This article discusses the optimization of the GSP4PDB database by implementing two techniques: denormalization and indexing. The empirical evaluation described in the article shows that combining these techniques enhances the efficiency of the database when querying both real and artificial graph-based structural patterns.
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Affiliation(s)
- Renzo Angles
- Department of Computer Science, Faculty of Engineering, Universidad de Talca, Camino a Los Niches Km. 1, Curicó, Región del Maule 3340000, Chile
- Millennium Institute for Foundational Research on Data (IMFD), Vicuña Mackenna 4860, Macul, Santiago, Región Metropolitana 7810000, Chile
| | - Mauricio Arenas-Salinas
- Centro de Bioinformática y Simulación Molecular (CBSM), Faculty of Engineering, Universidad de Talca, Av. Lircay s/n, Talca Región del Maule 34600000, Chile
| | - Roberto García
- Millennium Institute for Foundational Research on Data (IMFD), Vicuña Mackenna 4860, Macul, Santiago, Región Metropolitana 7810000, Chile
- Engineering Systems Doctoral Program, Faculty of Engineering, Universidad de Talca, Camino a Los Niches Km 1, Curicó, Región del Maule 3340000, Chile
| | - Ben Ingram
- School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK43 0AL, England
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4
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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5
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Sieg J, Rarey M. Searching similar local 3D micro-environments in protein structure databases with MicroMiner. Brief Bioinform 2023; 24:bbad357. [PMID: 37833838 DOI: 10.1093/bib/bbad357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/28/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023] Open
Abstract
The available protein structure data are rapidly increasing. Within these structures, numerous local structural sites depict the details characterizing structure and function. However, searching and analyzing these sites extensively and at scale poses a challenge. We present a new method to search local sites in protein structure databases using residue-defined local 3D micro-environments. We implemented the method in a new tool called MicroMiner and demonstrate the capabilities of residue micro-environment search on the example of structural mutation analysis. Usually, experimental structures for both the wild-type and the mutant are unavailable for comparison. With MicroMiner, we extracted $>255 \times 10^{6}$ amino acid pairs in protein structures from the PDB, exemplifying single mutations' local structural changes for single chains and $>45 \times 10^{6}$ pairs for protein-protein interfaces. We further annotate existing data sets of experimentally measured mutation effects, like $\Delta \Delta G$ measurements, with the extracted structure pairs to combine the mutation effect measurement with the structural change upon mutation. In addition, we show how MicroMiner can bridge the gap between mutation analysis and structure-based drug design tools. MicroMiner is available as a command line tool and interactively on the https://proteins.plus/ webserver.
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Affiliation(s)
- Jochen Sieg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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6
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TWN-RENCOD: A novel method for protein binding site comparison. Comput Struct Biotechnol J 2022; 21:425-431. [PMID: 36618985 PMCID: PMC9798139 DOI: 10.1016/j.csbj.2022.12.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/30/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).
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7
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Palei S, Weisner J, Vogt M, Gontla R, Buchmuller B, Ehrt C, Grabe T, Kleinbölting S, Müller M, Clever GH, Rauh D, Summerer D. A high-throughput effector screen identifies a novel small molecule scaffold for inhibition of ten-eleven translocation dioxygenase 2. RSC Med Chem 2022; 13:1540-1548. [PMID: 36545435 PMCID: PMC9749932 DOI: 10.1039/d2md00186a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/29/2022] [Indexed: 12/24/2022] Open
Abstract
Ten-eleven translocation dioxygenases (TETs) are the erasers of 5-methylcytosine (mC), the central epigenetic regulator of mammalian DNA. TETs convert mC to three oxidized derivatives with unique physicochemical properties and inherent regulatory potential, and it initializes active demethylation by the base excision repair pathway. Potent small molecule inhibitors would be useful tools to study TET functions by conditional control. To facilitate the discovery of such tools, we here report a high-throughput screening pipeline and its application to screen and validate 31.5k compounds for inhibition of TET2. Using a homogenous fluorescence assay, we discover a novel quinoline-based scaffold that we further validate with an orthogonal semi-high throughput MALDI-MS assay for direct monitoring of substrate turnover. Structure-activity relationship (SAR) studies involving >20 derivatives of this scaffold led to the identification of optimized inhibitors, and together with computational studies suggested a plausible model for its mode of action.
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Affiliation(s)
- Shubhendu Palei
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Jörn Weisner
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Melina Vogt
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Rajesh Gontla
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Benjamin Buchmuller
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Christiane Ehrt
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Tobias Grabe
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Silke Kleinbölting
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Matthias Müller
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Guido H Clever
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Daniel Rauh
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
| | - Daniel Summerer
- Department of Chemistry and Chemical Biology, TU Dortmund University and, Drug Discovery Hub Dortmund (DDHD), Zentrum für Integrierte Wirkstoffforschung (ZIW) Otto-Hahn Str. 4a 44227 Dortmund Germany
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8
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Konc J, Janežič D. Protein binding sites for drug design. Biophys Rev 2022; 14:1413-1421. [PMID: 36532870 PMCID: PMC9734416 DOI: 10.1007/s12551-022-01028-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/01/2022] [Indexed: 12/13/2022] Open
Abstract
Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules-ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov.
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Affiliation(s)
- Janez Konc
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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9
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Scott O, Gu J, Chan AE. Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network. J Chem Inf Model 2022; 62:5383-5396. [PMID: 36341715 PMCID: PMC9709917 DOI: 10.1021/acs.jcim.2c00832] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.
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10
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Konc J, Janežič D. ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites. J Chem Inf Model 2022; 62:5821-5829. [PMID: 36269348 DOI: 10.1021/acs.jcim.2c00947] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.
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Affiliation(s)
- Janez Konc
- Theory Department, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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11
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Grigorev V, Tinkov O, Grigoreva L, Rasdolsky A. Structural fractal analysis of the active sites of acetylcholinesterase from various organisms. J Mol Graph Model 2022; 116:108265. [PMID: 35816907 DOI: 10.1016/j.jmgm.2022.108265] [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: 04/26/2022] [Revised: 06/24/2022] [Accepted: 06/29/2022] [Indexed: 12/15/2022]
Abstract
Acetylcholinesterase (AChE) is the object of many studies due to the fact that it plays an important role in the vital activity of organisms. In particular, when new AChE inhibitors are developed, much attention is paid to the specificity of their action. One of the approaches used to study the specificity is to compare AChE taken from various organisms. In this work, crystallographic data are used to investigate the active sites of AChE (ASAs) in the free (uncomplexed) state for the following five organisms: Homo sapiens (HS), Mus musculus (MM), Torpedo californica (TC), Electrophorus electricus (EE), and Drosophila melanogaster (DM). The structural fractal analysis (SFA) proposed by us earlier is used as a research method. This method is based on the calculation and comparison of the fractal dimensions of molecular structures. SFA demonstrates that there are no significant structural differences between the active sites of human AChE and other AChEs. However, differences are found for the MM/EE pair. Further analysis of individual AARs has revealed two different areas of active sites. Ser203, Trp236, Phe338, and Tyr341 are found to belong to a variable region, and the remaining AARs belong to a conservative region of the ASAs. The fraction of "variability" is low, 0.8%.
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Affiliation(s)
- Veniamin Grigorev
- Department of Computer-aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severniy proezd 1, 142432, Chernogolovka, Moscow region, Russia.
| | - Oleg Tinkov
- Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Transnistrian State University, October 25 Str. 128, 3300, Tiraspol, Transdniestria, Republic of Moldova
| | - Ludmila Grigoreva
- Department of Fundamental Physical and Chemical Engineering, Moscow State University, Leninskiye Gory 1/51, 119991, Moscow, Russia
| | - Alexander Rasdolsky
- Department of Computer-aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severniy proezd 1, 142432, Chernogolovka, Moscow region, Russia
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12
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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13
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Shi W, Singha M, Pu L, Srivastava G, Ramanujam J, Brylinski M. GraphSite: Ligand Binding Site Classification with Deep Graph Learning. Biomolecules 2022; 12:biom12081053. [PMID: 36008947 PMCID: PMC9405584 DOI: 10.3390/biom12081053] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 12/10/2022] Open
Abstract
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.
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Affiliation(s)
- Wentao Shi
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Jagannathan Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
- Correspondence: ; Tel.: +1-(225)-578-2791; Fax: +1-(225)-578-2597
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14
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Luo L, Yang J, Wang C, Wu J, Li Y, Zhang X, Li H, Zhang H, Zhou Y, Lu A, Chen S. Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1123-1145. [PMID: 34705221 PMCID: PMC8548270 DOI: 10.1007/s11427-020-1959-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022]
Abstract
As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.
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Affiliation(s)
- Lu Luo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Jun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Cheng Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100006, China
| | - Jie Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yafang Li
- Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Xu Zhang
- weMED Health, Houston, 77054, USA
| | - Hui Li
- Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hui Zhang
- Akupunktur Akademiet, Aabyhoej, Aarhus, 8230, Denmark
| | - Yumei Zhou
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, 518033, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, China
| | - Shilin Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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15
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Valdés-Jiménez A, Jiménez-González D, Kiper AK, Rinné S, Decher N, González W, Reyes-Parada M, Núñez-Vivanco G. A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels. Front Pharmacol 2022; 13:855792. [PMID: 35370665 PMCID: PMC8971525 DOI: 10.3389/fphar.2022.855792] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 02/21/2022] [Indexed: 01/01/2023] Open
Abstract
The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.
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Affiliation(s)
- Alejandro Valdés-Jiménez
- Center for Bioinformatics, Simulations and Modelling, Faculty of Engineering, University of Talca, Talca, Chile
- Computer Architecture Department, Universitat Politécnica de Catalunya, Barcelona, Spain
| | - Daniel Jiménez-González
- Computer Architecture Department, Universitat Politécnica de Catalunya, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Aytug K. Kiper
- Institute for Physiology and Pathophysiology, Philipps-University Marburg, Marburg, Germany
| | - Susanne Rinné
- Institute for Physiology and Pathophysiology, Philipps-University Marburg, Marburg, Germany
| | - Niels Decher
- Institute for Physiology and Pathophysiology, Philipps-University Marburg, Marburg, Germany
| | - Wendy González
- Center for Bioinformatics, Simulations and Modelling, Faculty of Engineering, University of Talca, Talca, Chile
- Millennium Nucleus of Ion Channels-Associated Diseases (MiNICAD), Universidad de Talca, Talca, Chile
- *Correspondence: Wendy González, ; Miguel Reyes-Parada, ; Gabriel Núñez-Vivanco,
| | - Miguel Reyes-Parada
- Centro de Investigación Biomédica y Aplicada (CIBAP), Escuela de Medicina, Facultad de Ciencias Médicas, Universidad de Santiago de Chile, Santiago, Chile
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile
- *Correspondence: Wendy González, ; Miguel Reyes-Parada, ; Gabriel Núñez-Vivanco,
| | - Gabriel Núñez-Vivanco
- Departamento de Ciencias Naturales y Tecnología, Universidad de Aysén, Coyhaique, Chile
- *Correspondence: Wendy González, ; Miguel Reyes-Parada, ; Gabriel Núñez-Vivanco,
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16
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Konc J, Lešnik S, Škrlj B, Sova M, Proj M, Knez D, Gobec S, Janežič D. ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison. J Chem Inf Model 2022; 62:1573-1584. [PMID: 35289616 DOI: 10.1021/acs.jcim.1c01176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097-4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3-dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of l-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.
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Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Theory Department, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Samo Lešnik
- National Institute of Chemistry, Theory Department, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Blaž Škrlj
- National Institute of Chemistry, Theory Department, Hajdrihova 19, SI-1001 Ljubljana, Slovenia.,Jozef Stefan International Postgraduate School, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.,Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Matej Sova
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Matic Proj
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Damijan Knez
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Stanislav Gobec
- Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
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17
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Li G, Dai QQ, Li GB. MeCOM: A Method for Comparing Three-Dimensional Metalloenzyme Active Sites. J Chem Inf Model 2022; 62:730-739. [PMID: 35044164 DOI: 10.1021/acs.jcim.1c01335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Since metalloenzymes are a large collection of metal ion(s) dependent enzymes, comparison analyses of metalloenzyme active sites are critical for metalloenzyme de novo design, function investigation, and inhibitor development. Here, we report a method named MeCOM for comparing metalloenzyme active sites. It is characterized by metal ion(s) centric active site recognition and three-dimensional superimposition using α-carbon or pharmacophore features. The test results revealed that for the given metalloenzymes, MeCOM could effectively recognize the active sites, extract active site features, and superimpose the active sites; it also could correctly identify similar active sites, differentiate dissimilar active sites, and evaluate the similarity degree. Moreover, MeCOM showed potential to establish new associations between structurally distinct metalloenzymes by active site comparison. MeCOM is freely available at https://mecom.ddtmlab.org.
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Affiliation(s)
- Gen Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Qing-Qing Dai
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
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18
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Smilova MD, Curran PR, Radoux CJ, von Delft F, Cole JC, Bradley AR, Marsden BD. Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins. J Chem Inf Model 2022; 62:284-294. [PMID: 35020376 PMCID: PMC8790751 DOI: 10.1021/acs.jcim.1c00823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
![]()
Selectivity is a
crucial property in small molecule development.
Binding site comparisons within a protein family are a key piece of
information when aiming to modulate the selectivity profile of a compound.
Binding site differences can be exploited to confer selectivity for
a specific target, while shared areas can provide insights into polypharmacology.
As the quantity of structural data grows, automated methods are needed
to process, summarize, and present these data to users. We present
a computational method that provides quantitative and data-driven
summaries of the available binding site information from an ensemble
of structures of the same protein. The resulting ensemble maps identify
the key interactions important for ligand binding in the ensemble.
The comparison of ensemble maps of related proteins enables the identification
of selectivity-determining regions within a protein family. We applied
the method to three examples from the well-researched human bromodomain
and kinase families, demonstrating that the method is able to identify
selectivity-determining regions that have been used to introduce selectivity
in past drug discovery campaigns. We then illustrate how the resulting
maps can be used to automate comparisons across a target protein family.
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Affiliation(s)
- Mihaela D Smilova
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K
| | - Peter R Curran
- The Cambridge Crystallographic Data Centre (CCDC), Cambridge CB2 1EZ, U.K.,Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Chris J Radoux
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K
| | - Frank von Delft
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.,Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0DE, U.K.,Research Complex at Harwell. Harwell Science and Innovation Campus, Didcot OX11 0FA, U.K.,Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Jason C Cole
- The Cambridge Crystallographic Data Centre (CCDC), Cambridge CB2 1EZ, U.K
| | - Anthony R Bradley
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K
| | - Brian D Marsden
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.,Kennedy Institute of Rheumatology, NDORMS, University of Oxford, Oxford OX3 7DQ, U.K
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19
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Marotta G, Basagni F, Rosini M, Minarini A. Role of Fyn Kinase Inhibitors in Switching Neuroinflammatory Pathways. Curr Med Chem 2021; 29:4738-4755. [PMID: 34939537 DOI: 10.2174/0929867329666211221153719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 11/22/2022]
Abstract
Fyn kinase is a member of the Src non-receptor tyrosine kinase family. Fyn is involved in multiple signaling pathways extending from cell proliferation and differentiation to cell adhesion and cell motility, and it has been found to be overexpressed in various types of cancers. In the central nervous system, Fyn exerts several different functions such as axon-glial signal transduction, oligodendrocyte maturation and myelination, and it is implicated in neuroinflammatory processes. Based on these premises, Fyn emerges as an attractive target in cancer and neurodegenerative disease therapy, particularly Alzheimer disease (AD), based on its activation by Aβ via cellular prion protein and its interaction with tau protein. However, Fyn is also a challenging target since the Fyn inhibitors discovered so far, due to the relevant homology of Fyn with other kinases, suffer from off-target effects. This review covers the efforts performed in the last decade to identify and optimize small molecules that effectively inhibit Fyn, both in enzymatic and in cell assays, including drug repositioning practices, as an opportunity of therapeutic intervention in neurodegeneration.
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Affiliation(s)
- Giambattista Marotta
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126-Bologna. Italy
| | - Filippo Basagni
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126-Bologna. Italy
| | - Michela Rosini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126-Bologna. Italy
| | - Anna Minarini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126-Bologna. Italy
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20
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Li Y, Xu Y, Yu Y. CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery. Molecules 2021; 26:molecules26237257. [PMID: 34885843 PMCID: PMC8658888 DOI: 10.3390/molecules26237257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
Molecular latent representations, derived from autoencoders (AEs), have been widely used for drug or material discovery over the past couple of years. In particular, a variety of machine learning methods based on latent representations have shown excellent performance on quantitative structure–activity relationship (QSAR) modeling. However, the sequence feature of them has not been considered in most cases. In addition, data scarcity is still the main obstacle for deep learning strategies, especially for bioactivity datasets. In this study, we propose the convolutional recurrent neural network and transfer learning (CRNNTL) method inspired by the applications of polyphonic sound detection and electrocardiogram classification. Our model takes advantage of both convolutional and recurrent neural networks for feature extraction, as well as the data augmentation method. According to QSAR modeling on 27 datasets, CRNNTL can outperform or compete with state-of-art methods in both drug and material properties. In addition, the performances on one isomers-based dataset indicate that its excellent performance results from the improved ability in global feature extraction when the ability of the local one is maintained. Then, the transfer learning results show that CRNNTL can overcome data scarcity when choosing relative source datasets. Finally, the high versatility of our model is shown by using different latent representations as inputs from other types of AEs.
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Affiliation(s)
- Yaqin Li
- West China Tianfu Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (Y.L.); (Y.Y.)
| | - Yongjin Xu
- Department of Chemistry and Molecular Biology, University of Gothenburg, Kemivägen 10, 41296 Gothenburg, Sweden;
| | - Yi Yu
- Department of Chemistry and Molecular Biology, University of Gothenburg, Kemivägen 10, 41296 Gothenburg, Sweden;
- Correspondence: (Y.L.); (Y.Y.)
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21
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Thomas M, Boardman A, Garcia-Ortegon M, Yang H, de Graaf C, Bender A. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:1-59. [PMID: 34731463 DOI: 10.1007/978-1-0716-1787-8_1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andrew Boardman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Miguel Garcia-Ortegon
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
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22
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Vijayan RSK, Kihlberg J, Cross JB, Poongavanam V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today 2021; 27:967-984. [PMID: 34838731 DOI: 10.1016/j.drudis.2021.11.023] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/15/2021] [Accepted: 11/19/2021] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.
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Affiliation(s)
- R S K Vijayan
- Institute for Applied Cancer Science, MD Anderson Cancer Center, Houston, TX, USA
| | - Jan Kihlberg
- Department of Chemistry-BMC, Uppsala University, Uppsala, Sweden
| | - Jason B Cross
- Institute for Applied Cancer Science, MD Anderson Cancer Center, Houston, TX, USA.
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23
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Eguida M, Rognan D. Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision. J Cheminform 2021; 13:90. [PMID: 34814950 PMCID: PMC8609734 DOI: 10.1186/s13321-021-00567-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/06/2021] [Indexed: 11/10/2022] Open
Abstract
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France.
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24
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Akawa OB, Soremekun OS, Olotu FA, Soliman MES. Atomistic insights into the selective therapeutic activity of 6-(2,4-difluorophenoxy)-5-((ethylmethyl)pyridine-3-yl)-8-methylpyrrolo[1,2-a]pyrazin-1(2H)-one towards bromodomain-containing proteins. Comput Biol Chem 2021; 95:107592. [PMID: 34710811 DOI: 10.1016/j.compbiolchem.2021.107592] [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: 09/24/2020] [Revised: 09/29/2021] [Accepted: 10/12/2021] [Indexed: 10/20/2022]
Abstract
Cross-target effect has been one of the major mechanisms of drug toxicity, this has necessitated the design of inhibitors that are specifically tailored to target particular biomolecules. 6-(2,4-difluorophenoxy)-5-((ethylmethyl)pyridine-3-yl)-8-methylpyrrolo[1,2-a] pyrazin-1(2H)-one (Cpd38) is an inhibitor possessing high inhibition rate and tailored specificity towards bromodomain-containing protein 4 (BRD4). In this research, we used an array of computational techniques to provide insight at the atomistic level the specific targeting of BRD4 by Cpd38 relative to the binding of Cpd38 with E1A binding protein P300 (EP300); another bromodomain-containing protein (BCP). Comparatively, binding of Cpd38 improved the conformational stability and compactness of BRD4 protein when compared to the Cpd38 bound EP300. Also, Cpd38 induced a conformational change in the active site of BRD4 that facilitated a complementary pose between Cpd38 and BRD4 suitable for effective atomistic interactions. Expectedly, thermodynamic calculations revealed that the Cpd38-BRD4 system had higher binding energy (-36.11 Kcal/mol) than the Cpd38-EP300 system with a free binding energy of -15.86 Kcal/mol. Noteworthy is the opposing role Trp81 (acting as hydrogen bond acceptor) and Pro1074 (acting as hydrogen bond donor) found on the WPF and LPF loops respectively play in maintaining Cpd38 stability. Furthermore, the hydrogen bond acceptor/donator ratio was approximately 4:1 in Cpd38-BRD4 system compared with 2:1 in Cpd38-EP300 system. Taken together, atomistic insights and structural perspectives detailed in this report supplements the experimental report supporting the improved selectivity of Cpd38 for BRD4 ahead of other BCPs while providing leeway for the future design of BET selective agents with better pharmacological profile.
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Affiliation(s)
- Oluwole B Akawa
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Opeyemi S Soremekun
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa.
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Li S, Cai C, Gong J, Liu X, Li H. A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing. Proteins 2021; 89:1541-1556. [PMID: 34245187 DOI: 10.1002/prot.26176] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/26/2021] [Accepted: 06/30/2021] [Indexed: 01/18/2023]
Abstract
The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.
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Affiliation(s)
- Shiliang Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Chaoqian Cai
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Jiayu Gong
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Xiaofeng Liu
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Honglin Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China.,Research and Development Department, Jiangzhong Pharmaceutical Co., Ltd., Nanchang, China
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26
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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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27
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Bajusz D, Wade WS, Satała G, Bojarski AJ, Ilaš J, Ebner J, Grebien F, Papp H, Jakab F, Douangamath A, Fearon D, von Delft F, Schuller M, Ahel I, Wakefield A, Vajda S, Gerencsér J, Pallai P, Keserű GM. Exploring protein hotspots by optimized fragment pharmacophores. Nat Commun 2021; 12:3201. [PMID: 34045440 PMCID: PMC8159961 DOI: 10.1038/s41467-021-23443-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 04/29/2021] [Indexed: 02/04/2023] Open
Abstract
Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores at the most preferred hotspots. The efficiency of this approach is demonstrated with a pilot library of 96 fragment-sized compounds (SpotXplorer0) that is validated on popular target classes and emerging drug targets. Biochemical screening against a set of GPCRs and proteases retrieves compounds containing an average of 70% of known pharmacophores for these targets. More importantly, SpotXplorer0 screening identifies confirmed hits against recently established challenging targets such as the histone methyltransferase SETD2, the main protease (3CLPro) and the NSP3 macrodomain of SARS-CoV-2.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | | | - Grzegorz Satała
- Maj Institute of Pharmacology Polish Academy of Sciences, Kraków, Poland
| | - Andrzej J Bojarski
- Maj Institute of Pharmacology Polish Academy of Sciences, Kraków, Poland
| | - Janez Ilaš
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
| | - Jessica Ebner
- Institute for Medical Biochemistry, University of Veterinary Medicine, Vienna, Austria
| | - Florian Grebien
- Institute for Medical Biochemistry, University of Veterinary Medicine, Vienna, Austria
| | - Henrietta Papp
- National Laboratory of Virology, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Ferenc Jakab
- National Laboratory of Virology, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Alice Douangamath
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, UK
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, UK
| | - Frank von Delft
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, UK
- Structural Genomics Consortium, University of Oxford, Old Road Campus, Roosevelt Drive, Headington, OX3 7DQ, UK
- Centre for Medicines Discovery, University of Oxford, Old Road Campus, Roosevelt Drive, Headington, OX3 7DQ, UK
- Department of Biochemistry, University of Johannesburg, Auckland Park, 2006, South Africa
| | - Marion Schuller
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Ivan Ahel
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Amanda Wakefield
- Department of Chemistry, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sándor Vajda
- Department of Chemistry, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary.
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Rognan D. Modeling Protein-Ligand Interactions: Are We Ready for Deep Learning? SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11521-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Zhang S, Zhao Y, Wang S, Li M, Xu Y, Ran J, Geng X, He J, Meng J, Shao G, Zhou H, Ge Z, Chen G, Li R, Yang B. Discovery of novel diarylamides as orally active diuretics targeting urea transporters. Acta Pharm Sin B 2021; 11:181-202. [PMID: 33532188 PMCID: PMC7838058 DOI: 10.1016/j.apsb.2020.06.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/14/2020] [Accepted: 05/26/2020] [Indexed: 12/17/2022] Open
Abstract
Urea transporters (UT) play a vital role in the mechanism of urine concentration and are recognized as novel targets for the development of salt-sparing diuretics. Thus, UT inhibitors are promising for development as novel diuretics. In the present study, a novel UT inhibitor with a diarylamide scaffold was discovered by high-throughput screening. Optimization of the inhibitor led to the identification of a promising preclinical candidate, N-[4-(acetylamino)phenyl]-5-nitrofuran-2-carboxamide (1H), with excellent in vitro UT inhibitory activity at the submicromolar level. The half maximal inhibitory concentrations of 1H against UT-B in mouse, rat, and human erythrocyte were 1.60, 0.64, and 0.13 μmol/L, respectively. Further investigation suggested that 8 μmol/L 1H more powerfully inhibited UT-A1 at a rate of 86.8% than UT-B at a rate of 73.9% in MDCK cell models. Most interestingly, we found for the first time that oral administration of 1H at a dose of 100 mg/kg showed superior diuretic effect in vivo without causing electrolyte imbalance in rats. Additionally, 1H did not exhibit apparent toxicity in vivo and in vitro, and possessed favorable pharmacokinetic characteristics. 1H shows promise as a novel diuretic to treat hyponatremia accompanied with volume expansion and may cause few side effects.
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Key Words
- AQP1, aquaporin 1
- BCRP, breast cancer resistance protein
- CCK-8, cell counting kit-8
- CMC-Na, carboxymethylcellulose sodium
- DMF, N,N-dimethylformamide
- Diuretic
- Fa, fraction absorbance
- GFR, glomerular filtration rate
- HDL-C and LDL-C, high- and low-density lipoprotein
- IC50, half maximal inhibitory concentration
- IMCD, inner medulla collecting duct
- Oral administration
- P-gp, P-glycoprotein
- PBS, phosphate buffered saline
- Papp, apparent permeability
- Structure optimization
- THF, tetrahydrofuran
- UT, urea transporter
- Urea transporter inhibitor
- r.t., room temperature
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30
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Borba JVVB, Silva AC, Lima MNN, Mendonca SS, Furnham N, Costa FTM, Andrade CH. Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 124:187-223. [PMID: 33632465 DOI: 10.1016/bs.apcsb.2020.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.
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Affiliation(s)
- Joyce Villa Verde Bastos Borba
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Arthur Carvalho Silva
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Marilia Nunes Nascimento Lima
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Sabrina Silva Mendonca
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fabio Trindade Maranhão Costa
- Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Carolina Horta Andrade
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
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Mohammadi E, Benfeitas R, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers (Basel) 2020; 12:E2694. [PMID: 32967266 PMCID: PMC7563533 DOI: 10.3390/cancers12092694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.
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Affiliation(s)
- Elyas Mohammadi
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691 Stockholm, Sweden;
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, 25240 Erzurum, Turkey;
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, SE-41345 Gothenburg, Sweden;
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
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32
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Gelosa P, Castiglioni L, Camera M, Sironi L. Drug repurposing in cardiovascular diseases: Opportunity or hopeless dream? Biochem Pharmacol 2020; 177:113894. [DOI: 10.1016/j.bcp.2020.113894] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 02/27/2020] [Indexed: 12/14/2022]
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Eguida M, Rognan D. A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design. J Med Chem 2020; 63:7127-7142. [DOI: 10.1021/acs.jmedchem.0c00422] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Merveille Eguida
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
| | - Didier Rognan
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
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34
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Bellera CL, Alberca LN, Sbaraglini ML, Talevi A. In Silico Drug Repositioning for Chagas Disease. Curr Med Chem 2020; 27:662-675. [PMID: 31622200 DOI: 10.2174/0929867326666191016114839] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 09/12/2019] [Accepted: 09/23/2019] [Indexed: 12/18/2022]
Abstract
Chagas disease is an infectious tropical disease included within the group of neglected tropical diseases. Though historically endemic to Latin America, it has lately spread to high-income countries due to human migration. At present, there are only two available drugs, nifurtimox and benznidazole, approved for this treatment, both with considerable side-effects (which often result in treatment interruption) and limited efficacy in the chronic stage of the disease in adults. Drug repositioning involves finding novel therapeutic indications for known drugs, including approved, withdrawn, abandoned and investigational drugs. It is today a broadly applied approach to develop innovative medications, since indication shifts are built on existing safety, ADME and manufacturing information, thus greatly shortening development timeframes. Drug repositioning has been signaled as a particularly interesting strategy to search for new therapeutic solutions for neglected and rare conditions, which traditionally present limited commercial interest and are mostly covered by the public sector and not-for-profit initiatives and organizations. Here, we review the applications of computer-aided technologies as systematic approaches to drug repositioning in the field of Chagas disease. In silico screening represents the most explored approach, whereas other rational methods such as network-based and signature-based approximations have still not been applied.
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Affiliation(s)
- Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - María L Sbaraglini
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), La Plata, Argentina
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35
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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36
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Angles R, Arenas-Salinas M, García R, Reyes-Suarez JA, Pohl E. GSP4PDB: a web tool to visualize, search and explore protein-ligand structural patterns. BMC Bioinformatics 2020; 21:85. [PMID: 32164553 PMCID: PMC7068854 DOI: 10.1186/s12859-020-3352-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank. RESULTS We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand - amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can "draw" a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern. The user can further explore and analyse the results using a wide range of filters, or download their related information for external post-processing and analysis. CONCLUSIONS GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction.
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Affiliation(s)
- Renzo Angles
- Department of Computer Science, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
- Millennium Institute for Foundational Research on Data, Santiago, Chile
| | - Mauricio Arenas-Salinas
- Centro de Bioinformática y Simulación Molecular, Universidad de Talca, Talca, Chile
- Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
| | - Roberto García
- Millennium Institute for Foundational Research on Data, Santiago, Chile
- Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
| | - Jose Antonio Reyes-Suarez
- Centro de Bioinformática y Simulación Molecular, Universidad de Talca, Talca, Chile
- Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
| | - Ehmke Pohl
- Department of Chemistry, Durham University, Durham, DH1 3LE United Kingdom
- Department of Biosciences, Durham University, Durham, DH1 3LE United Kingdom
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37
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Simonovsky M, Meyers J. DeeplyTough: Learning Structural Comparison of Protein Binding Sites. J Chem Inf Model 2020; 60:2356-2366. [DOI: 10.1021/acs.jcim.9b00554] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Martin Simonovsky
- BenevolentAI, London W1T 5HD, U.K
- École des Ponts ParisTech, Champs sur Marne 77455, France
- Université Paris-Est, Champs sur Marne 77455, France
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Talevi A, Carrillo C, Comini M. The Thiol-polyamine Metabolism of Trypanosoma cruzi: Molecular Targets and Drug Repurposing Strategies. Curr Med Chem 2019; 26:6614-6635. [PMID: 30259812 DOI: 10.2174/0929867325666180926151059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/23/2018] [Accepted: 09/10/2018] [Indexed: 12/18/2022]
Abstract
Chagas´ disease continues to be a challenging and neglected public health problem in many American countries. The etiologic agent, Trypanosoma cruzi, develops intracellularly in the mammalian host, which hinders treatment efficacy. Progress in the knowledge of parasite biology and host-pathogen interaction has not been paralleled by the development of novel, safe and effective therapeutic options. It is then urgent to seek for novel therapeutic candidates and to implement drug discovery strategies that may accelerate the discovery process. The most appealing targets for pharmacological intervention are those essential for the pathogen and, whenever possible, absent or significantly different from the host homolog. The thiol-polyamine metabolism of T. cruzi offers interesting candidates for a rational design of selective drugs. In this respect, here we critically review the state of the art of the thiolpolyamine metabolism of T. cruzi and the pharmacological potential of its components. On the other hand, drug repurposing emerged as a valid strategy to identify new biological activities for drugs in clinical use, while significantly shortening the long time and high cost associated with de novo drug discovery approaches. Thus, we also discuss the different drug repurposing strategies available with a special emphasis in their applications to the identification of drug candidates targeting essential components of the thiol-polyamine metabolism of T. cruzi.
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Affiliation(s)
- Alan Talevi
- Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata, La Plata, Argentina
| | - Carolina Carrillo
- Instituto de Ciencias y Tecnología Dr. César Milstein (ICT Milstein) - CONICET. Ciudad Autónoma de Buenos Aires, Argentina
| | - Marcelo Comini
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
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Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Brief Bioinform 2019; 20:2167-2184. [PMID: 30169563 PMCID: PMC6954434 DOI: 10.1093/bib/bby078] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 01/06/2023] Open
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | | | - Omar Zade Kana
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Wei Pan Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
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40
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Da Silva F, Bret G, Teixeira L, Gonzalez CF, Rognan D. Exhaustive Repertoire of Druggable Cavities at Protein-Protein Interfaces of Known Three-Dimensional Structure. J Med Chem 2019; 62:9732-9742. [PMID: 31603323 DOI: 10.1021/acs.jmedchem.9b01184] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Protein-protein interactions (PPIs) offer the unique opportunity to tailor ligands aimed at specifically stabilizing or disrupting the corresponding interfaces and providing a safer alternative to conventional ligands targeting monomeric macromolecules. Selecting biologically relevant protein-protein interfaces for either stabilization or disruption by small molecules is usually biology-driven on a case-by-case basis and does not follow a structural rationale that could be applied to an entire interactome. We herewith provide a first step to the latter goal by using a fully automated and structure-based workflow, applicable to any PPI of known three-dimensional (3D) structure, to identify and prioritize druggable cavities at and nearby PPIs of pharmacological interest. When applied to the entire Protein Data Bank, 164 514 druggable cavities were identified and classified in four groups (interfacial, rim, allosteric, orthosteric) according to their properties and spatial locations. Systematic comparison of PPI cavities with pockets deduced from druggable protein-ligand complexes shows almost no overlap in property space, suggesting that even the most druggable PPI cavities are unlikely to be addressed with conventional drug-like compound libraries. The archive is freely accessible at http://drugdesign.unistra.fr/ppiome .
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Affiliation(s)
- Franck Da Silva
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Leandro Teixeira
- Department of Microbiology and Cell Science, Genetics Institute, Institute of Food and Agricultural Sciences , University of Florida , Gainesville , Florida 32610-3610 , United States
| | - Claudio F Gonzalez
- Department of Microbiology and Cell Science, Genetics Institute, Institute of Food and Agricultural Sciences , University of Florida , Gainesville , Florida 32610-3610 , United States
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
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41
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Yang G, Ma A, Qin ZS. An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure. Front Genet 2019; 10:916. [PMID: 31608126 PMCID: PMC6773955 DOI: 10.3389/fgene.2019.00916] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/29/2019] [Indexed: 12/20/2022] Open
Abstract
Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.
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Affiliation(s)
- Guodong Yang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Aiqun Ma
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
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42
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Andrade CH, Neves BJ, Melo-Filho CC, Rodrigues J, Silva DC, Braga RC, Cravo PVL. In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases. Curr Med Chem 2019. [DOI: 10.2174/0929867325666180309114824] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs)
have reached clinical trials in the last decades, underscoring the need for new, safe and effective
treatments. In such context, drug repositioning, which allows finding novel indications
for approved drugs whose pharmacokinetic and safety profiles are already known,
emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent
of the typical drug discovery process that involves the systematic screening of chemical
compounds against drug targets in high-throughput screening (HTS) efforts, for the identification
of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics
attempts to identify all potential ligands for all possible targets and diseases. In
this review, we summarize current methodological development efforts in drug repositioning
that use state-of-the-art computational ligand- and structure-based chemogenomics approaches.
Furthermore, we highlighted the recent progress in computational drug repositioning
for some NTDs, based on curation and modeling of genomic, biological, and chemical data.
Additionally, we also present in-house and other successful examples and suggest possible solutions
to existing pitfalls.
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Affiliation(s)
- Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Bruno Junior Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Cleber Camilo Melo-Filho
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Juliana Rodrigues
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Diego Cabral Silva
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Rodolpho Campos Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Pedro Vitor Lemos Cravo
- Laboratory of Cheminformatics, Centro Universitario de Anapolis (UniEVANGELICA), Anapolis, GO, 75083-515, Brazil
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43
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Paranjpe MD, Taubes A, Sirota M. Insights into Computational Drug Repurposing for Neurodegenerative Disease. Trends Pharmacol Sci 2019; 40:565-576. [PMID: 31326236 PMCID: PMC6771436 DOI: 10.1016/j.tips.2019.06.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/26/2019] [Accepted: 06/12/2019] [Indexed: 12/14/2022]
Abstract
Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.
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Affiliation(s)
- Manish D Paranjpe
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA.
| | - Alice Taubes
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA; Gladstone Institutes, San Francisco, CA 94158, USA.
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44
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3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns. Int J Mol Sci 2019; 20:ijms20133174. [PMID: 31261733 PMCID: PMC6651053 DOI: 10.3390/ijms20133174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 06/19/2019] [Accepted: 06/20/2019] [Indexed: 01/25/2023] Open
Abstract
Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.
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45
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Ehrt C, Brinkjost T, Koch O. Binding site characterization - similarity, promiscuity, and druggability. MEDCHEMCOMM 2019; 10:1145-1159. [PMID: 31391887 DOI: 10.1039/c9md00102f] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 05/31/2019] [Indexed: 12/19/2022]
Abstract
The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany.,Department of Computer Science , TU Dortmund University , Dortmund , Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
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46
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Ansar S, Sadhasivam A, Vetrivel U. PocketPipe: A computational pipeline for integratedPocketome prediction and comparison. Bioinformation 2019; 15:295-298. [PMID: 31285647 PMCID: PMC6599441 DOI: 10.6026/97320630015295] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 04/04/2019] [Accepted: 04/04/2019] [Indexed: 01/03/2023] Open
Abstract
Functional characterisation of proteins often depends on specific interactions with other molecules. In the drug discovery scenario, the
ability of a protein to bind with drug-like molecule with a high affinity is referred as druggability. Deciphering such druggable binding
pockets on proteins plays an important role in structure-based drug designing studies. Moreover, availability of plethora of structural data
poses a need automated pipelines which can efficiently integrate robust algorithms towards large-scale pocket identification and
comparison. These pipelines have direct applicability on off-target analysis, drug repurposing and structural prioritization of drug targets
in pathogenic microbes. However, currently there is a paucity of such efficient pipelines. Hence, by this study a highly optimized shell
script based pipeline (PocketPipe) has been developed with seamless integration of robust algorithms namely, P2Rank (predicts binding
sites based on machine learning) and PocketMatch-v2.1 (compares binding pockets by residue-based method), for pocketome generation
and comparison, respectively. The process of input workflow and various steps carried out by PocketPipe and the output results are well
documented in the operating manual. On execution, the pipeline features seamless operability, high scalability, dynamic file handling and
results parsing. PocketPipe is distributed freely under GNU GPL license and can be downloaded at
https://github.com/inpacdb/PocketPipe
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Affiliation(s)
- Samdani Ansar
- 1Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, SankaraNethralaya, Chennai - 600 006, Tamil Nadu, India.,2School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, India
| | - Anupriya Sadhasivam
- 1Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, SankaraNethralaya, Chennai - 600 006, Tamil Nadu, India
| | - Umashankar Vetrivel
- 1Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, SankaraNethralaya, Chennai - 600 006, Tamil Nadu, India
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Sydow D, Morger A, Driller M, Volkamer A. TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data. J Cheminform 2019; 11:29. [PMID: 30963287 PMCID: PMC6454689 DOI: 10.1186/s13321-019-0351-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/27/2019] [Indexed: 11/25/2022] Open
Abstract
Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCADD .
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Affiliation(s)
- Dominique Sydow
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Andrea Morger
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Maximilian Driller
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Andrea Volkamer
- In Silico Toxicology, Institute of Physiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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Abstract
INTRODUCTION The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.
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Affiliation(s)
- Janez Konc
- a Theory Department , National Institute of Chemistry , Ljubljana , Slovenia.,b Faculty of Pharmacy , University of Ljubljana , Ljubljana , Slovenia.,c Faculty of Mathematics , Natural Sciences and Information Technologies, University of Primorska , Koper , Slovenia.,d Faculty of Chemistry and Chemical Technology , University of Maribor , Maribor , Slovenia
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49
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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50
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Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
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