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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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2
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Firouzi R, Sowlati-Hashjin S, Chávez-García C, Ashouri M, Karimi-Jafari MH, Karttunen M. Identification of Catechins' Binding Sites in Monomeric A β42 through Ensemble Docking and MD Simulations. Int J Mol Sci 2023; 24:ijms24098161. [PMID: 37175868 PMCID: PMC10179585 DOI: 10.3390/ijms24098161] [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: 03/14/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aβ42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.
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Affiliation(s)
- Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran 1496813151, Iran
| | | | - Cecilia Chávez-García
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Mitra Ashouri
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran P.O. Box 14155-6619, Iran
| | - Mohammad Hossein Karimi-Jafari
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran P.O. Box 14155-6619, Iran
| | - Mikko Karttunen
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
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3
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fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions. PLoS Comput Biol 2022; 18:e1009783. [PMID: 35653385 PMCID: PMC9197077 DOI: 10.1371/journal.pcbi.1009783] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/14/2022] [Accepted: 05/06/2022] [Indexed: 11/19/2022] Open
Abstract
Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.
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Graef J, Ehrt C, Diedrich K, Poppinga M, Ritter N, Rarey M. Searching Geometric Patterns in Protein Binding Sites and Their Application to Data Mining in Protein Kinase Structures. J Med Chem 2021; 65:1384-1395. [PMID: 34491747 DOI: 10.1021/acs.jmedchem.1c01046] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The ever-growing number of protein-ligand complex structures can give fundamental insights into protein functions and protein-ligand interactions, especially in the field of protein kinase research. The number of tools to mine this data for individually defined structural motifs is restricted due to the challenging task of developing efficient index structures for 3D data in relational databases. Herein we present GeoMine, a database system with web front-end mining of more than 900 000 binding sites. It enables database searches for geometric (interaction) patterns in protein-ligand interfaces by, for example, textual, numerical, substructure, similarity, and 3D searches. GeoMine processes reasonably selective user-defined queries within minutes. We demonstrate its usability for advancing protein kinase research with a special emphasis on unusual interactions, their use in designing selective kinase inhibitors, and the analysis of reactive cysteine residues that are amenable to covalent kinase inhibitors. GeoMine is freely available as part of our modeling support server at https://proteins.plus.
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Affiliation(s)
- Joel Graef
- ZBH Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- ZBH Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
| | - Konrad Diedrich
- ZBH Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
| | - Martin Poppinga
- ZBH Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
| | - Norbert Ritter
- ZBH Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- ZBH Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
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5
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Bai B, Zou R, Chan HCS, Li H, Yuan S. MolADI: A Web Server for Automatic Analysis of Protein-Small Molecule Dynamic Interactions. Molecules 2021; 26:molecules26154625. [PMID: 34361778 PMCID: PMC8347168 DOI: 10.3390/molecules26154625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 11/16/2022] Open
Abstract
Protein-ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein-ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.
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Affiliation(s)
- Bing Bai
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Rongfeng Zou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- AlphaMol Science Ltd., 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - H. C. Stephen Chan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Hongchun Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- Correspondence: (H.L.); (S.Y.)
| | - Shuguang Yuan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- AlphaMol Science Ltd., 1068 Xueyuan Avenue, Shenzhen 518055, China
- Correspondence: (H.L.); (S.Y.)
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6
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Adasme MF, Linnemann KL, Bolz SN, Kaiser F, Salentin S, Haupt VJ, Schroeder M. PLIP 2021: expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res 2021; 49:W530-W534. [PMID: 33950214 PMCID: PMC8262720 DOI: 10.1093/nar/gkab294] [Citation(s) in RCA: 658] [Impact Index Per Article: 219.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/24/2021] [Accepted: 04/13/2021] [Indexed: 12/19/2022] Open
Abstract
With the growth of protein structure data, the analysis of molecular interactions between ligands and their target molecules is gaining importance. PLIP, the protein–ligand interaction profiler, detects and visualises these interactions and provides data in formats suitable for further processing. PLIP has proven very successful in applications ranging from the characterisation of docking experiments to the assessment of novel ligand–protein complexes. Besides ligand–protein interactions, interactions with DNA and RNA play a vital role in many applications, such as drugs targeting DNA or RNA-binding proteins. To date, over 7% of all 3D structures in the Protein Data Bank include DNA or RNA. Therefore, we extended PLIP to encompass these important molecules. We demonstrate the power of this extension with examples of a cancer drug binding to a DNA target, and an RNA–protein complex central to a neurological disease. PLIP is available online at https://plip-tool.biotec.tu-dresden.de and as open source code. So far, the engine has served over a million queries and the source code has been downloaded several thousand times.
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Affiliation(s)
- Melissa F Adasme
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Katja L Linnemann
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | | | - Sebastian Salentin
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | | | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Tatzberg 47-49, 01307 Dresden, Germany
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7
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Diedrich K, Graef J, Schöning-Stierand K, Rarey M. GeoMine: interactive pattern mining of protein-ligand interfaces in the Protein Data Bank. Bioinformatics 2021; 37:424-425. [PMID: 32735322 DOI: 10.1093/bioinformatics/btaa693] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 07/12/2020] [Accepted: 07/24/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY The searching of user-defined 3D queries in molecular interfaces is a computationally challenging problem that is not satisfactorily solved so far. Most of the few existing tools focused on that purpose are desktop based and not openly available. Besides that, they show a lack of query versatility, search efficiency and user-friendliness. We address this issue with GeoMine, a publicly available web application that provides textual, numerical and geometrical search functionality for protein-ligand binding sites derived from structural data contained in the Protein Data Bank (PDB). The query generation is supported by a 3D representation of a start structure that provides interactively selectable elements like atoms, bonds and interactions. GeoMine gives full control over geometric variability in the query while performing a deterministic, precise search. Reasonably selective queries are processed on the entire set of protein-ligand complexes in the PDB within a few minutes. GeoMine offers an interactive and iterative search process of successive result analyses and query adaptations. From the numerous potential applications, we picked two from the field of side-effect analyze showcasing the usefulness of GeoMine. AVAILABILITY AND IMPLEMENTATION GeoMine is part of the ProteinsPlus web application suite and freely available at https://proteins.plus. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Konrad Diedrich
- Universität Hamburg, ZBH - Center for Bioinformatics, 20146 Hamburg, Germany
| | - Joel Graef
- Universität Hamburg, ZBH - Center for Bioinformatics, 20146 Hamburg, Germany
| | | | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, 20146 Hamburg, Germany
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8
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Ghosh D, Bansode S, Joshi R, Kolte B, Gacche R. Molecular elucidation of pancreatic elastase inhibition by baicalein. J Biomol Struct Dyn 2021; 40:5759-5768. [PMID: 33446085 DOI: 10.1080/07391102.2021.1873189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The serine protease, elastase exists in various forms and plays diverse roles in the human body. Pharmacological inhibition of elastase has been investigated for its therapeutic role in managing conditions such as diabetes, pneumonia and arthritis. Sivelestat, a synthetic molecule, is the only elastase inhibitor to have been approved by any major drug regulatory authority (PMDA, in this case) - but still has failed to attain widespread clinical usage owing to its high price, cumbersome administration and obscure long-term safety profile. In order to find a relatively better-suited alternative, screening was conducted using plant flavonoids, which yielded baicalein, a molecule that showed robust inhibition against Pancreatic Elastase inhibition (IC50: 3.53 μM). Other than having a considerably lower IC50than sivelestat, baicalein is also cheaper, safer and easier to administer. While MicroScale Thermophoresis validated baicalein-elastase interaction, enzyme-kinetic studies, molecular docking and molecular dynamic simulation revealed the mode of inhibition to be non-competitive. Baicalein exhibited binding to a distinct allosteric site on the enzyme. The current study demonstrates the elastase inhibition properties of baicalein in an in-vitro and in-silico environment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Debajeet Ghosh
- Department of Biotechnology, Savitribai Phule Pune University, Pune, India
| | - Sneha Bansode
- Biochemical Sciences Division, CSIR - National Chemical Laboratory, Pune, India
| | - Rakesh Joshi
- Biochemical Sciences Division, CSIR - National Chemical Laboratory, Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Baban Kolte
- Department of Biotechnology, Savitribai Phule Pune University, Pune, India.,Department of Chemistry, Institute of Biochemistry and Molecular Biology, University of Hamburg, Hamburg, Germany
| | - Rajesh Gacche
- Department of Biotechnology, Savitribai Phule Pune University, Pune, India
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9
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Fassio AV, Santos LH, Silveira SA, Ferreira RS, de Melo-Minardi RC. nAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1317-1328. [PMID: 30629512 DOI: 10.1109/tcbb.2019.2892099] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions. Nonetheless, there is not an all-in-one tool that couples large-scale statistical, visual, and interactive analysis of conserved protein-ligand interactions. Therefore, we propose nAPOLI (Analysis of PrOtein-Ligand Interactions), a web server that combines large-scale analysis of conserved interactions in protein-ligand complexes at the atomic-level, interactive visual representations, and comprehensive reports of the interacting residues/atoms to detect and explore conserved non-covalent interactions. We demonstrate the potential of nAPOLI in detecting important conserved interacting residues through four case studies: two involving a human cyclin-dependent kinase 2 (CDK2), one related to ricin, and other to the human nuclear receptor subfamily 3 (hNR3). nAPOLI proved to be suitable to identify conserved interactions according to literature, as well as highlight additional interactions. Finally, we illustrate, with a virtual screening ligand selection, how nAPOLI can be widely applied in structural biology and drug design. nAPOLI is freely available at bioinfo.dcc.ufmg.br/napoli/.
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10
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From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library. J Biotechnol 2017; 261:207-214. [DOI: 10.1016/j.jbiotec.2017.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 05/31/2017] [Accepted: 06/07/2017] [Indexed: 02/06/2023]
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11
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Inhester T, Bietz S, Hilbig M, Schmidt R, Rarey M. Index-Based Searching of Interaction Patterns in Large Collections of Protein–Ligand Interfaces. J Chem Inf Model 2017; 57:148-158. [DOI: 10.1021/acs.jcim.6b00561] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Therese Inhester
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Stefan Bietz
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Hilbig
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Robert Schmidt
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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12
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Korb O, Kuhn B, Hert J, Taylor N, Cole J, Groom C, Stahl M. Interactive and Versatile Navigation of Structural Databases. J Med Chem 2016; 59:4257-66. [DOI: 10.1021/acs.jmedchem.5b01756] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Oliver Korb
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Bernd Kuhn
- Roche
Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Jérôme Hert
- Roche
Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Neil Taylor
- Desert Scientific Software Pty Ltd., Level 5 Nexus Building, Norwest Business Park, 4 Columbia Court, Baulkham Hills, NSW 2153, Australia
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Colin Groom
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Martin Stahl
- Roche
Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
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13
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Bartolowits M, Davisson VJ. Considerations of Protein Subpockets in Fragment-Based Drug Design. Chem Biol Drug Des 2015; 87:5-20. [PMID: 26307335 DOI: 10.1111/cbdd.12631] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.
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Affiliation(s)
- Matthew Bartolowits
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Dr., West Lafayette, IN, 47907, USA
| | - V Jo Davisson
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Dr., West Lafayette, IN, 47907, USA
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14
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Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 2015; 43:W443-7. [PMID: 25873628 PMCID: PMC4489249 DOI: 10.1093/nar/gkv315] [Citation(s) in RCA: 1212] [Impact Index Per Article: 134.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 03/28/2015] [Indexed: 11/14/2022] Open
Abstract
The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.
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Affiliation(s)
- Sebastian Salentin
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Sven Schreiber
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - V Joachim Haupt
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Melissa F Adasme
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany Escuela de Ingeniería en Bioinformática, Universidad de Talca, Avda. Lircay s/n Talca, 3460000, Chile
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
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15
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Inhester T, Rarey M. Protein-ligand interaction databases: advanced tools to mine activity data and interactions on a structural level. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2014. [DOI: 10.1002/wcms.1192] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Therese Inhester
- Center for Bioinformatics; University of Hamburg; Hamburg Germany
| | - Matthias Rarey
- Center for Bioinformatics; University of Hamburg; Hamburg Germany
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C-H…pi interactions in proteins: prevalence, pattern of occurrence, residue propensities, location, and contribution to protein stability. J Mol Model 2014; 20:2136. [DOI: 10.1007/s00894-014-2136-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Accepted: 01/02/2014] [Indexed: 11/25/2022]
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17
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Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K. The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 96:1-37. [DOI: 10.1016/bs.apcsb.2014.06.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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18
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19
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Rognan D. Towards the Next Generation of Computational Chemogenomics Tools. Mol Inform 2013; 32:1029-34. [PMID: 27481148 DOI: 10.1002/minf.201300054] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Accepted: 06/11/2013] [Indexed: 01/07/2023]
Affiliation(s)
- D Rognan
- UMR 7200 CNRS-Université de Strasbourg, MEDALIS Drug Discovery Center, 74 route du Rhin, 67400, Illkirch, France.
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Schreyer AM, Blundell TL. CREDO: a structural interactomics database for drug discovery. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat049. [PMID: 23868908 PMCID: PMC3715132 DOI: 10.1093/database/bat049] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
CREDO is a unique relational database storing all pairwise atomic interactions of inter- as well as intra-molecular contacts between small molecules and macromolecules found in experimentally determined structures from the Protein Data Bank. These interactions are integrated with further chemical and biological data. The database implements useful data structures and algorithms such as cheminformatics routines to create a comprehensive analysis platform for drug discovery. The database can be accessed through a web-based interface, downloads of data sets and web services at http://www-cryst.bioc.cam.ac.uk/credo. Database URL:http://www-cryst.bioc.cam.ac.uk/credo
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Affiliation(s)
- Adrian M Schreyer
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, CB2 1GA Cambridge, UK.
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Desaphy J, Raimbaud E, Ducrot P, Rognan D. Encoding protein-ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 2013; 53:623-37. [PMID: 23432543 DOI: 10.1021/ci300566n] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
We herewith present a novel and universal method to convert protein-ligand coordinates into a simple fingerprint of 210 integers registering the corresponding molecular interaction pattern. Each interaction (hydrophobic, aromatic, hydrogen bond, ionic bond, metal complexation) is detected on the fly and physically described by a pseudoatom centered either on the interacting ligand atom, the interacting protein atom, or the geometric center of both interacting atoms. Counting all possible triplets of interaction pseudoatoms within six distance ranges, and pruning the full integer vector to keep the most frequent triplets enables the definition of a simple (210 integers) and coordinate frame-invariant interaction pattern descriptor (TIFP) that can be applied to compare any pair of protein-ligand complexes. TIFP fingerprints have been calculated for ca. 10,000 druggable protein-ligand complexes therefore enabling a wide comparison of relationships between interaction pattern similarity and ligand or binding site pairwise similarity. We notably show that interaction pattern similarity strongly depends on binding site similarity. In addition to the TIFP fingerprint which registers intermolecular interactions between a ligand and its target protein, we developed two tools (Ishape, Grim) to align protein-ligand complexes from their interaction patterns. Ishape is based on the overlap of interaction pseudoatoms using a smooth Gaussian function, whereas Grim utilizes a standard clique detection algorithm to match interaction pattern graphs. Both tools are complementary and enable protein-ligand complex alignments capitalizing on both global and local pattern similarities. The new fingerprint and companion alignment tools have been successfully used in three scenarios: (i) interaction-biased alignment of protein-ligand complexes, (ii) postprocessing docking poses according to known interaction patterns for a particular target, and (iii) virtual screening for bioisosteric scaffolds sharing similar interaction patterns.
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Affiliation(s)
- Jérémy Desaphy
- Laboratory for Therapeutical Innovation, UMR 7200 Université de Strabsourg/CNRS , MEDALIS Drug Discovery Center, F-67400 Illkirch, France
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Kalliokoski T, Olsson TSG, Vulpetti A. Subpocket analysis method for fragment-based drug discovery. J Chem Inf Model 2013; 53:131-41. [PMID: 23327721 DOI: 10.1021/ci300523r] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Although two binding sites might be dissimilar overall, they might still bind the same fragments if they share suitable subpockets. Information about shared subpockets can be therefore used in fragment-based drug design to suggest new fragments or to replace existing fragments within an already known compound. A novel computational method called SubCav is described which allows the similarity searching and alignment of subpockets from a PDB-wide database against a user-defined query. The method is based on pharmacophoric fingerprints combined with a subpocket alignment algorithm. SubCav was shown to be effective in producing reasonable alignments for subpockets with low sequence similarity and be able to retrieve relevant subpockets from a large database of structures including those with different folds. It can also be used to analyze subpockets inside a protein family to facilitate drug design and to rationalize compound selectivity.
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Affiliation(s)
- Tuomo Kalliokoski
- Novartis Institutes for Biomedical Research, Postfach, CH-4002 Basel, Switzerland
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