51
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Wagner JR, Sørensen J, Hensley N, Wong C, Zhu C, Perison T, Amaro RE. POVME 3.0: Software for Mapping Binding Pocket Flexibility. J Chem Theory Comput 2017; 13:4584-4592. [PMID: 28800393 DOI: 10.1021/acs.jctc.7b00500] [Citation(s) in RCA: 141] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
We present a substantial update to the open-source POVME binding pocket analysis software. New capabilities of POVME 3.0 include a flexible chemical coloring scheme for feature identification, postanalysis tools for comparing large ensembles of pockets (e.g., from molecular dynamics simulations), and the introduction of scripts and methods that facilitate binding pocket comparison and analysis. We envision the use of this software for visualization of binding pocket dynamics, selection of representative structures for ensemble docking, and incorporation of molecular dynamics results into ligand design efforts.
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Affiliation(s)
- Jeffrey R Wagner
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States
| | - Jesper Sørensen
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States
| | - Nathan Hensley
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States
| | - Celia Wong
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States
| | - Clare Zhu
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States
| | - Taylor Perison
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093, United States.,National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
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52
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Drwal MN, Jacquemard C, Perez C, Desaphy J, Kellenberger E. Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands? J Chem Inf Model 2017; 57:1197-1209. [PMID: 28414463 DOI: 10.1021/acs.jcim.6b00769] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, β-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.
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Affiliation(s)
- Malgorzata N Drwal
- Laboratoire d'Innovation Thérapeutique UMR 7200, CNRS-Université de Strasbourg , 74 Route du Rhin, 674000 Illkirch, France
| | - Célien Jacquemard
- Laboratoire d'Innovation Thérapeutique UMR 7200, CNRS-Université de Strasbourg , 74 Route du Rhin, 674000 Illkirch, France
| | - Carlos Perez
- Eli Lilly Research Laboratories , Avenida de la Industria 30, 28108 Alcobendas, Madrid, Spain
| | - Jérémy Desaphy
- Lilly Research Laboratories, Eli Lilly and Company , Lilly Corporate Center, Indianapolis, Indiana 46285, United States
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique UMR 7200, CNRS-Université de Strasbourg , 74 Route du Rhin, 674000 Illkirch, France
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53
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Abstract
Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.
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Affiliation(s)
- Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400 Illkirch, France.
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54
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Anighoro A, Pinzi L, Marverti G, Bajorath J, Rastelli G. Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space. RSC Adv 2017. [DOI: 10.1039/c7ra05889f] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
With the aid of computational design, we show that Hsp90 and B-Raf inhibitors have overlapping chemical space and we disclose the first-in-class dual inhibitors.
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Affiliation(s)
- A. Anighoro
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität
- D-53113 Bonn
| | - L. Pinzi
- Department of Life Sciences
- University of Modena and Reggio Emilia
- Modena
- Italy
| | - G. Marverti
- Department of Biomedical
- Metabolic and Neurosciences
- University of Modena and Reggio Emilia
- Modena
- Italy
| | - J. Bajorath
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität
- D-53113 Bonn
| | - G. Rastelli
- Department of Life Sciences
- University of Modena and Reggio Emilia
- Modena
- Italy
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55
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Global vision of druggability issues: applications and perspectives. Drug Discov Today 2016; 22:404-415. [PMID: 27939283 DOI: 10.1016/j.drudis.2016.11.021] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 10/10/2016] [Accepted: 11/25/2016] [Indexed: 02/04/2023]
Abstract
During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.
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56
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Lewis R, Deheuvels J, Ertl P, Pirard B, Sirockin F. Building Compound Archives for the Future. Mol Inform 2016; 35:580-582. [PMID: 27870238 DOI: 10.1002/minf.201600042] [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: 03/26/2016] [Accepted: 05/02/2016] [Indexed: 11/11/2022]
Abstract
Will the targets of the future be covered by the compound libraries of today? This communication will cover a critical review of past strategies before turning to a new measure of diversity, protein pockets. A fingerprint descriptor for pockets will be described.
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57
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Comparing atom-based with residue-based descriptors in predicting binding site similarity: do backbone atoms matter? Future Med Chem 2016; 8:1871-1885. [PMID: 27629811 DOI: 10.4155/fmc-2016-0077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
AIM We question the level of detail required in protein 3D-representation to detect site similarity which is relevant for polypharmacology prediction. RESULTS We modified the in-house program SiteAlign to replace generic pharmacophoric descriptors of cavity-lining amino acids by descriptors accounting for solvent exposure. Benchmarking the novel, atom-based, method (SiteAlign2) revealed no global improvement of performance. However, in the rare cases of no sequence or global structure similarities between the compared proteins, SiteAlign2 was more successful if backbone atoms are key determinants of ligand binding. CONCLUSION SiteAlign suits the comparison of binding sites for close or distant homologs. SiteAlign2 provides a better insight into the physical model of site similarity between nonhomologs, but at the expense of an increased sensitivity to atomic coordinates.
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58
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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59
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Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors. Sci Rep 2016; 6:23815. [PMID: 27034268 PMCID: PMC4817116 DOI: 10.1038/srep23815] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 03/15/2016] [Indexed: 02/03/2023] Open
Abstract
Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates.
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60
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Da Silva F, Desaphy J, Bret G, Rognan D. IChemPIC: A Random Forest Classifier of Biological and Crystallographic Protein–Protein Interfaces. J Chem Inf Model 2015; 55:2005-14. [DOI: 10.1021/acs.jcim.5b00190] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Franck Da Silva
- Laboratoire d’Innovation
Thérapeutique, UMR 7200 CNRS−Université de Strasbourg, 67400 Illkirch, France
| | - Jérémy Desaphy
- 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
| | - Didier Rognan
- Laboratoire d’Innovation
Thérapeutique, UMR 7200 CNRS−Université de Strasbourg, 67400 Illkirch, France
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61
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Sarkar A, Brenk R. To Hit or Not to Hit, That Is the Question - Genome-wide Structure-Based Druggability Predictions for Pseudomonas aeruginosa Proteins. PLoS One 2015; 10:e0137279. [PMID: 26360059 PMCID: PMC4567284 DOI: 10.1371/journal.pone.0137279] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 07/15/2015] [Indexed: 12/23/2022] Open
Abstract
Pseudomonas aeruginosa is a Gram-negative bacterium known to cause opportunistic infections in immune-compromised or immunosuppressed individuals that often prove fatal. New drugs to combat this organism are therefore sought after. To this end, we subjected the gene products of predicted perturbative genes to structure-based druggability predictions using DrugPred. Making this approach suitable for large-scale predictions required the introduction of new methods for calculation of descriptors, development of a workflow to identify suitable pockets in homologous proteins and establishment of criteria to obtain valid druggability predictions based on homologs. We were able to identify 29 perturbative proteins of P. aeruginosa that may contain druggable pockets, including some of them with no or no drug-like inhibitors deposited in ChEMBL. These proteins form promising novel targets for drug discovery against P. aeruginosa.
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Affiliation(s)
- Aurijit Sarkar
- Division of Biological Chemistry & Drug Discovery, College of Life Sciences, University of Dundee, Dow Street, Dundee, United Kingdom
| | - Ruth Brenk
- Division of Biological Chemistry & Drug Discovery, College of Life Sciences, University of Dundee, Dow Street, Dundee, United Kingdom
- Institut für Pharmazie und Biochemie, Johannes Gutenberg-Universität Mainz, Mainz, Germany
- University of Bergen, Department for Biomedicine, Bergen, Norway
- * E-mail:
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62
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Chartier M, Najmanovich R. Detection of Binding Site Molecular Interaction Field Similarities. J Chem Inf Model 2015; 55:1600-15. [PMID: 26158641 DOI: 10.1021/acs.jcim.5b00333] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available.
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Affiliation(s)
- Matthieu Chartier
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke , 12e Avenue Nord, Sherbrooke, J1H 5N4 Québec, Canada
| | - Rafael Najmanovich
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke , 12e Avenue Nord, Sherbrooke, J1H 5N4 Québec, Canada
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63
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Trejo-Soto PJ, Aguayo-Ortiz R, Yépez-Mulia L, Hernández-Campos A, Medina-Franco JL, Castillo R. Insights into the structure and inhibition of Giardia intestinalis arginine deiminase: homology modeling, docking, and molecular dynamics studies. J Biomol Struct Dyn 2015; 34:732-48. [PMID: 26017138 DOI: 10.1080/07391102.2015.1051115] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Giardia intestinalis arginine deiminase (GiADI) is an important metabolic enzyme involved in the energy production and defense of this protozoan parasite. The lack of this enzyme in the human host makes GiADI an attractive target for drug design against G. intestinalis. One approach in the design of inhibitors of GiADI could be computer-assisted studies of its crystal structure, such as docking; however, the required crystallographic structure of the enzyme still remains unresolved. Because of its relevance, in this work, we present a three-dimensional structure of GiADI obtained from its amino acid sequence using the homology modeling approximation. Furthermore, we present an approximation of the most stable dimeric structure of GiADI identified through molecular dynamics simulation studies. An in silico analysis of druggability using the structure of GiADI was carried out in order to know if it is a good target for design and optimization of selective inhibitors. Potential GiADI inhibitors were identified by docking of a set of 3196 commercial and 19 in-house benzimidazole derivatives, and molecular dynamics simulation studies were used to evaluate the stability of the ligand-enzyme complexes.
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Affiliation(s)
- Pedro Josué Trejo-Soto
- a Facultad de Química, Departamento de Farmacia , Universidad Nacional Autónoma de México , México, DF 04510 , Mexico
| | - Rodrigo Aguayo-Ortiz
- a Facultad de Química, Departamento de Farmacia , Universidad Nacional Autónoma de México , México, DF 04510 , Mexico
| | - Lilián Yépez-Mulia
- b Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, IMSS , México, DF 06720 , Mexico
| | - Alicia Hernández-Campos
- a Facultad de Química, Departamento de Farmacia , Universidad Nacional Autónoma de México , México, DF 04510 , Mexico
| | - José Luis Medina-Franco
- a Facultad de Química, Departamento de Farmacia , Universidad Nacional Autónoma de México , México, DF 04510 , Mexico
| | - Rafael Castillo
- a Facultad de Química, Departamento de Farmacia , Universidad Nacional Autónoma de México , México, DF 04510 , Mexico
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64
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Hussein HA, Borrel A, Geneix C, Petitjean M, Regad L, Camproux AC. PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res 2015; 43:W436-42. [PMID: 25956651 PMCID: PMC4489252 DOI: 10.1093/nar/gkv462] [Citation(s) in RCA: 107] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 04/27/2015] [Indexed: 12/21/2022] Open
Abstract
Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.
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Affiliation(s)
- Hiba Abi Hussein
- INSERM, UMRS-973, MTi, Université Paris Diderot, 35 Rue Hélène Brion, 75205 Paris Cedex 13, case courier 7113, Paris, France Université Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
| | - Alexandre Borrel
- INSERM, UMRS-973, MTi, Université Paris Diderot, 35 Rue Hélène Brion, 75205 Paris Cedex 13, case courier 7113, Paris, France Université Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France Division of Pharmaceutical Chemistry, Faculty of pharmacy, University of Helsinki, Viikinkaari 9 (P.O. Box 56) FI-00014, Finland
| | - Colette Geneix
- INSERM, UMRS-973, MTi, Université Paris Diderot, 35 Rue Hélène Brion, 75205 Paris Cedex 13, case courier 7113, Paris, France Université Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
| | - Michel Petitjean
- INSERM, UMRS-973, MTi, Université Paris Diderot, 35 Rue Hélène Brion, 75205 Paris Cedex 13, case courier 7113, Paris, France Université Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
| | - Leslie Regad
- INSERM, UMRS-973, MTi, Université Paris Diderot, 35 Rue Hélène Brion, 75205 Paris Cedex 13, case courier 7113, Paris, France Université Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
| | - Anne-Claude Camproux
- INSERM, UMRS-973, MTi, Université Paris Diderot, 35 Rue Hélène Brion, 75205 Paris Cedex 13, case courier 7113, Paris, France Université Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
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65
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Borrel A, Regad L, Xhaard H, Petitjean M, Camproux AC. PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties. J Chem Inf Model 2015; 55:882-95. [PMID: 25835082 DOI: 10.1021/ci5006004] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.
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Affiliation(s)
- Alexandre Borrel
- †INSERM, UMRS-973, MTi, Paris, France.,‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France.,§University of Helsinki, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Helsinki, Finland
| | - Leslie Regad
- †INSERM, UMRS-973, MTi, Paris, France.,‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
| | - Henri Xhaard
- §University of Helsinki, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Helsinki, Finland
| | - Michel Petitjean
- †INSERM, UMRS-973, MTi, Paris, France.,‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
| | - Anne-Claude Camproux
- †INSERM, UMRS-973, MTi, Paris, France.,‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France
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66
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Krivák R, Hoksza D. Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features. J Cheminform 2015; 7:12. [PMID: 25932051 PMCID: PMC4414931 DOI: 10.1186/s13321-015-0059-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 02/24/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results. RESULTS We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms. CONCLUSIONS The positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at http://siret.ms.mff.cuni.cz/prank.
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Affiliation(s)
- Radoslav Krivák
- Department of Software Engineering, Charles University in Prague, Prague, Czech Republic
| | - David Hoksza
- Department of Software Engineering, Charles University in Prague, Prague, Czech Republic
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67
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Chen YC. Beware of docking! Trends Pharmacol Sci 2015; 36:78-95. [DOI: 10.1016/j.tips.2014.12.001] [Citation(s) in RCA: 344] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 11/23/2014] [Accepted: 12/02/2014] [Indexed: 12/16/2022]
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68
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Abstract
Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.
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Affiliation(s)
- Didier Rognan
- Laboratory for Therapeutical Innovation
- UMR7200 CNRS-Université de Strasbourg
- MEDALIS Drug Discovery Center
- 67400 Illkirch
- France
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69
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Cortés-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Méndez-Lucio O, IJzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A. Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. MEDCHEMCOMM 2015. [DOI: 10.1039/c4md00216d] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.
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Affiliation(s)
- Isidro Cortés-Ciriano
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Qurrat Ul Ain
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | | | - Eelke B. Lenselink
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
| | - Adriaan P. IJzerman
- Division of Medicinal Chemistry
- Leiden Academic Centre for Drug Research
- Leiden
- The Netherlands
| | - Gerd Wohlfahrt
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Peteris Prusis
- Computer-Aided Drug Design
- Orion Pharma
- FIN-02101 Espoo
- Finland
| | - Thérèse E. Malliavin
- Unité de Bioinformatique Structurale
- Institut Pasteur and CNRS UMR 3825
- Structural Biology and Chemistry Department
- 75 724 Paris
- France
| | - Gerard J. P. van Westen
- European Molecular Biology Laboratory
- European Bioinformatics Institute
- Wellcome Trust Genome Campus
- Hinxton
- UK
| | - Andreas Bender
- Unilever Centre for Molecular Informatics
- Department of Chemistry
- CB2 1EW Cambridge
- UK
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70
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P2RANK: Knowledge-Based Ligand Binding Site Prediction Using Aggregated Local Features. ALGORITHMS FOR COMPUTATIONAL BIOLOGY 2015. [DOI: 10.1007/978-3-319-21233-3_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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71
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Krotzky T, Rickmeyer T, Fober T, Klebe G. Extraction of protein binding pockets in close neighborhood of bound ligands makes comparisons simple due to inherent shape similarity. J Chem Inf Model 2014; 54:3229-37. [PMID: 25345905 DOI: 10.1021/ci500553a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Methods for comparing protein binding sites are frequently validated on data sets of pockets that were obtained simply by extracting the protein area next to the bound ligands. With this strategy, any unoccupied pocket will remain unconsidered. Furthermore, a large amount of ligand-biased intrinsic shape information is predefined, inclining the subsequent comparisons as rather trivial even in data sets that hardly contain redundancies in sequence information. In this study, we present the results of a very simplistic and shape-biased comparison approach, which stress that unrestricted cavity extraction is essential to enable unexpected cross-reactivity predictions among proteins and function annotations of orphan proteins.
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Affiliation(s)
- Timo Krotzky
- Institute of Pharmaceutical Chemistry, University of Marburg , Marbacher Weg 6-10, 35032 Marburg, Germany
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72
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Ahmed A, Smith RD, Clark JJ, Dunbar JB, Carlson HA. Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures. Nucleic Acids Res 2014; 43:D465-9. [PMID: 25378330 PMCID: PMC4383918 DOI: 10.1093/nar/gku1088] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
For over 10 years, Binding MOAD (Mother of All Databases; http://www.BindingMOAD.org) has been one of the largest resources for high-quality protein-ligand complexes and associated binding affinity data. Binding MOAD has grown at the rate of 1994 complexes per year, on average. Currently, it contains 23,269 complexes and 8156 binding affinities. Our annual updates curate the data using a semi-automated literature search of the references cited within the PDB file, and we have recently upgraded our website and added new features and functionalities to better serve Binding MOAD users. In order to eliminate the legacy application server of the old platform and to accommodate new changes, the website has been completely rewritten in the LAMP (Linux, Apache, MySQL and PHP) environment. The improved user interface incorporates current third-party plugins for better visualization of protein and ligand molecules, and it provides features like sorting, filtering and filtered downloads. In addition to the field-based searching, Binding MOAD now can be searched by structural queries based on the ligand. In order to remove redundancy, Binding MOAD records are clustered in different families based on 90% sequence identity. The new Binding MOAD, with the upgraded platform, features and functionalities, is now equipped to better serve its users.
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Affiliation(s)
- Aqeel Ahmed
- Department of Medicinal Chemistry, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065, USA
| | - Richard D Smith
- Department of Medicinal Chemistry, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065, USA
| | - Jordan J Clark
- Department of Medicinal Chemistry, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065, USA
| | - James B Dunbar
- Department of Medicinal Chemistry, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065, USA
| | - Heather A Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065, USA
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73
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Desaphy J, Bret G, Rognan D, Kellenberger E. sc-PDB: a 3D-database of ligandable binding sites--10 years on. Nucleic Acids Res 2014; 43:D399-404. [PMID: 25300483 PMCID: PMC4384012 DOI: 10.1093/nar/gku928] [Citation(s) in RCA: 146] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The sc-PDB database (available at http://bioinfo-pharma.u-strasbg.fr/scPDB/) is a comprehensive and up-to-date selection of ligandable binding sites of the Protein Data Bank. Sites are defined from complexes between a protein and a pharmacological ligand. The database provides the all-atom description of the protein, its ligand, their binding site and their binding mode. Currently, the sc-PDB archive registers 9283 binding sites from 3678 unique proteins and 5608 unique ligands. The sc-PDB database was publicly launched in 2004 with the aim of providing structure files suitable for computational approaches to drug design, such as docking. During the last 10 years we have improved and standardized the processes for (i) identifying binding sites, (ii) correcting structures, (iii) annotating protein function and ligand properties and (iv) characterizing their binding mode. This paper presents the latest enhancements in the database, specifically pertaining to the representation of molecular interaction and to the similarity between ligand/protein binding patterns. The new website puts emphasis in pictorial analysis of data.
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Affiliation(s)
- Jérémy Desaphy
- Laboratoire d'innovation thérapeutique, Medalis Drug Discovery Center, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France
| | - Guillaume Bret
- Laboratoire d'innovation thérapeutique, Medalis Drug Discovery Center, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France
| | - Didier Rognan
- Laboratoire d'innovation thérapeutique, Medalis Drug Discovery Center, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France
| | - Esther Kellenberger
- Laboratoire d'innovation thérapeutique, Medalis Drug Discovery Center, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France
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74
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Todoroff N, Kunze J, Schreuder H, Hessler G, Baringhaus KH, Schneider G. Fractal Dimensions of Macromolecular Structures. Mol Inform 2014; 33:588-596. [PMID: 26213587 PMCID: PMC4502991 DOI: 10.1002/minf.201400090] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Accepted: 06/30/2014] [Indexed: 11/11/2022]
Abstract
Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less-explored geometric features of macromolecules is molecular surface irregularity, or 'roughness', which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large-scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand-binding pockets and protein-protein interfaces, exhibit greater local fluctuations in their fractal dimensions than 'inert' surface areas. We expect approximately 22 % of a protein's surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.
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Affiliation(s)
- Nickolay Todoroff
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied BiosciencesVladimir-Prelog-Weg 4, 8093 Zurich, Switzerland fax: (+41) 44 633 13 79
| | - Jens Kunze
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied BiosciencesVladimir-Prelog-Weg 4, 8093 Zurich, Switzerland fax: (+41) 44 633 13 79
| | | | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH R&DFrankfurt am Main, Germany
| | | | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied BiosciencesVladimir-Prelog-Weg 4, 8093 Zurich, Switzerland fax: (+41) 44 633 13 79
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75
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Radusky L, Defelipe LA, Lanzarotti E, Luque J, Barril X, Marti MA, Turjanski AG. TuberQ: a Mycobacterium tuberculosis protein druggability database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau035. [PMID: 24816183 PMCID: PMC4014675 DOI: 10.1093/database/bau035] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In 2012 an estimated 8.6 million people developed tuberculosis (TB) and 1.3 million died from the disease [including 320 000 deaths among human immunodeficiency virus (HIV)-positive people]. There is an urgent need for new anti-TB drugs owing to the following: the fact that current treatments have severe side effects, the increasing emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb), the negative drug-drug interactions with certain HIV (or other disease) treatments and the ineffectiveness against dormant Mtb. In this context we present here the TuberQ database, a novel resource for all researchers working in the field of drug development in TB. The main feature of TuberQ is to provide a druggability analysis of Mtb proteins in a consistent and effective manner, contributing to a better selection of potential drug targets for screening campaigns and the analysis of targets for structure-based drug design projects. The structural druggability analysis is combined with features related to the characteristics of putative inhibitor binding pockets and with functional and biological data of proteins. The structural analysis is performed on all available unique Mtb structures and high-quality structural homology-based models. This information is shown in an interactive manner, depicting the protein structure, the pockets and the associated characteristics for each protein. TuberQ also provides information about gene essentiality information, as determined from whole cell-based knockout experiments, and expression information obtained from microarray experiments done in different stress-related conditions. We hope that TuberQ will be a powerful tool for researchers working in TB and eventually will lead to the identification of novel putative targets and progresses in therapeutic activities. Database URL: http://tuberq.proteinq.com.ar/
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Affiliation(s)
- Leandro Radusky
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Buenos Aires C1428EHA, Argentina, INQUIMAE/UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Buenos Aires C1428EHA, Argentina, Department of Physical Chemistry, Faculty of Pharmacy and Institute of Biomedicine (IBUB), University of Barcelona, Campus de l'Alimentació Torribera, Avgda. Prat de la Riba 171, Santa Coloma de Gramenet 08921, Spain, Department of Physical Chemistry, Faculty of Pharmacy and Institute of Biomedicine (IBUB), University of Barcelona, Avgda. Diagonal 643, Barcelona 08028, Spain and Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
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76
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Abstract
The amount of known protein structures is continuously growing, exhibited in over 95,000 3D structures freely available via the PDB. Over the last decade, pharmaceutical research has sparked interest in computationally extracting information from this large data pool, resulting in a homology-driven knowledge transfer from annotated to new structures. Studying protein structures with respect to understanding and modulating their functional behavior means analyzing their centers of action. Therefore, the detection and description of potential binding sites on the protein surface is a major step towards protein classification and assessment. Subsequently, these representations can be incorporated to compare proteins, and to predict their druggability or function. Especially in the context of target identification and polypharmacology, automated tools for large-scale target comparisons are highly needed. In this article, developments for automated structure-based target assessment are reviewed and remaining challenges as well as future perspectives are discussed.
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77
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Identifying druggable targets by protein microenvironments matching: application to transcription factors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e93. [PMID: 24452614 PMCID: PMC3910014 DOI: 10.1038/psp.2013.66] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Accepted: 10/19/2013] [Indexed: 01/17/2023]
Abstract
Druggability of a protein is its potential to be modulated by drug-like molecules. It is important in the target selection phase. We hypothesize that: (i) known drug-binding sites contain advantageous physicochemical properties for drug binding, or “druggable microenvironments” and (ii) given a target, the presence of multiple druggable microenvironments similar to those seen previously is associated with a high likelihood of druggability. We developed DrugFEATURE to quantify druggability by assessing the microenvironments in potential small-molecule binding sites. We benchmarked DrugFEATURE using two data sets. One data set measures druggability using NMR-based screening. DrugFEATURE correlates well with this metric. The second data set is based on historical drug discovery outcomes. Using the DrugFEATURE cutoffs derived from the first, we accurately discriminated druggable and difficult targets in the second. We further identified novel druggable transcription factors with implications for cancer therapy. DrugFEATURE provides useful insight for drug discovery, by evaluating druggability and suggesting specific regions for interacting with drug-like molecules.
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78
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Rognan D, Mus-Veteau I. Three-Dimensional Structure of the Smoothened Receptor: Implications for Drug Discovery. TOPICS IN MEDICINAL CHEMISTRY 2014. [DOI: 10.1007/7355_2014_64] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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79
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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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80
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Meslamani J, Bhajun R, Martz F, Rognan D. Computational profiling of bioactive compounds using a target-dependent composite workflow. J Chem Inf Model 2013; 53:2322-33. [PMID: 23941602 DOI: 10.1021/ci400303n] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Computational target fishing is a chemoinformatic method aimed at determining main and secondary targets of bioactive compounds in order to explain their mechanism of action, anticipate potential side effects, or repurpose existing drugs for novel therapeutic indications. Many existing successes in this area have been based on a use of a single computational method to estimate potentially new target-ligand associations. We herewith present an automated workflow using several methods to optimally browse target-ligand space according to existing knowledge on either ligand and target space under investigation. The protocol uses four ligand-based (SVM classification, SVR affinity prediction, nearest neighbors interpolation, shape similarity) and two structure-based approaches (docking, protein-ligand pharmacophore match) in series, according to well-defined ligand and target property checks. The workflow was remarkably accurate (72%) in identifying the main target of 189 clinical candidates and proposed two novel off-targets which could be experimentally validated. Rolofylline, an adenosine A1 receptor antagonist, was confirmed to inhibit phosphodiesterase 5 with a moderate affinity (IC50 = 13.8 μM). More interestingly, we describe a strong binding (IC50 = 142 nM) of a claimed selective phosphodiesterase 10 A inhibitor (PF-2545920) with the cysteinyl leukotriene type 1 G protein-coupled receptor.
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Affiliation(s)
- Jamel Meslamani
- Laboratory for Therapeutical Innovation, UMR 7200 Université de Strasbourg/CNRS, MEDALIS Drug Discovery Center , F-67400 Illkirch, France
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81
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MacCuish JD, MacCuish NE. Chemoinformatics applications of cluster analysis. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2013. [DOI: 10.1002/wcms.1152] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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82
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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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83
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Koelling F. PREDator - a new structure-based approach for cross-reactivity predictions. J Cheminform 2013. [PMCID: PMC3606190 DOI: 10.1186/1758-2946-5-s1-p6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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84
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von Behren MM, Volkamer A, Henzler AM, Schomburg KT, Urbaczek S, Rarey M. Fast protein binding site comparison via an index-based screening technology. J Chem Inf Model 2013; 53:411-22. [PMID: 23390978 DOI: 10.1021/ci300469h] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies.
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Affiliation(s)
- Mathias M von Behren
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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85
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Druggability predictions: methods, limitations, and applications. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1134] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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86
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Sturm N, Desaphy J, Quinn RJ, Rognan D, Kellenberger E. Structural insights into the molecular basis of the ligand promiscuity. J Chem Inf Model 2012; 52:2410-21. [PMID: 22920885 DOI: 10.1021/ci300196g] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Selectivity is a key factor in drug development. In this paper, we questioned the Protein Data Bank to better understand the reasons for the promiscuity of bioactive compounds. We assembled a data set of >1000 pairs of three-dimensional structures of complexes between a "drug-like" ligand (as its physicochemical properties overlap that of approved drugs) and two distinct "druggable" protein targets (as their binding sites are likely to accommodate "drug-like" ligands). Studying the similarity between the ligand-binding sites in the different targets revealed that the lack of selectivity of a ligand can be due (i) to the fact that Nature has created the same binding pocket in different proteins, which do not necessarily have otherwise sequence or fold similarity, or (ii) to specific characteristics of the ligand itself. In particular, we demonstrated that many ligands can adapt to different protein environments by changing their conformation, by using different chemical moieties to anchor to different targets, or by adopting unusual extreme binding modes (e.g., only apolar contact between the ligand and the protein, even though polar groups are present on the ligand or at the protein surface). Lastly, we provided new elements in support to the recent studies which suggest that the promiscuity of a ligand might be inferred from its molecular complexity.
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Affiliation(s)
- Noé Sturm
- UMR 7200 CNRS/Université de Strasbourg, MEDALIS Drug Discovery Center, 74 Route du Rhin, 67401 Illkirch, France
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