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Pearce NM, Skyner R, Krojer T. Experiences From Developing Software for Large X-Ray Crystallography-Driven Protein-Ligand Studies. Front Mol Biosci 2022; 9:861491. [PMID: 35480897 PMCID: PMC9035521 DOI: 10.3389/fmolb.2022.861491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
The throughput of macromolecular X-ray crystallography experiments has surged over the last decade. This remarkable gain in efficiency has been facilitated by increases in the availability of high-intensity X-ray beams, (ultra)fast detectors and high degrees of automation. These developments have in turn spurred the development of several dedicated centers for crystal-based fragment screening which enable the preparation and collection of hundreds of single-crystal diffraction datasets per day. Crystal structures of target proteins in complex with small-molecule ligands are of immense importance for structure-based drug design (SBDD) and their rapid turnover is a prerequisite for accelerated development cycles. While the experimental part of the process is well defined and has by now been established at several synchrotron sites, it is noticeable that software and algorithmic aspects have received far less attention, as well as the implications of new methodologies on established paradigms for structure determination, analysis, and visualization. We will review three key areas of development of large-scale protein-ligand studies. First, we will look into new software developments for batch data processing, followed by a discussion of the methodological changes in the analysis, modeling, refinement and deposition of structures for SBDD, and the changes in mindset that these new methods require, both on the side of depositors and users of macromolecular models. Finally, we will highlight key new developments for the presentation and analysis of the collections of structures that these experiments produce, and provide an outlook for future developments.
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Affiliation(s)
- Nicholas M. Pearce
- Department of Chemistry and Pharmaceutical Sciences, VU University Amsterdam, Amsterdam, Netherlands
| | - Rachael Skyner
- OMass Therapeutics, The Oxford Science Park, Oxford, United Kingdom
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Smilova MD, Curran PR, Radoux CJ, von Delft F, Cole JC, Bradley AR, Marsden BD. Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins. J Chem Inf Model 2022; 62:284-294. [PMID: 35020376 PMCID: PMC8790751 DOI: 10.1021/acs.jcim.1c00823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
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Selectivity is a
crucial property in small molecule development.
Binding site comparisons within a protein family are a key piece of
information when aiming to modulate the selectivity profile of a compound.
Binding site differences can be exploited to confer selectivity for
a specific target, while shared areas can provide insights into polypharmacology.
As the quantity of structural data grows, automated methods are needed
to process, summarize, and present these data to users. We present
a computational method that provides quantitative and data-driven
summaries of the available binding site information from an ensemble
of structures of the same protein. The resulting ensemble maps identify
the key interactions important for ligand binding in the ensemble.
The comparison of ensemble maps of related proteins enables the identification
of selectivity-determining regions within a protein family. We applied
the method to three examples from the well-researched human bromodomain
and kinase families, demonstrating that the method is able to identify
selectivity-determining regions that have been used to introduce selectivity
in past drug discovery campaigns. We then illustrate how the resulting
maps can be used to automate comparisons across a target protein family.
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Affiliation(s)
- Mihaela D Smilova
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K
| | - Peter R Curran
- The Cambridge Crystallographic Data Centre (CCDC), Cambridge CB2 1EZ, U.K.,Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Chris J Radoux
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K
| | - Frank von Delft
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.,Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0DE, U.K.,Research Complex at Harwell. Harwell Science and Innovation Campus, Didcot OX11 0FA, U.K.,Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Jason C Cole
- The Cambridge Crystallographic Data Centre (CCDC), Cambridge CB2 1EZ, U.K
| | - Anthony R Bradley
- Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K
| | - Brian D Marsden
- Centre for Medicines Discovery, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, U.K.,Kennedy Institute of Rheumatology, NDORMS, University of Oxford, Oxford OX3 7DQ, U.K
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Naveja JJ, Vogt M. Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications. Molecules 2021; 26:5291. [PMID: 34500724 PMCID: PMC8433811 DOI: 10.3390/molecules26175291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 01/21/2023] Open
Abstract
Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.
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Affiliation(s)
- José J. Naveja
- Instituto de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5-6, 53115 Bonn, Germany
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Guest EE, Pickett SD, Hirst JD. Structural variation of protein-ligand complexes of the first bromodomain of BRD4. Org Biomol Chem 2021; 19:5632-5641. [PMID: 34105560 DOI: 10.1039/d1ob00658d] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The bromodomain-containing protein 4 (BRD4), a member of the bromodomain and extra-terminal domain (BET) family, plays a key role in several diseases, especially cancers. With increased interest in BRD4 as a therapeutic target, many X-ray crystal structures of the protein in complex with small molecule inhibitors are publicly available over the recent decade. In this study, we use this structural information to investigate the conformations of the first bromodomain (BD1) of BRD4. Structural alignment of 297 BRD4-BD1 complexes shows a high level of similarity between the structures of BRD4-BD1, regardless of the bound ligand. We employ WONKA, a tool for detailed analyses of protein binding sites, to compare the active site of over 100 of these crystal structures. The positions of key binding site residues show a high level of conformational similarity, with the exception of Trp81. A focused analysis on the highly conserved water network in the binding site of BRD4-BD1 is performed to identify the positions of these water molecules across the crystal structures. The importance of the water network is illustrated using molecular docking and absolute free energy perturbation simulations. 82% of the ligand poses were better predicted when including water molecules as part of the receptor. Our analysis provides guidance for the design of new BRD4-BD1 inhibitors and the selection of the best structure of BRD4-BD1 to use in structure-based drug design, an important approach for faster and more cost-efficient lead discovery.
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Affiliation(s)
- Ellen E Guest
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
| | - Stephen D Pickett
- GlaxoSmithKline R&D Pharmaceuticals, Computational Chemistry, Stevenage, UK
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
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Pearce NM, Bradley AR, Krojer T, Marsden BD, Deane CM, von Delft F. Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2017; 4:032104. [PMID: 28345007 PMCID: PMC5336473 DOI: 10.1063/1.4974176] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 01/04/2017] [Indexed: 06/06/2023]
Abstract
Crystallographic fragment screening uses low molecular weight compounds to probe the protein surface and although individual protein-fragment interactions are high quality, fragments commonly bind at low occupancy, historically making identification difficult. However, our new Pan-Dataset Density Analysis method readily identifies binders missed by conventional analysis: for fragment screening data of lysine-specific demethylase 4D (KDM4D), the hit rate increased from 0.9% to 10.6%. Previously unidentified fragments reveal multiple binding sites and demonstrate: the versatility of crystallographic fragment screening; that surprisingly large conformational changes are possible in crystals; and that low crystallographic occupancy does not by itself reflect a protein-ligand complex's significance.
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Affiliation(s)
| | - Anthony R Bradley
- Structural Genomics Consortium, University of Oxford , Oxford OX3 7DQ, United Kingdom
| | - Tobias Krojer
- Structural Genomics Consortium, University of Oxford , Oxford OX3 7DQ, United Kingdom
| | | | - Charlotte M Deane
- Department of Statistics, University of Oxford , 24-29 St Giles, Oxford OX1 3LB, United Kingdom
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Deane CM, Wall ID, Green DVS, Marsden BD, Bradley AR. WONKA and OOMMPPAA: analysis of protein-ligand interaction data to direct structure-based drug design. Acta Crystallogr D Struct Biol 2017; 73:279-285. [PMID: 28291763 PMCID: PMC5349440 DOI: 10.1107/s2059798316009529] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 06/13/2016] [Indexed: 11/18/2022] Open
Abstract
In this work, two freely available web-based interactive computational tools that facilitate the analysis and interpretation of protein-ligand interaction data are described. Firstly, WONKA, which assists in uncovering interesting and unusual features (for example residue motions) within ensembles of protein-ligand structures and enables the facile sharing of observations between scientists. Secondly, OOMMPPAA, which incorporates protein-ligand activity data with protein-ligand structural data using three-dimensional matched molecular pairs. OOMMPPAA highlights nuanced structure-activity relationships (SAR) and summarizes available protein-ligand activity data in the protein context. In this paper, the background that led to the development of both tools is described. Their implementation is outlined and their utility using in-house Structural Genomics Consortium (SGC) data sets and openly available data from the PDB and ChEMBL is described. Both tools are freely available to use and download at http://wonka.sgc.ox.ac.uk/WONKA/ and http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/.
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Affiliation(s)
- Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24–29 St Giles, Oxford OX1 3LB, England
| | - Ian D. Wall
- Computational and Structural Chemistry, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage SG1 2NY, England
| | - Darren V. S. Green
- Computational and Structural Chemistry, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage SG1 2NY, England
| | - Brian D. Marsden
- SGC, Nuffield Department of Medicine, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, England
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, England
| | - Anthony R. Bradley
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24–29 St Giles, Oxford OX1 3LB, England
- SGC, Nuffield Department of Medicine, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, England
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Tanramluk D, Narupiyakul L, Akavipat R, Gong S, Charoensawan V. MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein-ligand fragment interaction, pathways and SNPs. Nucleic Acids Res 2016; 44:W514-21. [PMID: 27131358 PMCID: PMC4987895 DOI: 10.1093/nar/gkw314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 04/07/2016] [Accepted: 04/13/2016] [Indexed: 11/15/2022] Open
Abstract
Protein-ligand interaction analysis is an important step of drug design and protein engineering in order to predict the binding affinity and selectivity between ligands to the target proteins. To date, there are more than 100 000 structures available in the Protein Data Bank (PDB), of which ∼30% are protein-ligand (MW below 1000 Da) complexes. We have developed the integrative web server MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) with the aim of providing a user-friendly web interface to assist structural study and design of protein-ligand interactions. In brief, the server allows the users to input the chemical fragments and present all the unique molecular interactions to the target proteins with available three-dimensional structures in the PDB. The users can also link the ligands of interest to assess possible off-target proteins, human variants and pathway information using our all-in-one integrated tools. Taken together, we envisage that the server will facilitate and improve the study of protein-ligand interactions by allowing observation and comparison of ligand interactions with multiple proteins at the same time. (http://manoraa.org).
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Affiliation(s)
- Duangrudee Tanramluk
- Institute of Molecular Biosciences, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
| | - Lalita Narupiyakul
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Computer Engineering, Faculty of Engineering, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand
| | - Ruj Akavipat
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Computer Science, Faculty of Science, Kasetsart University, Chatuchak, Bangkok 10900, Thailand
| | - Sungsam Gong
- Department of Obstetrics and Gynaecology, University of Cambridge, The Rosie Hospital, Cambridge CB2 0SW, UK
| | - Varodom Charoensawan
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Department of Biochemistry, Faculty of Science, Mahidol University, Ratchathewi, Bangkok 10400, Thailand
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