Exploring protein hotspots by optimized fragment pharmacophores.
Animals
Cell Survival
Chlorocebus aethiops
Computational Chemistry
Coronavirus 3C Proteases
/ chemistry
Coronavirus Papain-Like Proteases
/ chemistry
Crystallography, X-Ray
Databases, Protein
Drug Design
Drug Development
/ methods
Drug Discovery
/ methods
HEK293 Cells
High-Throughput Screening Assays
/ methods
Histone-Lysine N-Methyltransferase
/ chemistry
Humans
Hydrogen Bonding
Hydrophobic and Hydrophilic Interactions
Ligands
Protein Binding
Receptors, G-Protein-Coupled
/ chemistry
SARS-CoV-2
/ chemistry
Small Molecule Libraries
Vero Cells
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
27 05 2021
27 05 2021
Historique:
received:
06
11
2020
accepted:
29
04
2021
entrez:
28
5
2021
pubmed:
29
5
2021
medline:
11
6
2021
Statut:
epublish
Résumé
Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores at the most preferred hotspots. The efficiency of this approach is demonstrated with a pilot library of 96 fragment-sized compounds (SpotXplorer0) that is validated on popular target classes and emerging drug targets. Biochemical screening against a set of GPCRs and proteases retrieves compounds containing an average of 70% of known pharmacophores for these targets. More importantly, SpotXplorer0 screening identifies confirmed hits against recently established challenging targets such as the histone methyltransferase SETD2, the main protease (3CLPro) and the NSP3 macrodomain of SARS-CoV-2.
Identifiants
pubmed: 34045440
doi: 10.1038/s41467-021-23443-y
pii: 10.1038/s41467-021-23443-y
pmc: PMC8159961
doi:
Substances chimiques
Ligands
0
Receptors, G-Protein-Coupled
0
Small Molecule Libraries
0
Histone-Lysine N-Methyltransferase
EC 2.1.1.43
SETD2 protein, human
EC 2.1.1.43
Coronavirus Papain-Like Proteases
EC 3.4.22.2
papain-like protease, SARS-CoV-2
EC 3.4.22.2
Coronavirus 3C Proteases
EC 3.4.22.28
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3201Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM118078
Pays : United States
Références
Williams, G., Ferenczy, G. G., Ulander, J. & Keserű, G. M. Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery. Drug Discov. Today 22, 681–689 (2017).
pubmed: 27916639
doi: 10.1016/j.drudis.2016.11.019
Ferenczy, G. G. & Keserű, G. M. Thermodynamic profiling for fragment-based lead discovery and optimization. Expert Opin. Drug Discov. 15, 117–129 (2020).
pubmed: 31741402
doi: 10.1080/17460441.2020.1691166
Kozakov, D. et al. Ligand deconstruction: Why some fragment binding positions are conserved and others are not. Proc. Natl Acad. Sci. 112, E2585–E2594 (2015).
pubmed: 25918377
doi: 10.1073/pnas.1501567112
pmcid: 4443342
Murray, C. W. & Verdonk, M. L. The consequences of translational and rotational entropy lost by small molecules on binding to proteins. J. Comput. Aided Mol. Des. 16, 741–753 (2002).
pubmed: 12650591
doi: 10.1023/A:1022446720849
Ferenczy, G. G. & Keserű, G. M. Thermodynamics of fragment binding. J. Chem. Inf. Model. 52, 1039–1045 (2012).
pubmed: 22458364
doi: 10.1021/ci200608b
Giordanetto, F., Jin, C., Willmore, L., Feher, M. & Shaw, D. E. Fragment hits: what do they look like and how do they bind? J. Med. Chem. 62, 3381–3394 (2019).
pubmed: 30875465
pmcid: 6466478
doi: 10.1021/acs.jmedchem.8b01855
Ferenczy, G. G. & Keserű, G. M. On the enthalpic preference of fragment binding. MedChemComm 7, 332–337 (2016).
doi: 10.1039/C5MD00542F
Hall, D. R., Kozakov, D., Whitty, A. & Vajda, S. Lessons from hot spot analysis for fragment-based drug discovery. Trends Pharmacol. Sci. 36, 724–736 (2015).
pubmed: 26538314
pmcid: 4640985
doi: 10.1016/j.tips.2015.08.003
Kutchukian, P. S. et al. Large scale meta-analysis of fragment-based screening campaigns: privileged fragments and complementary technologies. J. Biomol. Screen. 20, 588–596 (2015).
pubmed: 25550355
doi: 10.1177/1087057114565080
Drwal, M. N., Bret, G. & Kellenberger, E. Multi-target fragments display versatile binding modes. Mol. Inf. 36, 1700042 (2017).
doi: 10.1002/minf.201700042
Barelier, S., Pons, J., Marcillat, O., Lancelin, J. M. & Krimm, I. Fragment-based deconstruction of Bcl-xL inhibitors. J. Med. Chem. 53, 2577–2588 (2010).
pubmed: 20192224
doi: 10.1021/jm100009z
Ehrt, C., Brinkjost, T. & Koch, O. Impact of binding site comparisons on medicinal chemistry and rational molecular design. J. Med. Chem. 59, 4121–4151 (2016).
pubmed: 27046190
doi: 10.1021/acs.jmedchem.6b00078
Fehlmann, T. & Hutter, M. C. Conservation and relevance of pharmacophore point types. J. Chem. Inf. Model. 59, 1314–1323 (2019).
pubmed: 30807146
doi: 10.1021/acs.jcim.8b00757
Skucha, A. et al. MLL-fusion-driven leukemia requires SETD2 to safeguard genomic integrity. Nat. Commun. 9, 1983 (2018).
pubmed: 29777171
pmcid: 5959866
doi: 10.1038/s41467-018-04329-y
Murray, C. W. & Rees, D. C. Opportunity knocks: organic chemistry for fragment-based drug discovery (FBDD). Angew. Chem. Int. Ed. 55, 488–492 (2016).
doi: 10.1002/anie.201506783
Kozakov, D. et al. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat. Protoc. 10, 733–755 (2015).
pubmed: 25855957
pmcid: 4762777
doi: 10.1038/nprot.2015.043
Brenke, R. et al. Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics 25, 621–627 (2009).
pubmed: 19176554
pmcid: 2647826
doi: 10.1093/bioinformatics/btp036
Salam, N. K., Nuti, R. & Sherman, W. Novel method for generating structure-based pharmacophores using energetic analysis. J. Chem. Inf. Model. 49, 2356–2368 (2009).
pubmed: 19761201
doi: 10.1021/ci900212v
Friesner, R. A. et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem. 49, 6177–6196 (2006).
pubmed: 17034125
doi: 10.1021/jm051256o
Bajusz, D., Ferenczy, G. G. & Keserű, G. M. Structure-based virtual screening approaches in kinase-directed drug discovery. Curr. Top. Med. Chem. 17, 2235–2259 (2017).
pubmed: 28240180
doi: 10.2174/1568026617666170224121313
Keserű, G. M. et al. Design principles for fragment libraries: maximizing the value of learnings from pharma fragment-Based drug discovery (FBDD) programs for use in academia. J. Med. Chem. 59, 8189–8206 (2016).
pubmed: 27124799
doi: 10.1021/acs.jmedchem.6b00197
Baell, J. B. & Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53, 2719–2740 (2010).
pubmed: 20131845
doi: 10.1021/jm901137j
Ashton, M. et al. Identification of diverse database subsets using property-based and fragment-based molecular descriptions. Quant. Struct. Relatsh. 21, 598–604 (2002).
doi: 10.1002/qsar.200290002
Erlanson, D. Poll results: library vendors. < http://practicalfragments.blogspot.com/2018/12/poll-results-library-vendors.html > (2018).
Beliveau, V. et al. A high-resolution in vivo atlas of the human brain’s serotonin system. J. Neurosci. 37, 120–128 (2017).
pubmed: 28053035
pmcid: 5214625
doi: 10.1523/JNEUROSCI.2830-16.2016
Patel, N. R., Patel, D. V., Murumkar, P. R. & Yadav, M. R. Contemporary developments in the discovery of selective factor Xa inhibitors: a review. Eur. J. Med. Chem. 121, 671–698 (2016).
pubmed: 27322757
doi: 10.1016/j.ejmech.2016.05.039
Walker, C. P. R. & Royston, D. Thrombin generation and its inhibition: a review of the scientific basis and mechanism of action of anticoagulant therapies. Br. J. Anaesth. 88, 848–863 (2002).
pubmed: 12173205
doi: 10.1093/bja/88.6.848
Glennon, R. A. Higher-end serotonin receptors: 5-HT5, 5-HT6, and 5-HT7. J. Med. Chem. 46, 2795–2812 (2003).
pubmed: 12825922
doi: 10.1021/jm030030n
Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).
pubmed: 21948594
doi: 10.1093/nar/gkr777
Edmunds, J. W., Mahadevan, L. C. & Clayton, A. L. Dynamic histone H3 methylation during gene induction: HYPB/Setd2 mediates all H3K36 trimethylation. EMBO J. 27, 406–420 (2008).
pubmed: 18157086
doi: 10.1038/sj.emboj.7601967
Zheng, W. et al. Sinefungin derivatives as inhibitors and structure probes of protein lysine methyltransferase SETD2. J. Am. Chem. Soc. 134, 18004–18014 (2012).
pubmed: 23043551
pmcid: 3504124
doi: 10.1021/ja307060p
Gorbalenya, A. E. et al. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol. 5, 536–544 (2020).
doi: 10.1038/s41564-020-0695-z
Coronavirus Disease (COVID-2019) Situation Reports, World Health Organization. < https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports > (2020).
Jin, Z. et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 582, 289–293 (2020).
pubmed: 32272481
doi: 10.1038/s41586-020-2223-y
Liu, C. et al. Research and development on therapeutic agents and vaccines for COVID-19 and related human coronavirus diseases. ACS Cent. Sci. 6, 315–331 (2020).
pubmed: 32226821
doi: 10.1021/acscentsci.0c00272
Cao, B. et al. A trial of Lopinavir–Ritonavir in adults hospitalized with severe covid-19. N. Engl. J. Med. 382, 1787–1799 (2020).
pubmed: 32187464
doi: 10.1056/NEJMoa2001282
Sheahan, T. P. et al. Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV. Nat. Commun. 11, 1–14 (2020).
doi: 10.1038/s41467-019-13940-6
WHO Solidarity Trial Consortium. Repurposed antiviral drugs for covid-19—interim WHO solidarity trial results. N. Engl. J. Med. 384, 497–511 (2021).
doi: 10.1056/NEJMoa2023184
Fehr, A. R. et al. The conserved coronavirus macrodomain promotes virulence and suppresses the innate immune response during severe acute respiratory syndrome coronavirus infection. MBio 7, e01721–16 (2016).
pubmed: 27965448
pmcid: 5156301
doi: 10.1128/mBio.01721-16
Rack, J. G. M. et al. Viral macrodomains: a structural and evolutionary assessment of the pharmacological potential. Open Biol. 10, 200237 (2020).
Patel, D., Bauman, J. D. & Arnold, E. Advantages of crystallographic fragment screening: functional and mechanistic insights from a powerful platform for efficient drug discovery. Prog. Biophys. Mol. Biol. 116, 92–100 (2014).
pubmed: 25117499
pmcid: 4501029
doi: 10.1016/j.pbiomolbio.2014.08.004
Rees, D. C., Congreve, M., Murray, C. W. & Carr, R. Fragment-based lead discovery. Nat. Rev. Drug Discov. 3, 660–672 (2004).
pubmed: 15286733
doi: 10.1038/nrd1467
Collins, P. M. et al. Gentle, fast and effective crystal soaking by acoustic dispensing. Acta Crystallogr. Sect. D 73, 246–255 (2017).
doi: 10.1107/S205979831700331X
Krojer, T. et al. The XChemExplorer graphical workflow tool for routine or large-scale protein-ligand structure determination. Acta Crystallogr. Sect. D 73, 267–278 (2017).
doi: 10.1107/S2059798316020234
Pearce, N. M. et al. A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. Nat. Commun. 8, 15123 (2017).
pubmed: 28436492
pmcid: 5413968
doi: 10.1038/ncomms15123
Malla, T. R. et al. Mass spectrometry reveals potential of β-lactams as SARS-CoV-2 M pro inhibitors. Chem. Commun. 57, 1430–1433 (2021).
doi: 10.1039/D0CC06870E
Over, B. et al. Natural-product-derived fragments for fragment-based ligand discovery. Nat. Chem. 5, 21–28 (2013).
pubmed: 23247173
doi: 10.1038/nchem.1506
Koes, D. R. & Camacho, C. J. Pharmer: efficient and exact pharmacophore search. J. Chem. Inf. Model. 51, 1307–1314 (2011).
pubmed: 21604800
pmcid: 3124593
doi: 10.1021/ci200097m
Koes, D. R. & Camacho, C. J. ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40, W409–W414 (2012).
pubmed: 22553363
pmcid: 3394271
doi: 10.1093/nar/gks378
Leach, A. R., Gillet, V. J., Lewis, R. A. & Taylor, R. Three-dimensional pharmacophore methods in drug discovery. J. Med. Chem. 53, 539–558 (2010).
pubmed: 19831387
doi: 10.1021/jm900817u
McGregor, M. J. & Muskal, S. M. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J. Chem. Inf. Comput. Sci. 39, 569–574 (1999).
pubmed: 10361729
doi: 10.1021/ci980159j
McGregor, M. J. & Muskal, S. M. Pharmacophore fingerprinting. 2. Application to primary library design. J. Chem. Inf. Comput. Sci. 40, 117–125 (2000).
pubmed: 10661558
doi: 10.1021/ci990313h
Stiefl, N., Watson, I. A., Baumann, K. & Zaliani, A. ErG: 2D pharmacophore descriptions for scaffold hopping. J. Chem. Inf. Model. 46, 208–220 (2006).
pubmed: 16426057
doi: 10.1021/ci050457y
Saeh, J. C., Lyne, P. D., Takasaki, B. K. & Cosgrove, D. A. Lead hopping using SVM and 3D pharmacophore fingerprints. J. Chem. Inf. Model. 45, 1122–1133 (2005).
pubmed: 16045307
doi: 10.1021/ci049732r
Mason, J. S. et al. New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J. Med. Chem. 42, 3251–3264 (1999).
pubmed: 10464012
doi: 10.1021/jm9806998
Wollenhaupt, J. et al. F2X-universal and F2X-entry: structurally diverse compound libraries for crystallographic fragment screening. Structure 28, 694–706 (2020).
pubmed: 32413289
doi: 10.1016/j.str.2020.04.019
Schuller, M. et al. Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking. Sci. Adv. 7, eabf8711 (2021).
pubmed: 33853786
pmcid: 8046379
doi: 10.1126/sciadv.abf8711
Douangamath, A. et al. Crystallographic and electrophilic fragment screening of the SARS-CoV-2 main protease. Nat. Commun. 11, 5047 (2020).
pubmed: 33028810
pmcid: 7542442
doi: 10.1038/s41467-020-18709-w
Bajusz, D., Rácz, A. & Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform. 7, 20 (2015).
pubmed: 26052348
pmcid: 4456712
doi: 10.1186/s13321-015-0069-3
Alhammad, Y. M. O. et al. The SARS-CoV-2 conserved macrodomain is a mono-ADP-ribosylhydrolase. J. Virol. 95, 1969–1989 (2020).