Abstract
Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors – vemurafenib, venetoclax, and erdafitinib, FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as: surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure–activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).
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References
Shuker SB, Hajduk PJ, Meadows RP, Fesik SW (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274:1531–1534. https://doi.org/10.1126/science.274.5292.1531
Congreve M, Carr R, Murray C, Jhoti H (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877. https://doi.org/10.1016/S1359-6446(03)02831-9
Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41(3):856–864. https://doi.org/10.1021/ci000403i
Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K (2019) In silico methods for design of kinase inhibitors as anticancer drugs. Front Chem 7:873. https://doi.org/10.3389/fchem.2019.00873
Bian Y, Xie XQS (2018) Computational fragment-based drug design: current trends, strategies, and applications. AAPS J 20(3):59. https://doi.org/10.1208/s12248-018-0216-7
Otava Chemicals (2020) Otava chelator fragment library. https://otavachemicalscom/products/fragment-libraries/general-fragment-library Accessed 22 Feb 2018
Life Chemicals (2020) Targeted and Focused Screening Libraries. https://lifechemicals.com/screening-libraries/targeted-and-focused-screening-libraries/ Accessed 22 Feb 2018
Barelier S, Pons J, Marcillat O, Lancelin JM, Krimm I (2010) Fragment-based deconstruction of Bcl-xL inhibitors. J Med Chem 53(6):2577–2588. https://doi.org/10.1021/jm100009z
Zhang X, Yuan Z, Zhang Y, Yong S, Salas-Burgos A, Koomen J, Olashaw N, Parsons JT, Yang XJ, Dent SR, Yao TP, Lane WS, Seto E (2007) HDAC6 modulates cell motility by altering the acetylation level of cortactin. Mol Cell 27(2):197–213. https://doi.org/10.1016/j.molcel.2007.05.033
Seidel C, Schnekenburger M, Dicato M, Diederich M (2015) Histone deacetylase 6 in health and disease. Epigenomics 7(1):103–118. https://doi.org/10.2217/epi.14.69
Bradner JE, West N, Grachan ML, Greenberg EF, Haggarty SJ, Warnow T, Mazitschek R (2010) Chemical phylogenetics of histone deacetylases. Nat Chem Biol 6(3):238. https://doi.org/10.1038/nchembio.313
Talete srl, DRAGON (Software for Molecular Descriptor Calculation), Version 6.0–2010 - http://www.talete.mi.it/
Porter NJ, Mahendran A, Breslow R, Christianson DW (2017) Unusual zinc-binding mode of HDAC6-selective hydroxamate inhibitors. Proc Natl Acad Sci U S A 114(51):13459–13464. https://doi.org/10.1073/pnas.1718823114
Ruzic D, Petkovic M, Agbaba D, Ganesan A, Nikolic K (2019) Combined ligand and fragment-based drug Design of Selective Histone Deacetylase–6 inhibitors. Mol Inform 38(5):e1800083. https://doi.org/10.1002/minf.201800083
Irwin JJ, Shoichet BK (2005) ZINC− a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182. https://doi.org/10.1021/ci049714
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(D1):D1100–D1107. https://doi.org/10.1093/nar/gkr777
Enamine Ltd. (www.enamine.net)
CambridgeSoft, Corporation (2013) ChemBio3D Ultra, Version 13.0, Cambridge
Dassault Systemes BIOVIA. (2016). Discovery Studio v17.2.0.. https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/visualization-download.php
Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748. https://doi.org/10.1006/jmbi.1996.0897
Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445. https://doi.org/10.1023/a:1007996124545
Chem Axon. (2017). Instant J Chem V17.3.27.0.. https://chemaxon.com/products/instant-jchem
Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53(7):2719–2740. https://doi.org/10.1021/jm901137j
Lagorce D, Bouslama L, Becot J, Miteva MA, Villoutreix BO (2017) FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics 33(22):3658–3660. https://doi.org/10.1093/bioinformatics/btx491
Porter NJ, Wagner FF, Christianson DW (2018) Entropy as a driver of selectivity for inhibitor binding to histone deacetylase 6. Biochemistry 57(26):3916–3924. https://doi.org/10.1021/acs.biochem.8b00367
Chem Axon. (2017). Marvin Sketch V17.27. https://chemaxon.com/products/marvin
Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR et al (2016) Gaussian 09, Revision A.02. Gaussian, Inc., Wallingford CT
Hehre WJ, Radom L, Schleyer PR, Pople JA (1986) Ab initio molecular orbital theory Vol 1. Wiley, New York
Mannhold R, Kubinyi H, Folkers G (2006) Molecular interaction fields. Applications in drug discovery and ADME prediction, vol 27. Wiley-VCH, Zurich
Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276. https://doi.org/10.1016/S1093-3263(01)00123-1
Morgen M, Steimbach RR, Géraldy M, Hellweg L, Sehr P, Ridinger J, Witt O, Oehme I, Herbst-Gervasoni CJ, Osko JD, Porter NJ, Christianson DW, Gunkel N, Miller AK (2020) Design and synthesis of Dihydroxamic acids as HDAC6/8/10 inhibitors. ChemMedChem 15:1–13. https://doi.org/10.1002/cmdc.202000149
Chen K, Zhang X, Wu YD, Wiest O (2014) Inhibition and mechanism of HDAC8 revisited. JACS 136(33):11636–11643. https://doi.org/10.1021/ja501548p
Wu R, Lu Z, Cao Z, Zhang Y (2011) Zinc chelation with hydroxamate in histone deacetylases modulated by water access to the linker binding channel. J Am Chem Soc 133(16):6110–6113. https://doi.org/10.1021/ja111104p
Bell EW, Zhang Y (2019) DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism. J Cheminformatics 11(1):40. https://doi.org/10.1186/s13321-019-0362-7
Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701. https://doi.org/10.1002/qsar.200610151
Acknowledgments
This work was supported by the Ministry of Science and Technological Development of the Republic of Serbia, Contract No. 451-03-68/2020-14/200161. We kindly acknowledge European COST Action CM1406 (EpiChemBio). Numerical simulations were run on the PARADOX-IV supercomputing facility at the Scientific Computing Laboratory, National Center of Excellence for the Study of Complex Systems, Institute of Physics Belgrade, supported in part by the Ministry of Education, Science, and Technological Development of the Republic of Serbia.
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Ruzic, D., Djokovic, N., Nikolic, K. (2021). Fragment-Based Drug Design of Selective HDAC6 Inhibitors. In: Ballante, F. (eds) Protein-Ligand Interactions and Drug Design. Methods in Molecular Biology, vol 2266. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1209-5_9
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