1
|
Khatun S, Prasad Bhagat R, Dutta R, Datta A, Jaiswal A, Halder S, Jha T, Amin SA, Gayen S. Unraveling HDAC11: Epigenetic orchestra in different diseases and structural insights for inhibitor design. Biochem Pharmacol 2024; 225:116312. [PMID: 38788962 DOI: 10.1016/j.bcp.2024.116312] [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: 02/05/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 05/26/2024]
Abstract
Histone deacetylase 11 (HDAC11), a member of the HDAC family, has emerged as a critical regulator in numerous physiological as well as pathological processes. Due to its diverse roles, HDAC11 has been a focal point of research in recent times. Different non-selective inhibitors are already approved, and research is going on to find selective HDAC11 inhibitors. The objective of this review is to comprehensively explore the role of HDAC11 as a pivotal regulator in a multitude of physiological and pathological processes. It aims to delve into the intricate details of HDAC11's structural and functional aspects, elucidating its molecular interactions and implications in different disease contexts. With a primary focus on elucidating the structure-activity relationships (SARs) of HDAC11 inhibitors, this review also aims to provide a holistic understanding of how its molecular architecture influences its inhibition. Additionally, by integrating both established knowledge and recent research, the review seeks to contribute novel insights into the potential therapeutic applications of HDAC11 inhibitors. Overall, the scope of this review spans from fundamental research elucidating the complexities of HDAC11 biology to the potential of targeting HDAC11 in therapeutic interventions.
Collapse
Affiliation(s)
- Samima Khatun
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India
| | - Rinki Prasad Bhagat
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India
| | - Ritam Dutta
- Department of Pharmaceutical Technology, JIS University, 81, Nilgunj Road, Agarpara, Kolkata 700109, West Bengal, India
| | - Anwesha Datta
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India
| | - Abhishek Jaiswal
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India
| | - Swapnamay Halder
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India.
| | - Sk Abdul Amin
- Department of Pharmaceutical Technology, JIS University, 81, Nilgunj Road, Agarpara, Kolkata 700109, West Bengal, India.
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, West Bengal, India.
| |
Collapse
|
2
|
Li J, Chen X, Liu R, Liu X, Shu M. Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations. Int J Biol Macromol 2024; 262:129810. [PMID: 38340912 DOI: 10.1016/j.ijbiomac.2024.129810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 12/20/2023] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetylase 11 (HDAC11) to the pathogenesis of several metabolic diseases makes it a promising drug target for curbing metabolic disorders. The present study aims to design new specific HDAC11 inhibitors for the treatment of metabolic diseases. Deep learning was performed to learn the properties of existing HDAC11 inhibitors and yield a novel compound library containing 23,122 molecules. Subsequently, the compound library was screened by ADMET properties, Lipinski & Veber rules, traditional machine classification models, and molecular docking, and 10 compounds were screened as candidate HDAC11 inhibitors. The stability of the 10 new molecules was further evaluated by deploying RMSD, RMSF, MM/GBSA, free energy landscape mapping, and PCA analysis in molecular dynamics simulations. As a result, ten compounds, Cpd_17556, Cpd_2184, Cpd_8907, Cpd_7771, Cpd_14959, Cpd_7108, Cpd_12383, Cpd_13153, Cpd_14500and Cpd_21811, were characterized as good HDAC11 inhibitors and are expected to be promising drug candidates for metabolic disorders, and further in vitro, in vivo and clinical trials to demonstrate in the future.
Collapse
Affiliation(s)
- Jiali Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China; Key Laboratory of Screening and activity evaluation of targeted drugs, Chongqing 400054, China
| | - XiaoDie Chen
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China; Key Laboratory of Screening and activity evaluation of targeted drugs, Chongqing 400054, China
| | - Rong Liu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China; Key Laboratory of Screening and activity evaluation of targeted drugs, Chongqing 400054, China
| | - Xingyu Liu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China; Key Laboratory of Screening and activity evaluation of targeted drugs, Chongqing 400054, China
| | - Mao Shu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China; Key Laboratory of Screening and activity evaluation of targeted drugs, Chongqing 400054, China.
| |
Collapse
|
3
|
Baselious F, Hilscher S, Robaa D, Barinka C, Schutkowski M, Sippl W. Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor. Int J Mol Sci 2024; 25:1358. [PMID: 38279359 PMCID: PMC10816272 DOI: 10.3390/ijms25021358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/14/2024] [Accepted: 01/20/2024] [Indexed: 01/28/2024] Open
Abstract
HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.
Collapse
Affiliation(s)
- Fady Baselious
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| | - Sebastian Hilscher
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| | - Dina Robaa
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| | - Cyril Barinka
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, 252 50 Vestec, Czech Republic;
| | - Mike Schutkowski
- Charles Tanford Protein Center, Department of Enzymology, Institute of Biochemistry and Biotechnology, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany;
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| |
Collapse
|
4
|
Baselious F, Robaa D, Sippl W. Utilization of AlphaFold models for drug discovery: Feasibility and challenges. Histone deacetylase 11 as a case study. Comput Biol Med 2023; 167:107700. [PMID: 37972533 DOI: 10.1016/j.compbiomed.2023.107700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
Histone deacetylase 11 (HDAC11), an enzyme that cleaves acyl groups from acylated lysine residues, is the sole member of class IV of HDAC family with no reported crystal structure so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms which complicates the conventional template-based homology modeling. AlphaFold is a neural network machine learning approach for predicting the 3D structures of proteins with atomic accuracy even in absence of similar structures. However, the structures predicted by AlphaFold are missing small molecules as ligands and cofactors. In our study, we first optimized the HDAC11 AlphaFold model by adding the catalytic zinc ion followed by assessment of the usability of the model by docking of the selective inhibitor FT895. Minimization of the optimized model in presence of transplanted inhibitors, which have been described as HDAC11 inhibitors, was performed. Four complexes were generated and proved to be stable using three replicas of 50 ns MD simulations and were successfully utilized for docking of the selective inhibitors FT895, MIR002 and SIS17. For SIS17, The most reasonable pose was selected based on structural comparison between HDAC6, HDAC8 and the HDAC11 optimized AlphaFold model. The manually optimized HDAC11 model is thus able to explain the binding behavior of known HDAC11 inhibitors and can be used for further structure-based optimization.
Collapse
Affiliation(s)
- Fady Baselious
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
| | - Dina Robaa
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany.
| |
Collapse
|
5
|
Liu Y, Tong X, Hu W, Chen D. HDAC11: A novel target for improved cancer therapy. Biomed Pharmacother 2023; 166:115418. [PMID: 37659201 DOI: 10.1016/j.biopha.2023.115418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023] Open
Abstract
Histone deacetylase 11 (HDAC11) is a unique member of the histone deacetylase family that plays an important role in the regulation of gene expression and protein function. In recent years, research on the role of HDAC11 in tumors has attracted increasing attention. This review summarizes the current knowledge on the subcellular localization, structure, expression, and functions of HDAC11 in tumors, as well as the regulatory mechanisms involved in its network, including ncRNA and substrates. Moreover, we focus on the progress made in targeting HDAC11 to overcome tumor therapy resistance, and the development of HDAC11 inhibitors for cancer treatment. Collectively, this review provides comprehensive insights into the potential clinical implications of HDAC11 for cancer therapy.
Collapse
Affiliation(s)
- Yan Liu
- First Department of Oncology, the Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning, China
| | - Xuechao Tong
- Department of Emergency, the Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning, China
| | - Weina Hu
- Department of General Practice, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning, China.
| | - Da Chen
- Department of Emergency, the Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning, China.
| |
Collapse
|