1
|
Crivelli-Decker JE, Beckwith Z, Tom G, Le L, Khuttan S, Salomon-Ferrer R, Beall J, Gómez-Bombarelli R, Bortolato A. Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening. J Chem Theory Comput 2024. [PMID: 39146234 DOI: 10.1021/acs.jctc.4c00399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.
Collapse
Affiliation(s)
| | - Zane Beckwith
- SandboxAQ, Palo Alto, California 94301, United States
| | - Gary Tom
- SandboxAQ, Palo Alto, California 94301, United States
- Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5S 3H6, Canada
| | - Ly Le
- SandboxAQ, Palo Alto, California 94301, United States
| | - Sheenam Khuttan
- SandboxAQ, Palo Alto, California 94301, United States
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11367, United States
| | | | - Jackson Beall
- SandboxAQ, Palo Alto, California 94301, United States
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | | |
Collapse
|
2
|
Díaz-Holguín A, Saarinen M, Vo DD, Sturchio A, Branzell N, Cabeza de Vaca I, Hu H, Mitjavila-Domènech N, Lindqvist A, Baranczewski P, Millan MJ, Yang Y, Carlsson J, Svenningsson P. AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1. SCIENCE ADVANCES 2024; 10:eadn1524. [PMID: 39110804 PMCID: PMC11305387 DOI: 10.1126/sciadv.adn1524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 06/28/2024] [Indexed: 08/10/2024]
Abstract
Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.
Collapse
Affiliation(s)
- Alejandro Díaz-Holguín
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Marcus Saarinen
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Duc Duy Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Andrea Sturchio
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
| | - Niclas Branzell
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Israel Cabeza de Vaca
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Huabin Hu
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Núria Mitjavila-Domènech
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Annika Lindqvist
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Pawel Baranczewski
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Mark J. Millan
- Neuroinflammation Therapeutic Area, Institut de Recherches Servier, Centre de Recherches de Croissy, Paris, France and Institute of Neuroscience and Psychology, College of Medicine, Vet and Life Sciences, Glasgow University, Scotland, Glasgow, UK
| | - Yunting Yang
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Per Svenningsson
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| |
Collapse
|
3
|
Wu W, Chen Z, Wen H, Zhang H. Unveiling potential drug targets for lung squamous cell carcinoma through the integration of druggable genome and genome-wide association data. Front Genet 2024; 15:1431684. [PMID: 39175755 PMCID: PMC11338847 DOI: 10.3389/fgene.2024.1431684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 07/29/2024] [Indexed: 08/24/2024] Open
Abstract
Background: Lung squamous cell carcinoma (LSCC) is a major subtype of lung cancer with poor prognosis and low survival rate. Compared with lung adenocarcinoma, yet no FDA-approved targeted-therapy has been found for lung squamous cell carcinoma. Methods: To identify potential drug targets for LSCC, Summary-data-based Mendelian randomization (SMR) analysis was used to examine the potential association between 4,543 druggable genes and LSCC, followed by colocalization analysis and HEIDI tests to confirm the robustness of the result. Phenome-wide association study (PheWAS) explored potential side effects of candidate drug targets. Enrichment analysis and protein-protein interaction networks revealed the function and significance of therapeutic targets. Single-cell expression analysis was used to examine cell types with enrichment expression of druggable genes in LSCC tissue. Drug prediction included screening potential drug candidates and evaluating their interactions with targets through molecular docking. Results: This research has identified ten significant drug targets for LSCC through a comprehensive SMR analysis. These targets included (COPA, PKD2L1, CCR1, C2, CYP21A2, and NCSTN as risk factors, and CCNA2, C4A, APOM, and LPAR2 as protective factors). PheWAS demonstrated that C2, CCNA2, LPAR2, and NCSTN exhibited associations with other phenotypes at the genetic level. Then, we found four potentially effective drugs with the Dsigdb database. Subsequently, molecular docking indicated that favorable binding interactions between drug candidates and potential target molecules. In the druggability evaluation, five out of ten drug target genes have been used in drug development (APOM, C4A, CCNA2, COPA, and PKD2L1). Six out of ten druggable genes showed significant expression in LSCC tissues (COPA, PKD2L1, CCR1, C2, NCSTN, LPAR2). Besides, Single-cell expression analysis revealed that C2 and CCNA2 were primarily enriched in macrophages, while COPA and NCSTN were enriched in both macrophages and epithelial cells. Conclusion: Our research revealed ten potential druggable genes for LSCC treatment, which might help to advance the precise and efficient therapeutic approaches of LSCC.
Collapse
Affiliation(s)
- Wenhua Wu
- The Second Clinical Medical College, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhengrui Chen
- The Second Clinical Medical College, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Haiteng Wen
- The Second Clinical Medical College, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Haiyun Zhang
- Department of Pulmonary and Critical Care Medicine, Zhujiang Hosptial, Southern Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
4
|
Carlsson J, Luttens A. Structure-based virtual screening of vast chemical space as a starting point for drug discovery. Curr Opin Struct Biol 2024; 87:102829. [PMID: 38848655 DOI: 10.1016/j.sbi.2024.102829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 06/09/2024]
Abstract
Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.
Collapse
Affiliation(s)
- Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-751 24 Uppsala, Sweden.
| | - Andreas Luttens
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| |
Collapse
|
5
|
Wang M, Zan T, Fan C, Li Z, Wang D, Li Q, Zhang C. Advances in GPCR-targeted drug development in dermatology. Trends Pharmacol Sci 2024; 45:678-690. [PMID: 39060127 DOI: 10.1016/j.tips.2024.06.007] [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: 05/23/2024] [Revised: 06/16/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024]
Abstract
Achieving the efficacy and specificity of G-protein-coupled receptor (GPCR) targeting-drugs in the skin remains challenging. Understanding the molecular mechanism underlying GPCR dysfunction is crucial for developing targeted therapies. Recent advances in genetic, signal transduction, and structural studies have significantly improved our understanding of cutaneous GPCR functions in both normal and pathological states. In this review, we summarize recent discoveries of pathogenic GPCRs in dermal injuries, chronic inflammatory dermatoses, cutaneous malignancies, as well as the development of potent potential drugs. We also discuss targeting of cutaneous GPCR complexes via the transient receptor potential (TRP) channel and structure elucidation, which provide new opportunities for therapeutic targeting of GPCRs involved in skin disorders. These insights are expected to lead to more effective and specific treatments for various skin conditions.
Collapse
Affiliation(s)
- Meng Wang
- Songjiang Research Institute, Songjiang Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China
| | - Tao Zan
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Chengang Fan
- Department of Orthopedics and Precision Research Center for Refractory Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhouxiao Li
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Danru Wang
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
| | - Qingfeng Li
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
| | - Chao Zhang
- Department of Orthopedics and Precision Research Center for Refractory Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
| |
Collapse
|
6
|
Zhang H, Im W. Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods. J Chem Inf Model 2024; 64:5671-5679. [PMID: 38959405 PMCID: PMC11267607 DOI: 10.1021/acs.jcim.4c00764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/12/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024]
Abstract
Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.
Collapse
Affiliation(s)
- Han Zhang
- Departments of Biological
Sciences and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological
Sciences and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| |
Collapse
|
7
|
Huang R, Yu Q, Tamalunas A, Stief CG, Hennenberg M. Ligand-Receptor Interactions and Structure-Function Relationships in Off-Target Binding of the β 3-Adrenergic Agonist Mirabegron to α 1A-Adrenergic Receptors. Int J Mol Sci 2024; 25:7468. [PMID: 39000575 PMCID: PMC11242030 DOI: 10.3390/ijms25137468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open
Abstract
The β3-adrenoceptor agonist mirabegron is available for the treatment of storage symptoms of overactive bladder, including frequency, urgency, and incontinence. The off-target effects of mirabegron include binding to α1-adrenoceptors, which are central in the treatment of voiding symptoms. Here, we examined the structure-function relationships in the binding of mirabegron to a cryo-electron microscopy structure of α1A. The binding was simulated by docking mirabegron to a 3D structure of a human α1A-adrenoceptor (7YMH) using Autodock Vina. The simulations identified two binding states: slope orientation involving 10 positions and horizontal binding to the receptor surface involving 4 positions. No interactions occurred with positions constituting the α1A binding pocket, including Asp-106, Ser-188, or Phe-312, despite the positioning of the phenylethanolamine moiety in transmembrane regions close to the binding pocket by contact with Phe-288, -289, and Val-107. Contact with the unique positions of α1A included the transmembrane Met-292 during slope binding and exosite Phe-86 during horizontal binding. Exosite binding in slope orientation involved contact of the anilino part, rather than the aminothiazol end, to Ile-178, Ala-103, and Asn-179. In conclusion, contact with Met-292 and Phe-86, which are unique positions of α1A, accounts for mirabegron binding to α1A. Because of its lack of interactions with the binding pocket, mirabegron has lower affinity compared to α1A-blockers and no effects on voiding symptoms.
Collapse
Affiliation(s)
- Ru Huang
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510180, China; (R.H.); (Q.Y.)
| | - Qingfeng Yu
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510180, China; (R.H.); (Q.Y.)
| | - Alexander Tamalunas
- Department of Urology, LMU University Hospital, LMU Munich, 80539 Munich, Germany; (A.T.)
| | - Christian G. Stief
- Department of Urology, LMU University Hospital, LMU Munich, 80539 Munich, Germany; (A.T.)
| | - Martin Hennenberg
- Department of Urology, LMU University Hospital, LMU Munich, 80539 Munich, Germany; (A.T.)
- Urologische Klinik und Poliklinik, Marchioninistr. 15, 81377 Munich, Germany
| |
Collapse
|
8
|
Zhang T, An W, You S, Chen S, Zhang S. G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine. Chin Med 2024; 19:92. [PMID: 38956679 PMCID: PMC11218379 DOI: 10.1186/s13020-024-00964-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
G protein-coupled receptors (GPCRs) widely exist in vivo and participate in many physiological processes, thus emerging as important targets for drug development. Approximately 30% of the Food and Drug Administration (FDA)-approved drugs target GPCRs. To date, the 'one disease, one target, one molecule' strategy no longer meets the demands of drug development. Meanwhile, small-molecule drugs account for 60% of FDA-approved drugs. Traditional Chinese medicine (TCM) has garnered widespread attention for its unique theoretical system and treatment methods. TCM involves multiple components, targets and pathways. Centered on GPCRs and TCM, this paper discusses the similarities and differences between TCM and GPCRs from the perspectives of syndrome of TCM, the consistency of TCM's multi-component and multi-target approaches and the potential of GPCRs and TCM in the development of novel drugs. A novel strategy, 'simultaneous screening of drugs and targets', was proposed and applied to the study of GPCRs. We combine GPCRs with TCM to facilitate the modernisation of TCM, provide valuable insights into the rational application of TCM and facilitate the research and development of novel drugs. This study offers theoretical support for the modernisation of TCM and introduces novel ideas for development of safe and effective drugs.
Collapse
Affiliation(s)
- Ting Zhang
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611100, China
| | - Wenqiao An
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611100, China
| | - Shengjie You
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Shilin Chen
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| | - Sanyin Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611100, China.
| |
Collapse
|
9
|
Chen D, Wang Y, Xiao S, Cheng G, Liu Y, Zhao T, Cao J, Wen Y. Investigation on the mechanism of androsta-4,6,8,14-tetraene-3,11,16-trione against acute lymphoblastic leukemia. J Steroid Biochem Mol Biol 2024; 243:106573. [PMID: 38909867 DOI: 10.1016/j.jsbmb.2024.106573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/01/2024] [Accepted: 06/20/2024] [Indexed: 06/25/2024]
Abstract
Steroids are potential anti-leukemia agents, and Epigynum auritum is a Yunnan folk medicine with high levels of androsterone, pregnane, and steroid derivatives. However, the underlying therapeutic mechanism of androsta-4,6,8,14-tetraene-3,11,16-trione (ATT), an androsterone isolated from Epigynum auritum, is not yet clear. This study aimed to explore the anti-leukemia mechanism of ATT using molecular biology, network pharmacology, and molecular docking technology. The cell viability results showed that ATT had an anti-proliferation effect in acute lymphoblastic leukemia cells (CEM/C1, MOLT-4, Jurkat, BALL-1, Nalm-6, and RS4;11). Further studies showed that ATT reduced the mitochondrial membrane potential in B-cell acute lymphoblastic leukemia cell lines (BALL-1, Nalm-6, and RS4;11) and induced cell cycle arrest in MOLT-4 and BALL-1. ATT induced BALL-1 cell apoptosis by activating Caspase 3/7 activity and causing DNA fragmentation. Network pharmacology results suggested that ATT exerts its anti-leukemia activity via the PI3K/Akt signaling pathway. In addition, molecular docking analysis showed that ATT had high scores in docking with PTGS2, NR3C1, and AR. Western blotting results showed that ATT reduced the relative protein level of P-PI3K and P-Akt, thereby increasing the relative level of pro-apoptosis protein Bax and reducing the relative level of anti-apoptosis protein Bcl-2, the apoptosis downstream protein pro-caspase3, and cell proliferation-related proteins (P-GSK3B and CyclinD1). In conclusion, these results demonstrated that ATT could be a potential candidate drug with apoptosis-induction and cell cycle arrest effects for further investigation in acute lymphoblastic leukemia therapy.
Collapse
Affiliation(s)
- Dongjie Chen
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Yongpeng Wang
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Shanshan Xiao
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Guiguang Cheng
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Yaping Liu
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Tianrui Zhao
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Jianxin Cao
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
| | - Yan Wen
- Department of Hematology, The First People's Hospital of Yunnan Province, Yunnan Province Clinical Research Center for Hematologic Disease, Yunnan Province Clinical Center for Hematologic Disease, Kunming 650032, China.
| |
Collapse
|
10
|
Tomašević N, Emser FS, Muratspahić E, Gattringer J, Hasinger S, Hellinger R, Keov P, Felkl M, Gertsch J, Becker CFW, Gruber CW. Discovery and development of macrocyclic peptide modulators of the cannabinoid 2 receptor. J Biol Chem 2024; 300:107330. [PMID: 38679329 PMCID: PMC11154713 DOI: 10.1016/j.jbc.2024.107330] [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: 02/21/2024] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024] Open
Abstract
The cannabinoid type 2 receptor (CB2R), a G protein-coupled receptor, is an important regulator of immune cell function and a promising target to treat chronic inflammation and fibrosis. While CB2R is typically targeted by small molecules, including endo-, phyto-, and synthetic cannabinoids, peptides-owing to their size-may offer a different interaction space to facilitate differential interactions with the receptor. Here, we explore plant-derived cyclic cystine-knot peptides as ligands of the CB2R. Cyclotides are known for their exceptional biochemical stability. Recently, they gained attention as G protein-coupled receptor modulators and as templates for designing peptide ligands with improved pharmacokinetic properties over linear peptides. Cyclotide-based ligands for CB2R were profiled based on a peptide-enriched extract library comprising nine plants. Employing pharmacology-guided fractionation and peptidomics, we identified the cyclotide vodo-C1 from sweet violet (Viola odorata) as a full agonist of CB2R with an affinity (Ki) of 1 μM and a potency (EC50) of 8 μM. Leveraging deep learning networks, we verified the structural topology of vodo-C1 and modeled its molecular volume in comparison to the CB2R ligand binding pocket. In a fragment-based approach, we designed and characterized vodo-C1-based bicyclic peptides (vBCL1-4), aiming to reduce size and improve potency. Opposite to vodo-C1, the vBCL peptides lacked the ability to activate the receptor but acted as negative allosteric modulators or neutral antagonists of CB2R. This study introduces a macrocyclic peptide phytocannabinoid, which served as a template for the development of synthetic CB2R peptide modulators. These findings offer opportunities for future peptide-based probe and drug development at cannabinoid receptors.
Collapse
Affiliation(s)
- Nataša Tomašević
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Fabiola Susanna Emser
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Edin Muratspahić
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Jasmin Gattringer
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Simon Hasinger
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Roland Hellinger
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Peter Keov
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia; ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Manuel Felkl
- Institute of Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Jürg Gertsch
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bern, Switzerland
| | - Christian F W Becker
- Institute of Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Christian W Gruber
- Center for Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria.
| |
Collapse
|
11
|
Fan Z, Liu X, Wang N, Yu S, Bi C, Si Y, Ling X, Liu C, Wang J, Sun H. Utilizing a structure-based virtual screening approach to discover potential LSD1 inhibitors. J Cancer Res Clin Oncol 2024; 150:253. [PMID: 38748285 PMCID: PMC11096237 DOI: 10.1007/s00432-024-05784-5] [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: 04/15/2024] [Accepted: 05/06/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Lysine-specific demethylase 1 (LSD1) is highly expressed in a variety of malignant tumors, rendering it a crucial epigenetic target for anti-tumor therapy. Therefore, the inhibition of LSD1 activity has emerged as a promising innovative therapeutic approach for targeted cancer treatment. METHODS In our study, we employed innovative structure-based drug design methods to meticulously select compounds from the ZINC15 database. Utilizing virtual docking, we evaluated docking scores and binding modes to identify potential inhibitors. To further validate our findings, we harnessed molecular dynamic simulations and conducted meticulous biochemical experiments to deeply analyze the binding interactions between the protein and compounds. RESULTS Our results showcased that ZINC10039815 exhibits an exquisite binding mode with LSD1, fitting perfectly into the active pocket and forming robust interactions with multiple critical residues of the protein. CONCLUSIONS With its significant inhibitory effect on LSD1 activity, ZINC10039815 emerges as a highly promising candidate for the development of novel LSD1 inhibitors.
Collapse
Affiliation(s)
- Zhehao Fan
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Xiaofeng Liu
- Internal Medicine Department, Haian Hospital of Traditional Chinese Medicine, Nantong, China
| | - Ning Wang
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Shiyi Yu
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Caili Bi
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Yue Si
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Xinyue Ling
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Chenxu Liu
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China
| | - Jingcheng Wang
- Yangzhou University Affiliated Northern Jiangsu People's Hospital, Yangzhou, China
| | - Haibo Sun
- Institute of Translational Medicine, Medical College, Yangzhou University, Jiangyangzhonglu No. 136, Yangzhou, Jiangsu, China.
- Jiangsu Key Laboratory of Experimental & Translational Non-Coding RNA Research, Yangzhou, China.
| |
Collapse
|
12
|
Zhang X, Lyu D, Li S, Xiao H, Qiu Y, Xu K, Chen N, Deng L, Huang H, Wu R. Discovery of a SUCNR1 antagonist for potential treatment of diabetic nephropathy: In silico and in vitro studies. Int J Biol Macromol 2024; 268:131898. [PMID: 38677680 DOI: 10.1016/j.ijbiomac.2024.131898] [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: 03/10/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 04/29/2024]
Abstract
Diabetic nephropathy (DN) is one of the most severe complications of diabetes mellitus. Succinate Receptor 1 (SUCNR1), a member of the G-protein-coupled receptor (GPCR) family, represents a potential target for treatment of DN. Here, utilizing multi-strategy in silico virtual screening methods containing AlphaFold2 modelling, molecular dynamics (MD) simulation, ligand-based pharmacophore screening, molecular docking and machine learning-based similarity clustering, we successfully identified a novel antagonist of SUCNR1, AK-968/12117473 (Cpd3). Through extensive in vitro experiments, including dual-luciferase reporter assay, cellular thermal shift assay, immunofluorescence, and western blotting, we substantiated that Cpd3 could specifically target SUCNR1, inhibit the activation of NF-κB pathway, and ameliorate epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) deposition in renal tubular epithelial cells (NRK-52E) under high glucose conditions. Further in silico simulations revealed the molecular basis of the SUCNR1-Cpd3 interaction, and the in vitro metabolic stability assay indicated favorable drug-like pharmacokinetic properties of Cpd3. This work not only successfully pinpointed Cpd3 as a specific antagonist of SUCNR1 to serve as a promising candidate in the realm of therapeutic interventions for DN, but also provides a paradigm of dry-wet combined discovery strategies for GPCR-based therapeutics.
Collapse
Affiliation(s)
- Xuting Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China; Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou 510801, China
| | - Dongxin Lyu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Shanshan Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Haiming Xiao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yufan Qiu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Kangwei Xu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Nianhang Chen
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Li Deng
- College of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.
| | - Heqing Huang
- Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou 510801, China.
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
| |
Collapse
|
13
|
Jeje O, Otun S, Aloke C, Achilonu I. Exploring NAD + metabolism and NNAT: Insights from structure, function, and computational modeling. Biochimie 2024; 220:84-98. [PMID: 38182101 DOI: 10.1016/j.biochi.2024.01.002] [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: 10/29/2023] [Revised: 12/18/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024]
Abstract
Nicotinamide Adenine Dinucleotide (NAD+), a coenzyme, is ubiquitously distributed and serves crucial functions in diverse biological processes, encompassing redox reactions, energy metabolism, and cellular signalling. This review article explores the intricate realm of NAD + metabolism, with a particular emphasis on the complex relationship between its structure, function, and the pivotal enzyme, Nicotinate Nucleotide Adenylyltransferase (NNAT), also known as nicotinate mononucleotide adenylyltransferase (NaMNAT), in the process of its biosynthesis. Our findings indicate that NAD + biosynthesis in humans and bacteria occurs via the same de novo synthesis route and the pyridine ring salvage pathway. Maintaining NAD homeostasis in bacteria is imperative, as most bacterial species cannot get NAD+ from their surroundings. However, due to lower sequence identity and structurally distant relationship of bacteria, including E. faecium and K. pneumonia, to its human counterpart, inhibiting NNAT, an indispensable enzyme implicated in NAD + biosynthesis, is a viable alternative in curtailing infections orchestrated by E. faecium and K. pneumonia. By merging empirical and computational discoveries and connecting the intricate NAD + metabolism network with NNAT's crucial role, it becomes clear that the synergistic effect of these insights may lead to a more profound understanding of the coenzyme's function and its potential applications in the fields of therapeutics and biotechnology.
Collapse
Affiliation(s)
- Olamide Jeje
- Protein Structure-Function and Research Unit, School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Braamfontein, Johannesburg, 2050, South Africa
| | - Sarah Otun
- Protein Structure-Function and Research Unit, School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Braamfontein, Johannesburg, 2050, South Africa.
| | - Chinyere Aloke
- Protein Structure-Function and Research Unit, School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Braamfontein, Johannesburg, 2050, South Africa; Department of Medical Biochemistry, Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State, Nigeria
| | - Ikechukwu Achilonu
- Protein Structure-Function and Research Unit, School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Braamfontein, Johannesburg, 2050, South Africa
| |
Collapse
|
14
|
Chen Z, Yu J, Wang H, Xu P, Fan L, Sun F, Huang S, Zhang P, Huang H, Gu S, Zhang B, Zhou Y, Wan X, Pei G, Xu HE, Cheng J, Wang S. Flexible scaffold-based cheminformatics approach for polypharmacological drug design. Cell 2024; 187:2194-2208.e22. [PMID: 38552625 DOI: 10.1016/j.cell.2024.02.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 04/28/2024]
Abstract
Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.
Collapse
Affiliation(s)
- Zhangcheng Chen
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Jing Yu
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Huan Wang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Peiyu Xu
- State Key Laboratory of Drug Research, Center for Structure and Function of Drug Targets, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Luyu Fan
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Fengxiu Sun
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Sijie Huang
- State Key Laboratory of Drug Research, Center for Structure and Function of Drug Targets, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Pei Zhang
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Shuo Gu
- ComMedX, Beijing 100094, China
| | | | - Yue Zhou
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Gang Pei
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - H Eric Xu
- State Key Laboratory of Drug Research, Center for Structure and Function of Drug Targets, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
| | - Jianjun Cheng
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Sheng Wang
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
| |
Collapse
|
15
|
Marin E, Kovaleva M, Kadukova M, Mustafin K, Khorn P, Rogachev A, Mishin A, Guskov A, Borshchevskiy V. Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking. J Chem Inf Model 2024; 64:2612-2623. [PMID: 38157481 PMCID: PMC11005039 DOI: 10.1021/acs.jcim.3c01661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.
Collapse
Affiliation(s)
- Egor Marin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Margarita Kovaleva
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Maria Kadukova
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- University
Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Khalid Mustafin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Polina Khorn
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Andrey Rogachev
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
| | - Alexey Mishin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Albert Guskov
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Valentin Borshchevskiy
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
| |
Collapse
|
16
|
Puhl AC, Lewicki SA, Gao ZG, Pramanik A, Makarov V, Ekins S, Jacobson KA. Machine learning-aided search for ligands of P2Y 6 and other P2Y receptors. Purinergic Signal 2024:10.1007/s11302-024-10003-4. [PMID: 38526670 DOI: 10.1007/s11302-024-10003-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/12/2024] [Indexed: 03/27/2024] Open
Abstract
The P2Y6 receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet few potent and selective antagonists are known to date. This prompted us to use machine learning as a novel approach to aid ligand discovery, with pharmacological evaluation at three P2YR subtypes: initially P2Y6 and subsequently P2Y1 and P2Y14. Relying on extensive published data for P2Y6R agonists, we generated and validated an array of classification machine learning model using the algorithms deep learning (DL), adaboost classifier (ada), Bernoulli NB (bnb), k-nearest neighbors (kNN) classifier, logistic regression (lreg), random forest classifier (rf), support vector classification (SVC), and XGBoost (XGB) classifier models, and the common consensus was applied to molecular selection of 21 diverse structures. Compounds were screened using human P2Y6R-induced functional calcium transients in transfected 1321N1 astrocytoma cells and fluorescent binding inhibition at closely related hP2Y14R expressed in CHO cells. The hit compound ABBV-744, an experimental anticancer drug with a 6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridine scaffold, had multifaceted interactions with the P2YR family: hP2Y6R inhibition in a non-surmountable fashion, suggesting that noncompetitive antagonism, and hP2Y1R enhancement, but not hP2Y14R binding inhibition. Other machine learning-selected compounds were either weak (experimental anti-asthmatic drug AZD5423 with a phenyl-1H-indazole scaffold) or inactive in inhibiting the hP2Y6R. Experimental drugs TAK-593 and GSK1070916 (100 µM) inhibited P2Y14R fluorescent binding by 50% and 38%, respectively, and all other compounds by < 20%. Thus, machine learning has led the way toward revealing previously unknown modulators of several P2YR subtypes that have varied effects.
Collapse
Affiliation(s)
- Ana C Puhl
- Collaborations Pharmaceuticals, Inc, 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Sarah A Lewicki
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Zhan-Guo Gao
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Asmita Pramanik
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Vadim Makarov
- Research Center of Biotechnology RAS, Leninsky Prospekt 33-2, 119071, Moscow, Russian Federation
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc, 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
| | - Kenneth A Jacobson
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.
| |
Collapse
|
17
|
Jobe A, Vijayan R. Orphan G protein-coupled receptors: the ongoing search for a home. Front Pharmacol 2024; 15:1349097. [PMID: 38495099 PMCID: PMC10941346 DOI: 10.3389/fphar.2024.1349097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/15/2024] [Indexed: 03/19/2024] Open
Abstract
G protein-coupled receptors (GPCRs) make up the largest receptor superfamily, accounting for 4% of protein-coding genes. Despite the prevalence of such transmembrane receptors, a significant number remain orphans, lacking identified endogenous ligands. Since their conception, the reverse pharmacology approach has been used to characterize such receptors. However, the multifaceted and nuanced nature of GPCR signaling poses a great challenge to their pharmacological elucidation. Considering their therapeutic relevance, the search for native orphan GPCR ligands continues. Despite limited structural input in terms of 3D crystallized structures, with advances in machine-learning approaches, there has been great progress with respect to accurate ligand prediction. Though such an approach proves valuable given that ligand scarcity is the greatest hurdle to orphan GPCR deorphanization, the future pairings of the remaining orphan GPCRs may not necessarily take a one-size-fits-all approach but should be more comprehensive in accounting for numerous nuanced possibilities to cover the full spectrum of GPCR signaling.
Collapse
Affiliation(s)
- Amie Jobe
- Department of Biology, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Ranjit Vijayan
- Department of Biology, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates
- The Big Data Analytics Center, United Arab Emirates University, Al Ain, United Arab Emirates
- Zayed Bin Sultan Center for Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| |
Collapse
|
18
|
Yao H, Wang X, Chi J, Chen H, Liu Y, Yang J, Yu J, Ruan Y, Xiang X, Pi J, Xu JF. Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures. Molecules 2024; 29:964. [PMID: 38474476 DOI: 10.3390/molecules29050964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.
Collapse
Affiliation(s)
- Hanbo Yao
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaodong Wang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaxin Chi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Haorong Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yilin Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiayi Yang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaqi Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yongdui Ruan
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
| | - Xufu Xiang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jun-Fa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| |
Collapse
|
19
|
Dawson JRD, Wadman GM, Zhang P, Tebben A, Carter PH, Gu S, Shroka T, Borrega-Roman L, Salanga CL, Handel TM, Kufareva I. Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.15.567150. [PMID: 38014122 PMCID: PMC10680698 DOI: 10.1101/2023.11.15.567150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.
Collapse
Affiliation(s)
- John R D Dawson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Grant M Wadman
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | | | | | - Percy H Carter
- Bristol Myers Squibb Company, Princeton, NJ, USA
- (current affiliation) Blueprint Medicines, Cambridge, MA, USA
| | - Siyi Gu
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- (current affiliation) Lycia Therapeutics, South San Francisco, CA
| | - Thomas Shroka
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- (current affiliation) Avidity Biosciences Inc., San Diego, CA
| | - Leire Borrega-Roman
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Catherina L Salanga
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Tracy M Handel
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| |
Collapse
|
20
|
Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
Collapse
Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| |
Collapse
|
21
|
Cai H, Shen C, Jian T, Zhang X, Chen T, Han X, Yang Z, Dang W, Hsieh CY, Kang Y, Pan P, Ji X, Song J, Hou T, Deng Y. CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training. Chem Sci 2024; 15:1449-1471. [PMID: 38274053 PMCID: PMC10806797 DOI: 10.1039/d3sc05552c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.
Collapse
Affiliation(s)
- Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xiaoqi Han
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Zhuo Yang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Wei Dang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiangyang Ji
- Department of Automation, Tsinghua University Beijing 100084 China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| |
Collapse
|
22
|
Chen C, Zhang B, Tu J, Peng Y, Zhou Y, Yang X, Yu Q, Tan X. Discovery of 4-aminophenylacetamide derivatives as intestine-specific farnesoid X receptor antagonists for the potential treatment of nonalcoholic steatohepatitis. Eur J Med Chem 2024; 264:115992. [PMID: 38043493 DOI: 10.1016/j.ejmech.2023.115992] [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: 09/20/2023] [Revised: 11/21/2023] [Accepted: 11/21/2023] [Indexed: 12/05/2023]
Abstract
Farnesoid X receptor (FXR) plays a key role in bile acid homeostasis, inflammation, fibrosis, lipid and glucose metabolism and is emerging as a promising therapeutic target for nonalcoholic steatohepatitis (NASH). Emerging evidence suggested that intestine-specific FXR antagonists exhibited remarkable metabolic improvements and slowed NASH progression. In this study, we discovered several potent FXR antagonists using a multistage ligand- and structure-based virtual screening approach. Notably, compound V023-9340, which possesses a 4-aminophenylacetamide scaffold, emerged as the most potent FXR antagonist with an IC50 value of 4.27 μM. In vivo, V023-9340 demonstrated selective accumulation in the intestine, substantially ameliorating high-fat diet (HFD)-induced NASH in mice by mitigating hepatic steatosis and inflammation. Mechanistic studies revealed that V023-9340 strongly inhibited intestinal FXR while concurrently feedback-activated hepatic FXR. Further structure-activity relationship optimization employing V023-9340 has resulted in the synthesis of a more efficacious compound V02-8 with an IC50 value of 0.89 μM, which exhibited a 4.8-fold increase in FXR antagonistic activity compared to V023-9340. In summary, 4-aminophenylacetamide derivative V023-9340 represented a novel intestine-specific FXR antagonist and showed improved effects against HFD-induced NASH in mice, which may serve as a promising lead in discovering potential therapeutic drugs for NASH treatment.
Collapse
Affiliation(s)
- Cong Chen
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China
| | - Bing Zhang
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China
| | - Jiaojiao Tu
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China
| | - Yanfen Peng
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China
| | - Yihuan Zhou
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China
| | - Xinping Yang
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China
| | - Qiming Yu
- Guangxi Key Laboratory of Environmental Exposure Omics and Life Cycle Health, College of Public Health, Guilin Medical University, Guilin 541199, China.
| | - Xiangduan Tan
- Guangxi Key Laboratory of Drug Discovery and Optimization, College of Pharmacy, Guilin Medical University, Guilin 541199, China.
| |
Collapse
|
23
|
Singh K, Bhushan B, Singh B. Advances in Drug Discovery and Design using Computer-aided Molecular Modeling. Curr Comput Aided Drug Des 2024; 20:697-710. [PMID: 37711101 DOI: 10.2174/1573409920666230914123005] [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: 07/04/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023]
Abstract
Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.
Collapse
Affiliation(s)
- Kuldeep Singh
- Department of Pharmacology, Rajiv Academy for Pharmacy, Mathura Uttar Pradesh, India
| | - Bharat Bhushan
- Department of Pharmacology, Institute of Pharmaceutical Research, GLA University, Mathura Uttar Pradesh, India
| | - Bhoopendra Singh
- Department of Pharmacy, B.S.A. College of Engineering & Technology, Mathura Uttar Pradesh India
| |
Collapse
|
24
|
Huang J, Xie Y, Chen B, Xia Y, Jiang Y, Sun Z, Liu Y. GPR146 regulates pulmonary vascular remodeling by promoting pulmonary artery smooth muscle cell proliferation through 5-lipoxygenase. Eur J Pharmacol 2023; 961:176123. [PMID: 37926274 DOI: 10.1016/j.ejphar.2023.176123] [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: 04/28/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/07/2023]
Abstract
The pathological feature of hypoxic pulmonary hypertension (PH) is pulmonary vascular remodeling (PVR), primarily attributed to the hyperproliferation and apoptosis resistance of pulmonary artery smooth muscle cells (PASMCs). Existing PH-targeted drugs have difficulties in reversing PVR. Therefore, it is vital to discover a new regulatory mechanism for PVR and develop new targeted drugs. G protein-coupled receptor 146 (GPR146) is believed to participate in this process. This study aimed to investigate the role of GPR146 in PASMCs during PH. We investigated the role of GPR146 in PVR and its underlying mechanism using hypoxic PASMCs and mouse model (Sugen 5416 (20 mg/kg)/hypoxia). In our recent study, we have observed a significant increase in the expression of GPR146 protein in animal models of PH as well as in patients diagnosed with pulmonary arterial hypertension (PAH). Through immunohistochemistry, we found that GPR146 was mainly localized in the smooth muscle and endothelial layers of the pulmonary vasculature. GPR146 deficiency induction exhibited protective effects against hypoxia-induced elevation of right ventricular systolic blood pressure (RVSP), right ventricular hypertrophy, and pulmonary vascular remodeling in mice. In particular, the deletion of GPR146 attenuated the hypoxia-triggered proliferation of PASMCs. Furthermore, 5-lipoxygenase (5-LO) was related to PH development. Hypoxia and overexpression of GPR146 increased 5-LO expression, which was reversed through GPR146 knockdown or siRNA intervention. Our study discovered that GPR146 exhibited high expression in the pulmonary vessels of pulmonary hypertension. Subsequent research revealed that GPR146 played a crucial role in the development of hypoxic PH by promoting lipid peroxidation and 5-LO expression. In conclusion, GPR146 may regulate pulmonary vascular remodeling by promoting PASMCs proliferation through 5-LO, which presents a feasible target for PH prevention and treatment.
Collapse
Affiliation(s)
- Jie Huang
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China
| | - Yongpeng Xie
- Department of Emergency and Critical Care Medicine, Lianyungang Clinical College of Nanjing Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China
| | - Bing Chen
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China
| | - Yu Xia
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China
| | - Yanjiao Jiang
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China
| | - Zengxian Sun
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China; Department of Emergency and Critical Care Medicine, Lianyungang Clinical College of Nanjing Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China
| | - Yun Liu
- Department of Pharmacy, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China; Department of Pharmacy, Lianyungang Clinical College of Nanjing Medical University/The First People's Hospital of Lianyungang, Lianyungang, 222061, China.
| |
Collapse
|
25
|
Choi C, Bae J, Kim S, Lee S, Kang H, Kim J, Bang I, Kim K, Huh WK, Seok C, Park H, Im W, Choi HJ. Understanding the molecular mechanisms of odorant binding and activation of the human OR52 family. Nat Commun 2023; 14:8105. [PMID: 38062020 PMCID: PMC10703812 DOI: 10.1038/s41467-023-43983-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Structural and mechanistic studies on human odorant receptors (ORs), key in olfactory signaling, are challenging because of their low surface expression in heterologous cells. The recent structure of OR51E2 bound to propionate provided molecular insight into odorant recognition, but the lack of an inactive OR structure limited understanding of the activation mechanism of ORs upon odorant binding. Here, we determined the cryo-electron microscopy structures of consensus OR52 (OR52cs), a representative of the OR52 family, in the ligand-free (apo) and octanoate-bound states. The apo structure of OR52cs reveals a large opening between transmembrane helices (TMs) 5 and 6. A comparison between the apo and active structures of OR52cs demonstrates the inward and outward movements of the extracellular and intracellular segments of TM6, respectively. These results, combined with molecular dynamics simulations and signaling assays, shed light on the molecular mechanisms of odorant binding and activation of the OR52 family.
Collapse
Affiliation(s)
- Chulwon Choi
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jungnam Bae
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seonghan Kim
- Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Seho Lee
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyunook Kang
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jinuk Kim
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Injin Bang
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Kiheon Kim
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Won-Ki Huh
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hahnbeom Park
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Wonpil Im
- Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA
- Departments of Biological Sciences, Chemistry, and Computer Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Hee-Jung Choi
- Department of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
| |
Collapse
|
26
|
Zhao C, Jiang X, Peng L, Zhang Y, Li H, Zhang Q, Wang Y, Yang F, Wu J, Wen Z, He Z, Shen J, Chen C, Wang DW. Glimepiride, a novel soluble epoxide hydrolase inhibitor, protects against heart failure via increasing epoxyeicosatrienoic acids. J Mol Cell Cardiol 2023; 185:13-25. [PMID: 37871528 DOI: 10.1016/j.yjmcc.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND Epoxyeicosatrienoic acids (EETs), which exert multiple endogenous protective effects, are hydrolyzed into less active dihydroxyeicosatrienoic acids (DHETs) by soluble epoxide hydrolase (sEH). However, commercial drugs related to EETs or sEH are not yet in clinical use. METHODS Firstly, the plasma concentration of EETs and DHETs of 316 patients with heart failure (HF) were detected and quantitated by liquid chromatography-tandem mass spectrometry. Then, transverse aortic constriction (TAC)-induced HF was introduced in cardiomyocyte-specific Ephx2-/- mice. Moreover, Western blot, real-time PCR, luciferase reporter, ChIP assays were employed to explore the underlying mechanism. Finally, multiple sEH inhibitors were designed, synthesized, and validated in vitro and in vivo. RESULTS The ratios of DHETs/EETs were increased in the plasma from patients with HF. Meanwhile, the expression of sEH was upregulated in the heart of patients and mice with HF, especially in cardiomyocytes. Cardiomyocyte-specific Ephx2-/- mice ameliorated cardiac dysfunction induced by TAC. Consistently, Ephx2 knockdown protected Angiotensin II (AngII)-treated cardiomyocytes via increasing EETs in vitro. Mechanistically, AngII could enhance the expression of transcript factor Krüppel-like factor 15 (KLF15), which in turn upregulated sEH. Importantly, glimepiride was identified as a novel sEH inhibitor, which benefited from the elevated EETs during HF. CONCLUSIONS Glimepiride attenuates HF in mice in part by increasing EETs. CLINICAL TRIAL IDENTIFIER NCT03461107 (https://clinicaltrials.gov).
Collapse
Affiliation(s)
- Chengcheng Zhao
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Xiangrui Jiang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; Shandong Laboratory of Yantai Drug Discovery, Bohai Rim Advanced Research Institute for Drug Discovery, Yantai, China.
| | - Liyuan Peng
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Yan Zhang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Huihui Li
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Qiumeng Zhang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yinhui Wang
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Feipu Yang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Junfang Wu
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Zheng Wen
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Zuowen He
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Jingshan Shen
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
| | - Chen Chen
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China.
| | - Dao Wen Wang
- Division of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China.
| |
Collapse
|
27
|
Liu R, Gu J, Ye Y, Zhang Y, Zhang S, Lin Q, Yuan S, Chen Y, Lu X, Tong Y, Lv S, Chen L, Sun G. A Natural Compound Containing a Disaccharide Structure of Glucose and Rhamnose Identified as Potential N-Glycanase 1 (NGLY1) Inhibitors. Molecules 2023; 28:7758. [PMID: 38067490 PMCID: PMC10707914 DOI: 10.3390/molecules28237758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/13/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
N-glycanase 1 (NGLY1) is an essential enzyme involved in the deglycosylation of misfolded glycoproteins through the endoplasmic reticulum (ER)-associated degradation (ERAD) pathway, which could hydrolyze N-glycan from N-glycoprotein or N-glycopeptide in the cytosol. Recent studies indicated that NGLY1 inhibition is a potential novel drug target for antiviral therapy. In this study, structure-based virtual analysis was applied to screen candidate NGLY1 inhibitors from 2960 natural compounds. Three natural compounds, Poliumoside, Soyasaponin Bb, and Saikosaponin B2 showed significantly inhibitory activity of NGLY1, isolated from traditional heat-clearing and detoxifying Chinese herbs. Furthermore, the core structural motif of the three NGLY1 inhibitors was a disaccharide structure with glucose and rhamnose, which might exert its action by binding to important active sites of NGLY1, such as Lys238 and Trp244. In traditional Chinese medicine, many compounds containing this disaccharide structure probably targeted NGLY1. This study unveiled the leading compound of NGLY1 inhibitors with its core structure, which could guide future drug development.
Collapse
Affiliation(s)
- Ruijie Liu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China; (R.L.); (Y.Y.); (Y.Z.); (S.Z.); (Q.L.)
| | - Jingjing Gu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China;
| | - Yilin Ye
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China; (R.L.); (Y.Y.); (Y.Z.); (S.Z.); (Q.L.)
| | - Yuxin Zhang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China; (R.L.); (Y.Y.); (Y.Z.); (S.Z.); (Q.L.)
| | - Shaoxing Zhang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China; (R.L.); (Y.Y.); (Y.Z.); (S.Z.); (Q.L.)
| | - Qiange Lin
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China; (R.L.); (Y.Y.); (Y.Z.); (S.Z.); (Q.L.)
| | - Shuying Yuan
- Department of Clinical Laboratory, Jiaxing Maternity and Child Health Care Hospital, Jiaxing 314001, China;
| | - Yanwen Chen
- Central Laboratory, Ningbo Hospital, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Ningbo 315336, China;
| | - Xinrong Lu
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (X.L.); (Y.T.); (S.L.)
| | - Yongliang Tong
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (X.L.); (Y.T.); (S.L.)
| | - Shaoxian Lv
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (X.L.); (Y.T.); (S.L.)
| | - Li Chen
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; (X.L.); (Y.T.); (S.L.)
| | - Guiqin Sun
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China; (R.L.); (Y.Y.); (Y.Z.); (S.Z.); (Q.L.)
| |
Collapse
|
28
|
Wu D, Wang L, Li W, Li X. Identifying a New Target for BtOBP8: Discovery of a Small Amino Ketone Molecule Containing Benzothiazole Fragments. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:17635-17645. [PMID: 37651643 DOI: 10.1021/acs.jafc.3c02594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Insects rely on odorant-binding proteins (OBPs) for chemical perception, making OBPs a promising target for studying attractants and repellents of pests, such as Bemisia tabaci. However, no reports have reported using B. tabaci OBPs (BtOBPs) as pesticide screening targets. To fill this gap, we obtained BtOBP8 through prokaryotic expression and purification. Then, we confirmed its identity using western blotting and mass spectrometry. Next, we used the sitting drop and hanging drop methods to screen its crystal conditions. Using microscale thermophoresis and isothermal titration calorimetry, we identified the highest affinity ligand, 3l, from 30 compounds. Furthermore, point mutation techniques identified Val119 as a key amino acid residue in binding 31 to BtOBP8. Finally, we tested the bioactivity of B. tabaci Mediterranean and found that 3l more effectively inhibits the bioactivity of B. tabaci MED than imidacloprid. This study presents a new approach for developing green insecticides specific to B. tabaci MED by targeting OBPs. Conclusively, identifying and targeting specific OBPs can create more targeted and effective pest control strategies without relying on toxic chemicals.
Collapse
Affiliation(s)
- Danxia Wu
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Li Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Wei Li
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Xiangyang Li
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| |
Collapse
|
29
|
Nicoli A, Weber V, Bon C, Steuer A, Gustincich S, Gainetdinov RR, Lang R, Espinoza S, Di Pizio A. Structure-Based Discovery of Mouse Trace Amine-Associated Receptor 5 Antagonists. J Chem Inf Model 2023; 63:6667-6680. [PMID: 37847527 PMCID: PMC10647090 DOI: 10.1021/acs.jcim.3c00755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Indexed: 10/18/2023]
Abstract
Trace amine-associated receptors (TAARs) were discovered in 2001 as new members of class A G protein-coupled receptors (GPCRs). With the only exception of TAAR1, TAAR members (TAAR2-9, also known as noncanonical olfactory receptors) were originally described exclusively in the olfactory epithelium and believed to mediate the innate perception of volatile amines. However, most noncanonical olfactory receptors are still orphan receptors. Given its recently discovered nonolfactory expression and therapeutic potential, TAAR5 has been the focus of deorphanization campaigns that led to the discovery of a few druglike antagonists. Here, we report four novel TAAR5 antagonists identified through high-throughput screening, which, along with the four ligands published in the literature, constituted our starting point to design a computational strategy for the identification of TAAR5 ligands. We developed a structure-based virtual screening protocol that allowed us to identify three new TAAR5 antagonists with a hit rate of 10%. Despite lacking an experimental structure, we accurately modeled the TAAR5 binding site by integrating comparative sequence- and structure-based analyses of serotonin receptors with homology modeling and side-chain optimization. In summary, we have identified seven new TAAR5 antagonists that could serve as lead candidates for the development of new treatments for depression, anxiety, and neurodegenerative diseases.
Collapse
Affiliation(s)
- Alessandro Nicoli
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Chemoinformatics
and Protein Modelling, Department of Molecular Life Sciences, School
of Life Sciences, Technical University of
Munich, 85354 Freising, Germany
| | - Verena Weber
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Institute
for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine
(INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany
- Faculty
of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062 Germany
| | - Carlotta Bon
- Istituto
Italiano di Tecnologia, 16163 Genova, Italy
| | - Alexandra Steuer
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Chemoinformatics
and Protein Modelling, Department of Molecular Life Sciences, School
of Life Sciences, Technical University of
Munich, 85354 Freising, Germany
| | | | - Raul R. Gainetdinov
- Institute
of Translational Biomedicine and Saint Petersburg University Hospital,
Saint Petersburg State University, Saint Petersburg 199034, Russia
| | - Roman Lang
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
| | - Stefano Espinoza
- Istituto
Italiano di Tecnologia, 16163 Genova, Italy
- Dipartimento
di Scienze della Salute, Università
del Piemonte Orientale, 28100 Novara, Italy
| | - Antonella Di Pizio
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Chemoinformatics
and Protein Modelling, Department of Molecular Life Sciences, School
of Life Sciences, Technical University of
Munich, 85354 Freising, Germany
| |
Collapse
|
30
|
Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics 2023; 15:2619. [PMID: 38004597 PMCID: PMC10675155 DOI: 10.3390/pharmaceutics15112619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.
Collapse
Affiliation(s)
- Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, Japan
| | - Reiko Watanabe
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| | - Kenji Mizuguchi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| |
Collapse
|
31
|
Mei X, Ouyang H, Zhang H, Jia W, Lu B, Zhang J, Ji L. Scutellarin suppresses the metastasis of triple-negative breast cancer via targeting TNFα/TNFR2-RUNX1-triggered G-CSF expression in endothelial cells. Biochem Pharmacol 2023; 217:115808. [PMID: 37716622 DOI: 10.1016/j.bcp.2023.115808] [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: 07/04/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023]
Abstract
Triple-negative breast cancer (TNBC) is heterogeneous and aggressive, with high vascularity and frequent metastasis. We have already found natural flavonoid scutellarin (SC) suppressed spontaneous TNBC metastasis via normalizing tumor vasculature in vivo. In this study, supernatant from tumor necrosis factorα (TNFα)-treated human mammary microvascular endothelial cell (HMMEC) promoted cell migration and pseudopod formation in TNBC cells, but these phenomena were disappeared in SC-co-treated HMMEC. TNFα enhanced the expression of granulocyte colony-stimulating factor (G-CSF) and granulocyte-macrophage colony-stimulating factor (GM-CSF) in both HMMEC and human umbilical vein endothelial cell (HUVEC). G-CSF promoted TNBC migration and invasion in vitro, while G-CSF neutralization antibody and SC both inhibited TNBC metastasis in Balb/c mice. SC had no inhibition on the G-CSF-induced TNBC cell migration, but reduced G-CSF content in TNBC tumor tissues and TNFα-stimulated endothelial cells (ECs). SC restricted the nuclear translocation of runt-related transcription factor 1 (RUNX1) in TNBC tumor vessels and TNFα-treated ECs. RUNX1 was found to directly bind to the promoter of G-CSF in TNBC tumor vessels and regulated G-CSF expression. TNF receptor 2 (TNFR2) was crucial for regulating the TNFα-induced RUNX1 activation and G-CSF expression. Notably, SC hindered the interaction between TNFα and TNFR2 via binding to TNFR2. This work demonstrated that SC reduced TNBC metastasis by targeting TNFα/TNFR2-initiated RUNX1 activation and subsequent G-CSF production in TNBC-associated ECs.
Collapse
Affiliation(s)
- Xiyu Mei
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Key Laboratory of Research and Development of Chinese Medicine of Zhejiang Province, Key Laboratory of Pharmacodynamic Material Basis Research in Chinese Medicine of Zhejiang Province, Institute of Basic Medicine, Zhejiang Academy of Traditional Chinese Medicine, Hangzhou 310007, China
| | - Hao Ouyang
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Hong Zhang
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Wangya Jia
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Bin Lu
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jingnan Zhang
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Lili Ji
- The MOE Key Laboratory for Standardization of Chinese Medicines, Shanghai Key Laboratory of Compound Chinese Medicines and The SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| |
Collapse
|
32
|
Shaikh S, Ali S, Lim JH, Ahmad K, Han KS, Lee EJ, Choi I. Virtual Insights into Natural Compounds as Potential 5α-Reductase Type II Inhibitors: A Structure-Based Screening and Molecular Dynamics Simulation Study. Life (Basel) 2023; 13:2152. [PMID: 38004292 PMCID: PMC10671996 DOI: 10.3390/life13112152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
Androgenic alopecia (AGA) is a dermatological disease with psychosocial consequences for those who experience hair loss. AGA is linked to an increase in androgen levels caused by an excess of dihydrotestosterone in blood capillaries produced from testosterone by 5α-reductase type II (5αR2), which is expressed in scalp hair follicles; 5αR2 activity and dihydrotestosterone levels are elevated in balding scalps. The diverse health benefits of flavonoids have been widely reported in epidemiological studies, and research interest continues to increase. In this study, a virtual screening approach was used to identify compounds that interact with active site residues of 5αR2 by screening a library containing 241 flavonoid compounds. Here, we report two potent flavonoid compounds, eriocitrin and silymarin, that interacted strongly with 5αR2, with binding energies of -12.1 and -11.7 kcal/mol, respectively, which were more significant than those of the control, finasteride (-11.2 kcal/mol). Molecular dynamic simulations (200 ns) were used to optimize the interactions between compounds and 5αR2 and revealed that the interaction of eriocitrin and silymarin with 5αR2 was stable. The study shows that eriocitrin and silymarin provide developmental bases for novel 5αR2 inhibitors for the management of AGA.
Collapse
Affiliation(s)
- Sibhghatulla Shaikh
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Shahid Ali
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Jeong Ho Lim
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Ki Soo Han
- Neo Cremar Co., Ltd., Seoul 05702, Republic of Korea;
| | - Eun Ju Lee
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea; (S.S.); (S.A.); (J.H.L.); (K.A.); (E.J.L.)
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan 38541, Republic of Korea
| |
Collapse
|
33
|
Challapa-Mamani MR, Tomás-Alvarado E, Espinoza-Baigorria A, León-Figueroa DA, Sah R, Rodriguez-Morales AJ, Barboza JJ. Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania donovani: An Update and Literature Review. Trop Med Infect Dis 2023; 8:457. [PMID: 37888585 PMCID: PMC10610989 DOI: 10.3390/tropicalmed8100457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/28/2023] Open
Abstract
Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.
Collapse
Affiliation(s)
- Mabel R. Challapa-Mamani
- Escuela de Medicina, Universidad Cesar Vallejo, Trujillo 13007, Peru;
- Sociedad Científica de Estudiantes de Medicina de la Universidad César Vallejo, Trujillo 13007, Peru
| | - Eduardo Tomás-Alvarado
- Hospital General Regional 17, Instituto Mexicano del Seguro Social, Cancún 75533, Mexico;
| | | | | | - Ranjit Sah
- Department of Clinical Microbiology, Institute of Medicine, Tribhuvan University Teaching Hospital, Kathmandu 44600, Nepal;
- Department of Microbiology, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil Vidyapeeth, Pune 411018, Maharashtra, India
| | - Alfonso J. Rodriguez-Morales
- Faculty of Health Sciences, Universidad Científica del Sur, Lima 150152, Peru;
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut 350000, Lebanon
| | | |
Collapse
|
34
|
Li C, Wu Y, Wang W, Xu L, Zhou Y, Yue Y, Wu T, Yang M, Qiu Y, Huang M, Zhou F, Zhou Y, Hao P, Lin Z, Wang MW, Zhao S, Yang D, Xu F, Tao H. Structure-Based Ligand Discovery Targeting the Transmembrane Domain of Frizzled Receptor FZD7. J Med Chem 2023; 66:11855-11868. [PMID: 37669317 DOI: 10.1021/acs.jmedchem.2c01795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Despite the essential roles of Frizzled receptors (FZDs) in mediating Wnt signaling in embryonic development and tissue homeostasis, ligands targeting FZDs are rare. A few antibodies and peptide modulators have been developed that mainly bind to the family-conserved extracellular cysteine-rich domain of FZDs, while the canonical binding sites in the transmembrane domain (TMD) are far from sufficiently addressed. Based on the recent structures of FZDs, we explored small-molecule ligand discovery by targeting TMD. From the ChemDiv library with ∼1.6 million compounds, we identified compound F7H as an antagonist of FZD7 with an IC50 at 1.25 ± 0.38 μM. Focusing on this hit, the structural dissection study, together with computing studies such as molecular docking, molecular dynamics simulation, and free energy perturbation calculations, defined the binding pocket with key residue recognition. Our results revealed the structural basis of ligand recognition and demonstrated the feasibility of structure-guided ligand discovery for FZD7-TMD.
Collapse
Affiliation(s)
- Cuixia Li
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yiran Wu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Wenli Wang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Lu Xu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Yan Zhou
- The National Center for Drug Screening, The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203 Shanghai, China
| | - Yang Yue
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Tingting Wu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Meifang Yang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yanli Qiu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Frontiers Science Center of TCM Chemical Biology, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Minhao Huang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Fangfang Zhou
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Yiqing Zhou
- School of Biotechnology and Food Engineering, Changshu Institute of Technology, Suzhou 215500, China
| | - Piliang Hao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zhixiong Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Ming-Wei Wang
- The National Center for Drug Screening, The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203 Shanghai, China
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Dehua Yang
- The National Center for Drug Screening, The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203 Shanghai, China
| | - Fei Xu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Houchao Tao
- Shanghai Frontiers Science Center of TCM Chemical Biology, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| |
Collapse
|
35
|
Wang P, Lv L, Li H, Wang CY, Zhou J. Opportunities and challenges in drug discovery targeting the orphan receptor GPR12. Drug Discov Today 2023; 28:103698. [PMID: 37422169 DOI: 10.1016/j.drudis.2023.103698] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 07/10/2023]
Abstract
G-protein-coupled receptor 12 (GPR12) is a brain-specific expression orphan G-protein-coupled receptor (oGPCR) that regulates various physiological processes. It is an emerging therapeutic target for central nervous system (CNS) disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), attention deficit hyperactivity disorder (ADHD), and schizophrenia, as well as other human diseases, such as cancer, obesity, and metabolic disorders. GPR12 remains a less extensively investigated oGPCR, particularly in terms of its biological functions, signaling pathways, and ligand discovery. The discovery of drug-like small-molecule modulators to probe the brain functions of GPR12 or to act as a potential drug candidates, as well as the identification of reliable biomarkers, are vital to elucidate the roles of this receptor in various human diseases and develop novel target-based therapeutics.
Collapse
Affiliation(s)
- Pingyuan Wang
- Key Laboratory of Evolution and Marine Biodiversity Ministry of Education, Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266003, China; School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China.
| | - Ling Lv
- Key Laboratory of Evolution and Marine Biodiversity Ministry of Education, Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266003, China; College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China
| | - Haoran Li
- Key Laboratory of Evolution and Marine Biodiversity Ministry of Education, Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266003, China; School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
| | - Chang-Yun Wang
- Key Laboratory of Evolution and Marine Biodiversity Ministry of Education, Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266003, China; School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Laboratory for Marine Drugs and Bioproducts, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China.
| | - Jia Zhou
- Chemical Biology Program, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX 77555, USA.
| |
Collapse
|
36
|
Goßen J, Ribeiro RP, Bier D, Neumaier B, Carloni P, Giorgetti A, Rossetti G. AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology. Chem Sci 2023; 14:8651-8661. [PMID: 37592985 PMCID: PMC10430665 DOI: 10.1039/d3sc02352d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/20/2023] [Indexed: 08/19/2023] Open
Abstract
Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson's disease and cancer. The identified antagonists were tested here in a radio ligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310 ± 23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.
Collapse
Affiliation(s)
- Jonas Goßen
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Faculty of Mathematics, Computer Science and Natural Sciences RWTH Aachen University Aachen Germany
| | - Rui Pedro Ribeiro
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
| | - Dirk Bier
- Institute of Neuroscience and Medicine, Nuclear Chemistry (INM-5), Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany
| | - Bernd Neumaier
- Institute of Neuroscience and Medicine, Nuclear Chemistry (INM-5), Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany
- Institute of Radiochemistry and Experimental Molecular Imaging, University of Cologne, Faculty of Medicine and University Hospital Cologne Kerpener Straße 62 50937 Cologne Germany
| | - Paolo Carloni
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Faculty of Mathematics, Computer Science and Natural Sciences RWTH Aachen University Aachen Germany
- JARA-Institut Molecular Neuroscience and Neuroimaging (INM-11) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
| | - Alejandro Giorgetti
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Department of Biotechnology University of Verona Verona Italy
| | - Giulia Rossetti
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany
- Department of Neurology University Hospital Aachen (UKA), RWTH Aachen University Aachen Germany
| |
Collapse
|
37
|
Shen C, Zhang X, Hsieh CY, Deng Y, Wang D, Xu L, Wu J, Li D, Kang Y, Hou T, Pan P. A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem Sci 2023; 14:8129-8146. [PMID: 37538816 PMCID: PMC10395315 DOI: 10.1039/d3sc02044d] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/05/2023] Open
Abstract
Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.
Collapse
Affiliation(s)
- Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology Changzhou 213001 China
| | - Jian Wu
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| |
Collapse
|
38
|
Zsidó BZ, Bayarsaikhan B, Börzsei R, Szél V, Mohos V, Hetényi C. The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering. Int J Mol Sci 2023; 24:11784. [PMID: 37511543 PMCID: PMC10381018 DOI: 10.3390/ijms241411784] [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: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.
Collapse
Affiliation(s)
- Balázs Zoltán Zsidó
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Bayartsetseg Bayarsaikhan
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Rita Börzsei
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Viktor Szél
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Violetta Mohos
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Csaba Hetényi
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| |
Collapse
|
39
|
Zhang Y, Pan H, Yu C, Liu R, Xing B, Jia B, He J, Jia X, Feng X, Zhang Q, Dang W, Hu Z, Deng X, Guo P, Liu Z, Pan W. Phytoestrogen-derived multifunctional ligands for targeted therapy of breast cancer. Asian J Pharm Sci 2023; 18:100827. [PMID: 37588993 PMCID: PMC10425851 DOI: 10.1016/j.ajps.2023.100827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/20/2023] [Accepted: 05/31/2023] [Indexed: 08/18/2023] Open
Abstract
Nano-targeted delivery systems have been widely used for breast tumor drug delivery. Estrogen receptors are considered to be significant drug delivery target receptors due to their overexpression in a variety of tumor cells. However, targeted ligands have a significant impact on the safety and effectiveness of active delivery systems, limiting the clinical transformation of nanoparticles. Phytoestrogens have shown good biosafety characteristics and some affinity with the estrogen receptor. In the present study, molecular docking was used to select tanshinone IIA (Tan IIA) among phytoestrogens as a target ligand to be used in nanodelivery systems with some modifications. Modified Tan IIA (Tan-NH2) showed a good biosafety profile and demonstrated tumor-targeting, anti-tumor and anti-tumor metastasis effects. Moreover, the ligand was utilized with the anti-tumor drug Dox-loaded mesoporous silica nanoparticles via chemical modification to generate a nanocomposite Tan-Dox-MSN. Tan-Dox-MSN had a uniform particle size, good dispersibility and high drug loading capacity. Validation experiments in vivo and in vitro showed that it also had a better targeting ability, anti-tumor effect and lower toxicity in normal organs. These results supported the idea that phytoestrogens with high affinity for the estrogen receptor could improve the therapeutic efficacy of nano-targeted delivery systems in breast tumors.
Collapse
Affiliation(s)
- Ying Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Hao Pan
- School of Pharmacy, Liaoning University, Shenyang 110036, China
| | - Changxiang Yu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Rui Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Bin Xing
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Bei Jia
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jiachen He
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xintao Jia
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiaojiao Feng
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Qingqing Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Wenli Dang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zheming Hu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiuping Deng
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Pan Guo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zhidong Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Weisan Pan
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China
| |
Collapse
|
40
|
Yang T, Luo Y, Liu J, Liu F, Ma Z, Liu G, LI H, Wen J, Chen C, Zeng X. A novel signature incorporating lipid metabolism- and immune-related genes to predict the prognosis and immune landscape in hepatocellular carcinoma. Front Oncol 2023; 13:1182434. [PMID: 37346073 PMCID: PMC10279962 DOI: 10.3389/fonc.2023.1182434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/23/2023] [Indexed: 06/23/2023] Open
Abstract
Background Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients. Methods We used the "DEseq2" R package and the "Venn" R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature's prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients' sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR). Results We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend. Conclusions We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.
Collapse
Affiliation(s)
- Ti Yang
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Yurong Luo
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Junhao Liu
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Fang Liu
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Zengxin Ma
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Gai Liu
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Hailiang LI
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Jianfan Wen
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Chengcong Chen
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Xiancheng Zeng
- Department of Hepatobiliary-Pancreatic and Hernia Surgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| |
Collapse
|
41
|
Zuo K, Kranjc A, Capelli R, Rossetti G, Nechushtai R, Carloni P. Metadynamics simulations of ligands binding to protein surfaces: a novel tool for rational drug design. Phys Chem Chem Phys 2023; 25:13819-13824. [PMID: 37184538 DOI: 10.1039/d3cp01388j] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces.
Collapse
Affiliation(s)
- Ke Zuo
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
- Department of Physics, Università degli Studi di Ferrara, Ferrara 44121, Italy
| | - Agata Kranjc
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
| | - Riccardo Capelli
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan 20133, Italy
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Jülich Supercomputing Center (JSC), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen 52074, Germany
| | - Rachel Nechushtai
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- JARA Institute: Molecular Neuroscience and Imaging, Institute of Neuroscience and Medicine INM-11, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| |
Collapse
|
42
|
Singh I, Seth A, Billesbølle CB, Braz J, Rodriguiz RM, Roy K, Bekele B, Craik V, Huang XP, Boytsov D, Pogorelov VM, Lak P, O'Donnell H, Sandtner W, Irwin JJ, Roth BL, Basbaum AI, Wetsel WC, Manglik A, Shoichet BK, Rudnick G. Structure-based discovery of conformationally selective inhibitors of the serotonin transporter. Cell 2023; 186:2160-2175.e17. [PMID: 37137306 PMCID: PMC10306110 DOI: 10.1016/j.cell.2023.04.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 02/05/2023] [Accepted: 04/06/2023] [Indexed: 05/05/2023]
Abstract
The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has unusual anti-depressant and substance-withdrawal effects, and stabilizes the inward-open conformation. Unfortunately, ibogaine's promiscuity and cardiotoxicity limit the understanding of inward-open state ligands. We docked over 200 million small molecules against the inward-open state of the SERT. Thirty-six top-ranking compounds were synthesized, and thirteen inhibited; further structure-based optimization led to the selection of two potent (low nanomolar) inhibitors. These stabilized an outward-closed state of the SERT with little activity against common off-targets. A cryo-EM structure of one of these bound to the SERT confirmed the predicted geometry. In mouse behavioral assays, both compounds had anxiolytic- and anti-depressant-like activity, with potencies up to 200-fold better than fluoxetine (Prozac), and one substantially reversed morphine withdrawal effects.
Collapse
Affiliation(s)
- Isha Singh
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA
| | - Anubha Seth
- Department of Pharmacology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8066, USA
| | - Christian B Billesbølle
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA
| | - Joao Braz
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ramona M Rodriguiz
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC 27710, USA
| | - Kasturi Roy
- Department of Pharmacology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8066, USA
| | - Bethlehem Bekele
- Department of Pharmacology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8066, USA
| | - Veronica Craik
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Xi-Ping Huang
- Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Danila Boytsov
- Institute of Pharmacology, Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Vladimir M Pogorelov
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA
| | - Parnian Lak
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA
| | - Henry O'Donnell
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA
| | - Walter Sandtner
- Institute of Pharmacology, Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA
| | - Bryan L Roth
- Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Allan I Basbaum
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - William C Wetsel
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC 27710, USA; Departments of Cell Biology and Neurobiology, Duke University Medical Center, Durham, NC 27710, USA.
| | - Aashish Manglik
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA; Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA 94115, USA.
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St., Byers Hall Suite 508D, San Francisco, CA 94143, USA.
| | - Gary Rudnick
- Department of Pharmacology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8066, USA.
| |
Collapse
|
43
|
Boytsov D, Schicker K, Hellsberg E, Freissmuth M, Sandtner W. Allosteric modulators of solute carrier function: a theoretical framework. Front Physiol 2023; 14:1166450. [PMID: 37250134 PMCID: PMC10210158 DOI: 10.3389/fphys.2023.1166450] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 04/24/2023] [Indexed: 05/31/2023] Open
Abstract
Large-scale drug screening is currently the basis for the identification of new chemical entities. This is a rather laborious approach, because a large number of compounds must be tested to cover the chemical space in an unbiased fashion. However, the structures of targetable proteins have become increasingly available. Thus, a new era has arguably been ushered in with the advent of methods, which allow for structure-based docking campaigns (i.e., virtual screens). Solute carriers (SLCs) are among the most promising drug targets. This claim is substantiated by the fact that a large fraction of the 400 solute carrier genes is associated with human diseases. The ability to dock large ligand libraries into selected structures of solute carriers has set the stage for rational drug design. In the present study, we show that these structure-based approaches can be refined by taking into account how solute carriers operate. We specifically address the feasibility of targeting solute carriers with allosteric modulators, because their actions differ fundamentally from those of ligands, which bind to the substrate binding site. For the pertinent analysis we used transition state theory in conjunction with the linear free energy relationship (LFER). These provide the theoretical framework to understand how allosteric modulators affect solute carrier function.
Collapse
Affiliation(s)
- D. Boytsov
- Center of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - K. Schicker
- Center of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - E. Hellsberg
- Computational Structural Biology Unit, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, United States
| | - M. Freissmuth
- Center of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - W. Sandtner
- Center of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
44
|
Matricon P, Nguyen AT, Vo DD, Baltos JA, Jaiteh M, Luttens A, Kampen S, Christopoulos A, Kihlberg J, May LT, Carlsson J. Structure-based virtual screening discovers potent and selective adenosine A 1 receptor antagonists. Eur J Med Chem 2023; 257:115419. [PMID: 37301076 DOI: 10.1016/j.ejmech.2023.115419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 06/12/2023]
Abstract
Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A1 and A2A adenosine receptors (A1R and A2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A1R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A1R selective ligands were predicted. Of these, seven antagonized the A1R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A1R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.
Collapse
Affiliation(s)
- Pierre Matricon
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Anh Tn Nguyen
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Duc Duy Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Jo-Anne Baltos
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Mariama Jaiteh
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Andreas Luttens
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Stefanie Kampen
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Arthur Christopoulos
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jan Kihlberg
- Department of Chemistry - BMC, Uppsala University, SE-751 23, Uppsala, Sweden
| | - Lauren Therese May
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia.
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden.
| |
Collapse
|
45
|
Thomas M, Bender A, de Graaf C. Integrating structure-based approaches in generative molecular design. Curr Opin Struct Biol 2023; 79:102559. [PMID: 36870277 DOI: 10.1016/j.sbi.2023.102559] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/23/2023] [Accepted: 01/31/2023] [Indexed: 03/06/2023]
Abstract
Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field.
Collapse
Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. https://twitter.com/@AndreasBenderUK
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK. https://twitter.com/@Chris_de_Graaf
| |
Collapse
|
46
|
The chronological evolution of fluorescent GPCR probes for bioimaging. Coord Chem Rev 2023. [DOI: 10.1016/j.ccr.2023.215040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
|
47
|
Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 158] [Impact Index Per Article: 158.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
Collapse
Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
| |
Collapse
|
48
|
Szwabowski GL, Baker DL, Parrill AL. Application of computational methods for class A GPCR Ligand discovery. J Mol Graph Model 2023; 121:108434. [PMID: 36841204 DOI: 10.1016/j.jmgm.2023.108434] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/22/2023]
Abstract
G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.
Collapse
Affiliation(s)
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
| |
Collapse
|
49
|
Nicoli A, Haag F, Marcinek P, He R, Kreißl J, Stein J, Marchetto A, Dunkel A, Hofmann T, Krautwurst D, Di Pizio A. Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1. J Chem Inf Model 2023; 63:2014-2029. [PMID: 36696962 PMCID: PMC10091413 DOI: 10.1021/acs.jcim.2c00752] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L1043.32 and L2556.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.
Collapse
Affiliation(s)
- Alessandro Nicoli
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Franziska Haag
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Patrick Marcinek
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Ruiming He
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany.,Department of Chemistry, Technical University of Munich, 85748 Garching, Germany
| | - Johanna Kreißl
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Jörg Stein
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Alessandro Marchetto
- Computational Biomedicine, Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Biology, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52074 Aachen, Germany
| | - Andreas Dunkel
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Thomas Hofmann
- Chair of Food Chemistry and Molecular Sensory Science, Technical University of Munich, 85354 Freising, Germany
| | - Dietmar Krautwurst
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| |
Collapse
|
50
|
Luo Y, Wang P, Mou M, Zheng H, Hong J, Tao L, Zhu F. A novel strategy for designing the magic shotguns for distantly related target pairs. Brief Bioinform 2023; 24:6984790. [PMID: 36631399 DOI: 10.1093/bib/bbac621] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/09/2022] [Accepted: 12/17/2022] [Indexed: 01/13/2023] Open
Abstract
Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.
Collapse
Affiliation(s)
- Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanqi Zheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| |
Collapse
|