1
|
Retchin M, Wang Y, Takaba K, Chodera JD. DrugGym: A testbed for the economics of autonomous drug discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596296. [PMID: 38854082 PMCID: PMC11160604 DOI: 10.1101/2024.05.28.596296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.
Collapse
Affiliation(s)
- Michael Retchin
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Simons Center for Computational Chemistry and Center for Data Science, New York University, New York, NY 10004
| | - Kenichiro Takaba
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation, Shizuoka 410-2321, Japan
| | - John D. Chodera
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| |
Collapse
|
2
|
Namballa HK, Dorogan M, Gudipally AR, Okafor S, Gadhiya S, Harding WW. Discovery of Selective Dopamine Receptor Ligands Derived from (-)-Stepholidine via C-3 Alkoxylation and C-3/C-9 Dialkoxylation. J Med Chem 2023. [PMID: 37421373 DOI: 10.1021/acs.jmedchem.3c00976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2023]
Abstract
We evaluated C-3 alkoxylated and C-3/C-9 dialkoxylated (-)-stepholidine analogues to probe the tolerance at the C-3 and C-9 positions of the tetrahydroprotoberberine (THPB) template toward affinity for dopamine receptors. A C-9 ethoxyl substituent appears optimal for D1R affinity since high D1R affinities were observed for compounds that contain an ethyl group at C-9, with larger C-9 substituents tending to decrease D1R affinity. A number of novel ligands were identified, such as compounds 12a and 12b, with nanomolar affinities for D1R and no affinity for either D2R or D3R, with compound 12a being identified as a D1R antagonist for both G-protein- and β-arrestin-based signaling. Compound 23b was identified as the most potent and selective D3R ligand containing a THPB template to date and functions as an antagonist for both G-protein- and β-arrestin-based signaling. Molecular docking and molecular dynamics studies validated the D1R and D3R affinity and selectivity of 12a, 12b, and 23b.
Collapse
Affiliation(s)
- Hari K Namballa
- Department of Chemistry, Hunter College, City University of New York, 695 Park Avenue, New York, New York 10065, United States
| | - Michael Dorogan
- Department of Chemistry, Hunter College, City University of New York, 695 Park Avenue, New York, New York 10065, United States
| | - Ashok R Gudipally
- Department of Chemistry, Hunter College, City University of New York, 695 Park Avenue, New York, New York 10065, United States
- Ph.D. Program in Chemistry, CUNY Graduate Center, 365 5th Avenue, New York, New York 10016, United States
| | - Sunday Okafor
- Department of Pharmaceutical and Medicinal Chemistry, University of Nigeria, 410011 Nsukka, Enugu State, Nigeria
- Center for Biomedical Research, New York, New York 10065, United States
| | - Satishkumar Gadhiya
- Department of Chemistry, Hunter College, City University of New York, 695 Park Avenue, New York, New York 10065, United States
- Ph.D. Program in Chemistry, CUNY Graduate Center, 365 5th Avenue, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, CUNY Graduate Center, 365 5th Avenue, New York, New York 10016, United States
| | - Wayne W Harding
- Department of Chemistry, Hunter College, City University of New York, 695 Park Avenue, New York, New York 10065, United States
- Ph.D. Program in Chemistry, CUNY Graduate Center, 365 5th Avenue, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, CUNY Graduate Center, 365 5th Avenue, New York, New York 10016, United States
| |
Collapse
|
3
|
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
|
4
|
Guo B, Zheng H, Jiang H, Li X, Guan N, Zuo Y, Zhang Y, Yang H, Wang X. Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy. Brief Bioinform 2023; 24:6995409. [PMID: 36682005 DOI: 10.1093/bib/bbac628] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/12/2022] [Accepted: 12/25/2022] [Indexed: 01/23/2023] Open
Abstract
Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.
Collapse
Affiliation(s)
- Binjie Guo
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zheng
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Haohan Jiang
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Xiaodan Li
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Naiyu Guan
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yanming Zuo
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yicheng Zhang
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Hengfu Yang
- School of Computer Science, Hunan First Normal University, Changsha, 410205 Hunan, China
| | - Xuhua Wang
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, 226001 Jiangsu, China
| |
Collapse
|
5
|
Hsieh CJ, Giannakoulias S, Petersson EJ, Mach RH. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals (Basel) 2023; 16:317. [PMID: 37259459 PMCID: PMC9964981 DOI: 10.3390/ph16020317] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 11/19/2023] Open
Abstract
The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).
Collapse
Affiliation(s)
- Chia-Ju Hsieh
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sam Giannakoulias
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E. James Petersson
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert H. Mach
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
6
|
In silico selectivity modeling of pyridine and pyrimidine based CYP11B1 and CYP11B2 inhibitors: A case study. J Mol Graph Model 2022; 116:108238. [PMID: 35691091 DOI: 10.1016/j.jmgm.2022.108238] [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: 01/28/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 12/14/2022]
Abstract
DESIGN of selective drug candidates for highly structural similar targets is a challenging task for researchers. The main objective of this study was to explore the selectivity modeling of pyridine and pyrimidine scaffold towards the highly homologous targets CYP11B1 and CYP11B2 enzymes by in silico (Molecular docking and QSAR) approaches. In this regard, a big dataset (n = 228) of CYP11B1 and CYP11B2 inhibitors were gathered and classified based on heterocyclic ring and the exhaustive analysis was carried out for pyridine and pyrimidinescaffolds. The LibDock algorithm was used to explore the binding pattern, screening, and identify the structural feature responsible for the selectivity of the ligands towards the studied targets. Finally, QSAR analysis was done to explore the correlation between various binding parameters and structural features responsible for the inhibitory activity and selectivity of the ligands in a quantitative way. The docking and QSAR analysis clearly revealed and distinguished the importance of structural features, functional groups attached for CYP11B2 and CYP11B1 selectivity for pyridine and pyrimidine analogs. Additionally, the docking analysis highlighted the differentiating amino acids residues for selectivity for ligands for each of the enzymes. The results obtained from this research work will be helpful in designing the selective CYP11B1/CYP11B2 inhibitors.
Collapse
|
7
|
Moreira BP, Batista ICA, Tavares NC, Armstrong T, Gava SG, Torres GP, Mourão MM, Falcone FH. Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni. Front Cell Infect Microbiol 2022; 12:913301. [PMID: 35865824 PMCID: PMC9294739 DOI: 10.3389/fcimb.2022.913301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/02/2022] [Indexed: 01/02/2023] Open
Abstract
Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.
Collapse
Affiliation(s)
- Bernardo Pereira Moreira
- Institut für Parasitologie, Biomedizinisches Forschungszentrum Seltersberg (BFS), Justus-Liebig-Universität Giessen, Giessen, Germany
| | | | - Naiara Clemente Tavares
- Grupo de Helmintologia e Malacologia Médica, Instituto René Rachou, Fundação Oswaldo Cruz-FIOCRUZ, Belo Horizonte, Brazil
| | - Tom Armstrong
- School of Chemistry, University of Nottingham, Nottingham, United Kingdom
| | - Sandra Grossi Gava
- Grupo de Helmintologia e Malacologia Médica, Instituto René Rachou, Fundação Oswaldo Cruz-FIOCRUZ, Belo Horizonte, Brazil
| | - Gabriella Parreiras Torres
- Grupo de Helmintologia e Malacologia Médica, Instituto René Rachou, Fundação Oswaldo Cruz-FIOCRUZ, Belo Horizonte, Brazil
| | - Marina Moraes Mourão
- Grupo de Helmintologia e Malacologia Médica, Instituto René Rachou, Fundação Oswaldo Cruz-FIOCRUZ, Belo Horizonte, Brazil
- *Correspondence: Franco H. Falcone, ; Marina Moraes Mourão,
| | - Franco H. Falcone
- Institut für Parasitologie, Biomedizinisches Forschungszentrum Seltersberg (BFS), Justus-Liebig-Universität Giessen, Giessen, Germany
- *Correspondence: Franco H. Falcone, ; Marina Moraes Mourão,
| |
Collapse
|
8
|
Krishna Deepak RNV, Verma RK, Hartono YD, Yew WS, Fan H. Recent Advances in Structure, Function, and Pharmacology of Class A Lipid GPCRs: Opportunities and Challenges for Drug Discovery. Pharmaceuticals (Basel) 2021; 15:12. [PMID: 35056070 PMCID: PMC8779880 DOI: 10.3390/ph15010012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/17/2021] [Accepted: 12/17/2021] [Indexed: 01/01/2023] Open
Abstract
Great progress has been made over the past decade in understanding the structural, functional, and pharmacological diversity of lipid GPCRs. From the first determination of the crystal structure of bovine rhodopsin in 2000, much progress has been made in the field of GPCR structural biology. The extraordinary progress in structural biology and pharmacology of GPCRs, coupled with rapid advances in computational approaches to study receptor dynamics and receptor-ligand interactions, has broadened our comprehension of the structural and functional facets of the receptor family members and has helped usher in a modern age of structure-based drug design and development. First, we provide a primer on lipid mediators and lipid GPCRs and their role in physiology and diseases as well as their value as drug targets. Second, we summarize the current advancements in the understanding of structural features of lipid GPCRs, such as the structural variation of their extracellular domains, diversity of their orthosteric and allosteric ligand binding sites, and molecular mechanisms of ligand binding. Third, we close by collating the emerging paradigms and opportunities in targeting lipid GPCRs, including a brief discussion on current strategies, challenges, and the future outlook.
Collapse
Affiliation(s)
- R. N. V. Krishna Deepak
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
| | - Ravi Kumar Verma
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
| | - Yossa Dwi Hartono
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore;
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Wen Shan Yew
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore;
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Hao Fan
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore;
| |
Collapse
|
9
|
Ballante F, Kooistra AJ, Kampen S, de Graaf C, Carlsson J. Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You? Pharmacol Rev 2021; 73:527-565. [PMID: 34907092 DOI: 10.1124/pharmrev.120.000246] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.
Collapse
Affiliation(s)
- Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Albert J Kooistra
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Stefanie Kampen
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Chris de Graaf
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| |
Collapse
|
10
|
Kricker JA, Page CP, Gardarsson FR, Baldursson O, Gudjonsson T, Parnham MJ. Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development. Pharmacol Rev 2021; 73:233-262. [PMID: 34716226 DOI: 10.1124/pharmrev.121.000300] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.
Collapse
Affiliation(s)
- Jennifer A Kricker
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Clive P Page
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Fridrik Runar Gardarsson
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Olafur Baldursson
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Thorarinn Gudjonsson
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Michael J Parnham
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| |
Collapse
|
11
|
Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors. Acta Pharmacol Sin 2021; 42:1354-1367. [PMID: 33122823 PMCID: PMC8285414 DOI: 10.1038/s41401-020-00541-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/15/2020] [Indexed: 02/06/2023] Open
Abstract
Group I metabotropic glutamate receptors (mGlu1 and mGlu5) are promising targets for multiple psychiatric and neurodegenerative disorders. Understanding the subtype selectivity of mGlu1 and mGlu5 allosteric sites is essential for the rational design of novel modulators with single- or dual-target mechanism of action. In this study, starting from the deposited mGlu1 and mGlu5 crystal structures, we utilized computational modeling approaches integrating docking, molecular dynamics simulation, and efficient post-trajectory analysis to reveal the subtype-selective mechanism of mGlu1 and mGlu5 to 10 diverse drug scaffolds representing known negative allosteric modulators (NAMs) in the literature. The results of modeling identified six pairs of non-conserved residues and four pairs of conserved ones as critical features to distinguish the selective NAMs binding to the corresponding receptors. In addition, nine pairs of residues are beneficial to the development of novel dual-target NAMs of group I metabotropic glutamate receptors. Furthermore, the binding modes of a reported dual-target NAM (VU0467558) in mGlu1 and mGlu5 were predicted to verify the identified residues that play key roles in the receptor selectivity and the dual-target binding. The results of this study can guide rational structure-based design of novel NAMs, and the approach can be generally applicable to characterize the features of selectivity for other G-protein-coupled receptors.
Collapse
|
12
|
Titov IY, Stroylov VS, Rusina P, Svitanko IV. Preliminary modelling as the first stage of targeted organic synthesis. RUSSIAN CHEMICAL REVIEWS 2021. [DOI: 10.1070/rcr5012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.
The bibliography includes 353 references.
Collapse
|
13
|
Kingdon ADH, Alderwick LJ. Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis. Comput Struct Biotechnol J 2021; 19:3708-3719. [PMID: 34285773 PMCID: PMC8258792 DOI: 10.1016/j.csbj.2021.06.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 12/12/2022] Open
Abstract
Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.
Collapse
Key Words
- CV, collective variable
- Docking
- Drug discovery
- In silico
- LIE, Linear Interaction Energy
- MD, Molecular Dynamic
- MDR, multi-drug resistant
- MMPB(GB)SA, Molecular Mechanics with Poisson Boltzmann (or generalised Born) and Surface Area solvation
- Machine learning
- Mt, Mycobacterium tuberculosis
- Mycobacterium tuberculosis
- PTC, peptidyl transferase centre
- RMSD, root-mean square-deviation
- Tuberculosis, TB
- cMD, Classical Molecular Dynamic
- cryo-EM, cryogenic electron microscopy
- ns, nanosecond
Collapse
Affiliation(s)
- Alexander D H Kingdon
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Luke J Alderwick
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| |
Collapse
|
14
|
Abstract
Opioids such as morphine and oxycodone are analgesics frequently prescribed for the treatment of moderate or severe pain. Unfortunately, these medications are associated with exceptionally high abuse potentials and often cause fatal side effects, mainly through the μ-opioid receptor (MOR). Efforts to discover novel, safer, and more efficacious analgesics targeting MOR have encountered challenges. In this review, we summarize alternative strategies and targets that could be used to develop safer nonopioid analgesics. A molecular understanding of G protein-coupled receptor activation and signaling has illuminated not only the complexities of receptor pharmacology but also the potential for pathway-selective agonists and allosteric modulators as safer medications. The availability of structures of pain-related receptors, in combination with high-throughput computational tools, has accelerated the discovery of multitarget ligands with promising pharmacological profiles. Emerging clinical evidence also supports the notion that drugs targeting peripheral opioid receptors have potential as improved analgesic agents.
Collapse
Affiliation(s)
- Tao Che
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA; .,Center for Clinical Pharmacology, University of Health Sciences and Pharmacy and Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599, USA;
| |
Collapse
|
15
|
Schultz KJ, Colby SM, Lin VS, Wright AT, Renslow RS. Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists. J Chem Inf Model 2021; 61:481-492. [PMID: 33404240 DOI: 10.1021/acs.jcim.0c01019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate-active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insights into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. We further describe how such assessment techniques can be applied to putative chemical probes with a case study involving proposed medetomidine-based probes.
Collapse
Affiliation(s)
- Katherine J Schultz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sean M Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Vivian S Lin
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Aaron T Wright
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99163, United States
| | - Ryan S Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.,The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99163, United States
| |
Collapse
|
16
|
Bento AP, Hersey A, Félix E, Landrum G, Gaulton A, Atkinson F, Bellis LJ, De Veij M, Leach AR. An open source chemical structure curation pipeline using RDKit. J Cheminform 2020; 12:51. [PMID: 33431044 PMCID: PMC7458899 DOI: 10.1186/s13321-020-00456-1] [Citation(s) in RCA: 128] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/24/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The ChEMBL database is one of a number of public databases that contain bioactivity data on small molecule compounds curated from diverse sources. Incoming compounds are typically not standardised according to consistent rules. In order to maintain the quality of the final database and to easily compare and integrate data on the same compound from different sources it is necessary for the chemical structures in the database to be appropriately standardised. RESULTS A chemical curation pipeline has been developed using the open source toolkit RDKit. It comprises three components: a Checker to test the validity of chemical structures and flag any serious errors; a Standardizer which formats compounds according to defined rules and conventions and a GetParent component that removes any salts and solvents from the compound to create its parent. This pipeline has been applied to the latest version of the ChEMBL database as well as uncurated datasets from other sources to test the robustness of the process and to identify common issues in database molecular structures. CONCLUSION All the components of the structure pipeline have been made freely available for other researchers to use and adapt for their own use. The code is available in a GitHub repository and it can also be accessed via the ChEMBL Beaker webservices. It has been used successfully to standardise the nearly 2 million compounds in the ChEMBL database and the compound validity checker has been used to identify compounds with the most serious issues so that they can be prioritised for manual curation.
Collapse
Affiliation(s)
- A Patrícia Bento
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Anne Hersey
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Eloy Félix
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | | | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Francis Atkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
- The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Louisa J Bellis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Marleen De Veij
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, Cambridgeshire, UK.
| |
Collapse
|
17
|
Vanhaelen Q, Lin YC, Zhavoronkov A. The Advent of Generative Chemistry. ACS Med Chem Lett 2020; 11:1496-1505. [PMID: 32832015 PMCID: PMC7429972 DOI: 10.1021/acsmedchemlett.0c00088] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.
Collapse
Affiliation(s)
- Quentin Vanhaelen
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Yen-Chu Lin
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
- Insilico
Taiwan, Taipei City 115, Taiwan, R.O.C
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| |
Collapse
|
18
|
Lester HA, Dougherty DA. Nicotine Bound to Its Receptors: New Structures for a Vexing Pathopharmacological Problem. Neuron 2020; 104:431-432. [PMID: 31697916 DOI: 10.1016/j.neuron.2019.10.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this issue of Neuron, Gharpure et al. (2019) nearly complete atomic-level descriptions for binding, permeation, and subunit interactions at the two major heteropentameric nicotinic receptors-those governing nicotine's rewarding and aversive effects. Can we finally design highly selective and useful nicotinic drugs?
Collapse
Affiliation(s)
- Henry A Lester
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Dennis A Dougherty
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| |
Collapse
|
19
|
Patel N, Huang XP, Grandner JM, Johansson LC, Stauch B, McCorvy JD, Liu Y, Roth B, Katritch V. Structure-based discovery of potent and selective melatonin receptor agonists. eLife 2020; 9:e53779. [PMID: 32118583 PMCID: PMC7080406 DOI: 10.7554/elife.53779] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 02/26/2020] [Indexed: 12/12/2022] Open
Abstract
Melatonin receptors MT1 and MT2 are involved in synchronizing circadian rhythms and are important targets for treating sleep and mood disorders, type-2 diabetes and cancer. Here, we performed large scale structure-based virtual screening for new ligand chemotypes using recently solved high-resolution 3D crystal structures of agonist-bound MT receptors. Experimental testing of 62 screening candidates yielded the discovery of 10 new agonist chemotypes with sub-micromolar potency at MT receptors, with compound 21 reaching EC50 of 0.36 nM. Six of these molecules displayed selectivity for MT2 over MT1. Moreover, two most potent agonists, including 21 and a close derivative of melatonin, 28, had dramatically reduced arrestin recruitment at MT2, while compound 37 was devoid of Gi signaling at MT1, implying biased signaling. This study validates the suitability of the agonist-bound orthosteric pocket in the MT receptor structures for the structure-based discovery of selective agonists.
Collapse
Affiliation(s)
- Nilkanth Patel
- Department of Biological Sciences and Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern CaliforniaLos AngelesUnited States
| | - Xi Ping Huang
- Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
- National Institute of Mental Health Psychoactive Drug Screening Program, Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
| | - Jessica M Grandner
- Department of Biological Sciences and Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern CaliforniaLos AngelesUnited States
| | - Linda C Johansson
- Department of Biological Sciences and Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern CaliforniaLos AngelesUnited States
| | - Benjamin Stauch
- Department of Biological Sciences and Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern CaliforniaLos AngelesUnited States
| | - John D McCorvy
- Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
- National Institute of Mental Health Psychoactive Drug Screening Program, Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
| | - Yongfeng Liu
- Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
- National Institute of Mental Health Psychoactive Drug Screening Program, Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
| | - Bryan Roth
- Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
- National Institute of Mental Health Psychoactive Drug Screening Program, Department of Pharmacology, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
- Division of Chemical Biology and Medicinal Chemistry, University of North Carolina Chapel Hill Medical SchoolChapel HillUnited States
| | - Vsevolod Katritch
- Department of Biological Sciences and Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern CaliforniaLos AngelesUnited States
| |
Collapse
|
20
|
Jaiteh M, Rodríguez-Espigares I, Selent J, Carlsson J. Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity. PLoS Comput Biol 2020; 16:e1007680. [PMID: 32168319 PMCID: PMC7135368 DOI: 10.1371/journal.pcbi.1007680] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/06/2020] [Accepted: 01/23/2020] [Indexed: 12/15/2022] Open
Abstract
Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.
Collapse
Affiliation(s)
- Mariama Jaiteh
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| |
Collapse
|
21
|
Stein RM, Kang HJ, McCorvy JD, Glatfelter GC, Jones AJ, Che T, Slocum S, Huang XP, Savych O, Moroz YS, Stauch B, Johansson LC, Cherezov V, Kenakin T, Irwin JJ, Shoichet BK, Roth BL, Dubocovich ML. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature 2020; 579:609-614. [PMID: 32040955 PMCID: PMC7134359 DOI: 10.1038/s41586-020-2027-0] [Citation(s) in RCA: 156] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Accepted: 01/31/2020] [Indexed: 01/12/2023]
Affiliation(s)
- Reed M Stein
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Hye Jin Kang
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John D McCorvy
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Grant C Glatfelter
- Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (SUNY), The State University of New York, Buffalo, NY, USA.,Designer Drug Research Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD, USA
| | - Anthony J Jones
- Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (SUNY), The State University of New York, Buffalo, NY, USA
| | - Tao Che
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel Slocum
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xi-Ping Huang
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Yurii S Moroz
- National Taras Shevchenko University of Kyiv, Kiev, Ukraine.,Chemspace, Monmouth Junction, NJ, USA
| | - Benjamin Stauch
- Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Linda C Johansson
- Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Vadim Cherezov
- Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Terry Kenakin
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA.
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Margarita L Dubocovich
- Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (SUNY), The State University of New York, Buffalo, NY, USA.
| |
Collapse
|
22
|
Terrón-Díaz ME, Wright SJ, Agosto MA, Lichtarge O, Wensel TG. Residues and residue pairs of evolutionary importance differentially direct signaling bias of D2 dopamine receptors. J Biol Chem 2019; 294:19279-19291. [PMID: 31676688 DOI: 10.1074/jbc.ra119.008068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 10/16/2019] [Indexed: 01/11/2023] Open
Abstract
The D2 dopamine receptor and the serotonin 5-hydroxytryptamine 2A receptor (5-HT2A) are closely-related G-protein-coupled receptors (GPCRs) from the class A bioamine subfamily. Despite structural similarity, they respond to distinct ligands through distinct downstream pathways, whose dysregulation is linked to depression, bipolar disorder, addiction, and psychosis. They are important drug targets, and it is important to understand how their bias toward G-protein versus β-arrestin signaling pathways is regulated. Previously, evolution-based computational approaches, difference Evolutionary Trace and Evolutionary Trace-Mutual information (ET-Mip), revealed residues and residue pairs that, when switched in the D2 receptor to the corresponding residues from 5-HT2A, altered ligand potency and G-protein activation efficiency. We have tested these residue swaps for their ability to trigger recruitment of β-arrestin2 in response to dopamine or serotonin. The results reveal that the selected residues modulate agonist potency, maximal efficacy, and constitutive activity of β-arrestin2 recruitment. Whereas dopamine potency for most variants was similar to that for WT and lower than for G-protein activation, potency in β-arrestin2 recruitment for N124H3.42 was more than 5-fold higher. T205M5.54 displayed high constitutive activity, enhanced dopamine potency, and enhanced efficacy in β-arrestin2 recruitment relative to WT, and L379F6.41 was virtually inactive. These striking differences from WT activity were largely reversed by a compensating mutation (T205M5.54/L379F6.41) at residues previously identified by ET-Mip as functionally coupled. The observation that the signs and relative magnitudes of the effects of mutations in several cases are at odds with their effects on G-protein activation suggests that they also modulate signaling bias.
Collapse
Affiliation(s)
- María E Terrón-Díaz
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030.,Graduate Program in Integrative Molecular and Biomedical Sciences, Baylor College of Medicine, Houston, Texas 77030
| | - Sara J Wright
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology Baylor College of Medicine, Houston, Texas 77030
| | - Melina A Agosto
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology Baylor College of Medicine, Houston, Texas 77030
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology Baylor College of Medicine, Houston, Texas 77030.,Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030
| | - Theodore G Wensel
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas 77030
| |
Collapse
|
23
|
Molecular pharmacology of metabotropic receptors targeted by neuropsychiatric drugs. Nat Struct Mol Biol 2019; 26:535-544. [PMID: 31270468 DOI: 10.1038/s41594-019-0252-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/15/2019] [Indexed: 12/30/2022]
Abstract
Metabotropic receptors are responsible for so-called 'slow synaptic transmission' and mediate the effects of hundreds of peptide and non-peptide neurotransmitters and neuromodulators. Over the past decade or so, a revolution in membrane-protein structural determination has clarified the molecular determinants responsible for the actions of these receptors. This Review focuses on the G protein-coupled receptors (GPCRs) that are targets of neuropsychiatric drugs and shows how insights into the structure and function of these important synaptic proteins are accelerating understanding of their actions. Notably, elucidating the structure and function of GPCRs should enhance the structure-guided discovery of novel chemical tools with which to manipulate and understand these synaptic proteins.
Collapse
|
24
|
Rzęsikowska K, Krawczuk A, Kalinowska-Tłuścik J. Electrostatic potential and non-covalent interactions analysis for the design of selective 5-HT 7 ligands. J Mol Graph Model 2019; 91:130-139. [PMID: 31226573 DOI: 10.1016/j.jmgm.2019.06.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 06/04/2019] [Accepted: 06/05/2019] [Indexed: 01/01/2023]
Abstract
Despite the significant improvement of methodology in the field of the in silico drug discovery, the search for selective drugs is still far from trivial. This is especially relevant in the case of designing new medicaments for treatment of central nervous system disorders. In this work, we present a new approach based on the molecular docking and the following electronic properties analysis of ligands' binding poses (electrostatic potential distribution analysis and quantitative topological analysis of the electron density distribution). The proposed protocol significantly increases the success rate of the selective 5-HT7R ligands against 5-HT1AR selection from the prepared databases (the rise from 33.3% to 77.8% and from 22.7% to 62.5% for training and testing sets, respectively). The presented approach can be applied as a supportive method in the virtual screening of ligands databases.
Collapse
Affiliation(s)
- Katarzyna Rzęsikowska
- Department of Crystal Chemistry and Crystal Physics, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Poland
| | - Anna Krawczuk
- Department of Crystal Chemistry and Crystal Physics, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Poland
| | - Justyna Kalinowska-Tłuścik
- Department of Crystal Chemistry and Crystal Physics, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Poland.
| |
Collapse
|
25
|
Cunningham CW, Elballa WM, Vold SU. Bifunctional opioid receptor ligands as novel analgesics. Neuropharmacology 2019; 151:195-207. [PMID: 30858102 DOI: 10.1016/j.neuropharm.2019.03.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/30/2019] [Accepted: 03/02/2019] [Indexed: 12/12/2022]
Abstract
Prolonged treatment of chronic severe pain with opioid analgesics is frought with problematic adverse effects including tolerance, dependence, and life-threatening respiratory depression. Though these effects are mediated predominately through preferential activation of μ opioid peptide (μOP) receptors, there is an emerging appreciation that actions at κOP and δOP receptors contribute to the observed pharmacologic and behavioral profile of μOP receptor agonists and may be targeted simultaneously to afford improved analgesic effects. Recent developments have also identified the related nociceptin opioid peptide (NOP) receptor as a key modulator of the effects of μOP receptor signaling. We review here the available literature describing OP neurotransmitter systems and highlight recent drug and probe design strategies.
Collapse
Affiliation(s)
| | - Waleed M Elballa
- Department of Pharmaceutical Sciences, Concordia University Wisconsin, Mequon, WI, USA.
| | - Stephanie U Vold
- Department of Pharmaceutical Sciences, Concordia University Wisconsin, Mequon, WI, USA.
| |
Collapse
|