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Odunitan TT, Saibu OA, Apanisile BT, Omoboyowa DA, Balogun TA, Awe AV, Ajayi TM, Olagunju GV, Mahmoud FM, Akinboade M, Adeniji CB, Abdulazeez WO. Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets. Comput Biol Med 2024; 168:107737. [PMID: 38000249 DOI: 10.1016/j.compbiomed.2023.107737] [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: 10/05/2023] [Revised: 11/03/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023]
Abstract
Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.
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Affiliation(s)
- Tope T Odunitan
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria; Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | - Oluwatosin A Saibu
- Department of Chemistry and Biochemistry, New Mexico State University, Las Cruces, NM, USA.
| | - Boluwatife T Apanisile
- Department of Nutrition and Dietetics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Damilola A Omoboyowa
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Oyo State, Nigeria
| | - Toheeb A Balogun
- Department of Biological Sciences, University of California, San Diego, CA, USA
| | - Adeyoola V Awe
- Department of Medical Laboratory Science, Lead City, University, Ibadan, Oyo State, Nigeria
| | - Temitope M Ajayi
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Grace V Olagunju
- Department of Molecular Biology, New Mexico State University, Las Cruces, NM, USA
| | - Fatimah M Mahmoud
- Department of Molecular Biology, New Mexico State University, Las Cruces, NM, USA
| | - Modinat Akinboade
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Catherine B Adeniji
- Department of Environmental Management and Toxicology, Lead City University, Ibadan, Oyo State, Nigeria
| | - Waliu O Abdulazeez
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
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Zhang C, Liu J, Sui Y, Liu S, Yang M. In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches. Sci Rep 2023; 13:21404. [PMID: 38049492 PMCID: PMC10696093 DOI: 10.1038/s41598-023-48398-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/26/2023] [Indexed: 12/06/2023] Open
Abstract
The pandemic of coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a significant impact on the economy and public health worldwide. Therapeutic options such as drugs and vaccines for this newly emerged disease are eagerly desired due to the high mortality. Using the U.S. Food and Drug Administration (FDA) approved drugs to treat a new disease or entirely different diseases, in terms of drug repurposing, minimizes the time and cost of drug development compared to the de novo design of a new drug. Drug repurposing also has some other advantages such as reducing safety evaluation to accelerate drug application on time. Carvedilol, a non-selective beta-adrenergic blocker originally designed to treat high blood pressure and manage heart disease, has been shown to impact SARS-CoV-2 infection in clinical observation and basic studies. Here, we applied computer-aided approaches to investigate the possibility of repurposing carvedilol to combat SARS-CoV-2 infection. The molecular mechanisms and potential molecular targets of carvedilol were identified by evaluating the interactions of carvedilol with viral proteins. Additionally, the binding affinities of in vivo metabolites of carvedilol with selected targets were evaluated. The docking scores for carvedilol and its metabolites with RdRp were - 10.0 kcal/mol, - 9.8 kcal/mol (1-hydroxyl carvedilol), - 9.7 kcal/mol (3-hydroxyl carvedilol), - 9.8 kcal/mol (4-hydroxyl carvedilol), - 9.7 kcal/mol (5-hydroxyl carvedilol), - 10.0 kcal/mol (8-hydroxyl carvedilol), and - 10.1 kcal/mol (O-desmethyl carvedilol), respectively. Using the molecular dynamics simulation (100 ns) method, we further confirmed the stability of formed complexes of RNA-dependent RNA polymerase (RdRp) and carvedilol or its metabolites. Finally, the drug-target interaction mechanisms that contribute to the complex were investigated. Overall, this study provides the molecular targets and mechanisms of carvedilol and its metabolites as repurposed drugs to fight against SARS-CoV-2 infection.
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Affiliation(s)
- Chunye Zhang
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65212, USA
| | - Jiazheng Liu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau, 999078, China
| | - Yuxiang Sui
- School of Life Science, Shanxi Normal University, Linfen, 041004, Shanxi, China
| | - Shuai Liu
- The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Ming Yang
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA.
- NextGen Precision Health Institution, University of Missouri, Columbia, MO, 65212, USA.
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3
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High P, Carmon KS. G protein-coupled receptor-targeting antibody-drug conjugates: Current status and future directions. Cancer Lett 2023; 564:216191. [PMID: 37100113 PMCID: PMC11270908 DOI: 10.1016/j.canlet.2023.216191] [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: 03/20/2023] [Revised: 04/10/2023] [Accepted: 04/14/2023] [Indexed: 04/28/2023]
Abstract
In recent years, antibody-drug conjugates (ADCs) have emerged as promising anti-cancer therapeutic agents with several having already received market approval for the treatment of solid tumor and hematological malignancies. As ADC technology continues to improve and the range of indications treatable by ADCs increases, the repertoire of target antigens has expanded and will undoubtedly continue to grow. G protein-coupled receptors (GPCRs) are well-characterized therapeutic targets implicated in many human pathologies, including cancer, and represent a promising emerging target of ADCs. In this review, we will discuss the past and present therapeutic targeting of GPCRs and describe ADCs as therapeutic modalities. Moreover, we will summarize the status of existing preclinical and clinical GPCR-targeted ADCs and address the potential of GPCRs as novel targets for future ADC development.
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Affiliation(s)
- Peyton High
- Center for Translational Cancer Research, The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Kendra S Carmon
- Center for Translational Cancer Research, The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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Monteleone S, Fedorov DG, Townsend-Nicholson A, Southey M, Bodkin M, Heifetz A. Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method. J Chem Inf Model 2022; 62:3784-3799. [PMID: 35939049 DOI: 10.1021/acs.jcim.2c00457] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).
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Affiliation(s)
- Stefania Monteleone
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Dmitri G Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
| | - Andrea Townsend-Nicholson
- Institute of Structural & Molecular Biology, Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Michelle Southey
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Michael Bodkin
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Alexander Heifetz
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
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Abstract
Membrane proteins (MPs) play essential roles in numerous cellular processes. Because around 70% of the currently marketed drugs target MPs, a detailed understanding of their structure, binding properties, and functional dynamics in a physiologically relevant environment is crucial for a more detailed understanding of this important protein class. We here summarize the benefits of using lipid nanodiscs for NMR structural investigations and provide a detailed overview of the currently used lipid nanodisc systems as well as their applications in solution-state NMR. Despite the increasing use of other structural methods for the structure determination of MPs in lipid nanodiscs, solution NMR turns out to be a versatile tool to probe a wide range of MP features, ranging from the structure determination of small to medium-sized MPs to probing ligand and partner protein binding as well as functionally relevant dynamical signatures in a lipid nanodisc setting. We will expand on these topics by discussing recent NMR studies with lipid nanodiscs and work out a key workflow for optimizing the nanodisc incorporation of an MP for subsequent NMR investigations. With this, we hope to provide a comprehensive background to enable an informed assessment of the applicability of lipid nanodiscs for NMR studies of a particular MP of interest.
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Affiliation(s)
- Umut Günsel
- Bavarian NMR Center (BNMRZ) at the Department of Chemistry, Technical University of Munich, Ernst-Otto-Fischer-Strasse 2, 85748 Garching, Germany
| | - Franz Hagn
- Bavarian NMR Center (BNMRZ) at the Department of Chemistry, Technical University of Munich, Ernst-Otto-Fischer-Strasse 2, 85748 Garching, Germany.,Institute of Structural Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
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Ghadermarzi S, Krawczyk B, Song J, Kurgan L. XRRpred: Accurate Predictor of Crystal Structure Quality from Protein Sequence. Bioinformatics 2021; 37:4366-4374. [PMID: 34247234 DOI: 10.1093/bioinformatics/btab509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/10/2021] [Accepted: 07/06/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION X-ray crystallography was used to produce nearly 90% of protein structures. These efforts were supported by numerous sequence-based tools that accurately predict crystallizable proteins. However, protein structures vary widely in their quality, typically measured with resolution and R-free. This impacts the ability to use these structures for some applications including rational drug design and molecular docking and motivates development of methods that accurately predict structure quality. RESULTS We introduce XRRpred, the first predictor of the resolution and R-free values from protein sequences. XRRpred relies on original sequence profiles, hand-crafted features, empirically selected and parametrized regressors, and modern resampling techniques. Using an independent test dataset, we show that XRRpred provides accurate predictions of resolution and R-free. We demonstrate that XRRpred's predictions correctly model relationship between the resolution and R-free and reproduce structure quality relations between structural classes of proteins. We also show that XRRpred significantly outperforms indirect alternative ways to predict the structure quality that include predictors of crystallization propensity and an alignment-based approach. XRRpred is available as a convenient webserver that allows batch predictions and offers informative visualization of the results. AVAILABILITY http://biomine.cs.vcu.edu/servers/XRRPred/.
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Affiliation(s)
- Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Bartosz Krawczyk
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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Abstract
The understanding of binding interactions between a protein and a small molecule plays a key role in the rationalization of potency and selectivity and in design of new ideas. However, even when a target of interest is structurally enabled, visual inspection and force field-based molecular mechanics calculations cannot always explain the full complexity of the molecular interactions that are critical in drug design. Quantum mechanical methods have the potential to address this shortcoming, but traditionally, computational expense has made the application of these calculations impractical. The fragment molecular orbital (FMO) method offers a solution that combines accuracy, speed, and the ability to characterize important interactions (i.e. its strength in kcal/mol and chemical nature: hydrophobic, electrostatic, etc) that would otherwise be hard to detect. In this chapter, we describe the FMO method and illustrate its application in the discovery of the benzothiazole (BZT) series as novel tyrosine kinase ITK inhibitors for treatment of allergic asthma.
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9
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Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods Mol Biol 2021. [PMID: 32016893 DOI: 10.1007/978-1-0716-0282-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed, and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions.
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Huang Y, Deng Y, Zhang J, Meng L, Li X. Direct ligand screening against membrane proteins on live cells enabled by DNA-programmed affinity labelling. Chem Commun (Camb) 2021; 57:3769-3772. [DOI: 10.1039/d1cc00961c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
DNA-programmed affinity labelling (DPAL) enables the screening of chemical compounds against membrane proteins directly on live cells.
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Affiliation(s)
- Yiran Huang
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry
- The University of Hong Kong
- Pokfulam Road
- Hong Kong SAR
- China
| | - Yuqing Deng
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry
- The University of Hong Kong
- Pokfulam Road
- Hong Kong SAR
- China
| | - Jianfu Zhang
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry
- The University of Hong Kong
- Pokfulam Road
- Hong Kong SAR
- China
| | - Ling Meng
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry
- The University of Hong Kong
- Pokfulam Road
- Hong Kong SAR
- China
| | - Xiaoyu Li
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry
- The University of Hong Kong
- Pokfulam Road
- Hong Kong SAR
- China
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Guleria V, Pal T, Sharma B, Chauhan S, Jaiswal V. Pharmacokinetic and molecular docking studies to design antimalarial compounds targeting Actin I. Int J Health Sci (Qassim) 2021; 15:4-15. [PMID: 34916893 PMCID: PMC8589829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/30/2022] Open
Abstract
OBJECTIVE Malaria is an ancient disease that still causes more than 200 million of cases 7 with high mortality globally. Identification of new drug targets and development of novel antimalarial drugs with unique mode of action encounter the drug resistance and reduce the mortality by Plasmodium parasites. Actin protein is one of the key proteins in Plasmodium falciparum playing multifarious important roles including transport, cell motility, cell division, and shape determination. This study investigated Actin I as a drug target, in silico screening of diverse molecules through molecular docking was considered. Further, pharmacokinetic parameters of the selected molecules from the docking and interaction studies were planned to propose the lead molecules.b. METHODS Molecules were selected according to score and protein ligand interaction and selected molecules were subjected for pharmacokinetic studies to investigate important drug parameters. RESULTS The docked molecules were ranked according to the binding score and good interaction pattern was observed with Actin I within top 20 scoring molecules. The selected molecules also had optimum pharmacokinetic parameters. CONCLUSION The current study provides a set of hit molecules which can be further explored through in vitro and in vivo experiments for the development of potential drugs against malaria, there by encountering drug resistance and establishing Actin I as an important drug target.
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Affiliation(s)
- Vandana Guleria
- Department of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan, Himachal Pradesh, India
| | - Tarun Pal
- Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India
| | - Bhanu Sharma
- Department of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan, Himachal Pradesh, India
| | - Shweta Chauhan
- Department of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan, Himachal Pradesh, India
| | - Varun Jaiswal
- Department of Engineering, School of Electrical and Computer Science Engineering, Shoolini University of Biotechnology and Management Sciences, Solan, Himachal Pradesh, India,Department of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam-si 13120, Gyeonggi-do, Korea,Address for correspondence: Dr. Varun Jaiswal, Department of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam-si 13120, Gyeonggi-do, Korea. E-mail:
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Torrens-Fontanals M, Stepniewski TM, Aranda-García D, Morales-Pastor A, Medel-Lacruz B, Selent J. How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs. Int J Mol Sci 2020; 21:E5933. [PMID: 32824756 PMCID: PMC7460635 DOI: 10.3390/ijms21165933] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/11/2020] [Accepted: 08/15/2020] [Indexed: 01/08/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.
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Affiliation(s)
- Mariona Torrens-Fontanals
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Tomasz Maciej Stepniewski
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
- InterAx Biotech AG, PARK innovAARE, 5234 Villigen, Switzerland
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - David Aranda-García
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Adrián Morales-Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Brian Medel-Lacruz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
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13
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Wang P, Huang X, Qiu W, Xiao X. Identifying GPCR-drug interaction based on wordbook learning from sequences. BMC Bioinformatics 2020; 21:150. [PMID: 32312232 PMCID: PMC7171867 DOI: 10.1186/s12859-020-3488-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 04/13/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND G protein-coupled receptors (GPCRs) mediate a variety of important physiological functions, are closely related to many diseases, and constitute the most important target family of modern drugs. Therefore, the research of GPCR analysis and GPCR ligand screening is the hotspot of new drug development. Accurately identifying the GPCR-drug interaction is one of the key steps for designing GPCR-targeted drugs. However, it is prohibitively expensive to experimentally ascertain the interaction of GPCR-drug pairs on a large scale. Therefore, it is of great significance to predict the interaction of GPCR-drug pairs directly from the molecular sequences. With the accumulation of known GPCR-drug interaction data, it is feasible to develop sequence-based machine learning models for query GPCR-drug pairs. RESULTS In this paper, a new sequence-based method is proposed to identify GPCR-drug interactions. For GPCRs, we use a novel bag-of-words (BoW) model to extract sequence features, which can extract more pattern information from low-order to high-order and limit the feature space dimension. For drug molecules, we use discrete Fourier transform (DFT) to extract higher-order pattern information from the original molecular fingerprints. The feature vectors of two kinds of molecules are concatenated and input into a simple prediction engine distance-weighted K-nearest-neighbor (DWKNN). This basic method is easy to be enhanced through ensemble learning. Through testing on recently constructed GPCR-drug interaction datasets, it is found that the proposed methods are better than the existing sequence-based machine learning methods in generalization ability, even an unconventional method in which the prediction performance was further improved by post-processing procedure (PPP). CONCLUSIONS The proposed methods are effective for GPCR-drug interaction prediction, and may also be potential methods for other target-drug interaction prediction, or protein-protein interaction prediction. In addition, the new proposed feature extraction method for GPCR sequences is the modified version of the traditional BoW model and may be useful to solve problems of protein classification or attribute prediction. The source code of the proposed methods is freely available for academic research at https://github.com/wp3751/GPCR-Drug-Interaction.
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Affiliation(s)
- Pu Wang
- Computer School, Hubei University of Arts and Science, Xiangyang, 441053 China
| | - Xiaotong Huang
- Computer School, Hubei University of Arts and Science, Xiangyang, 441053 China
| | - Wangren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, 333403 China
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, 333403 China
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14
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Heifetz A, Morao I, Babu MM, James T, Southey MWY, Fedorov DG, Aldeghi M, Bodkin MJ, Townsend-Nicholson A. Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method. J Chem Theory Comput 2020; 16:2814-2824. [PMID: 32096994 PMCID: PMC7161079 DOI: 10.1021/acs.jctc.9b01136] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.
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Affiliation(s)
- Alexander Heifetz
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
- Institute
of Structural & Molecular Biology, Research Department of Structural
& Molecular Biology, Division of Biosciences, University College London, London, WC1E 6BT, United Kingdom
- E-mail: (A.H.)
| | - Inaki Morao
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
- E-mail: (I.M.)
| | - M. Madan Babu
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Tim James
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
| | | | - Dmitri G. Fedorov
- CD-FMat,
National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
| | - Matteo Aldeghi
- Department
of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany
| | - Michael J. Bodkin
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
| | - Andrea Townsend-Nicholson
- Institute
of Structural & Molecular Biology, Research Department of Structural
& Molecular Biology, Division of Biosciences, University College London, London, WC1E 6BT, United Kingdom
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15
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Ishchenko A, Stauch B, Han GW, Batyuk A, Shiriaeva A, Li C, Zatsepin N, Weierstall U, Liu W, Nango E, Nakane T, Tanaka R, Tono K, Joti Y, Iwata S, Moraes I, Gati C, Cherezov V. Toward G protein-coupled receptor structure-based drug design using X-ray lasers. IUCRJ 2019; 6:1106-1119. [PMID: 31709066 PMCID: PMC6830214 DOI: 10.1107/s2052252519013137] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Accepted: 09/23/2019] [Indexed: 06/10/2023]
Abstract
Rational structure-based drug design (SBDD) relies on the availability of a large number of co-crystal structures to map the ligand-binding pocket of the target protein and use this information for lead-compound optimization via an iterative process. While SBDD has proven successful for many drug-discovery projects, its application to G protein-coupled receptors (GPCRs) has been limited owing to extreme difficulties with their crystallization. Here, a method is presented for the rapid determination of multiple co-crystal structures for a target GPCR in complex with various ligands, taking advantage of the serial femtosecond crystallography approach, which obviates the need for large crystals and requires only submilligram quantities of purified protein. The method was applied to the human β2-adrenergic receptor, resulting in eight room-temperature co-crystal structures with six different ligands, including previously unreported structures with carvedilol and propranolol. The generality of the proposed method was tested with three other receptors. This approach has the potential to enable SBDD for GPCRs and other difficult-to-crystallize membrane proteins.
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Affiliation(s)
- Andrii Ishchenko
- Bridge Institute, Departments of Chemistry and Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Benjamin Stauch
- Bridge Institute, Departments of Chemistry and Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Gye Won Han
- Bridge Institute, Departments of Chemistry and Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Alexander Batyuk
- Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Anna Shiriaeva
- Bridge Institute, Departments of Chemistry and Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Chufeng Li
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Nadia Zatsepin
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Chemistry and Physics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Uwe Weierstall
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Wei Liu
- School of Molecular Sciences and Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
| | - Eriko Nango
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
- Department of Cell Biology, Graduate School of Medicine, Kyoto University, Yoshidakonoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Takanori Nakane
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 2-11-16 Yayoi, Bunkyo, Tokyo 113-0032, Japan
| | - Rie Tanaka
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
- Department of Cell Biology, Graduate School of Medicine, Kyoto University, Yoshidakonoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Kensuke Tono
- Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5198, Japan
| | - Yasumasa Joti
- Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5198, Japan
| | - So Iwata
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
- Department of Cell Biology, Graduate School of Medicine, Kyoto University, Yoshidakonoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Isabel Moraes
- National Physical Laboratory, Hampton Road, Teddington TW11 0LW, England
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot OX11 0FA, England
| | - Cornelius Gati
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
- Biosciences Division, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Vadim Cherezov
- Bridge Institute, Departments of Chemistry and Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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16
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Bruno A, Costantino G, Sartori L, Radi M. The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization. Curr Med Chem 2019; 26:3838-3873. [PMID: 29110597 DOI: 10.2174/0929867324666171107101035] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. METHODS In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. RESULTS A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. CONCLUSION The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.
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Affiliation(s)
- Agostino Bruno
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Gabriele Costantino
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Luca Sartori
- Experimental Therapeutics Unit, IFOM - The FIRC Institute for Molecular Oncology Foundation, Via Adamello 16 - 20139 Milano, Italy
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del Farmaco, Universita degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
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17
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Jana S, Ghosh S, Muk S, Levy B, Vaidehi N. Prediction of Conformation Specific Thermostabilizing Mutations for Class A G Protein-Coupled Receptors. J Chem Inf Model 2019; 59:3744-3754. [PMID: 31408606 PMCID: PMC6944194 DOI: 10.1021/acs.jcim.9b00175] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
G protein-coupled receptors (GPCRs) are highly flexible and prone to denaturation during protein extraction in detergents and purification. This poses a huge challenge to purify a conformationally homogeneous solution of GPCRs. Thermostabilizing mutations have been used widely to purify and obtain crystal structures of several GPCRs. However, identifying thermostabilizing mutations for GPCRs remains a tedious and expensive task as they are not transferable even among closely related GPCRs. Additionally, the mutations stabilizing one conformational state of a GPCR do not always stabilize other conformational state(s) of the same GPCR. Previously we developed a computational method, LiticonDesign, for rapid prediction of thermostabilizing mutations for a specific GPCR conformation. In this study, we have used LiticonDesign to predict thermostabilizing mutations for the agonist bound active-intermediate state of the human adenosine receptor (A2AR) using the structure of the inactive state of the same GPCR and vice versa. Our study shows that the thermostable mutation predictions using LiticonDesign, for an active-intermediate state of a GPCR (A2AR in our case), requires a homology model that is derived from an active/active-intermediate state GPCR structure as a template. Similarly, the homology models derived from inactive state GPCR conformations are better in predicting the thermostable mutations for the inactive state of A2AR. Overall, LiticonDesign method is not only efficient in predicting thermostabilizing mutations for a given GPCR sequence but also can recover conformation specific mutations for a state of interest, if a suitable starting structure of desired conformation is chosen.
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Affiliation(s)
- Suvamay Jana
- Department of Computation and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1500 E. Duarte Road, Duarte, CA 91010
| | - Soumadwip Ghosh
- Department of Computation and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1500 E. Duarte Road, Duarte, CA 91010
| | - Sanychen Muk
- Department of Computation and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1500 E. Duarte Road, Duarte, CA 91010
| | - Benjamin Levy
- Department of Computation and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1500 E. Duarte Road, Duarte, CA 91010
| | - Nagarajan Vaidehi
- Department of Computation and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1500 E. Duarte Road, Duarte, CA 91010
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18
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Zou Y, Ewalt J, Ng HL. Recent Insights from Molecular Dynamics Simulations for G Protein-Coupled Receptor Drug Discovery. Int J Mol Sci 2019; 20:E4237. [PMID: 31470676 PMCID: PMC6747122 DOI: 10.3390/ijms20174237] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 08/26/2019] [Accepted: 08/27/2019] [Indexed: 02/06/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. Some ligands that bind to GPCRs activate different downstream signaling pathways. G protein activation, or -arrestin biased signaling, involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. -arrestin biased signaling has become a hot target for structure-based drug discovery. However, challenges include that there are few crystal structures available in the Protein Data Bank and that GPCRs are highly dynamic. Hence, molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions with receptors and ligands. Here, we highlight recent MD simulation studies and enhanced sampling methods used to study biased G protein-coupled receptor signaling and their conformational dynamics as well as applications to drug discovery.
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Affiliation(s)
- Ye Zou
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66506, USA
| | - John Ewalt
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66506, USA
| | - Ho-Leung Ng
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66506, USA.
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19
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Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction. Int J Mol Sci 2019; 20:ijms20092175. [PMID: 31052500 PMCID: PMC6539940 DOI: 10.3390/ijms20092175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 04/14/2019] [Accepted: 04/29/2019] [Indexed: 01/11/2023] Open
Abstract
Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identify molecules with the expected pharmacological activity. Therefore, it is urgent and essential to develop more representative descriptors and reliable classification methods to accurately predict molecular activity. In this paper, we investigate the potential of a novel representation based on Spherical Harmonics fed into Probabilistic Classification Vector Machines classifier, namely SHPCVM, to compound the activity prediction task. We make use of representation learning to acquire the features which describe the molecules as precise as possible. To verify the performance of SHPCVM ten-fold cross-validation tests are performed on twenty-one G protein-coupled receptors (GPCRs). Experimental outcomes (accuracy of 0.86) assessed by the classification accuracy, precision, recall, Matthews’ Correlation Coefficient and Cohen’s kappa reveal that using our Spherical Harmonics-based representation which is relatively short and Probabilistic Classification Vector Machines can achieve very satisfactory performance results for GPCRs.
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20
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Abstract
G protein-coupled receptors (GPCRs) represent both the largest class of drug targets and the largest family of human membrane proteins. Recent three-dimensional structures have revealed the presence of many water molecules buried inside GPCRs, but the functional role of these water molecules has been unclear. Using extensive atomic-level computer simulations, we find that, although most of these waters are highly mobile, a few are stable. These stable water molecules mediate state-dependent and state-independent polar networks that are conserved across diverse GPCRs. These findings promise to help guide the rational design of GPCR-targeted drugs. G protein-coupled receptors (GPCRs) have evolved to recognize incredibly diverse extracellular ligands while sharing a common architecture and structurally conserved intracellular signaling partners. It remains unclear how binding of diverse ligands brings about GPCR activation, the common structural change that enables intracellular signaling. Here, we identify highly conserved networks of water-mediated interactions that play a central role in activation. Using atomic-level simulations of diverse GPCRs, we show that most of the water molecules in GPCR crystal structures are highly mobile. Several water molecules near the G protein-coupling interface, however, are stable. These water molecules form two kinds of polar networks that are conserved across diverse GPCRs: (i) a network that is maintained across the inactive and the active states and (ii) a network that rearranges upon activation. Comparative analysis of GPCR crystal structures independently confirms the striking conservation of water-mediated interaction networks. These conserved water-mediated interactions near the G protein-coupling region, along with diverse water-mediated interactions with extracellular ligands, have direct implications for structure-based drug design and GPCR engineering.
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21
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22
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Wu ZQ, Liu YM, Liu C, Chen JJ, Chen LL, Zhang TD, Zhou RB, Yang CQ, Shang P, Yin DC. A first attempt investigation on crystallization screening and crystal quality of lysozyme under different simulated gravities in a large-gradient magnetic field. CrystEngComm 2019. [DOI: 10.1039/c9ce00730j] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A magnetic field has been proved useful in protein crystallization in that it can help to improve the crystal quality, which is essential for high-resolution diffraction using crystallography.
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23
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GPCR drug discovery: integrating solution NMR data with crystal and cryo-EM structures. Nat Rev Drug Discov 2018; 18:59-82. [PMID: 30410121 DOI: 10.1038/nrd.2018.180] [Citation(s) in RCA: 159] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The 826 G protein-coupled receptors (GPCRs) in the human proteome regulate key physiological processes and thus have long been attractive drug targets. With the crystal structures of more than 50 different human GPCRs determined over the past decade, an initial platform for structure-based rational design has been established for drugs that target GPCRs, which is currently being augmented with cryo-electron microscopy (cryo-EM) structures of higher-order GPCR complexes. Nuclear magnetic resonance (NMR) spectroscopy in solution is one of the key approaches for expanding this platform with dynamic features, which can be accessed at physiological temperature and with minimal modification of the wild-type GPCR covalent structures. Here, we review strategies for the use of advanced biochemistry and NMR techniques with GPCRs, survey projects in which crystal or cryo-EM structures have been complemented with NMR investigations and discuss the impact of this integrative approach on GPCR biology and drug discovery.
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24
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Membrane protein engineering to the rescue. Biochem Soc Trans 2018; 46:1541-1549. [PMID: 30381335 DOI: 10.1042/bst20180140] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 09/03/2018] [Accepted: 09/05/2018] [Indexed: 02/07/2023]
Abstract
The inherent hydrophobicity of membrane proteins is a major barrier to membrane protein research and understanding. Their low stability and solubility in aqueous environments coupled with poor expression levels make them a challenging area of research. For many years, the only way of working with membrane proteins was to optimise the environment to suit the protein, through the use of different detergents, solubilising additives, and other adaptations. However, with innovative protein engineering methodologies, the membrane proteins themselves are now being adapted to suit the environment. This mini-review looks at the types of adaptations which are applied to membrane proteins from a variety of different fields, including water solubilising fusion tags, thermostabilising mutation screening, scaffold proteins, stabilising protein chimeras, and isolating water-soluble domains.
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25
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Hu G, Wang K, Song J, Uversky VN, Kurgan L. Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity. Proteomics 2018; 18:e1800243. [PMID: 30198635 DOI: 10.1002/pmic.201800243] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 08/30/2018] [Indexed: 12/14/2022]
Abstract
Growth rate of the protein sequence universe dramatically exceeds the speed of expansion for the protein structure universe, generating an immense dark proteome that includes proteins with unknown structure. A whole-proteome scale analysis of 5.4 million proteins from 987 proteomes in the three domains of life and viruses to systematically dissect an interplay between structural coverage, degree of putative intrinsic disorder, and predicted propensity for structure determination is performed. It has been found that Archaean and Bacterial proteomes have relatively high structural coverage and low amounts of disorder, whereas Eukaryotic and Viral proteomes are characterized by a broad spread of structural coverage and higher disorder levels. The analysis reveals that dark proteomes (i.e., proteomes containing high fractions of proteins with unknown structure) have significantly elevated amounts of intrinsic disorder and are predicted to be difficult to solve structurally. Although the majority of dark proteomes are of viral origin, many dark viral proteomes have at least modest crystallization propensity and only a handful of them are enriched in the intrinsic disorder. The disorder, structural coverage, and propensity are mapped for structural determination onto a novel proteome-level sequence similarity network to analyze the interplay of these characteristics in the taxonomic landscape.
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Affiliation(s)
- Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, 33612, USA.,Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, 142290, Russia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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26
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McGuinness KN, Pan W, Sheridan RP, Murphy G, Crespo A. Role of simple descriptors and applicability domain in predicting change in protein thermostability. PLoS One 2018; 13:e0203819. [PMID: 30192891 PMCID: PMC6128648 DOI: 10.1371/journal.pone.0203819] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 08/28/2018] [Indexed: 01/07/2023] Open
Abstract
The melting temperature (Tm) of a protein is the temperature at which half of the protein population is in a folded state. Therefore, Tm is a measure of the thermostability of a protein. Increasing the Tm of a protein is a critical goal in biotechnology and biomedicine. However, predicting the change in melting temperature (dTm) due to mutations at a single residue is difficult because it depends on an intricate balance of forces. Existing methods for predicting dTm have had similar levels of success using generally complex models. We find that training a machine learning model with a simple set of easy to calculate physicochemical descriptors describing the local environment of the mutation performed as well as more complicated machine learning models and is 2-6 orders of magnitude faster. Importantly, unlike in most previous publications, we perform a blind prospective test on our simple model by designing 96 variants of a protein not in the training set. Results from retrospective and prospective predictions reveal the limited applicability domain of each model. This study highlights the current deficiencies in the available dTm dataset and is a call to the community to systematically design a larger and more diverse experimental dataset of mutants to prospectively predict dTm with greater certainty.
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Affiliation(s)
- Kenneth N. McGuinness
- Modeling and Informatics, Merck & Co., Inc., Kenilworth, New Jersey, United States of America
| | - Weilan Pan
- Biochemical Engineering and Structure, Merck & Co., Inc., Rahway, New Jersey, United States of America
| | - Robert P. Sheridan
- Modeling and Informatics, Merck & Co., Inc., Kenilworth, New Jersey, United States of America
| | - Grant Murphy
- Biochemical Engineering and Structure, Merck & Co., Inc., Rahway, New Jersey, United States of America
| | - Alejandro Crespo
- Modeling and Informatics, Merck & Co., Inc., Kenilworth, New Jersey, United States of America
- * E-mail:
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27
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Weiss D, Karpiak J, Huang XP, Sassano MF, Lyu J, Roth BL, Shoichet BK. Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets. J Med Chem 2018; 61:6830-6845. [PMID: 29990431 PMCID: PMC6105036 DOI: 10.1021/acs.jmedchem.8b00718] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Indexed: 12/14/2022]
Abstract
To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field.
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Affiliation(s)
- Dahlia
R. Weiss
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
| | - Joel Karpiak
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
| | - Xi-Ping Huang
- Department
of Pharmacology and National Institute of Mental Health Psychoactive
Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Maria F. Sassano
- Department
of Pharmacology and National Institute of Mental Health Psychoactive
Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jiankun Lyu
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
| | - Bryan L. Roth
- Department
of Pharmacology and National Institute of Mental Health Psychoactive
Drug Screening Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Brian K. Shoichet
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California 94158-2550, United States
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Yoo JI, O’Malley MA. Tuning Vector Stability and Integration Frequency Elevates Functional GPCR Production and Homogeneity in Saccharomyces cerevisiae. ACS Synth Biol 2018; 7:1763-1772. [PMID: 29871481 DOI: 10.1021/acssynbio.8b00036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Membrane proteins play a valuable role in biotechnology, yet the difficulty of producing high yields of functional membrane protein limits their use in synthetic biology. The practical application of G protein-coupled receptors in whole cell biosensors, for example, is restricted to those that are functionally produced at the cell surface in the chosen host, limiting the range of detectable molecules. Here, we present a facile approach to significantly improve the yield and homogeneity of functional membrane proteins in Saccharomyces cerevisiae by altering only the choice of expression vector. Expression of a model GPCR, the human adenosine A2a receptor, from commonly used centromeric and episomal vectors leads to low yields and cellular heterogeneity due to plasmid loss in 20-90% of the cell population. In contrast, homogeneous production of GPCR is attained using a multisite integrating vector or a novel, modified high copy vector that does not require genomic integration or addition of any selection agents. Finally, we introduce a FACS-based screen, which enables rapid isolation of cells with 4- to 15-fold increases in gene dosage and up to a 9-fold increase in functional protein yield without loss of homogeneity compared to a strain isolated through conventional, low-throughput methods. These results can be extended to improve the cellular homogeneity and yield of other membrane proteins, expanding the repertoire of useful receptors for synthetic biology applications.
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Affiliation(s)
- Justin I. Yoo
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, United States
| | - Michelle A. O’Malley
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, United States
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Won J, Lee GR, Park H, Seok C. GalaxyGPCRloop: Template-Based and Ab Initio Structure Sampling of the Extracellular Loops of G-Protein-Coupled Receptors. J Chem Inf Model 2018; 58:1234-1243. [DOI: 10.1021/acs.jcim.8b00148] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Jonghun Won
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Hahnbeom Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
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30
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Meng F, Murray GF, Kurgan L, Donahue HJ. Functional and structural characterization of osteocytic MLO-Y4 cell proteins encoded by genes differentially expressed in response to mechanical signals in vitro. Sci Rep 2018; 8:6716. [PMID: 29712973 PMCID: PMC5928037 DOI: 10.1038/s41598-018-25113-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/09/2018] [Indexed: 12/29/2022] Open
Abstract
The anabolic response of bone to mechanical load is partially the result of osteocyte response to fluid flow-induced shear stress. Understanding signaling pathways activated in osteocytes exposed to fluid flow could identify novel signaling pathways involved in the response of bone to mechanical load. Bioinformatics allows for a unique perspective and provides key first steps in understanding these signaling pathways. We examined proteins encoded by genes differentially expressed in response to fluid flow in murine osteocytic MLO-Y4 cells. We considered structural and functional characteristics including putative intrinsic disorder, evolutionary conservation, interconnectedness in protein-protein interaction networks, and cellular localization. Our analysis suggests that proteins encoded by fluid flow activated genes have lower than expected conservation, are depleted in intrinsic disorder, maintain typical levels of connectivity for the murine proteome, and are found in the cytoplasm and extracellular space. Pathway analyses reveal that these proteins are associated with cellular response to stress, chemokine and cytokine activity, enzyme binding, and osteoclast differentiation. The lower than expected disorder of proteins encoded by flow activated genes suggests they are relatively specialized.
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Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Graeme F Murray
- Bone Engineering, Science and Technology (BEST) Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, United States of America.
| | - Henry J Donahue
- Bone Engineering, Science and Technology (BEST) Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America.
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31
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Lagarias P, Vrontaki E, Lambrinidis G, Stamatis D, Convertino M, Ortore G, Mavromoustakos T, Klotz KN, Kolocouris A. Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding Mode. J Chem Inf Model 2018; 58:794-815. [PMID: 29485875 DOI: 10.1021/acs.jcim.7b00455] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
An intense effort is made by pharmaceutical and academic research laboratories to identify and develop selective antagonists for each adenosine receptor (AR) subtype as potential clinical candidates for "soft" treatment of various diseases. Crystal structures of subtypes A2A and A1ARs offer exciting opportunities for structure-based drug design. In the first part of the present work, Maybridge HitFinder library of 14400 compounds was utilized to apply a combination of structure-based against the crystal structure of A2AAR and ligand-based methodologies. The docking poses were rescored by CHARMM energy minimization and calculation of the desolvation energy using Poisson-Boltzmann equation electrostatics. Out of the eight selected and tested compounds, five were found positive hits (63% success). Although the project was initially focused on targeting A2AAR, the identified antagonists exhibited low micromolar or micromolar affinity against A2A/A3, ARs, or A3AR, respectively. Based on these results, 19 compounds characterized by novel chemotypes were purchased and tested. Sixteen of them were identified as AR antagonists with affinity toward combinations of the AR family isoforms (A2A/A3, A1/A3, A1/A2A/A3, and A3). The second part of this work involves the performance of hundreds of molecular dynamics (MD) simulations of complexes between the ARs and a total of 27 ligands to resolve the binding interactions of the active compounds, which were not achieved by docking calculations alone. This computational work allowed the prediction of stable and unstable complexes which agree with the experimental results of potent and inactive compounds, respectively. Of particular interest is that the 2-amino-thiophene-3-carboxamides, 3-acylamino-5-aryl-thiophene-2-carboxamides, and carbonyloxycarboximidamide derivatives were found to be selective and possess a micromolar to low micromolar affinity for the A3 receptor.
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Affiliation(s)
- Panagiotis Lagarias
- Division of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences , National and Kapodistrian University of Athens , Panepistimiopolis-Zografou , 15771 Athens , Greece
| | - Eleni Vrontaki
- Division of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences , National and Kapodistrian University of Athens , Panepistimiopolis-Zografou , 15771 Athens , Greece
| | - George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences , National and Kapodistrian University of Athens , Panepistimiopolis-Zografou , 15771 Athens , Greece
| | - Dimitrios Stamatis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences , National and Kapodistrian University of Athens , Panepistimiopolis-Zografou , 15771 Athens , Greece
| | - Marino Convertino
- Department of Biochemistry & Biophysics , University of North Carolina at Chapel Hill , 120 Mason Farm Road , Chapel Hill , North Carolina 27599 , United States
| | - Gabriella Ortore
- Department of Pharmacy , University of Pisa , 56126 Pisa , Italy
| | - Thomas Mavromoustakos
- Division of Organic Chemistry, Department of Chemistry, School of Science , National and Kapodistrian University of Athens , Panepistimiopolis-Zografou , 15771 Athens , Greece
| | - Karl-Norbert Klotz
- Institute of Pharmacology and Toxicology , University of Würzburg Versbacher Str. 9 , 97078 Würzburg , Germany
| | - Antonios Kolocouris
- Division of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences , National and Kapodistrian University of Athens , Panepistimiopolis-Zografou , 15771 Athens , Greece
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Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, Choi S. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol 2018; 9:128. [PMID: 29593527 PMCID: PMC5854945 DOI: 10.3389/fphar.2018.00128] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/06/2018] [Indexed: 01/14/2023] Open
Abstract
The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
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Affiliation(s)
| | | | | | | | | | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
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Moreno E, Canet J, Gracia E, Lluís C, Mallol J, Canela EI, Cortés A, Casadó V. Molecular Evidence of Adenosine Deaminase Linking Adenosine A 2A Receptor and CD26 Proteins. Front Pharmacol 2018; 9:106. [PMID: 29497379 PMCID: PMC5818423 DOI: 10.3389/fphar.2018.00106] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/30/2018] [Indexed: 01/05/2023] Open
Abstract
Adenosine is an endogenous purine nucleoside that acts in all living systems as a homeostatic network regulator through many pathways, which are adenosine receptor (AR)-dependent and -independent. From a metabolic point of view, adenosine deaminase (ADA) is an essential protein in the regulation of the total intracellular and extracellular adenosine in a tissue. In addition to its cytosolic localization, ADA is also expressed as an ecto-enzyme on the surface of different cells. Dipeptidyl peptidase IV (CD26) and some ARs act as binding proteins for extracellular ADA in humans. Since CD26 and ARs interact with ADA at opposite sites, we have investigated if ADA can function as a cell-to-cell communication molecule by bridging the anchoring molecules CD26 and A2AR present on the surfaces of the interacting cells. By combining site-directed mutagenesis of ADA amino acids involved in binding to A2AR and a modification of the bioluminescence resonance energy transfer (BRET) technique that allows detection of interactions between two proteins expressed in different cell populations with low steric hindrance (NanoBRET), we show direct evidence of the specific formation of trimeric complexes CD26-ADA-A2AR involving two cells. By dynamic mass redistribution assays and ligand binding experiments, we also demonstrate that A2AR-NanoLuc fusion proteins are functional. The existence of this ternary complex is in good agreement with the hypothesis that ADA could bridge T-cells (expressing CD26) and dendritic cells (expressing A2AR). This is a new metabolic function for ecto-ADA that, being a single chain protein, it has been considered as an example of moonlighting protein, because it performs more than one functional role (as a catalyst, a costimulator, an allosteric modulator and a cell-to-cell connector) without partitioning these functions in different subunits.
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Affiliation(s)
- Estefanía Moreno
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Júlia Canet
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Eduard Gracia
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Carme Lluís
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Josefa Mallol
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Enric I. Canela
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Antoni Cortés
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Vicent Casadó
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
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34
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Heifetz A, Southey M, Morao I, Townsend-Nicholson A, Bodkin MJ. Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery. Methods Mol Biol 2018; 1705:375-394. [PMID: 29188574 DOI: 10.1007/978-1-4939-7465-8_19] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.
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Affiliation(s)
- Alexander Heifetz
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK. .,Division of Biosciences, Research Department of Structural and Molecular Biology, Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
| | - Michelle Southey
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Inaki Morao
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Andrea Townsend-Nicholson
- Division of Biosciences, Research Department of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK
| | - Mike J Bodkin
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
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35
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Carpenter B. Current applications of mini G proteins to study the structure and function of G protein-coupled receptors. AIMS BIOENGINEERING 2018. [DOI: 10.3934/bioeng.2018.4.209] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
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36
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Gao J, Wu Z, Hu G, Wang K, Song J, Joachimiak A, Kurgan L. Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures. Curr Protein Pept Sci 2018; 19:200-210. [PMID: 28933304 PMCID: PMC7001581 DOI: 10.2174/1389203718666170921114437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 09/14/2017] [Accepted: 09/14/2017] [Indexed: 11/22/2022]
Abstract
Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.
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Affiliation(s)
- Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Jiangning Song
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Australia
| | - Andrzej Joachimiak
- Midwest Center for Structural Genomics, Argonne, USA
- Structural Biology Center, Biosciences, Argonne National Laboratory, Argonne, USA
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, USA
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37
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Chudyk EI, Sarrat L, Aldeghi M, Fedorov DG, Bodkin MJ, James T, Southey M, Robinson R, Morao I, Heifetz A. Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods Mol Biol 2018; 1705:179-195. [PMID: 29188563 DOI: 10.1007/978-1-4939-7465-8_8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity. It is essential for an efficient structure-based drug design (SBDD) process. FMO enables ab initio approaches to be applied to systems that conventional quantum-mechanical (QM) methods would find challenging. The key advantage of the Fragment Molecular Orbital Method (FMO) is that it can reveal atomistic details about the individual contributions and chemical nature of each residue and water molecule toward ligand binding which would otherwise be difficult to detect without using QM methods. In this chapter, we demonstrate the typical use of FMO to analyze 19 crystal structures of β1 and β2 adrenergic receptors with their corresponding agonists and antagonists.
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Affiliation(s)
- Ewa I Chudyk
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Laurie Sarrat
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Matteo Aldeghi
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Dmitri G Fedorov
- CD-FMat, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568, Japan
| | - Mike J Bodkin
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Tim James
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Michelle Southey
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Roger Robinson
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Inaki Morao
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Alexander Heifetz
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK.
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38
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Carpenter B, Lebon G. Human Adenosine A 2A Receptor: Molecular Mechanism of Ligand Binding and Activation. Front Pharmacol 2017; 8:898. [PMID: 29311917 PMCID: PMC5736361 DOI: 10.3389/fphar.2017.00898] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 11/24/2017] [Indexed: 11/29/2022] Open
Abstract
Adenosine receptors (ARs) comprise the P1 class of purinergic receptors and belong to the largest family of integral membrane proteins in the human genome, the G protein-coupled receptors (GPCRs). ARs are classified into four subtypes, A1, A2A, A2B, and A3, which are all activated by extracellular adenosine, and play central roles in a broad range of physiological processes, including sleep regulation, angiogenesis and modulation of the immune system. ARs are potential therapeutic targets in a variety of pathophysiological conditions, including sleep disorders, cancer, and dementia, which has made them important targets for structural biology. Over a decade of research and innovation has culminated with the publication of more than 30 crystal structures of the human adenosine A2A receptor (A2AR), making it one of the best structurally characterized GPCRs at the atomic level. In this review we analyze the structural data reported for A2AR that described for the first time the binding of mode of antagonists, including newly developed drug candidates, synthetic and endogenous agonists, sodium ions and an engineered G protein. These structures have revealed the key conformational changes induced upon agonist and G protein binding that are central to signal transduction by A2AR, and have highlighted both similarities and differences in the activation mechanism of this receptor compared to other class A GPCRs. Finally, comparison of A2AR with the recently solved structures of A1R has provided the first structural insight into the molecular determinants of ligand binding specificity in different AR subtypes.
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Affiliation(s)
- Byron Carpenter
- Warwick Integrative Synthetic Biology Centre, School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Guillaume Lebon
- Institut de Génomique Fonctionnelle, Neuroscience Department, UMR CNRS 5203, INSERM U1191, Université de Montpellier, Montpellier, France
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Hauser AS, Attwood MM, Rask-Andersen M, Schiöth HB, Gloriam DE. Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov 2017; 16:829-842. [PMID: 29075003 PMCID: PMC6882681 DOI: 10.1038/nrd.2017.178] [Citation(s) in RCA: 1587] [Impact Index Per Article: 226.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially novel GPCR targets without an approved drug, and the number of biological drugs, allosteric modulators and biased agonists has increased. The major disease indications for GPCR modulators show a shift towards diabetes, obesity and Alzheimer disease, although several central nervous system disorders are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug discovery.
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Affiliation(s)
- Alexander S Hauser
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Misty M Attwood
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, 751 05 Uppsala, Sweden
| | - Mathias Rask-Andersen
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, University of Uppsala, 751 05 Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Neuroscience, Functional Pharmacology, University of Uppsala, 751 05 Uppsala, Sweden
| | - David E Gloriam
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
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40
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Fish I, Stößel A, Eitel K, Valant C, Albold S, Huebner H, Möller D, Clark MJ, Sunahara RK, Christopoulos A, Shoichet BK, Gmeiner P. Structure-Based Design and Discovery of New M 2 Receptor Agonists. J Med Chem 2017; 60:9239-9250. [PMID: 29094937 DOI: 10.1021/acs.jmedchem.7b01113] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Muscarinic receptor agonists are characterized by apparently strict restraints on their tertiary or quaternary amine and their distance to an ester or related center. On the basis of the active state crystal structure of the muscarinic M2 receptor in complex with iperoxo, we explored potential agonists that lacked the highly conserved functionalities of previously known ligands. Using structure-guided pharmacophore design followed by docking, we found two agonists (compounds 3 and 17), out of 19 docked and synthesized compounds, that fit the receptor well and were predicted to form a hydrogen-bond conserved among known agonists. Structural optimization led to compound 28, which was 4-fold more potent than its parent 3. Fortified by the discovery of this new scaffold, we sought a broader range of chemotypes by docking 2.2 million fragments, which revealed another three micromolar agonists unrelated either to 28 or known muscarinics. Even pockets as tightly defined and as deeply studied as that of the muscarinic reveal opportunities for the structure-based design and the discovery of new chemotypes.
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Affiliation(s)
- Inbar Fish
- Department of Pharmaceutical Chemistry, University of California, San Francisco , San Francisco, California 94158, United States.,Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel-Aviv University , Ramat Aviv, Israel
| | - Anne Stößel
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich Alexander University , Schuhstraße 19, 91052 Erlangen, Germany
| | - Katrin Eitel
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich Alexander University , Schuhstraße 19, 91052 Erlangen, Germany
| | - Celine Valant
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University , Parkville Victoria 3052, Australia
| | - Sabine Albold
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University , Parkville Victoria 3052, Australia
| | - Harald Huebner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich Alexander University , Schuhstraße 19, 91052 Erlangen, Germany
| | - Dorothee Möller
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich Alexander University , Schuhstraße 19, 91052 Erlangen, Germany
| | - Mary J Clark
- Department of Pharmacology, University of California, San Diego , La Jolla, California 92093, United States
| | - Roger K Sunahara
- Department of Pharmacology, University of California, San Diego , La Jolla, California 92093, United States
| | - Arthur Christopoulos
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University , Parkville Victoria 3052, Australia
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco , San Francisco, California 94158, United States
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich Alexander University , Schuhstraße 19, 91052 Erlangen, Germany
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41
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Sahlgren C, Meinander A, Zhang H, Cheng F, Preis M, Xu C, Salminen TA, Toivola D, Abankwa D, Rosling A, Karaman DŞ, Salo-Ahen OMH, Österbacka R, Eriksson JE, Willför S, Petre I, Peltonen J, Leino R, Johnson M, Rosenholm J, Sandler N. Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems. Adv Healthc Mater 2017; 6. [PMID: 28892296 DOI: 10.1002/adhm.201700258] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 05/04/2017] [Indexed: 12/13/2022]
Abstract
Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.
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Affiliation(s)
- Cecilia Sahlgren
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Annika Meinander
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Hongbo Zhang
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Fang Cheng
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Maren Preis
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Chunlin Xu
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Tiina A. Salminen
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Diana Toivola
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Center for Disease Modeling; University of Turku; FI-20520 Turku Finland
| | - Daniel Abankwa
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Ari Rosling
- Faculty of Science and Engineering; Polymer Technologies; Åbo Akademi University; FI-20500 Turku Finland
| | - Didem Şen Karaman
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Outi M. H. Salo-Ahen
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Ronald Österbacka
- Faculty of Science and Engineering; Physics; Åbo Akademi University; FI-20500 Turku Finland
| | - John E. Eriksson
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
| | - Stefan Willför
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Ion Petre
- Faculty of Science and Engineering; Computer Science; Åbo Akademi University; FI-20500 Turku Finland
| | - Jouko Peltonen
- Faculty of Science and Engineering; Physical Chemistry; Åbo Akademi University; FI-20500 Turku Finland
| | - Reko Leino
- Faculty of Science and Engineering; Organic Chemistry; Johan Gadolin Process Chemistry Centre; Åbo Akademi University; FI-20500 Turku Finland
| | - Mark Johnson
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Jessica Rosenholm
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Niklas Sandler
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
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42
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Sahlgren C, Meinander A, Zhang H, Cheng F, Preis M, Xu C, Salminen TA, Toivola D, Abankwa D, Rosling A, Karaman DŞ, Salo-Ahen OMH, Österbacka R, Eriksson JE, Willför S, Petre I, Peltonen J, Leino R, Johnson M, Rosenholm J, Sandler N. Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems. Adv Healthc Mater 2017. [DOI: 10.1002/adhm.201700258 10.1002/adhm.201700258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Affiliation(s)
- Cecilia Sahlgren
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Annika Meinander
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Hongbo Zhang
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Fang Cheng
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
| | - Maren Preis
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Chunlin Xu
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Tiina A. Salminen
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Diana Toivola
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Center for Disease Modeling; University of Turku; FI-20520 Turku Finland
| | - Daniel Abankwa
- Department of Biomedical Engineering; Technical University of Eindhoven; 5613 DR Eindhoven Netherlands
| | - Ari Rosling
- Faculty of Science and Engineering; Polymer Technologies; Åbo Akademi University; FI-20500 Turku Finland
| | - Didem Şen Karaman
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Outi M. H. Salo-Ahen
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Ronald Österbacka
- Faculty of Science and Engineering; Physics; Åbo Akademi University; FI-20500 Turku Finland
| | - John E. Eriksson
- Faculty of Science and Engineering; Cell Biology; Åbo Akademi University; FI-20520 Turku Finland
- Turku Centre for Biotechnology; Åbo Akademi University and University of Turku; FI-20520 Turku Finland
| | - Stefan Willför
- Faculty of Science and Engineering; Natural Materials Technology; Åbo Akademi University; FI-20500 Turku Finland
| | - Ion Petre
- Faculty of Science and Engineering; Computer Science; Åbo Akademi University; FI-20500 Turku Finland
| | - Jouko Peltonen
- Faculty of Science and Engineering; Physical Chemistry; Åbo Akademi University; FI-20500 Turku Finland
| | - Reko Leino
- Faculty of Science and Engineering; Organic Chemistry; Johan Gadolin Process Chemistry Centre; Åbo Akademi University; FI-20500 Turku Finland
| | - Mark Johnson
- Faculty of Science and Engineering; Structural Bioinformatics Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Jessica Rosenholm
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
| | - Niklas Sandler
- Faculty of Science and Engineering; Pharmaceutical Sciences Laboratory; Åbo Akademi University; FI-20520 Turku Finland
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43
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Woolley MJ, Conner AC. Understanding the common themes and diverse roles of the second extracellular loop (ECL2) of the GPCR super-family. Mol Cell Endocrinol 2017; 449:3-11. [PMID: 27899324 DOI: 10.1016/j.mce.2016.11.023] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 11/02/2016] [Accepted: 11/24/2016] [Indexed: 11/16/2022]
Abstract
The extracellular loops (ECLs) of G protein-coupled receptors (GPCRs) can bind directly to docked orthosteric or allosteric ligands, they can contain transient contact points for ligand entry into the transmembrane (TM) bundle and they can regulate the activation of the receptor signalling pathways. Of the three ECLs, ECL2 is the largest and most structurally diverse reflecting its functional importance. This has been shown through biochemical techniques and has been supported by the many subsequent crystal structures of GPCRs bound to both agonists and antagonists. ECL2 shares common structural features between (and sometimes across) receptor sub-families and can facilitate ligand entry to the TM core or act directly as a surface of the ligand-binding pocket. Structural similarities seem to underpin common binding mechanisms; however, where these exist, variations in primary sequence ensure ligand-binding specificity. This review will compare current understanding of the structural themes and main functional roles of ECL2 in ligand binding, activation and regulation of the major families of GPCRs.
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Affiliation(s)
- Michael J Woolley
- Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Alex C Conner
- Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK.
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44
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Morao I, Fedorov DG, Robinson R, Southey M, Townsend‐Nicholson A, Bodkin MJ, Heifetz A. Rapid and accurate assessment of GPCR-ligand interactions Using the fragment molecular orbital-based density-functional tight-binding method. J Comput Chem 2017; 38:1987-1990. [PMID: 28675443 PMCID: PMC5600120 DOI: 10.1002/jcc.24850] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/07/2017] [Accepted: 05/16/2017] [Indexed: 01/17/2023]
Abstract
The reliable and precise evaluation of receptor–ligand interactions and pair‐interaction energy is an essential element of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been used to accelerate QM calculations, and by combining FMO with the density‐functional tight‐binding (DFTB) method we are able to decrease computational cost 1000 times, achieving results in seconds, instead of hours. We have applied FMO‐DFTB to three different GPCR–ligand systems. Our results correlate well with site directed mutagenesis data and findings presented in the published literature, demonstrating that FMO‐DFTB is a rapid and accurate means of GPCR–ligand interactions. © 2017 Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Inaki Morao
- Computational ChemistryEvotec (UK) Ltd114 Innovation Drive, Milton ParkAbingdonOxfordshireOX14 4RZUnited Kingdom
| | - Dmitri G. Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat)National Institute of Advanced Industrial Science and Technology (AIST)1‐1‐1 UmezonoTsukubaIbaraki305‐8568Japan
| | - Roger Robinson
- Computational ChemistryEvotec (UK) Ltd114 Innovation Drive, Milton ParkAbingdonOxfordshireOX14 4RZUnited Kingdom
| | - Michelle Southey
- Computational ChemistryEvotec (UK) Ltd114 Innovation Drive, Milton ParkAbingdonOxfordshireOX14 4RZUnited Kingdom
| | - Andrea Townsend‐Nicholson
- Institute of Structural & Molecular Biology, Research Department of Structural & Molecular Biology, Division of BiosciencesUniversity College LondonLondonWC1E 6BTUnited Kingdom
| | - Mike J. Bodkin
- Computational ChemistryEvotec (UK) Ltd114 Innovation Drive, Milton ParkAbingdonOxfordshireOX14 4RZUnited Kingdom
| | - Alexander Heifetz
- Computational ChemistryEvotec (UK) Ltd114 Innovation Drive, Milton ParkAbingdonOxfordshireOX14 4RZUnited Kingdom
- Institute of Structural & Molecular Biology, Research Department of Structural & Molecular Biology, Division of BiosciencesUniversity College LondonLondonWC1E 6BTUnited Kingdom
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45
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Congreve M, Oswald C, Marshall FH. Applying Structure-Based Drug Design Approaches to Allosteric Modulators of GPCRs. Trends Pharmacol Sci 2017. [PMID: 28648526 DOI: 10.1016/j.tips.2017.05.010] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Structural insights have been revealed from X-ray co-complexes of a range of G protein-coupled receptors (GPCRs) and their allosteric ligands. The understanding of how small molecules can modulate the function of this important class of receptors by binding to a diverse range of pockets on and inside the proteins has had a profound impact on the structure-based drug design (SBDD) of new classes of therapeutic agents. The types of allosteric pockets and the mode of modulation as well as the advantages and disadvantages of targeting allosteric pockets (as opposed to the natural orthosteric site) are considered in the context of these new structural findings.
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Affiliation(s)
- Miles Congreve
- Heptares Therapeutics Ltd, Biopark, Welwyn Garden City, UK
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46
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Muranaka H, Momose T, Handa C, Ozawa T. Photoaffinity Labeling of the Human A 2A Adenosine Receptor and Cross-link Position Analysis by Mass Spectrometry. ACS Med Chem Lett 2017. [PMID: 28626529 DOI: 10.1021/acsmedchemlett.7b00138] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Photoaffinity labeling (PAL) is widely used for the identification of ligand-binding proteins and elucidation of ligand-binding sites. PAL has also been employed for the characterization of G protein-coupled receptors (GPCRs); however, a limited number of reports has successfully identified their cross-linked amino acids. This report is the first of its kind to determine the cross-link position of the human A2A adenosine receptor by PAL with the novel diazirine-based photoaffinity probe 9.
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Affiliation(s)
- Hideyuki Muranaka
- Central Research Laboratories, Kissei Pharmaceutical Co., Ltd., 4365-1 Kashiwabara,
Hotaka, Azumino, Nagano 399-8304, Japan
| | - Takaki Momose
- Central Research Laboratories, Kissei Pharmaceutical Co., Ltd., 4365-1 Kashiwabara,
Hotaka, Azumino, Nagano 399-8304, Japan
| | - Chiaki Handa
- Central Research Laboratories, Kissei Pharmaceutical Co., Ltd., 4365-1 Kashiwabara,
Hotaka, Azumino, Nagano 399-8304, Japan
| | - Tomonaga Ozawa
- Central Research Laboratories, Kissei Pharmaceutical Co., Ltd., 4365-1 Kashiwabara,
Hotaka, Azumino, Nagano 399-8304, Japan
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47
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Deganutti G, Moro S. Supporting the Identification of Novel Fragment-Based Positive Allosteric Modulators Using a Supervised Molecular Dynamics Approach: A Retrospective Analysis Considering the Human A2A Adenosine Receptor as a Key Example. Molecules 2017; 22:molecules22050818. [PMID: 28509867 PMCID: PMC6154550 DOI: 10.3390/molecules22050818] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 05/03/2017] [Accepted: 05/10/2017] [Indexed: 12/31/2022] Open
Abstract
Structure-driven fragment-based (SDFB) approaches have provided efficient methods for the identification of novel drug candidates. This strategy has been largely applied in discovering several pharmacological ligand classes, including enzyme inhibitors, receptor antagonists and, more recently, also allosteric (positive and negative) modulators. Recently, Siegal and collaborators reported an interesting study, performed on a detergent-solubilized StaR adenosine A2A receptor, describing the existence of both fragment-like negative allosteric modulators (NAMs), and fragment-like positive allosteric modulators (PAMs). From this retrospective study, our results suggest that Supervised Molecular Dynamics (SuMD) simulations can support, on a reasonable time scale, the identification of fragment-like PAMs following their receptor recognition pathways and characterizing the possible allosteric binding sites.
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Affiliation(s)
- Giuseppe Deganutti
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padua, Italy.
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padua, Italy.
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Glukhova A, Thal DM, Nguyen AT, Vecchio EA, Jörg M, Scammells PJ, May LT, Sexton PM, Christopoulos A. Structure of the Adenosine A1 Receptor Reveals the Basis for Subtype Selectivity. Cell 2017; 168:867-877.e13. [DOI: 10.1016/j.cell.2017.01.042] [Citation(s) in RCA: 188] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Revised: 12/15/2016] [Accepted: 01/30/2017] [Indexed: 01/10/2023]
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49
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Janero DR, Korde A, Makriyannis A. Ligand-Assisted Protein Structure (LAPS): An Experimental Paradigm for Characterizing Cannabinoid-Receptor Ligand-Binding Domains. Methods Enzymol 2017; 593:217-235. [DOI: 10.1016/bs.mie.2017.06.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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50
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Using the fragment molecular orbital method to investigate agonist-orexin-2 receptor interactions. Biochem Soc Trans 2016; 44:574-81. [PMID: 27068972 PMCID: PMC5264495 DOI: 10.1042/bst20150250] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Indexed: 12/11/2022]
Abstract
The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in 'real-time' and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX2R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allowsab initioapproaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure-function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available.
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