1
|
Mordalski S, Kościółek T. Homology Modeling of the G Protein-Coupled Receptors. Methods Mol Biol 2023; 2627:167-181. [PMID: 36959447 DOI: 10.1007/978-1-0716-2974-1_9] [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] [Indexed: 04/25/2023]
Abstract
G protein-coupled receptors (GPCRs) are therapeutically important family of membrane proteins. Despite growing number of experimental structures available for GPCRs, homology modeling remains a relevant method for studying these receptors and for discovering new ligands for them. Here we describe the state-of-the-art methods for modeling GPCRs, starting from template selection, through fine-tuning sequence alignment to model refinement.
Collapse
Affiliation(s)
- Stefan Mordalski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland.
| | - Tomasz Kościółek
- Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| |
Collapse
|
2
|
Mordalski S, Wojtuch A, Podolak I, Kurczab R, Bojarski AJ. 2D SIFt: a matrix of ligand-receptor interactions. J Cheminform 2021; 13:66. [PMID: 34496955 PMCID: PMC8424890 DOI: 10.1186/s13321-021-00545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 08/21/2021] [Indexed: 11/10/2022] Open
Abstract
Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.
Collapse
Affiliation(s)
- Stefan Mordalski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland.
| | - Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
| | - Igor Podolak
- Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
| | - Rafał Kurczab
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland
| |
Collapse
|
3
|
Podlewska S, Bugno R, Kudla L, Bojarski AJ, Przewlocki R. Molecular Modeling of µ Opioid Receptor Ligands with Various Functional Properties: PZM21, SR-17018, Morphine, and Fentanyl-Simulated Interaction Patterns Confronted with Experimental Data. Molecules 2020; 25:E4636. [PMID: 33053718 PMCID: PMC7594085 DOI: 10.3390/molecules25204636] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/04/2020] [Accepted: 10/06/2020] [Indexed: 11/16/2022] Open
Abstract
Molecular modeling approaches are an indispensable part of the drug design process. They not only support the process of searching for new ligands of a given receptor, but they also play an important role in explaining particular activity pathways of a compound. In this study, a comprehensive molecular modeling protocol was developed to explain the observed activity profiles of selected µ opioid receptor agents: two G protein-biased µ opioid receptor agonists(PZM21 and SR-17018), unbiased morphine, and the β-arrestin-2-biased agonist,fentanyl. The study involved docking and molecular dynamics simulations carried out for three crystal structures of the target at a microsecond scale, followed by the statistical analysis of ligand-protein contacts. The interaction frequency between the modeled compounds and the subsequent residues of a protein during the simulation was also correlated with the output of in vitro and in vivo tests, resulting in the set of amino acids with the highest Pearson correlation coefficient values. Such indicated positions may serve as a guide for designing new G protein-biased ligands of the µ opioid receptor.
Collapse
Affiliation(s)
- Sabina Podlewska
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Cracow, Poland;
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland; (R.B.); (L.K.); (A.J.B.)
| | - Ryszard Bugno
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland; (R.B.); (L.K.); (A.J.B.)
| | - Lucja Kudla
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland; (R.B.); (L.K.); (A.J.B.)
| | - Andrzej J. Bojarski
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland; (R.B.); (L.K.); (A.J.B.)
| | - Ryszard Przewlocki
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland; (R.B.); (L.K.); (A.J.B.)
| |
Collapse
|
4
|
Jastrzębski S, Szymczak M, Pocha A, Mordalski S, Tabor J, Bojarski AJ, Podlewska S. Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening. J Chem Inf Model 2020; 60:4246-4262. [DOI: 10.1021/acs.jcim.9b01202] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Stanisław Jastrzębski
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Maciej Szymczak
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Agnieszka Pocha
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Stefan Mordalski
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Jacek Tabor
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Andrzej J. Bojarski
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Kraków, Poland
| |
Collapse
|
5
|
Mora Lagares L, Minovski N, Caballero Alfonso AY, Benfenati E, Wellens S, Culot M, Gosselet F, Novič M. Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies. Int J Mol Sci 2020; 21:ijms21114058. [PMID: 32517082 PMCID: PMC7312539 DOI: 10.3390/ijms21114058] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/28/2020] [Accepted: 06/02/2020] [Indexed: 12/12/2022] Open
Abstract
The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand–P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand–P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (hP-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π–sigma, π–alkyl, and π–π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand–protein complex in the binding site. It was also observed that some interacting residues in hP-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity.
Collapse
Affiliation(s)
- Liadys Mora Lagares
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Correspondence: (L.M.L.); (M.N.); Tel.: +386-01-476-0253 (L.M.L. & M.N.)
| | - Nikola Minovski
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
| | - Ana Yisel Caballero Alfonso
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia;
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche “Mario Negri”—IRCCS, 20156 Milano, Italy;
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche “Mario Negri”—IRCCS, 20156 Milano, Italy;
| | - Sara Wellens
- Laboratoire de la Barrière Hémato-Encéphalique (LBHE), University Artois, UR 2465, F-62300 Lens, France; (S.W.); (M.C.); (F.G.)
| | - Maxime Culot
- Laboratoire de la Barrière Hémato-Encéphalique (LBHE), University Artois, UR 2465, F-62300 Lens, France; (S.W.); (M.C.); (F.G.)
| | - Fabien Gosselet
- Laboratoire de la Barrière Hémato-Encéphalique (LBHE), University Artois, UR 2465, F-62300 Lens, France; (S.W.); (M.C.); (F.G.)
| | - Marjana Novič
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
- Correspondence: (L.M.L.); (M.N.); Tel.: +386-01-476-0253 (L.M.L. & M.N.)
| |
Collapse
|
6
|
Liu YY, Chen XR, Gao LF, Chen M, Cui WQ, Ding WY, Chen XY, God'spower BO, Li YH. Spectrum-Effect Relationships Between the Bioactive Ingredient of Syringa oblata Lindl. Leaves and Its Role in Inhibiting the Biofilm Formation of Streptococcus suis. Front Pharmacol 2018; 9:570. [PMID: 29922159 PMCID: PMC5996274 DOI: 10.3389/fphar.2018.00570] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 05/14/2018] [Indexed: 12/17/2022] Open
Abstract
Syringa oblata Lindl. (S. oblata) has been used in herbal medicines for treating bacterial diseases. It is also thought to inhibit Streptococcus suis (S. suis) biofilm formation. However, due to the inherent nature of the complexity in its chemical properties, it is difficult to understand the possible bioactive ingredients of S. oblata. The spectrum-effect relationships method was applied to screen the main active ingredients in S. oblata obtained from Heilongjiang Province based on gray relational analysis. The results revealed that Sub-MICs obtained from 10 batches of S. oblata could inhibit biofilm formation by S. suis. Gray relational analysis revealed variations in the contents of 15 main peaks and rutin was discovered to be the main active ingredient. Then, the function of rutin was further verified by inhibiting S. suis biofilm formation using crystal violet staining. Computational studies revealed that rutin may target the chloramphenicol acetyltransferase protein in the biofilm formation of S. suis. In conclusion, this study revealed that the spectrum-effect relationships and computational studies are useful tools to associate the active ingredient with the potential anti-biofilm effects of S. oblata. Here, our findings would provide foundation for the further understanding of the mechanism of S. oblata intervention in biofilm formation.
Collapse
Affiliation(s)
- Yan-Yan Liu
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Xing-Ru Chen
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Ling-Fei Gao
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Mo Chen
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Wen-Qiang Cui
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Wen-Ya Ding
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Xue-Ying Chen
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Bello-Onaghise God'spower
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| | - Yan-Hua Li
- College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.,Heilongjiang Key Laboratory for Animal Disease Control and Pharmaceutical Development, Harbin, China
| |
Collapse
|
7
|
Huang Z, Wong CF. Inexpensive Method for Selecting Receptor Structures for Virtual Screening. J Chem Inf Model 2015; 56:21-34. [PMID: 26651874 DOI: 10.1021/acs.jcim.5b00299] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This article introduces a screening performance index (SPI) to help select from a number of experimental structures one or a few that are more likely to identify more actives among its top hits from virtual screening of a compound library. It achieved this by docking only known actives to the experimental structures without considering a large number of decoys to reduce computational costs. The SPI is calculated by using the docking energies of the actives to all the receptor structures. We evaluated the performance of the SPI by applying it to study eight protein systems: fatty acid binding protein adipocyte FABP4, serine/threonine-protein kinase BRAF, beta-1 adrenergic receptor ADRB1, TGF-beta receptor type I TGFR1, adenosylhomocysteinase SAHH, thyroid hormone receptor beta-1 THB, phospholipase A2 group IIA PA2GA, and cytochrome P450 3a4 CP3A4. We found that the SPI agreed with the results from other popular performance metrics such as Boltzmann-Enhanced Discrimination Receiver Operator Characteristics (BEDROC), Robust Initial Enhancement (RIE), Area Under Accumulation Curve (AUAC), and Enrichment Factor (EF) but is less expensive to calculate. SPI also performed better than the best docking energy, the molecular volume of the bound ligand, and the resolution of crystal structure in selecting good receptor structures for virtual screening. The implications of these findings were further discussed in the context of ensemble docking, in situations when no experimental structure for the targeted protein was available, or under circumstances when quick choices of receptor structures need to be made before quantitative indexes such as the SPI and BEDROC can be calculated.
Collapse
Affiliation(s)
- Zunnan Huang
- China-America Cancer Research Institute, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Dongguan Scientific Research Center, Guangdong Medical University , Dongguan, Guangdong Province, P. R. China , 523808
| | - Chung F Wong
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-Saint Louis , One University Boulevard, St. Louis, Missouri 63121, United States
| |
Collapse
|
8
|
Wasko MJ, Pellegrene KA, Madura JD, Surratt CK. A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets. Front Neurol 2015; 6:197. [PMID: 26441817 PMCID: PMC4566055 DOI: 10.3389/fneur.2015.00197] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 08/24/2015] [Indexed: 01/12/2023] Open
Abstract
Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.
Collapse
Affiliation(s)
- Michael J Wasko
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Kendy A Pellegrene
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Jeffry D Madura
- Department of Chemistry and Biochemistry, Center for Computational Sciences, Bayer School of Natural and Environmental Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Christopher K Surratt
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| |
Collapse
|