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Shaikh J, Patel S, Nagani A, Shah M, Ugharatdar S, Patel A, Shah D, Patel D. Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives. In Silico Pharmacol 2024; 12:79. [PMID: 39220602 PMCID: PMC11362452 DOI: 10.1007/s40203-024-00255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
In the quest to combat tuberculosis, DprE1, a challenging target for novel anti-tubercular agents due to its small size and membrane location, has been a focus of research. DprE1 catalyzes the transformation of DPR into Ketoribose DPX, with Benzothiazinone emerging as a potent pharmacophore for inhibiting DprE1. Clinical trial drugs such as BTZ043, BTZ038, PBTZ169, and TMC-207 have shown promising results as DprE1 inhibitors. This study employed pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives to identify crucial features for eliciting a biological response. Benzothiazinone (Ligand code: 73) emerged as a reference ligand with a fitness score of 3.000. ROC analysis validated the pharmacophore with an excellent score of 0.71. To build a 3D QSAR model, a series of Benzothiazinone congeneric derivatives were explored. The model exhibited strong performance, with a standard deviation of 0.1531, a correlation coefficient for the training set (R2) value of 0.9754, and a correlation coefficient for test set Q2 value of 0.7632, indicating robust predictive capabilities. Contour maps guided the design of novel benzothiazinone derivatives, emphasizing steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor groups for structure-activity relationships. Docking studies against PDB ID: 4NCR demonstrated favorable scores, with interactions aligning well with the in-built ligand 26 J. Docking validation via RMSD values supported the reliability of the docking results. This comprehensive approach aids in the design of novel benzothiazinone derivatives with potential anti-tubercular properties, contributing to the development of novel anti-tubercular agents which can be pivotal in the eradication of tuberculosis.
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Affiliation(s)
- Jahaan Shaikh
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
| | - Salman Patel
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
| | - Afzal Nagani
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
- Research and Development Cell, Parul University, Vadodara, Gujarat India
| | - Moksh Shah
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
| | - Siddik Ugharatdar
- Department of Pharmaceutical Chemistry, Laxminarayandev College of Pharmacy, Bholav, Bharuch, Gujarat India
| | - Ashish Patel
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT Campus, Changa, Anand, Gujarat India
| | - Drashti Shah
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT Campus, Changa, Anand, Gujarat India
| | - Dharti Patel
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT Campus, Changa, Anand, Gujarat India
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2
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Wuyun Q, Chen Y, Shen Y, Cao Y, Hu G, Cui W, Gao J, Zheng W. Recent Progress of Protein Tertiary Structure Prediction. Molecules 2024; 29:832. [PMID: 38398585 PMCID: PMC10893003 DOI: 10.3390/molecules29040832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.
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Affiliation(s)
- Qiqige Wuyun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yihan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Yifeng Shen
- Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0882, Kanagawa, Japan;
| | - Yang Cao
- College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Gang Hu
- NITFID, School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
| | - Wei Cui
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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Hsieh CJ, Giannakoulias S, Petersson EJ, Mach RH. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals (Basel) 2023; 16:317. [PMID: 37259459 PMCID: PMC9964981 DOI: 10.3390/ph16020317] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 11/19/2023] Open
Abstract
The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).
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Affiliation(s)
- Chia-Ju Hsieh
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sam Giannakoulias
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E. James Petersson
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert H. Mach
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Hakmi M, Bouricha EM, El Harti J, Amzazi S, Belyamani L, Khanfri JE, Ibrahimi A. Computational modeling and druggability assessment of Aggregatibacter actinomycetemcomitans leukotoxin. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 222:106952. [PMID: 35724475 DOI: 10.1016/j.cmpb.2022.106952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/30/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
The leukotoxin (LtxA) of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is a protein exotoxin belonging to the repeat-in-toxin family (RTX). Numerous studies have demonstrated that LtxA may play a critical role in the pathogenicity of A. actinomycetemcomitans since hyper-leukotoxic strains have been associated with severe disease. Accordingly, considerable effort has been made to elucidate the mechanisms by which LtxA interacts with host cells and induce their death. However, these attempts have been hampered by the unavailability of a tertiary structure of the toxin, which limits the understanding of its molecular properties and mechanisms. In this paper, we used homology and template free modeling algorithms to build the complete tertiary model of LtxA at atomic level in its calcium-bound Holo-state. The resulting model was refined by energy minimization, validated by Molprobity and ProSA tools, and subsequently subjected to a cumulative 600ns of all-atom classical molecular dynamics simulation to evaluate its structural aspects. The druggability of the proposed model was assessed using Fpocket and FTMap tools, resulting in the identification of four putative cavities and fifteen binding hotspots that could be targeted by rational drug design tools to find new ligands to inhibit LtxA activity.
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Affiliation(s)
- Mohammed Hakmi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - El Mehdi Bouricha
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - Jaouad El Harti
- Therapeutic Chemistry Laboratory, Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - Said Amzazi
- Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, and Genomic Center of Human Pathologies, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco
| | - Lahcen Belyamani
- Emergency Department, Military Hospital Mohammed V, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco
| | - Jamal Eddine Khanfri
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
| | - Azeddine Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco.
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Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med 2021; 137:104851. [PMID: 34520990 DOI: 10.1016/j.compbiomed.2021.104851] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/05/2021] [Accepted: 09/05/2021] [Indexed: 12/28/2022]
Abstract
In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.
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Affiliation(s)
- Bilal Shaker
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Sajjad Ahmad
- Department of Health and Biological Sciences, Abasyn University, Peshawar, 25000, Pakistan
| | - Jingyu Lee
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Chanjin Jung
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea.
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Bhunia SS, Saxena AK. Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors. Curr Top Med Chem 2021; 21:269-294. [PMID: 32901584 DOI: 10.2174/1568026620666200908165250] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/20/2020] [Accepted: 09/01/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery. OBJECTIVE The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs. METHODS The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures. RESULTS The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures. CONCLUSION The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.
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Affiliation(s)
- Shome S Bhunia
- Global Institute of Pharmaceutical Education and Research, Kashipur, Uttarakhand, India
| | - Anil K Saxena
- Division of Medicinal and Process Chemistry, CSIR-CDRI, Lucknow 226031, India
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Sharma S, Bhatia V. Appraisal of the Role of In silico Methods in Pyrazole Based Drug Design. Mini Rev Med Chem 2021; 21:204-216. [PMID: 32875985 DOI: 10.2174/1389557520666200901184146] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/19/2020] [Accepted: 07/06/2020] [Indexed: 11/22/2022]
Abstract
Pyrazole and its derivatives are a pharmacologically and significantly active scaffolds that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anti-cancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.
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Affiliation(s)
- Smriti Sharma
- Department of Chemistry, Miranda House, University of Delhi, India
| | - Vinayak Bhatia
- ICARE Eye Hospital and Postgraduate Institute, U.P., Noida, India
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8
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Pearce R, Zhang Y. Toward the solution of the protein structure prediction problem. J Biol Chem 2021; 297:100870. [PMID: 34119522 PMCID: PMC8254035 DOI: 10.1016/j.jbc.2021.100870] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 11/20/2022] Open
Abstract
Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.
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Affiliation(s)
- Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, USA.
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Hou T, Bian Y, McGuire T, Xie XQ. Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence. Biomolecules 2021; 11:biom11060870. [PMID: 34208096 PMCID: PMC8230833 DOI: 10.3390/biom11060870] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/30/2021] [Accepted: 06/08/2021] [Indexed: 01/01/2023] Open
Abstract
G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discovery of new GPCR allosteric modulators can greatly benefit the further understanding of GPCR cell signaling mechanisms. It is critical but also challenging to make an accurate distinction of modulators for different GPCR groups in an efficient and effective manner. In this study, we focus on an 11-class classification task with 10 GPCR subtype classes and a random compounds class. We used a dataset containing 34,434 compounds with allosteric modulators collected from classical GPCR families A, B, and C, as well as random drug-like compounds. Six types of machine learning models, including support vector machine, naïve Bayes, decision tree, random forest, logistic regression, and multilayer perceptron, were trained using different combinations of features including molecular descriptors, Atom-pair fingerprints, MACCS fingerprints, and ECFP6 fingerprints. The performances of trained machine learning models with different feature combinations were closely investigated and discussed. To the best of our knowledge, this is the first work on the multi-class classification of GPCR allosteric modulators. We believe that the classification models developed in this study can be used as simple and accurate tools for the discovery and development of GPCR allosteric modulators.
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Affiliation(s)
- Tianling Hou
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen (CCGS) Center and Pharmacometrics System Pharmacology Program, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (T.H.); (Y.B.); (T.M.)
- NIH National Center of Excellence for Computational Drug Abuse Research (CDAR), University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yuemin Bian
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen (CCGS) Center and Pharmacometrics System Pharmacology Program, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (T.H.); (Y.B.); (T.M.)
- NIH National Center of Excellence for Computational Drug Abuse Research (CDAR), University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Terence McGuire
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen (CCGS) Center and Pharmacometrics System Pharmacology Program, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (T.H.); (Y.B.); (T.M.)
- NIH National Center of Excellence for Computational Drug Abuse Research (CDAR), University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen (CCGS) Center and Pharmacometrics System Pharmacology Program, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (T.H.); (Y.B.); (T.M.)
- Drug Discovery Institute, Departments of Computational Biology and of Structural Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Correspondence:
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Oliveira ASF, Ibarra AA, Bermudez I, Casalino L, Gaieb Z, Shoemark DK, Gallagher T, Sessions RB, Amaro RE, Mulholland AJ. A potential interaction between the SARS-CoV-2 spike protein and nicotinic acetylcholine receptors. Biophys J 2021; 120:983-993. [PMID: 33609494 PMCID: PMC7889469 DOI: 10.1016/j.bpj.2021.01.037] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/11/2021] [Accepted: 01/13/2021] [Indexed: 01/08/2023] Open
Abstract
Changeux et al. (Changeux et al. C. R. Biol. 343:33-39.) recently suggested that the SARS-CoV-2 spike protein may interact with nicotinic acetylcholine receptors (nAChRs) and that such interactions may be involved in pathology and infectivity. This hypothesis is based on the fact that the SARS-CoV-2 spike protein contains a sequence motif similar to known nAChR antagonists. Here, we use molecular simulations of validated atomically detailed structures of nAChRs and of the spike to investigate the possible binding of the Y674-R685 region of the spike to nAChRs. We examine the binding of the Y674-R685 loop to three nAChRs, namely the human α4β2 and α7 subtypes and the muscle-like αβγδ receptor from Tetronarce californica. Our results predict that Y674-R685 has affinity for nAChRs. The region of the spike responsible for binding contains a PRRA motif, a four-residue insertion not found in other SARS-like coronaviruses. The conformational behavior of the bound Y674-R685 is highly dependent on the receptor subtype; it adopts extended conformations in the α4β2 and α7 complexes but is more compact when bound to the muscle-like receptor. In the α4β2 and αβγδ complexes, the interaction of Y674-R685 with the receptors forces the loop C region to adopt an open conformation, similar to other known nAChR antagonists. In contrast, in the α7 complex, Y674-R685 penetrates deeply into the binding pocket in which it forms interactions with the residues lining the aromatic box, namely with TrpB, TyrC1, and TyrC2. Estimates of binding energy suggest that Y674-R685 forms stable complexes with all three nAChR subtypes. Analyses of simulations of the glycosylated spike show that the Y674-R685 region is accessible for binding. We suggest a potential binding orientation of the spike protein with nAChRs, in which they are in a nonparallel arrangement to one another.
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Affiliation(s)
- A Sofia F Oliveira
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom; Bristol Synthetic Biology Centre, BrisSynBio, Bristol, United Kingdom
| | - Amaurys Avila Ibarra
- Research Software Engineering, Advanced Computing Research Centre, University of Bristol, Bristol, United Kingdom
| | - Isabel Bermudez
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, United Kingdom
| | - Lorenzo Casalino
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Zied Gaieb
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Deborah K Shoemark
- School of Biochemistry, University of Bristol, Bristol, United Kingdom; Bristol Synthetic Biology Centre, BrisSynBio, Bristol, United Kingdom
| | - Timothy Gallagher
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
| | | | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom.
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Tóth AD, Garger D, Prokop S, Soltész-Katona E, Várnai P, Balla A, Turu G, Hunyady L. A general method for quantifying ligand binding to unmodified receptors using Gaussia luciferase. J Biol Chem 2021; 296:100366. [PMID: 33545176 PMCID: PMC7950324 DOI: 10.1016/j.jbc.2021.100366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 01/27/2021] [Accepted: 02/01/2021] [Indexed: 11/23/2022] Open
Abstract
Reliable measurement of ligand binding to cell surface receptors is of outstanding biological and pharmacological importance. Resonance energy transfer-based assays are powerful approaches to achieve this goal, but the currently available methods are hindered by the necessity of receptor tagging, which can potentially alter ligand binding properties. Therefore, we developed a tag-free system to measure ligand‒receptor interactions in live cells using the Gaussia luciferase (GLuc) as a bioluminescence resonance energy transfer donor. GLuc is as small as the commonly applied Nanoluciferase but has enhanced brightness, and its proper substrate is the frequently used coelenterazine. In our assay, bystander bioluminescence resonance energy transfer is detected between a GLuc-based extracellular surface biosensor and fluorescent ligands bound to their unmodified receptors. The broad spectrum of applications includes equilibrium and kinetic ligand binding measurements for both labeled and competitive unlabeled ligands, and the assay can be utilized for different classes of plasma membrane receptors. Furthermore, the assay is suitable for high-throughput screening, as evidenced by the identification of novel α1 adrenergic receptor ligands. Our data demonstrate that GLuc-based biosensors provide a simple, sensitive, and cost-efficient platform for drug characterization and development.
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Affiliation(s)
- András Dávid Tóth
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; MTA-SE Laboratory of Molecular Physiology, Eötvös Loránd Research Network, Budapest, Hungary; Department of Internal Medicine and Hematology, Semmelweis University, Budapest, Hungary
| | - Dániel Garger
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Susanne Prokop
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; Szentágothai János Doctoral School of Neuroscience, Semmelweis University, Budapest, Hungary
| | - Eszter Soltész-Katona
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; MTA-SE Laboratory of Molecular Physiology, Eötvös Loránd Research Network, Budapest, Hungary
| | - Péter Várnai
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; MTA-SE Laboratory of Molecular Physiology, Eötvös Loránd Research Network, Budapest, Hungary
| | - András Balla
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; MTA-SE Laboratory of Molecular Physiology, Eötvös Loránd Research Network, Budapest, Hungary
| | - Gábor Turu
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; MTA-SE Laboratory of Molecular Physiology, Eötvös Loránd Research Network, Budapest, Hungary
| | - László Hunyady
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary; MTA-SE Laboratory of Molecular Physiology, Eötvös Loránd Research Network, Budapest, Hungary.
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12
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Oliveira ASF, Ibarra AA, Bermudez I, Casalino L, Gaieb Z, Shoemark DK, Gallagher T, Sessions RB, Amaro RE, Mulholland AJ. Simulations support the interaction of the SARS-CoV-2 spike protein with nicotinic acetylcholine receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.07.16.206680. [PMID: 32743575 PMCID: PMC7386492 DOI: 10.1101/2020.07.16.206680] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Changeux et al. recently suggested that the SARS-CoV-2 spike (S) protein may interact with nicotinic acetylcholine receptors (nAChRs). Such interactions may be involved in pathology and infectivity. Here, we use molecular simulations of validated atomically detailed structures of nAChRs, and of the S protein, to investigate this 'nicotinic hypothesis'. We examine the binding of the Y674-R685 loop of the S protein to three nAChRs, namely the human α4β2 and α7 subtypes and the muscle-like αβγδ receptor from Tetronarce californica. Our results indicate that Y674-R685 has affinity for nAChRs and the region responsible for binding contains the PRRA motif, a four-residue insertion not found in other SARS-like coronaviruses. In particular, R682 has a key role in the stabilisation of the complexes as it forms interactions with loops A, B and C in the receptor's binding pocket. The conformational behaviour of the bound Y674-R685 region is highly dependent on the receptor subtype, adopting extended conformations in the α4β2 and α7 complexes and more compact ones when bound to the muscle-like receptor. In the α4β2 and αβγδ complexes, the interaction of Y674-R685 with the receptors forces the loop C region to adopt an open conformation similar to other known nAChR antagonists. In contrast, in the α7 complex, Y674-R685 penetrates deeply into the binding pocket where it forms interactions with the residues lining the aromatic box, namely with TrpB, TyrC1 and TyrC2. Estimates of binding energy suggest that Y674-R685 forms stable complexes with all three nAChR subtypes. Analyses of the simulations of the full-length S protein show that the Y674-R685 region is accessible for binding, and suggest a potential binding orientation of the S protein with nAChRs.
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Affiliation(s)
- A. Sofia F. Oliveira
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
| | - Amaurys Avila Ibarra
- Research Software Engineering, Advanced Computing Research Centre, University of Bristol, Bristol BS1 5QD, UK
| | - Isabel Bermudez
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford OX30BP, UK
| | - Lorenzo Casalino
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093 USA
| | - Zied Gaieb
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093 USA
| | | | - Timothy Gallagher
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
| | | | - Rommie E. Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093 USA
| | - Adrian J. Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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13
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Rallabandi HR, Ganesan P, Kim YJ. Targeting the C-Terminal Domain Small Phosphatase 1. Life (Basel) 2020; 10:life10050057. [PMID: 32397221 PMCID: PMC7281111 DOI: 10.3390/life10050057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/05/2020] [Accepted: 05/07/2020] [Indexed: 12/15/2022] Open
Abstract
The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.
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14
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Li Y, Sun Y, Song Y, Dai D, Zhao Z, Zhang Q, Zhong W, Hu LA, Ma Y, Li X, Wang R. Fragment-Based Computational Method for Designing GPCR Ligands. J Chem Inf Model 2019; 60:4339-4349. [PMID: 31652060 DOI: 10.1021/acs.jcim.9b00699] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, which is arguably the most important family of drug target. With the technology breakthroughs in X-ray crystallography and cryo-electron microscopy, more than 300 GPCR-ligand complex structures have been publicly reported since 2007, covering about 60 unique GPCRs. Such abundant structural information certainly will facilitate the structure-based drug design by targeting GPCRs. In this study, we have developed a fragment-based computational method for designing novel GPCR ligands. We first extracted the characteristic interaction patterns (CIPs) on the binding interfaces between GPCRs and their ligands. The CIPs were used as queries to search the chemical fragments derived from GPCR ligands, which were required to form similar interaction patterns with GPCR. Then, the selected chemical fragments were assembled into complete molecules by using the AutoT&T2 software. In this work, we chose β-adrenergic receptor (β-AR) and muscarinic acetylcholine receptor (mAChR) as the targets to validate this method. Based on the designs suggested by our method, samples of 63 compounds were purchased and tested in a cell-based functional assay. A total of 15 and 22 compounds were identified as active antagonists for β2-AR and mAChR M1, respectively. Molecular dynamics simulations and binding free energy analysis were performed to explore the key interactions (e.g., hydrogen bonds and π-π interactions) between those active compounds and their target GPCRs. In summary, our work presents a useful approach to the de novo design of GPCR ligands based on the relevant 3D structural information.
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Affiliation(s)
- Yan Li
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People's Republic of China.,Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yaping Sun
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Yunpeng Song
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Dongcheng Dai
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Zhixiong Zhao
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People's Republic of China
| | - Qing Zhang
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Wenge Zhong
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Liaoyuan A Hu
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Yingli Ma
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Xun Li
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Renxiao Wang
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People's Republic of China.,Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.,Shanxi Key Laboratory of Innovative Drugs for the Treatment of Serious Diseases Basing on Chronic Inflammation, College of Traditional Chinese Medicine, Shanxi University of Chinese Medicine, Taiyuan, Shanxi 030619, People's Republic of China
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15
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Banegas-Luna AJ, Cerón-Carrasco JP, Puertas-Martín S, Pérez-Sánchez H. BRUSELAS: HPC Generic and Customizable Software Architecture for 3D Ligand-Based Virtual Screening of Large Molecular Databases. J Chem Inf Model 2019; 59:2805-2817. [DOI: 10.1021/acs.jcim.9b00279] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Antonio J. Banegas-Luna
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos s/n, 30107 Murcia, Spain
| | - José P. Cerón-Carrasco
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos s/n, 30107 Murcia, Spain
| | - Savíns Puertas-Martín
- Supercomputing - Algorithms Research Group (SAL), Department of Informatics, University of Almería, Agrifood Campus of International Excellence, ceiA3, Almería, 04120, Spain
| | - Horacio Pérez-Sánchez
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Campus de los Jerónimos s/n, 30107 Murcia, Spain
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16
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Bian Y, Jing Y, Wang L, Ma S, Jun JJ, Xie XQ. Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers. Mol Pharm 2019; 16:2605-2615. [PMID: 31013097 DOI: 10.1021/acs.molpharmaceut.9b00182] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have classifiers to identify active ligands from inactive or random compounds and distinguish allosteric modulators from orthosteric ligands. In this study, supervised machine learning classifiers were built for two subtypes of cannabinoid receptors, CB1 and CB2. Three types of features, including molecular descriptors, MACCS fingerprints, and ECFP6 fingerprints, were calculated to evaluate the compound sets from diverse aspects. Deep neural networks, as well as conventional machine learning algorithms including support vector machine, naïve Bayes, logistic regression, and ensemble learning, were applied. Their performances on the classification with different types of features were compared and discussed. According to the receiver operating characteristic curves and the calculated metrics, the advantages and drawbacks of each algorithm were investigated. The feature ranking was followed to help extract useful knowledge about critical molecular properties, substructural keys, and circular fingerprints. The extracted features will then facilitate the research on cannabinoid receptors by providing guidance on preferred properties for compound modification and novel scaffold design. Besides using conventional molecular docking studies for compound virtual screening, machine-learning-based decision-making models provide alternative options. This study can be of value to the application of machine learning in the area of drug discovery and compound development.
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17
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Raza S, Ranaghan KE, van der Kamp MW, Woods CJ, Mulholland AJ, Azam SS. Visualizing protein-ligand binding with chemical energy-wise decomposition (CHEWD): application to ligand binding in the kallikrein-8 S1 Site. J Comput Aided Mol Des 2019; 33:461-475. [PMID: 30989572 DOI: 10.1007/s10822-019-00200-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 04/05/2019] [Indexed: 02/08/2023]
Abstract
Kallikrein-8, a serine protease, is a target for structure-based drug design due to its therapeutic potential in treating Alzheimer's disease and is also useful as a biomarker in ovarian cancer. We present a binding assessment of ligands to kallikrein-8 using a residue-wise decomposition of the binding energy. Binding of four putative inhibitors of kallikrein-8 is investigated through molecular dynamics simulation and ligand binding energy evaluation with two methods (MM/PBSA and WaterSwap). For visualization of the residue-wise decomposition of binding energies, chemical energy-wise decomposition or CHEWD is introduced as a plugin to UCSF Chimera and Pymol. CHEWD allows easy comparison between ligands using individual residue contributions to the binding energy. Molecular dynamics simulations indicate one ligand binds stably to the kallikrein-8 S1 binding site. Comparison with other members of the kallikrein family shows that residues responsible for binding are specific to kallikrein-8. Thus, ZINC02927490 is a promising lead for development of novel kallikrein-8 inhibitors.
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Affiliation(s)
- Saad Raza
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University Islamabad, Islamabad, 45320, Pakistan.,Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | - Kara E Ranaghan
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | - Marc W van der Kamp
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK.,School of Biochemistry, Biomedical Sciences Building, University of Bristol, University Walk, Bristol, BS8 1TD, UK.,BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol, BS8 1TQ, UK
| | - Christopher J Woods
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK.,BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol, BS8 1TQ, UK
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK. .,BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol, BS8 1TQ, UK.
| | - Syed Sikander Azam
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University Islamabad, Islamabad, 45320, Pakistan.
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18
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Evenseth LM, Warszycki D, Bojarski AJ, Gabrielsen M, Sylte I. In Silico Methods for the Discovery of Orthosteric GABA B Receptor Compounds. Molecules 2019; 24:E935. [PMID: 30866507 PMCID: PMC6429233 DOI: 10.3390/molecules24050935] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 02/20/2019] [Accepted: 03/01/2019] [Indexed: 12/27/2022] Open
Abstract
The GABAB receptor (GABAB-R) is a heterodimeric class C G protein-coupled receptor comprised of the GABAB1a/b and GABAB2 subunits. The endogenous orthosteric agonist γ-amino-butyric acid (GABA) binds within the extracellular Venus flytrap (VFT) domain of the GABAB1a/b subunit. The receptor is associated with numerous neurological and neuropsychiatric disorders including learning and memory deficits, depression and anxiety, addiction and epilepsy, and is an interesting target for new drug development. Ligand- and structure-based virtual screening (VS) are used to identify hits in preclinical drug discovery. In the present study, we have evaluated classical ligand-based in silico methods, fingerprinting and pharmacophore mapping and structure-based in silico methods, structure-based pharmacophores, docking and scoring, and linear interaction approximation (LIA) for their aptitude to identify orthosteric GABAB-R compounds. Our results show that the limited number of active compounds and their high structural similarity complicate the use of ligand-based methods. However, by combining ligand-based methods with different structure-based methods active compounds were identified in front of DUDE-E decoys and the number of false positives was reduced, indicating that novel orthosteric GABAB-R compounds may be identified by a combination of ligand-based and structure-based in silico methods.
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Affiliation(s)
- Linn M Evenseth
- Molecular Pharmacology and Toxicology, Department of Medical Biology, Faculty of Health Sciences, UiT-The Arctic University of Norway, NO-9037 Tromsø, Norway.
| | - Dawid Warszycki
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Science, Smetna 12, 31-343 Kraków, Poland.
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Science, Smetna 12, 31-343 Kraków, Poland.
| | - Mari Gabrielsen
- Molecular Pharmacology and Toxicology, Department of Medical Biology, Faculty of Health Sciences, UiT-The Arctic University of Norway, NO-9037 Tromsø, Norway.
| | - Ingebrigt Sylte
- Molecular Pharmacology and Toxicology, Department of Medical Biology, Faculty of Health Sciences, UiT-The Arctic University of Norway, NO-9037 Tromsø, Norway.
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19
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Screen efficiency comparisons of decision tree and neural network algorithms in machine learning assisted drug design. Sci China Chem 2019. [DOI: 10.1007/s11426-018-9412-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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20
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Srinivasan B, Tonddast-Navaei S, Roy A, Zhou H, Skolnick J. Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs. Med Res Rev 2018; 39:684-705. [PMID: 30192413 DOI: 10.1002/med.21538] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/16/2018] [Accepted: 08/09/2018] [Indexed: 12/15/2022]
Abstract
Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.
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Affiliation(s)
- Bharath Srinivasan
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia
| | - Sam Tonddast-Navaei
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia
| | - Ambrish Roy
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia
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21
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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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22
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Yuan X, Xu Y. Recent Trends and Applications of Molecular Modeling in GPCR⁻Ligand Recognition and Structure-Based Drug Design. Int J Mol Sci 2018; 19:ijms19072105. [PMID: 30036949 PMCID: PMC6073596 DOI: 10.3390/ijms19072105] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/12/2018] [Accepted: 07/12/2018] [Indexed: 01/14/2023] Open
Abstract
G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.
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Affiliation(s)
- Xiaojing Yuan
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai 201203, China.
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yechun Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences (CAS), Shanghai 201203, China.
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.
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23
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24
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Cross JB. Methods for Virtual Screening of GPCR Targets: Approaches and Challenges. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2017; 1705:233-264. [PMID: 29188566 DOI: 10.1007/978-1-4939-7465-8_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.
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Affiliation(s)
- Jason B Cross
- University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA.
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25
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Ghosh S, Kaushik A, Khurana S, Varshney A, Singh AK, Dahiya P, Thakur JK, Sarin SK, Gupta D, Malhotra P, Mukherjee SK, Bhatnagar RK. An RNAi-based high-throughput screening assay to identify small molecule inhibitors of hepatitis B virus replication. J Biol Chem 2017; 292:12577-12588. [PMID: 28584057 DOI: 10.1074/jbc.m117.775155] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 06/04/2017] [Indexed: 01/28/2023] Open
Abstract
Persistent or chronic infection with the hepatitis B virus (HBV) represents one of the most common viral diseases in humans. The hepatitis B virus deploys the hepatitis B virus X protein (HBx) as a suppressor of host defenses consisting of RNAi-based silencing of viral genes. Because of its critical role in countering host defenses, HBx represents an attractive target for antiviral drugs. Here, we developed and optimized a loss-of-function screening procedure, which identified a potential pharmacophore that abrogated HBx RNAi suppression activity. In a survey of 14,400 compounds in the Maybridge Screening Collection, we prioritized candidate compounds via high-throughput screening based on reversal of green fluorescent protein (GFP)-reported, RNAi-mediated silencing in a HepG2/GFP-shRNA RNAi sensor line. The screening yielded a pharmacologically active compound, N-(2,4-difluorophenyl)-N'-[3-(1H-imidazol-1-yl) propyl] thiourea (IR415), which blocked HBx-mediated RNAi suppression indicated by the GFP reporter assay. We also found that IR415 reversed the inhibitory effect of HBx protein on activity of the Dicer endoribonuclease. We further confirmed the results of the primary screen in IR415-treated, HBV-infected HepG2 cells, which exhibited a marked depletion of HBV core protein synthesis and down-regulation of pre-genomic HBV RNA. Using a molecular interaction analysis system, we confirmed that IR415 selectively targets HBx in a concentration-dependent manner. The screening assay presented here allows rapid and improved detection of small-molecule inhibitors of HBx and related viral proteins. The assay may therefore potentiate the development of next-generation RNAi pathway-based therapeutics and promises to accelerate our search for novel and effective drugs in antiviral research.
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Affiliation(s)
- Subhanita Ghosh
- Insect Resistance Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, 110067 New Delhi, India
| | - Abhinav Kaushik
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, 110067 New Delhi, India
| | - Sachin Khurana
- Malaria Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, 110067 New Delhi, India
| | - Aditi Varshney
- Institute of Liver and Biliary Sciences, D-1, Vasant Kunj, 110070 New Delhi, India
| | - Avishek Kumar Singh
- Institute of Liver and Biliary Sciences, D-1, Vasant Kunj, 110070 New Delhi, India
| | - Pradeep Dahiya
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, 110067 New Delhi, India
| | - Jitendra K Thakur
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, 110067 New Delhi, India
| | - Shiv Kumar Sarin
- Institute of Liver and Biliary Sciences, D-1, Vasant Kunj, 110070 New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, 110067 New Delhi, India
| | - Pawan Malhotra
- Malaria Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, 110067 New Delhi, India,.
| | - Sunil K Mukherjee
- Division of Plant Pathology, Indian Agriculture Research Institute, 110012 New Delhi, India.
| | - Raj K Bhatnagar
- Insect Resistance Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, 110067 New Delhi, India.
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Clark T. G-Protein coupled receptors: answers from simulations. Beilstein J Org Chem 2017; 13:1071-1078. [PMID: 28684986 PMCID: PMC5480328 DOI: 10.3762/bjoc.13.106] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 05/16/2017] [Indexed: 12/31/2022] Open
Abstract
Molecular-dynamics (MD) simulations are playing an increasingly important role in research into the modes of action of G-protein coupled receptors (GPCRs). In this field, MD simulations are unusually important as, because of the difficult experimental situation, they often offer the only opportunity to determine structural and mechanistic features in atomistic detail. Modern combinations of soft- and hardware have made MD simulations a powerful tool in GPCR research. This is important because GPCRs are targeted by approximately half of the drugs on the market, so that computer-aided drug design plays a major role in GPCR research.
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Affiliation(s)
- Timothy Clark
- Computer-Chemie-Centrum, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, 91052 Erlangen, Germany
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27
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Spyrakis F, Ahmed MH, Bayden AS, Cozzini P, Mozzarelli A, Kellogg GE. The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery. J Med Chem 2017; 60:6781-6827. [PMID: 28475332 DOI: 10.1021/acs.jmedchem.7b00057] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.
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Affiliation(s)
- Francesca Spyrakis
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino , Via Pietro Giuria 9, 10125 Torino, Italy
| | - Mostafa H Ahmed
- Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University , Richmond, Virginia 23298-0540, United States
| | - Alexander S Bayden
- CMD Bioscience , 5 Science Park, New Haven, Connecticut 06511, United States
| | - Pietro Cozzini
- Dipartimento di Scienze degli Alimenti e del Farmaco, Laboratorio di Modellistica Molecolare, Università degli Studi di Parma , Parco Area delle Scienze 59/A, 43121 Parma, Italy
| | - Andrea Mozzarelli
- Dipartimento di Scienze degli Alimenti e del Farmaco, Laboratorio di Biochimica, Università degli Studi di Parma , Parco Area delle Scienze 23/A, 43121 Parma, Italy.,Istituto di Biofisica, Consiglio Nazionale delle Ricerche , Via Moruzzi 1, 56124 Pisa, Italy
| | - Glen E Kellogg
- Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University , Richmond, Virginia 23298-0540, United States
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Arimont M, Sun SL, Leurs R, Smit M, de Esch IJP, de Graaf C. Structural Analysis of Chemokine Receptor-Ligand Interactions. J Med Chem 2017; 60:4735-4779. [PMID: 28165741 PMCID: PMC5483895 DOI: 10.1021/acs.jmedchem.6b01309] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
![]()
This
review focuses on the construction and application of structural chemokine
receptor models for the elucidation of molecular determinants of chemokine
receptor modulation and the structure-based discovery and design of
chemokine receptor ligands. A comparative analysis of ligand binding
pockets in chemokine receptors is presented, including a detailed
description of the CXCR4, CCR2, CCR5, CCR9, and US28 X-ray structures,
and their implication for modeling molecular interactions of chemokine
receptors with small-molecule ligands, peptide ligands, and large
antibodies and chemokines. These studies demonstrate how the integration
of new structural information on chemokine receptors with extensive
structure–activity relationship and site-directed mutagenesis
data facilitates the prediction of the structure of chemokine receptor–ligand
complexes that have not been crystallized. Finally, a review of structure-based
ligand discovery and design studies based on chemokine receptor crystal
structures and homology models illustrates the possibilities and challenges
to find novel ligands for chemokine receptors.
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Affiliation(s)
- Marta Arimont
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Shan-Liang Sun
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Martine Smit
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute of Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
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29
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Suen J, Adams M, Lim J, Madala P, Xu W, Cotterell A, He Y, Yau M, Hooper J, Fairlie D. Mapping transmembrane residues of proteinase activated receptor 2 (PAR 2 ) that influence ligand-modulated calcium signaling. Pharmacol Res 2017; 117:328-342. [DOI: 10.1016/j.phrs.2016.12.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 12/07/2016] [Accepted: 12/07/2016] [Indexed: 12/22/2022]
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Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci Rep 2016; 6:28288. [PMID: 27339552 PMCID: PMC4919634 DOI: 10.1038/srep28288] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 05/26/2016] [Indexed: 12/20/2022] Open
Abstract
The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of ß2-adrenoceptor (ß2R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and ß2R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.
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31
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Bednarski M, Otto M, Dudek M, Kołaczkowski M, Bucki A, Siwek A, Groszek G, Maziarz E, Wilk P, Sapa J. Synthesis and Pharmacological Activity of a New Series of 1-(1H-Indol-4-yloxy)-3-(2-(2-methoxyphenoxy)ethylamino)propan-2-ol Analogs. Arch Pharm (Weinheim) 2016; 349:211-23. [PMID: 26853441 DOI: 10.1002/ardp.201500234] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 01/19/2016] [Accepted: 01/20/2016] [Indexed: 12/18/2022]
Abstract
β-Adrenergic receptor antagonists are important therapeutics for the treatment of cardiovascular disorders. In the group of β-blockers, much attention is being paid to the third-generation drugs that possess important ancillary properties besides inhibiting β-adrenoceptors. Vasodilating activity of these drugs is produced through different mechanisms, such as nitric oxide (NO) release, β2 -agonistic action, α1 -blockade, antioxidant action, and Ca(2+) entry blockade. Here, a study on evaluation of the cardiovascular activity of five new compounds is presented. Compound 3a is a methyl and four of the tested compounds (3b-e) are dimethoxy derivatives of 1-(1H-indol-4-yloxy)-3-(2-(2-methoxyphenoxy)ethylamino)propan-2-ol. The obtained results confirmed that the methyl and dimethoxy derivatives of 1-(1H-indol-4-yloxy)-3-(2-(2-methoxyphenoxy)ethylamino)propan-2-ol and their enantiomers possess α1 - and β1 -adrenolytic activities and that the antiarrhythmic and hypotensive effects of the tested compounds are related to their adrenolytic properties.
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Affiliation(s)
- Marek Bednarski
- Faculty of Pharmacy, Department of Pharmacological Screening, Medical College, Jagiellonian University, Krakow, Poland
| | - Monika Otto
- Faculty of Pharmacy, Department of Pharmacological Screening, Medical College, Jagiellonian University, Krakow, Poland
| | - Magdalena Dudek
- Faculty of Pharmacy, Department of Pharmacological Screening, Medical College, Jagiellonian University, Krakow, Poland
| | - Marcin Kołaczkowski
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Medical College, Jagiellonian University, Krakow, Poland
| | - Adam Bucki
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Medical College, Jagiellonian University, Krakow, Poland
| | - Agata Siwek
- Faculty of Pharmacy, Department of Pharmacobiology, Medical College, Jagiellonian University, Krakow, Poland
| | - Grażyna Groszek
- Faculty of Chemistry, Rzeszów University of Technology, Rzeszów, Poland
| | | | - Piotr Wilk
- Nencki Institute of Experimental Biology, Warszawa, Poland
| | - Jacek Sapa
- Faculty of Pharmacy, Department of Pharmacological Screening, Medical College, Jagiellonian University, Krakow, Poland
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32
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Johnson DK, Karanicolas J. Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions. J Chem Inf Model 2016; 56:399-411. [PMID: 26726827 DOI: 10.1021/acs.jcim.5b00572] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.
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Affiliation(s)
- David K Johnson
- Center for Computational Biology, and ‡Department of Molecular Biosciences, University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - John Karanicolas
- Center for Computational Biology, and ‡Department of Molecular Biosciences, University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
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33
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Emerging Approaches to GPCR Ligand Screening for Drug Discovery. Trends Mol Med 2015; 21:687-701. [DOI: 10.1016/j.molmed.2015.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Revised: 09/02/2015] [Accepted: 09/04/2015] [Indexed: 01/07/2023]
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34
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Forli S. Charting a Path to Success in Virtual Screening. Molecules 2015; 20:18732-58. [PMID: 26501243 PMCID: PMC4630810 DOI: 10.3390/molecules201018732] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 10/07/2015] [Accepted: 10/12/2015] [Indexed: 12/27/2022] Open
Abstract
Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.
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Affiliation(s)
- Stefano Forli
- Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
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35
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Topiol S, Sabio M. The role of experimental and computational structural approaches in 7TM drug discovery. Expert Opin Drug Discov 2015. [DOI: 10.1517/17460441.2015.1072166] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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36
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Shahlaei M, Mousavi A. A Conformational Analysis Study on the Melanocortin 4 Receptor Using Multiple Molecular Dynamics Simulations. Chem Biol Drug Des 2015; 86:309-21. [DOI: 10.1111/cbdd.12495] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2014] [Revised: 05/29/2014] [Accepted: 06/13/2014] [Indexed: 12/28/2022]
Affiliation(s)
- Mohsen Shahlaei
- Novel Drug Delivery Research Center; School of Pharmacy; Kermanshah University of Medical Sciences; Parastar Bolvar 6734667149 Kermanshah Iran
| | - Atefeh Mousavi
- Student Research Committee; School of Pharmacy; Kermanshah University of Medical Sciences; Parastar Bolvar 6734667149 Kermanshah Iran
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37
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Kumar A, Zhang KYJ. Hierarchical virtual screening approaches in small molecule drug discovery. Methods 2015; 71:26-37. [PMID: 25072167 PMCID: PMC7129923 DOI: 10.1016/j.ymeth.2014.07.007] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 07/16/2014] [Accepted: 07/17/2014] [Indexed: 02/06/2023] Open
Abstract
Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.
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Affiliation(s)
- Ashutosh Kumar
- Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan.
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38
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Istyastono EP, Kooistra AJ, Vischer HF, Kuijer M, Roumen L, Nijmeijer S, Smits RA, de Esch IJP, Leurs R, de Graaf C. Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor. MEDCHEMCOMM 2015. [DOI: 10.1039/c5md00022j] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Structure-based virtual screening using H1R- and β2R-based histamine H4R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H4R.
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Affiliation(s)
- Enade P. Istyastono
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Albert J. Kooistra
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Henry F. Vischer
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Martien Kuijer
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Luc Roumen
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Saskia Nijmeijer
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | | | - Iwan J. P. de Esch
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Rob Leurs
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Chris de Graaf
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
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Whittaker CAP, Patching SG, Esmann M, Middleton DA. Ligand orientation in a membrane-embedded receptor site revealed by solid-state NMR with paramagnetic relaxation enhancement. Org Biomol Chem 2015; 13:2664-8. [DOI: 10.1039/c4ob02427c] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Paramagnetic relaxation-enhanced solid-state NMR reveals a ouabain analogue with an inverted orientation in the Na,K-ATPase inhibitory site.
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Affiliation(s)
| | | | - Mikael Esmann
- Department of Biomedicine
- Aarhus University
- Aarhus
- Denmark
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40
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41
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Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands. Structure 2014; 22:1140-1151. [PMID: 25043551 DOI: 10.1016/j.str.2014.05.017] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 05/05/2014] [Accepted: 05/27/2014] [Indexed: 01/23/2023]
Abstract
The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles.
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Stewart JM, Tarantal AF, Chen Y, Appleby NC, Fuentes TI, Lee CCI, Salvaris EJ, d'Apice AJF, Cowan PJ, Kearns-Jonker M. Anti-non-Gal-specific combination treatment with an anti-idiotypic Ab and an inhibitory small molecule mitigates the xenoantibody response. Xenotransplantation 2014; 21:254-66. [PMID: 24635144 DOI: 10.1111/xen.12096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Accepted: 02/14/2014] [Indexed: 11/28/2022]
Abstract
BACKGROUND B-cell depletion significantly extends survival of α-1,3-galactosyltranferase knockout (GTKO) porcine organs in pig-to-primate models. Our previous work demonstrated that the anti-non-Gal xenoantibody response is structurally restricted. Selective inhibition of xenoantigen/xenoantibody interactions could prolong xenograft survival while preserving B-cell-mediated immune surveillance. METHODS The anti-idiotypic antibody, B4N190, was selected from a synthetic human phage display library after enrichment against a recombinant anti-non-Gal xenoantibody followed by functional testing in vitro. The inhibitory small molecule, JMS022, was selected from the NCI diversity set III using virtual screening based on predicted xenoantibody structure. Three rhesus monkeys were pre-treated with anti-non-Gal-specific single-chain anti-idiotypic antibody, B4N190. A total of five monkeys, including two untreated controls, were then immunized with GTKO porcine endothelial cells to initiate an anti-non-α-1,3-Gal (non-Gal) xenoantibody response. The efficacy of the inhibitory small molecule specific for anti-non-Gal xenoantibody, JMS022, was tested in vitro. RESULTS After the combination of in vivo anti-id and in vitro small molecule treatments, IgM xenoantibody binding to GTKO cells was reduced to pre-immunization levels in two-thirds of animals; however, some xenoantibodies remained in the third animal. Furthermore, when treated with anti-id alone, all three experimental animals displayed a lower anti-non-Gal IgG xenoantibody response compared with controls. Treatment with anti-idiotypic antibody alone reduced IgM xenoantibody response intensity in only one of three monkeys injected with GTKO pig endothelial cells. In the one experimental animal, which displayed reduced IgM and IgG responses, select B-cell subsets were also reduced by anti-id therapy alone. Furthermore, natural antibody responses, including anti-laminin, anti-ssDNA, and anti-thyroglobulin antibodies were intact despite targeted depletion of anti-non-Gal xenoantibodies in vivo indicating that selective reduction of xenoantibodies can be accomplished without total B-cell depletion. CONCLUSIONS This preliminary study demonstrates the strength of approaches designed to selectively inhibit anti-non-Gal xenoantibody. Both anti-non-Gal-specific anti-idiotypic antibody and small molecules can be used to selectively limit xenoantibody responses.
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Affiliation(s)
- John M Stewart
- Department of Human Anatomy, Loma Linda University School of Medicine, Loma Linda, CA, USA
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From Three-Dimensional GPCR Structure to Rational Ligand Discovery. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 796:129-57. [DOI: 10.1007/978-94-007-7423-0_7] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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44
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Exploring the CXCR3 Chemokine Receptor with Small-Molecule Antagonists and Agonists. TOPICS IN MEDICINAL CHEMISTRY 2014. [DOI: 10.1007/7355_2014_75] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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45
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Webb B, Eswar N, Fan H, Khuri N, Pieper U, Dong G, Sali A. Comparative Modeling of Drug Target Proteins☆. REFERENCE MODULE IN CHEMISTRY, MOLECULAR SCIENCES AND CHEMICAL ENGINEERING 2014. [PMCID: PMC7157477 DOI: 10.1016/b978-0-12-409547-2.11133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.
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Girschick T, Puchbauer L, Kramer S. Improving structural similarity based virtual screening using background knowledge. J Cheminform 2013; 5:50. [PMID: 24341870 PMCID: PMC3928642 DOI: 10.1186/1758-2946-5-50] [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: 06/08/2013] [Accepted: 11/25/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods. RESULTS In virtual screening experiments we show that our approach clearly improves enrichment factors with both applied variants of our approach: the extension of the structural similarity measure with background knowledge in the form of a hand-selected relevant substructure or the extension of the similarity measure with background knowledge derived with data mining methods. CONCLUSION Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial. This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds.
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Affiliation(s)
- Tobias Girschick
- Technische Universität München, Institut für Informatik, Boltzmannstrasse 3, 85748 Garching b. München, Germany
| | - Lucia Puchbauer
- Technische Universität München, Institut für Informatik, Boltzmannstrasse 3, 85748 Garching b. München, Germany
| | - Stefan Kramer
- Johannes Gutenberg-Universität Mainz, Institut für Informatik, Staudingerweg 9, 55128 Mainz, Germany
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Yao S, Lu T, Zhou Z, Liu H, Yuan H, Ran T, Lu S, Zhang Y, Ke Z, Xu J, Xiong X, Chen Y. An efficient multistep ligand-based virtual screening approach for GPR40 agonists. Mol Divers 2013; 18:183-93. [PMID: 24307222 DOI: 10.1007/s11030-013-9493-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2013] [Accepted: 11/11/2013] [Indexed: 10/25/2022]
Abstract
G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein-ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.
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Affiliation(s)
- Sihui Yao
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
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Kalyaanamoorthy S, Chen YPP. Modelling and enhanced molecular dynamics to steer structure-based drug discovery. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2013; 114:123-36. [PMID: 23827463 DOI: 10.1016/j.pbiomolbio.2013.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Revised: 05/31/2013] [Accepted: 06/22/2013] [Indexed: 10/26/2022]
Abstract
The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.
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Affiliation(s)
- Subha Kalyaanamoorthy
- Department of Computer Science and Computer Engineering, Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, VIC 3086, Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Computer Engineering, Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, VIC 3086, Australia.
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Yoshikawa Y, Oishi S, Kubo T, Tanahara N, Fujii N, Furuya T. Optimized method of G-protein-coupled receptor homology modeling: its application to the discovery of novel CXCR7 ligands. J Med Chem 2013; 56:4236-51. [PMID: 23656360 DOI: 10.1021/jm400307y] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Homology modeling of G-protein-coupled seven-transmembrane receptors (GPCRs) remains a challenge despite the increasing number of released GPCR crystal structures. This challenge can be attributed to the low sequence identity and structural diversity of the ligand-binding pocket of GPCRs. We have developed an optimized GPCR structure modeling method based on multiple GPCR crystal structures. This method was designed to be applicable to distantly related receptors of known structural templates. CXC chemokine receptor (CXCR7) is a potential drug target for cancer chemotherapy. Homology modeling, docking, and virtual screening for CXCR7 were carried out using our method. The predicted docking poses of the known antagonists were different from the crystal structure of human CXCR4 with the small-molecule antagonist IT1t. Furthermore, 21 novel CXCR7 ligands with IC50 values of 1.29-11.4 μM with various scaffolds were identified by structure-based virtual screening.
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Affiliation(s)
- Yasushi Yoshikawa
- Drug Discovery Department, Research & Development Division, PharmaDesign Inc., 2-19-8 Hatchobori, Tokyo 104-0032, Japan
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Pala D, Beuming T, Sherman W, Lodola A, Rivara S, Mor M. Structure-based virtual screening of MT2 melatonin receptor: influence of template choice and structural refinement. J Chem Inf Model 2013; 53:821-35. [PMID: 23541165 DOI: 10.1021/ci4000147] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Developing GPCR homology models for structure-based virtual screening requires the choice of a suitable template and refinement of binding site residues. We explored this systematically for the MT2 melatonin receptor, with the aim to build a receptor homology model that is optimized for the enrichment of active melatoninergic ligands. A set of 12 MT2 melatonin receptor models was built using different GPCR X-ray structural templates and submitted to a virtual screening campaign on a set of compounds composed of 29 known melatonin receptor ligands and 2560 drug-like decoys. To evaluate the effect of including a priori information in receptor models, 12 representative melatonin receptor ligands were placed into the MT2 receptor models in poses consistent with known mutagenesis data and with assessed pharmacophore models. The receptor structures were then adapted to the ligands by induced-fit docking. Most of the 144 ligand-adapted MT2 receptor models showed significant improvements in screening enrichments compared to the unrefined homology models, with some template/refinement combinations giving excellent enrichment factors. The discriminating ability of the models was further tested on the 29 active ligands plus a set of 21 inactive or low-affinity compounds from the same chemical classes. Rotameric states of side chains for some residues, presumed to be involved in the binding process, were correlated with screening effectiveness, suggesting the existence of specific receptor conformations able to recognize active compounds. The top MT2 receptor model was able to identify 24 of 29 active ligands among the first 2% of the screened database. This work provides insights into the use of refined GPCR homology models for virtual screening.
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Affiliation(s)
- Daniele Pala
- Dipartimento di Farmacia, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
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