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Islam S, Amin MA, Rengasamy KR, Mohiuddin AKM, Mahmud S. Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach. Cancer Med 2024; 13:e70074. [PMID: 39101505 PMCID: PMC11299079 DOI: 10.1002/cam4.70074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/04/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/AIMS This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. METHODS This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. RESULTS The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. CONCLUSION Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.
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Affiliation(s)
- Sirajul Islam
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Md. Al Amin
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Kannan R.R. Rengasamy
- Laboratory of Natural Products and Medicinal Chemistry (LNPMC), Center for Global Health Research, Saveetha Medical College and HospitalSaveetha Institute of Medical and Technical Sciences (SIMATS)ThandalamChennai602105India
| | - A. K. M. Mohiuddin
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Shahin Mahmud
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
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2
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Saganuwan SA. Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy. Daru 2024:10.1007/s40199-024-00525-y. [PMID: 38935265 DOI: 10.1007/s40199-024-00525-y] [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: 11/20/2023] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVES Sometimes clinical efficacy and potential risk of therapeutic and toxic agents are difficult to predict over a long period of time. Hence there is need for literature search with a view to assessing cause of toxicity and less efficacy of drugs used in clinical practice. METHOD Hence literatures were searched for physicochemical properties, chemical formulas, molecular masses, pH values, ionization, receptor type, agonist and antagonist, therapeutic, toxic and structure-activity relationship of chemical compounds with pharmacophore and toxicophore, with a view to identifying high efficacious and relative low toxic agents. Inclusion criteria were manuscripts published on PubMed, Scopus, Web of Science, PubMed Central, Google Scholar among others, between 1960 and 2023. Keywords such as pharmacophore, toxicophore, structure-activity-relationship and disease where also searched. The exclusion criteria were the chemicals that lack pharmacophore, toxicophore and manuscripts published before 1960. RESULTS Findings have shown that pharmacophore and toxicophore functional groups determine clinical efficacy and safety of therapeutics, but if they overlap therapeutic and toxicity effects go concurrently. Hence the functional groups, dose, co-administration and concentration of drugs at receptor, drug-receptor binding and duration of receptor binding are the determining factors of pharmacophore and toxicophore activity. Molecular mass, chemical configuration, pH value, receptor affinity and binding capacity, multiple pharmacophores, hydrophilic/lipophilic nature of the chemical contribute greatly to functionality of pharmacophore and toxicophore. CONCLUSION Daily single therapy, avoidance of reversible pharmacology, drugs with covalent adduct, maintenance of therapeutic dose, and the use of multiple pharmacophores for terminal diseases will minimize toxicity and improve efficacy.
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Affiliation(s)
- Saganuwan Alhaji Saganuwan
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, Federal University of Agriculture, Makurdi, P.M.B. 2373, Benue State, Nigeria.
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3
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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Hoogstraten CA, Koenderink JB, van Straaten CE, Scheer-Weijers T, Smeitink JAM, Schirris TJJ, Russel FGM. Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies. Toxicol In Vitro 2024; 95:105740. [PMID: 38036072 DOI: 10.1016/j.tiv.2023.105740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/08/2023] [Accepted: 11/22/2023] [Indexed: 12/02/2023]
Abstract
During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.
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Affiliation(s)
- Charlotte A Hoogstraten
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Jan B Koenderink
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Carolijn E van Straaten
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Tom Scheer-Weijers
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Jan A M Smeitink
- Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Department of Pediatrics, Amalia Children's Hospital, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Khondrion BV, Nijmegen 6525 EX, the Netherlands
| | - Tom J J Schirris
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Frans G M Russel
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands.
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5
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Odunitan TT, Saibu OA, Apanisile BT, Omoboyowa DA, Balogun TA, Awe AV, Ajayi TM, Olagunju GV, Mahmoud FM, Akinboade M, Adeniji CB, Abdulazeez WO. Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets. Comput Biol Med 2024; 168:107737. [PMID: 38000249 DOI: 10.1016/j.compbiomed.2023.107737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/03/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023]
Abstract
Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.
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Affiliation(s)
- Tope T Odunitan
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria; Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | - Oluwatosin A Saibu
- Department of Chemistry and Biochemistry, New Mexico State University, Las Cruces, NM, USA.
| | - Boluwatife T Apanisile
- Department of Nutrition and Dietetics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Damilola A Omoboyowa
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Oyo State, Nigeria
| | - Toheeb A Balogun
- Department of Biological Sciences, University of California, San Diego, CA, USA
| | - Adeyoola V Awe
- Department of Medical Laboratory Science, Lead City, University, Ibadan, Oyo State, Nigeria
| | - Temitope M Ajayi
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Grace V Olagunju
- Department of Molecular Biology, New Mexico State University, Las Cruces, NM, USA
| | - Fatimah M Mahmoud
- Department of Molecular Biology, New Mexico State University, Las Cruces, NM, USA
| | - Modinat Akinboade
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
| | - Catherine B Adeniji
- Department of Environmental Management and Toxicology, Lead City University, Ibadan, Oyo State, Nigeria
| | - Waliu O Abdulazeez
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
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6
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Li S, Tian T, Zhang Z, Zou Z, Zhao D, Zeng J. PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction. Cell Syst 2023; 14:692-705.e6. [PMID: 37516103 DOI: 10.1016/j.cels.2023.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/25/2022] [Accepted: 05/19/2023] [Indexed: 07/31/2023]
Abstract
Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.
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Affiliation(s)
- Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Ziting Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| | - Ziheng Zou
- Silexon AI Technology, Nanjing, Jiangsu Province 210023, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
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7
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Szwabowski GL, Daigle BJ, Baker DL, Parrill AL. Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection. J Mol Graph Model 2023; 122:108488. [PMID: 37121167 DOI: 10.1016/j.jmgm.2023.108488] [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: 11/06/2022] [Accepted: 04/06/2023] [Indexed: 05/02/2023]
Abstract
Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand- or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model selection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmacophore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.
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Affiliation(s)
| | - Bernie J Daigle
- Departments of Biological Sciences and Computer Science, The University of Memphis, Memphis, TN, 38152, USA
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
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8
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Szwabowski GL, Baker DL, Parrill AL. Application of computational methods for class A GPCR Ligand discovery. J Mol Graph Model 2023; 121:108434. [PMID: 36841204 DOI: 10.1016/j.jmgm.2023.108434] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/22/2023]
Abstract
G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.
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Affiliation(s)
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
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9
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Szwabowski GL, Cole JA, Baker DL, Parrill AL. Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation. J Mol Graph Model 2023; 121:108429. [PMID: 36804368 DOI: 10.1016/j.jmgm.2023.108429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/18/2023] [Accepted: 02/06/2023] [Indexed: 02/13/2023]
Abstract
Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.
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Affiliation(s)
| | - Judith A Cole
- Department of Biological Sciences, The University of Memphis, Memphis, TN, 38152, USA
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
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10
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Singh MP, Singh N, Mishra D, Ehsan S, Chaturvedi VK, Chaudhary A, Singh V, Vamanu E. Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review. Curr Pharm Des 2023; 29:2601-2617. [PMID: 37916490 DOI: 10.2174/0113816128259795231023193419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/21/2023] [Indexed: 11/03/2023]
Abstract
The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.
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Affiliation(s)
- Mohan P Singh
- Centre of Biotechnology, University of Allahabad, Prayagraj 211002, India
| | - Nidhi Singh
- Centre of Bioinformatics, University of Allahabad, Prayagraj 211002, India
| | - Divya Mishra
- Centre of Bioinformatics, University of Allahabad, Prayagraj 211002, India
| | - Saba Ehsan
- Centre of Biotechnology, University of Allahabad, Prayagraj 211002, India
| | - Vivek K Chaturvedi
- Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Anupriya Chaudhary
- Centre of Biotechnology, University of Allahabad, Prayagraj 211002, India
| | - Veer Singh
- Department of Biochemistry, Rajendra Memorial Research Institute of Medical Sciences, Patna 800007, India
| | - Emanuel Vamanu
- Faculty of Biotechnology, University of Agricultural Sciences and Veterinary Medicine of Bucharest, Bucharest 011464, Romania
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Hernández-Silva D, Alcaraz-Pérez F, Pérez-Sánchez H, Cayuela ML. Virtual screening and zebrafish models in tandem, for drug discovery and development. Expert Opin Drug Discov 2022:1-13. [DOI: 10.1080/17460441.2022.2147503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- David Hernández-Silva
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Structural Bioinformatics and High-Performance Computing Research Group (BIOHPC), Computer Engineering Department, Universidad Católica de Murcia (UCAM), Guadalupe, 30107 Murcia, Spain
| | - Francisca Alcaraz-Pérez
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Horacio Pérez-Sánchez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Maria Luisa Cayuela
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
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12
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Vásquez AF, Gómez LA, González Barrios A, Riaño-Pachón DM. Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors. Int J Mol Sci 2022; 23:13946. [PMID: 36430425 PMCID: PMC9694616 DOI: 10.3390/ijms232213946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/02/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.
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Affiliation(s)
- Andrés Felipe Vásquez
- Grupo de Diseño de Productos y Procesos (GDPP), School of Chemical Engineering, Universidad de los Andes, Bogotá 111711, Colombia
- Naturalius SAS, Bogotá 110221, Colombia
| | - Luis Alberto Gómez
- Laboratorio de Fisiología Molecular, Instituto Nacional de Salud, Bogotá 111321, Colombia
- Department of Physiological Sciences, School of Medicine, Universidad Nacional de Colombia, Bogotá 11001, Colombia
| | - Andrés González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), School of Chemical Engineering, Universidad de los Andes, Bogotá 111711, Colombia
| | - Diego M. Riaño-Pachón
- Laboratório de Biologia Computacional, Evolutiva e de Sistemas, Centro de Energia Nuclear na Agricultura (CENA), Universidade de São Paulo, Piracicaba 05508-060, SP, Brazil
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13
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Liu S, Zhou J, Feng Z, Zhang J, Li S, Jin Z, Zhang C, Li S, He G, Li H. VRPharmer: Bringing Virtual Reality into Pharmacophore-based Virtual Screening with Interactive Exploration and Realistic Visualization. Bioinformatics 2022; 38:4953-4955. [PMID: 36073903 DOI: 10.1093/bioinformatics/btac615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/01/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Current pharmacophore-based virtual screening (VS) software has limited interactive capabilities and less intuitive screening processes. In this study, a novel tool named VRPharmer is proposed to perform the entire VS workflow in VR environments. VRPharmer enables users to interactively perceive computation processes and immersively observe molecular structures. Besides a typical screening mode (OPT mode), VRPharmer provides a unique interactive screening mode (SCORE mode) for freely exploring the optimal binding poses. Pharmacophore models are editable to study the impact of each feature and further refine the screening results. Moreover, molecular rendering algorithms are improved for precise representations. AVAILABILITY AND IMPLEMENTATION VRPharmer is open-source software under the MIT license. The released version is available at https://github.com/VRPharmer/VRPharmer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shun Liu
- School of Computer Science and Technology, East China Normal University, Shanghai, China, 200062
| | - Jianchao Zhou
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China, 200237
| | - Ziyan Feng
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, China, 200237
| | - Jiawen Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China, 200062
| | - Shuang Li
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, China, 200237
| | - Zilong Jin
- School of Computer Science and Technology, East China Normal University, Shanghai, China, 200062
| | - Chenfei Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China, 200062
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, China, 200237
| | - Gaoqi He
- School of Computer Science and Technology, East China Normal University, Shanghai, China, 200062
| | - Honglin Li
- School of Computer Science and Technology, East China Normal University, Shanghai, China, 200062.,Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, China, 200237.,Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, China, 200062
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14
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Drug Design by Pharmacophore and Virtual Screening Approach. Pharmaceuticals (Basel) 2022; 15:ph15050646. [PMID: 35631472 PMCID: PMC9145410 DOI: 10.3390/ph15050646] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/18/2022] [Accepted: 05/21/2022] [Indexed: 12/20/2022] Open
Abstract
Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.
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15
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Mapping of Protein Binding Sites using clustering algorithms development of a pharmacophore based drug discovery tool. J Mol Graph Model 2022; 115:108228. [DOI: 10.1016/j.jmgm.2022.108228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/28/2022] [Accepted: 05/19/2022] [Indexed: 11/22/2022]
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16
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Molecular Docking as a Potential Approach in Repurposing Drugs Against COVID-19: a Systematic Review and Novel Pharmacophore Models. CURRENT PHARMACOLOGY REPORTS 2022; 8:212-226. [PMID: 35381996 PMCID: PMC8970976 DOI: 10.1007/s40495-022-00285-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/14/2022] [Indexed: 12/12/2022]
Abstract
Purpose of Review This article provides a review of the recent literature related to the FDA-approved drugs that had been repurposed as potential drug candidates against COVID-19. Moreover, we performed a quality pharmacophore study for frequently studied targets, namely, the main protease, RNA-dependent RNA polymerase, and spike protein. Recent Findings Ever since the COVID-19 pandemic, the whole spectrum of scientific community is still unable to invent an absolute therapeutic agent for COVID-19. Considering such a fact, drug repurposing strategies seem a truly viable approach to develop novel therapeutic interventions. Summery Drug repurposing explores previously approved drugs of known safety and pharmacokinetics profile for possible new effects, reducing the cost, time, and predicting prospective side effects and drug interactions. COVID-19 virulent machinery appeared similar to other viruses, making antiviral agents widely repurposed in pursuit for curative candidates. Our main protease pharmacophoric study revealed multiple features and could be a probable starting point for upcoming research.
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17
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Tran QH, Nguyen QT, Vo NQH, Mai TT, Tran TTN, Tran TD, Le MT, Trinh DTT, Thai KM. Structure-based 3D-Pharmacophore modeling to discover novel interleukin 6 inhibitors: An in silico screening, molecular dynamics simulations and binding free energy calculations. PLoS One 2022; 17:e0266632. [PMID: 35385549 PMCID: PMC8986010 DOI: 10.1371/journal.pone.0266632] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/23/2022] [Indexed: 11/24/2022] Open
Abstract
Interleukin 6 (IL-6) is a cytokine with various biological functions in immune regulation, hematopoiesis, and inflammation. Elevated IL-6 levels have been identified in several severe disorders such as sepsis, acute respiratory distress syndrome (ARDS), and most recently, COVID-19. The biological activity of IL-6 relies on interactions with its specific receptor, IL-6Rα, including the membrane-bound IL-6 receptor (mIL-6R) and the soluble IL-6 receptor (sIL-6R). Thus, inhibition of the interaction between these two proteins would be a potential treatment for IL-6 related diseases. To date, no orally available small-molecule drug has been approved. This study focuses on finding potential small molecules that can inhibit protein-protein interactions between IL-6 and its receptor IL-6Rα using its crystal structure (PDB ID: 5FUC). First, two pharmacophore models were constructed based on the interactions between key residues of IL-6 (Phe74, Phe78, Leu178, Arg179, Arg182) and IL-6Rα (Phe229, Tyr230, Glu277, Glu278, Phe279). A database of approximately 22 million compounds was screened using 3D-pharmacophore models, molecular docking models, and ADMET properties. By analyzing the interactive capability of successfully docked compounds with important amino acids, 12 potential ligands were selected for further analysis via molecular dynamics simulations. Based on the stability of the complexes, the high interactions rate of each ligand with the key residues of IL-6/IL-6Rα, and the low binding free energy calculation, two compounds ZINC83804241 and ZINC02997430, were identified as the most potential IL-6 inhibitor candidates. These results will pave the way for the design and optimization of more specific compounds to combat cytokine storm in severe coronavirus patients.
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Affiliation(s)
- Que-Huong Tran
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
- Department of Pharmaceutical Chemistry Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Quoc-Thai Nguyen
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
- * E-mail: (QTN); (DTTT); , (KMT)
| | - Nguyen-Quynh-Huong Vo
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Tan Thanh Mai
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Thi-Thuy-Nga Tran
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
- Department of Pharmaceutical Chemistry Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Thanh-Dao Tran
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Minh-Tri Le
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
- School of Medicine, Vietnam National University Ho Chi Minh City, Linh Trung Ward., Thu Duc Dist., Ho Chi Minh City, Vietnam
| | - Dieu-Thuong Thi Trinh
- Faculty of Traditional Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
- * E-mail: (QTN); (DTTT); , (KMT)
| | - Khac-Minh Thai
- Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
- * E-mail: (QTN); (DTTT); , (KMT)
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18
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Kumar SP, Dixit NY, Patel CN, Rawal RM, Pandya HA. PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments. J Comput Chem 2022; 43:847-863. [PMID: 35301752 DOI: 10.1002/jcc.26840] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 11/09/2022]
Abstract
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.
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Affiliation(s)
- Sivakumar Prasanth Kumar
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Nandan Y Dixit
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Chirag N Patel
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Rakesh M Rawal
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Himanshu A Pandya
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
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19
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Identification of potential interleukin-8 inhibitors acting on the interactive site between chemokine and CXCR2 receptor: A computational approach. PLoS One 2022; 17:e0264385. [PMID: 35202450 PMCID: PMC8870564 DOI: 10.1371/journal.pone.0264385] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 02/09/2022] [Indexed: 11/19/2022] Open
Abstract
Interactions between interleukin (IL)-8 and its receptors, CXCR1, and CXCR2, serve crucial roles in inflammatory conditions and various types of cancers. Inhibition of this signaling pathway has been exploited as a promising strategy in treating these diseases. However, most studies only focused on the design of allosteric antagonists-bound receptors on the intracellular side of IL-8 receptors. Recently, the first cryo-EM structures of IL-8-CXCR2-Gi complexes have been solved, revealing the unique binding and activation modes of the endogenous chemokine IL-8. Hence, we set to identify small molecule inhibitors for IL-8 using critical protein-protein interaction between IL-8 and CXCR2 at the orthosteric binding site. The pharmacophore models and molecular docking screened compounds from DrugBank and NCI databases. The oral bioavailability of the top 23 ligands from the screening was then predicted by the SwissAMDE tool. Molecular dynamics simulation and free binding energy calculation were performed for the best compounds. The result indicated that DB14770, DB12121, and DB03916 could form strong interactions and stable protein-ligand complexes with IL-8. These three candidates are potential IL-8 inhibitors that can be further evaluated by in vitro experiments in the next stage.
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20
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Kandwal S, Fayne D. Repurposing drugs for treatment of SARS-CoV-2 infection: computational design insights into mechanisms of action. J Biomol Struct Dyn 2022; 40:1316-1330. [PMID: 32964805 PMCID: PMC7544922 DOI: 10.1080/07391102.2020.1825232] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 09/12/2020] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has negatively affected human life globally. It has led to economic crises and health emergencies across the world, spreading rapidly among the human population and has caused many deaths. Currently, there are no treatments available for COVID-19 so there is an urgent need to develop therapeutic interventions that could be used against the novel coronavirus infection. In this research, we used computational drug design technologies to repurpose existing drugs as inhibitors of SARS-CoV-2 viral proteins. The Broad Institute's Drug Repurposing Hub consists of in-development/approved drugs and was computationally screened to identify potential hits which could inhibit protein targets encoded by the SARS-CoV-2 genome. By virtually screening the Broad collection, using rationally designed pharmacophore features, we identified molecules which may be repurposed against viral nucleocapsid and non-structural proteins. The pharmacophore features were generated after careful visualisation of the interactions between co-crystalised ligands and the protein binding site. The ChEMBL database was used to determine the compound's level of inhibition of SARS-CoV-2 and correlate the predicted viral protein target with whole virus in vitro data. The results from this study may help to accelerate drug development against COVID-19 and the hit compounds should be progressed through further in vitro and in vivo studies on SARS-CoV-2.
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Affiliation(s)
- Shubhangi Kandwal
- Department of Clinical Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Darren Fayne
- Molecular Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
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21
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Kumar P, Mohanty D. Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease. Mol Inform 2022; 41:e2100178. [PMID: 34633768 PMCID: PMC8646684 DOI: 10.1002/minf.202100178] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/20/2021] [Indexed: 11/17/2022]
Abstract
Recent fragment-based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS-CoV-2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo- or small molecule fragment-bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS-CoV-2 Mpro . It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC-AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.
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Affiliation(s)
- Pawan Kumar
- National Institute of ImmunologyAruna Asaf Ali MargNew Delhi110067India
| | - Debasisa Mohanty
- National Institute of ImmunologyAruna Asaf Ali MargNew Delhi110067India
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22
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Castro LHE, Sant'Anna CMR. Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications. Curr Top Med Chem 2021; 22:333-346. [PMID: 34844540 DOI: 10.2174/1568026621666211129140958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/23/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
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Affiliation(s)
| | - Carlos Mauricio R Sant'Anna
- Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica. Brazil
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23
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N Vijayan A, Refaei MA, Silva RN, Tsang P, Zhang P. Detection of Sortase A and Identification of Its Inhibitors by Paramagnetic Nanoparticle-Assisted Nuclear Relaxation. Anal Chem 2021; 93:15430-15437. [PMID: 34757710 DOI: 10.1021/acs.analchem.1c03271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Sortase A is a virulence factor responsible for the attachment of surface proteins to Staphylococcus aureus and other Gram-positive bacteria. Inhibitors of this enzyme are potential anti-infective agents. Herein, a new highly selective magnetic relaxation-based method for screening potential sortase A inhibitors is described. A 13-amino acid-long peptide substrate of sortase A is conjugated to SiO2-EDTA-Gd NPs. In the presence of sortase A, the LPXTG motif on the peptide strand is cleaved resulting in a shortened peptide as well as a reduced water T2 value whose magnitude is dependent on the concentration of sortase A. The detection limit is determined to be 76 pM. In contrast, the presence of sortase A inhibitors causes the T2 to remain at a higher value. The proposed method is used to characterize inhibition of sortase A by curcumin and 4-(hydroxymercuri)benzoic acid with an IC50 value of 12.9 ± 1.6 μM and 130 ± 1.76 μM, respectively. Furthermore, this method was successfully applied to detect sortase A activity in bacterial suspensions. The feasibility to screen different inhibitors in Escherichia coli and S. aureus suspensions was demonstrated. This method is fast and potentially useful to rapidly screen possible inhibitors of sortase A in bacterial suspensions, thereby aiding in the development of antibacterial agents targeting Gram-positive bacteria.
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Affiliation(s)
- Anjaly N Vijayan
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221-0172, United States
| | - Mary Anne Refaei
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221-0172, United States
| | - Rebecca N Silva
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221-0172, United States
| | - Pearl Tsang
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221-0172, United States
| | - Peng Zhang
- Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 45221-0172, United States
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24
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Vásquez AF, Muñoz AR, Duitama J, González Barrios A. Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds. Front Chem 2021; 9:700802. [PMID: 34422762 PMCID: PMC8377161 DOI: 10.3389/fchem.2021.700802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/28/2021] [Indexed: 11/25/2022] Open
Abstract
Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.
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Affiliation(s)
- Andrés Felipe Vásquez
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia.,Naturalius S.A.S, Bogotá, Colombia
| | - Alejandro Reyes Muñoz
- Grupo de Biología Computacional y Ecología Microbiana (BCEM), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia.,Max Planck Tandem Group in Computational Biology, Universidad de Los Andes, Bogotá, Colombia
| | - Jorge Duitama
- Systems and Computing Engineering Department, Universidad de Los Andes, Bogotá, Colombia
| | - Andrés González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia
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25
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MUHAMMED MT, AKI-YALCIN E. Pharmacophore Modeling in Drug Discovery: Methodology and Current Status. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2021. [DOI: 10.18596/jotcsa.927426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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26
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Identification of potential COVID-19 main protease inhibitors using structure-based pharmacophore approach, molecular docking and repurposing studies. ACTA PHARMACEUTICA (ZAGREB, CROATIA) 2021; 71:163-174. [PMID: 33151166 DOI: 10.2478/acph-2021-0016] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/02/2020] [Indexed: 01/19/2023]
Abstract
The current outbreak of novel coronavirus (COVID-19) infections urges the need to identify potential therapeutic agents. Therefore, the repurposing of FDA-approved drugs against today's diseases involves the use of de-risked compounds with potentially lower costs and shorter development timelines. In this study, the recently resolved X-ray crystallographic structure of COVID-19 main protease (Mpro) was used to generate a pharmacophore model and to conduct a docking study to capture antiviral drugs as new promising COVID-19 main protease inhibitors. The developed pharmacophore successfully captured five FDA-approved antiviral drugs (lopinavir, remdesivir, ritonavir, saquinavir and raltegravir). The five drugs were successfully docked into the binding site of COVID-19 Mpro and showed several specific binding interactions that were comparable to those tying the co-crystallized inhibitor X77 inside the binding site of COVID-19 Mpro. Three of the captured drugs namely, remdesivir, lopinavir and ritonavir, were reported to have promising results in COVID-19 treatment and therefore increases the confidence in our results. Our findings suggest an additional possible mechanism of action for remdesivir as an antiviral drug inhibiting COVID-19 Mpro. Additionally, a combination of structure-based pharmacophore modeling with a docking study is expected to facilitate the discovery of novel COVID-19 Mpro inhibitors.
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Agrawal PK, Agrawal C, Blunden G. Rutin: A Potential Antiviral for Repurposing as a SARS-CoV-2 Main Protease (Mpro) Inhibitor. Nat Prod Commun 2021. [DOI: 10.1177/1934578x21991723] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Various computational studies, including in silico ones, have identified several existing compounds that could serve as effective inhibitors of the SARS-CoV-2 main protease (Mpro), and thus preventing replication of the virus. Among these, rutin has been identified as a potential hit, having prominent binding affinity to the virus. Moreover, its presence in several traditional antiviral medicines prescribed in China to infected patients with mild to moderate symptoms of COVID-19 justify its promise as a repurposed bioactive secondary metabolite against SARS-CoV-2.
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Affiliation(s)
| | | | - Gerald Blunden
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, UK
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28
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Temml V, Kutil Z. Structure-based molecular modeling in SAR analysis and lead optimization. Comput Struct Biotechnol J 2021; 19:1431-1444. [PMID: 33777339 PMCID: PMC7979990 DOI: 10.1016/j.csbj.2021.02.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/21/2021] [Accepted: 02/23/2021] [Indexed: 12/13/2022] Open
Abstract
In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.
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Affiliation(s)
- Veronika Temml
- Institute of Pharmacy, Department of Pharmaceutical and Medicinal Chemistry, Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, Austria
| | - Zsofia Kutil
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Vestec, Czech Republic
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29
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Unraveling the binding mechanism of an Oxovanadium(IV) - Curcumin complex on albumin, DNA and DNA gyrase by in vitro and in silico studies and evaluation of its hemocompatibility. J Inorg Biochem 2021; 221:111402. [PMID: 33975249 DOI: 10.1016/j.jinorgbio.2021.111402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 02/09/2021] [Accepted: 02/13/2021] [Indexed: 11/20/2022]
Abstract
An oxovanadium(IV) - curcumin based complex, viz. [VO(cur)(2,2´-bipy)(H2O)] where cur is curcumin and bipy is bipyridine, previously synthesized, has been studied for interaction with albumin and DNA. Fluorescence emission spectroscopy was used to evaluate the interaction of the complex with bovine serum albumin (BSA) and the BSA-binding constant (Kb) was calculated to be 2.56 x 105 M-1, whereas a single great-affinity binding site was revealed. Moreover, the hemocompatibility test demonstrated that the complex presented low hemolytic fraction (mostly below 1%), in all concentrations tested (0-250 μΜ of complex, 5% DMSO) assuring a safe application in interaction with blood. The binding of the complex to DNA was also investigated using absorption, fluorescence, and viscometry methods indicating a binding through a minor groove mode. From competitive studies with ethidium bromide the apparent binding constant value to DNA was estimated to be 4.82 x 106 M-1. Stern-Volmer quenching phenomenon gave a ΚSV constant [1.92 (± 0.05) x 104 M-1] and kq constant [8.33 (± 0.2) x 1011 M-1s-1]. Molecular docking simulations on the crystal structure of BSA, calf thymus DNA, and DNA gyrase, as well as pharmacophore analysis for BSA target, were also employed to study in silico the ability of [VO(cur)(2,2´-bipy)(H2O)] to bind to these target bio-macromolecules and explain the observed in vitro activity.
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30
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Lombino J, Gulotta MR, De Simone G, Mekni N, De Rosa M, Carbone D, Parrino B, Cascioferro SM, Diana P, Padova A, Perricone U. Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket. Mol Inform 2020; 40:e2000148. [PMID: 32833314 DOI: 10.1002/minf.202000148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/23/2020] [Indexed: 11/09/2022]
Abstract
The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.
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Affiliation(s)
- Jessica Lombino
- Fondazione Ri.MED, Via Bandiera 11, 90133, Palermo, Italy.,Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Via Archirafi 32, 90123, Palermo, Italy
| | - Maria Rita Gulotta
- Fondazione Ri.MED, Via Bandiera 11, 90133, Palermo, Italy.,Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Via Archirafi 32, 90123, Palermo, Italy
| | | | - Nedra Mekni
- Fondazione Ri.MED, Via Bandiera 11, 90133, Palermo, Italy
| | - Maria De Rosa
- Fondazione Ri.MED, Via Bandiera 11, 90133, Palermo, Italy
| | - Daniela Carbone
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Via Archirafi 32, 90123, Palermo, Italy
| | - Barbara Parrino
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Via Archirafi 32, 90123, Palermo, Italy
| | - Stella Maria Cascioferro
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Via Archirafi 32, 90123, Palermo, Italy
| | - Patrizia Diana
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Via Archirafi 32, 90123, Palermo, Italy
| | | | - Ugo Perricone
- Fondazione Ri.MED, Via Bandiera 11, 90133, Palermo, Italy
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31
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Jiang S, Feher M, Williams C, Cole B, Shaw DE. AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets. J Chem Inf Model 2020; 60:4326-4338. [PMID: 32639159 DOI: 10.1021/acs.jcim.0c00121] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Pharmacophore models are widely used in computational drug discovery (e.g., in the virtual screening of drug molecules) to capture essential information about interactions between ligands and a target protein. Generating pharmacophore models from protein structures is typically a manual process, but there has been growing interest in automated pharmacophore generation methods. Automation makes feasible the processing of large numbers of protein conformations, such as those generated by molecular dynamics (MD) simulations, and thus may help achieve the longstanding goal of incorporating protein flexibility into virtual screening workflows. Here, we present AutoPH4, a new automated method for generating pharmacophore models based on protein structures; we show that a virtual screening workflow incorporating AutoPH4 ranks compounds more accurately than any other pharmacophore-based virtual screening workflow for which results on a public benchmark have been reported. The strong performance of the virtual screening workflow indicates that the AutoPH4 component of the workflow generates high-quality pharmacophores, making AutoPH4 promising for use in future virtual screening workflows as well, such as ones that use conformations generated by MD simulations.
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Affiliation(s)
- Siduo Jiang
- D. E. Shaw Research, New York, New York 10036, United States
| | - Miklos Feher
- D. E. Shaw Research, New York, New York 10036, United States
| | - Chris Williams
- Chemical Computing Group, Montreal, Quebec H3A 2R7, Canada
| | - Brian Cole
- D. E. Shaw Research, New York, New York 10036, United States
| | - David E Shaw
- D. E. Shaw Research, New York, New York 10036, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, United States
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32
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D'Abadia PL, BailÃo EFLC, Lino JÚnior RS, Oliveira MG, Silva VB, Oliveira LAR, ConceiÇÃo EC, Melo-Reis PR, Borges LL, GonÇalves PJ, Almeida LM. Hancornia speciosa serum fraction latex stimulates the angiogenesis and extracellular matrix remodeling processes. AN ACAD BRAS CIENC 2020; 92:e20190107. [PMID: 32556049 DOI: 10.1590/0001-3765202020190107] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 05/03/2019] [Indexed: 11/21/2022] Open
Abstract
The Hancornia speciosa latex reveals angiogenic, osteogenic, and anti-inflammatory properties, which present its potential for developing of wound healing drugs; however, the latex compounds responsible for angiogenesis remain unknown. One strategy to screen these active compounds is evaluation of latex fractions. This study aimed to obtain different fractions of latex and evaluate its angiogenic activity separately using the chick chorioallantoic membrane (CAM) assay. The serum (SE) fraction was responsible for angiogenesis, which was subject to biochemical characterization and computational simulations in order to understand the contribution of H. speciosa latex in wound healing process. Our results revealed weak antioxidant potential and absence of antimicrobial activity in the SE fraction. Phytochemical analysis identified chlorogenic acids (CGA) as the main compound of SE fraction. CGA bioactivity predictions identify different molecules associated with extracellular matrix (ECM) remodeling, such as metalloproteinases, which also are overexpressed in our CAM assay experiment. Docking simulations revealed the interactions between CGA and matrix metalloproteinase 2. In conclusion, SE latex fraction stimulates angiogenesis and may influence ECM remodeling. These properties may contribute to the wound healing process, and also confirm the widespread use of this plant.
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Affiliation(s)
- PatrÍcia L D'Abadia
- Universidade Estadual de Goiás, Rodovia BR 153, nº 3105, Fazenda Barreiro do Meio, Campus Henrique Santillo, 75132-400 Anápolis, GO, Brazil
| | - Elisa FlÁvia Luiz C BailÃo
- Universidade Estadual de Goiás, Rodovia BR 153, nº 3105, Fazenda Barreiro do Meio, Campus Henrique Santillo, 75132-400 Anápolis, GO, Brazil
| | - Ruy S Lino JÚnior
- Universidade Federal de Goiás, Instituto de Patologia Tropical e Saúde Pública, Rua 235, s/n, Setor Leste Universitário, 74605-050 Goiânia, GO, Brazil
| | - Matheus Gabriel Oliveira
- Pontifícia Universidade Católica de Goiás, Escola de Ciências Médicas, Farmacêuticas e Biomédicas, Av. Universitária, n 1069, Setor Leste Universitário, 74605-010 Goiânia, GO, Brazil
| | - Vinicius B Silva
- Pontifícia Universidade Católica de Goiás, Escola de Ciências Médicas, Farmacêuticas e Biomédicas, Av. Universitária, n 1069, Setor Leste Universitário, 74605-010 Goiânia, GO, Brazil
| | - Leandra A R Oliveira
- Universidade Federal de Goiás, Laboratório de Pesquisa, Desenvolvimento & Inovação de Bioprodutos, Faculdade de Farmácia, Rua 240, s/n, Setor Leste Universitário, 74605-170 Goiânia, GO, Brazil
| | - Edemilson C ConceiÇÃo
- Universidade Federal de Goiás, Laboratório de Pesquisa, Desenvolvimento & Inovação de Bioprodutos, Faculdade de Farmácia, Rua 240, s/n, Setor Leste Universitário, 74605-170 Goiânia, GO, Brazil
| | - Paulo Roberto Melo-Reis
- Pontifícia Universidade Católica de Goiás, Laboratório de Estudos Experimentais e Biotecnológicos, Rua 232, nº 128, Setor Leste Universitário, 74605-120 Goiânia, GO, Brazil
| | - Leonardo Luiz Borges
- Universidade Estadual de Goiás, Rodovia BR 153, nº 3105, Fazenda Barreiro do Meio, Campus Henrique Santillo, 75132-400 Anápolis, GO, Brazil
| | - Pablo JosÉ GonÇalves
- Universidade Federal de Goiás, Instituto de Física, Av. Esperança, s/n, Campus Samambaia, 74690-900 Goiânia, GO, Brazil
| | - Luciane M Almeida
- Universidade Estadual de Goiás, Rodovia BR 153, nº 3105, Fazenda Barreiro do Meio, Campus Henrique Santillo, 75132-400 Anápolis, GO, Brazil
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Vásquez AF, Reyes Muñoz A, Duitama J, González Barrios A. Discovery of new potential CDK2/VEGFR2 type II inhibitors by fragmentation and virtual screening of natural products. J Biomol Struct Dyn 2020; 39:3285-3299. [DOI: 10.1080/07391102.2020.1763839] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Andrés Felipe Vásquez
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Alejandro Reyes Muñoz
- Grupo de Biología Computacional Ecología Microbiana (BCEM), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
- Max Planck Tandem Group in Computational Biology, Universidad de los Andes, Bogotá, Colombia
| | - Jorge Duitama
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Andrés González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical Engineering, Universidad de los Andes, Bogotá, Colombia
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Kumar SP, Patel CN, Rawal RM, Pandya HA. Energetic contributions of amino acid residues and its cross‐talk to delineate ligand‐binding mechanism. Proteins 2020; 88:1207-1225. [DOI: 10.1002/prot.25894] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/20/2020] [Accepted: 04/03/2020] [Indexed: 02/02/2023]
Affiliation(s)
| | - Chirag N. Patel
- Department of Botany, Bioinformatics, and Climate Change Impacts ManagementUniversity School of Sciences, Gujarat University Ahmedabad India
| | - Rakesh M. Rawal
- Department of Life SciencesUniversity School of Sciences, Gujarat University Ahmedabad India
| | - Himanshu A. Pandya
- Department of Life SciencesUniversity School of Sciences, Gujarat University Ahmedabad India
- Department of Botany, Bioinformatics, and Climate Change Impacts ManagementUniversity School of Sciences, Gujarat University Ahmedabad India
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35
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Liu Q, Miao Y, Wang X, Lv G, Peng Y, Li K, Li M, Qiu L, Lin J. Structure-based virtual screening and biological evaluation of novel non-bisphosphonate farnesyl pyrophosphate synthase inhibitors. Eur J Med Chem 2020; 186:111905. [DOI: 10.1016/j.ejmech.2019.111905] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 11/14/2019] [Accepted: 11/20/2019] [Indexed: 02/09/2023]
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36
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Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study. Sci Pharm 2019. [DOI: 10.3390/scipharm88010002] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Dengue infection is caused by a mosquito-borne virus, particularly in children, which may even cause death. No effective prevention or therapeutic agents to cure this disease are available up to now. The dengue viral envelope (E) protein was discovered to be a promising target for inhibition in several steps of viral infection. Structure-based virtual screening has become an important technique to identify first hits in a drug screening process, as it is possible to reduce the number of compounds to be assayed, allowing to save resources. In the present study, pharmacophore models were generated using the common hits approach (CHA), starting from trajectories obtained from molecular dynamics (MD) simulations of the E protein complexed with the active inhibitor, flavanone (FN5Y). Subsequently, compounds presented in various drug databases were screened using the LigandScout 4.2 program. The obtained hits were analyzed in more detail by molecular docking, followed by extensive MD simulations of the complexes. The highest-ranked compound from this procedure was then synthesized and tested on its inhibitory efficiency by experimental assays.
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37
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Tuffaha GO, Hatmal MM, Taha MO. Discovery of new JNK3 inhibitory chemotypes via QSAR-Guided selection of docking-based pharmacophores and comparison with other structure-based pharmacophore modeling methods. J Mol Graph Model 2019; 91:30-51. [PMID: 31158642 DOI: 10.1016/j.jmgm.2019.05.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 05/13/2019] [Accepted: 05/17/2019] [Indexed: 12/21/2022]
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38
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Al-Sha'er MA, Al-Aqtash RA, Taha MO. Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis. Med Chem 2019; 15:588-601. [PMID: 30799792 DOI: 10.2174/1573406415666190222125333] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/31/2019] [Accepted: 02/07/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. METHODS Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). RESULTS The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. CONCLUSION Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.
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Affiliation(s)
- Mahmoud A Al-Sha'er
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Rua'a A Al-Aqtash
- Faculty of Pharmacy, Zarqa University, P.O. Box 132222, Zarqa, 13132, Jordan
| | - Mutasem O Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan
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Mortier J, Dhakal P, Volkamer A. Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces. Molecules 2018; 23:molecules23081959. [PMID: 30082611 PMCID: PMC6222449 DOI: 10.3390/molecules23081959] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 07/27/2018] [Accepted: 07/27/2018] [Indexed: 12/19/2022] Open
Abstract
Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.
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Affiliation(s)
- Jérémie Mortier
- In-Silico Toxicology Group, Institute of Physiology, Charité-Universitätsmedizin Berlin, Virchowweg 6, 10117 Berlin, Germany.
| | - Pratik Dhakal
- In-Silico Toxicology Group, Institute of Physiology, Charité-Universitätsmedizin Berlin, Virchowweg 6, 10117 Berlin, Germany.
| | - Andrea Volkamer
- In-Silico Toxicology Group, Institute of Physiology, Charité-Universitätsmedizin Berlin, Virchowweg 6, 10117 Berlin, Germany.
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40
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Protein‐protein interactions as antibiotic targets: A medicinal chemistry perspective. Med Res Rev 2018; 40:469-494. [DOI: 10.1002/med.21519] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 05/28/2018] [Accepted: 06/03/2018] [Indexed: 12/27/2022]
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41
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Hatmal MM, Taha MO. Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand-Receptor Complexes. J Chem Inf Model 2018. [PMID: 29529367 DOI: 10.1021/acs.jcim.7b00708] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We previously combined molecular dynamics (classical or simulated annealing) with ligand-receptor contacts analysis as a means to extract valid pharmacophore model(s) from single ligand-receptor complexes. However, molecular dynamics methods are computationally expensive and time-consuming. Here we describe a novel method for extracting valid pharmacophore model(s) from a single crystallographic structure within a reasonable time scale. The new method is based on ligand-receptor contacts analysis following energy relaxation of a predetermined set of randomly deformed complexes generated from the targeted crystallographic structure. Ligand-receptor contacts maintained across many deformed/relaxed structures are assumed to be critical and used to guide pharmacophore development. This methodology was implemented to develop valid pharmacophore models for PI3K-γ, RENIN, and JAK1. The resulting pharmacophore models were validated by receiver operating characteristic (ROC) analysis against inhibitors extracted from the CHEMBL database. Additionally, we implemented pharmacophores extracted from PI3K-γ to search for new inhibitors from the National Cancer Institute list of compounds. The process culminated in new PI3K-γ/mTOR inhibitory leads of low micromolar IC50s.
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Affiliation(s)
- Ma'mon M Hatmal
- Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences , The Hashemite University , P.O. Box 330127 , Zarqa 13133 , Jordan
| | - Mutasem O Taha
- Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of Pharmacy , University of Jordan , Amman 11942 , Jordan
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Shiri F, Pirhadi S, Rahmani A. Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening. J Recept Signal Transduct Res 2017; 38:37-47. [PMID: 29254400 DOI: 10.1080/10799893.2017.1414844] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC50 values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski's rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.
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Affiliation(s)
- Fereshteh Shiri
- a Department of Chemistry , University of Zabol , Zabol , Iran
| | - Somayeh Pirhadi
- b Medicinal and Natural Products Chemistry Research Center , Shiraz University of Medical Sciences , Shiraz , Iran
| | - Azita Rahmani
- a Department of Chemistry , University of Zabol , Zabol , Iran
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Ghanakota P, Carlson HA. Comparing pharmacophore models derived from crystallography and NMR ensembles. J Comput Aided Mol Des 2017; 31:979-993. [PMID: 29047011 DOI: 10.1007/s10822-017-0077-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 10/12/2017] [Indexed: 10/18/2022]
Abstract
NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA.
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Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T. Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations. J Chem Inf Model 2017; 57:365-385. [PMID: 28072524 DOI: 10.1021/acs.jcim.6b00674] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.
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Affiliation(s)
- Marcus Wieder
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria.,Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna , Währingerstraße 17, 1090 Vienna, Austria
| | - Arthur Garon
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Ugo Perricone
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria.,Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo , Via Archirafi 32, Palermo, Italy
| | - Stefan Boresch
- Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna , Währingerstraße 17, 1090 Vienna, Austria
| | - Thomas Seidel
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Anna Maria Almerico
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo , Via Archirafi 32, Palermo, Italy
| | - Thierry Langer
- Faculty of Life Sciences, Department of Pharmaceutical Chemistry, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
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45
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Combining molecular dynamics simulation and ligand-receptor contacts analysis as a new approach for pharmacophore modeling: beta-secretase 1 and check point kinase 1 as case studies. J Comput Aided Mol Des 2016; 30:1149-1163. [PMID: 27722817 DOI: 10.1007/s10822-016-9984-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 10/03/2016] [Indexed: 01/19/2023]
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46
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Ngo T, Kufareva I, Coleman JL, Graham RM, Abagyan R, Smith NJ. Identifying ligands at orphan GPCRs: current status using structure-based approaches. Br J Pharmacol 2016; 173:2934-51. [PMID: 26837045 PMCID: PMC5341249 DOI: 10.1111/bph.13452] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 11/18/2015] [Accepted: 01/29/2016] [Indexed: 12/26/2022] Open
Abstract
GPCRs are the most successful pharmaceutical targets in history. Nevertheless, the pharmacology of many GPCRs remains inaccessible as their endogenous or exogenous modulators have not been discovered. Tools that explore the physiological functions and pharmacological potential of these 'orphan' GPCRs, whether they are endogenous and/or surrogate ligands, are therefore of paramount importance. Rates of receptor deorphanization determined by traditional reverse pharmacology methods have slowed, indicating a need for the development of more sophisticated and efficient ligand screening approaches. Here, we discuss the use of structure-based ligand discovery approaches to identify small molecule modulators for exploring the function of orphan GPCRs. These studies have been buoyed by the growing number of GPCR crystal structures solved in the past decade, providing a broad range of template structures for homology modelling of orphans. This review discusses the methods used to establish the appropriate signalling assays to test orphan receptor activity and provides current examples of structure-based methods used to identify ligands of orphan GPCRs. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc.
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Affiliation(s)
- Tony Ngo
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - James Lj Coleman
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
| | - Robert M Graham
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Nicola J Smith
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia.
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia.
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47
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Wieder M, Perricone U, Seidel T, Langer T. Pharmacophore Models Derived from Molecular Dynamics Simulations of Protein-Ligand Complexes: A Case Study. Nat Prod Commun 2016. [DOI: 10.1177/1934578x1601101019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
A single, merged pharmacophore hypothesis is derived combining 2000 pharmacophore models obtained during a 20 ns molecular dynamics simulation of a protein-ligand complex with one pharmacophore model derived from the initial PDB structure. This merged pharmacophore model contains all features that are present during the simulation and statistical information about the dynamics of the pharmacophore features. Based on the dynamics of the pharmacophore features we derive two distinctive feature patterns resulting in two different pharmacophore models for the analyzed system – the first model consists of features that are obtained from the PDB structure and the second uses two features that can only be derived from the molecular dynamics simulation. Both models can distinguish between active and decoy molecules in virtual screening. Our approach represents an objective way to add/remove features in pharmacophore models and can be of interest for the investigation of any naturally occurring system that relies on ligand-receptor interactions for its biological activity.
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Affiliation(s)
- Marcus Wieder
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Ugo Perricone
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche “STEBICEF”, Università di Palermo, Palermo, Italy
| | - Thomas Seidel
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
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48
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Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations. MONATSHEFTE FUR CHEMIE 2016; 147:553-563. [PMID: 27069282 PMCID: PMC4785218 DOI: 10.1007/s00706-016-1674-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 01/14/2016] [Indexed: 01/23/2023]
Abstract
ABSTRACT Pharmacophore modeling is a widely used technique in computer-aided drug discovery. Structure-based pharmacophore models of a ligand in complex with a protein have proven to be useful for supporting in silico hit discovery, hit to lead expansion, and lead optimization. As a structure-based approach it depends on the correct interpretation of ligand-protein interactions. There are legitimate concerns about the fidelity of the bound ligand and about non-physiological contacts with parts of the crystal and the solvent effects that influence the protein structure. A possible way to refine the structure of a protein-ligand system is to use the final structure of a given MD simulation. In this study we compare pharmacophore models built using the initial protein-ligand structure obtained from the protein data bank (PDB) with pharmacophore models built with the final structure of a molecular dynamics simulation. We show that the pharmacophore models differ in feature number and feature type and that the pharmacophore models built from the last structure of a MD simulation shows in some cases better ability to distinguish between active and decoy ligand structures. GRAPHICAL ABSTRACT
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49
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Wieder M, Perricone U, Boresch S, Seidel T, Langer T. Evaluating the stability of pharmacophore features using molecular dynamics simulations. Biochem Biophys Res Commun 2016; 470:685-689. [PMID: 26785387 DOI: 10.1016/j.bbrc.2016.01.081] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 01/14/2016] [Indexed: 01/10/2023]
Abstract
Molecular dynamics simulations of twelve protein-ligand systems were used to derive a single, structure based pharmacophore model for each system. These merged models combine the information from the initial experimental structure and from all snapshots saved during the simulation. We compared the merged pharmacophore models with the corresponding PDB pharmacophore models, i.e., the static models generated from an experimental structure in the usual manner. The frequency of individual features, of feature types and the occurrence of features not present in the static model derived from the experimental structure were analyzed. We observed both pharmacophore features not visible in the traditional approach, as well as features which disappeared rapidly during the molecular dynamics simulations and which may well be artifacts of the initial PDB structure-derived pharmacophore model. Our approach helps mitigate the sensitivity of structure based pharmacophore models to the single set of coordinates present in the experimental structure. Further, the frequency with which specific features occur during the MD simulation may aid in ranking the importance of individual features.
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Affiliation(s)
- Marcus Wieder
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria; Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
| | - Ugo Perricone
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche "STEBICEF", Università di Palermo, Palermo, Italy
| | - Stefan Boresch
- Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Thomas Seidel
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
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50
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Kumar A, Zhang KYJ. Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise. J Chem Inf Model 2015; 56:965-73. [PMID: 26247231 DOI: 10.1021/acs.jcim.5b00279] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
To evaluate the applicability of shape similarity in docking-based pose selection and virtual screening, we participated in the CSARdock2014 benchmark exercise for identifying the correct docking pose of inhibitors targeting factor XA, spleen tyrosine kinase, and tRNA methyltransferase. This exercise provides a valuable opportunity for researchers to test their docking programs, methods, and protocols in a blind testing environment. In the CSARdock2014 benchmark exercise, we have implemented an approach that uses ligand 3D shape similarity to facilitate docking-based pose selection and virtual screening. We showed here that ligand 3D shape similarity between bound poses could be used to identify the native-like pose from an ensemble of docking-generated poses. Our method correctly identified the native pose as the top-ranking pose for 73% of test cases in a blind testing environment. Moreover, the pose selection results also revealed an excellent correlation between ligand 3D shape similarity scores and RMSD to X-ray crystal structure ligand. In the virtual screening exercise, the average RMSD for our pose prediction was found to be 1.02 Å, and it was one of the top performances achieved in CSARdock2014 benchmark exercise. Furthermore, the inclusion of shape similarity improved virtual screening performance of docking-based scoring and ranking. The coefficient of determination (r(2)) between experimental activities and docking scores for 276 spleen tyrosine kinase inhibitors was found to be 0.365 but reached 0.614 when the ligand 3D shape similarity was included.
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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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