151
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Wong F, Krishnan A, Zheng EJ, Stärk H, Manson AL, Earl AM, Jaakkola T, Collins JJ. Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Mol Syst Biol 2022; 18:e11081. [PMID: 36065847 PMCID: PMC9446081 DOI: 10.15252/msb.202211081] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/12/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022] Open
Abstract
Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.
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Affiliation(s)
- Felix Wong
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aarti Krishnan
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Erica J Zheng
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Program in Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Hannes Stärk
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Abigail L Manson
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ashlee M Earl
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tommi Jaakkola
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - James J Collins
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
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152
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Hernandez CC, Tarfa RA, Miguel I Limcaoco J, Liu R, Mondal P, Hill C, Keith Duncan R, Tzounopoulos T, Stephenson CRJ, O'Meara MJ, Wipf P. Development of an automated screen for Kv7.2 potassium channels and discovery of a new agonist chemotype. Bioorg Med Chem Lett 2022; 71:128841. [PMID: 35671848 PMCID: PMC9469649 DOI: 10.1016/j.bmcl.2022.128841] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/11/2022] [Accepted: 06/01/2022] [Indexed: 01/11/2023]
Abstract
To identify pore domain ligands on Kv7.2 potassium ion channels, we compared wild-type (WT) and W236L mutant Kv7.2 channels in a series of assays with previously validated and novel agonist chemotypes. Positive controls were retigabine, flupirtine, and RL-81; i.e. Kv7.2 channel activators that significantly shift voltage-dependent activation to more negative potentials (ΔV50) at 5 µM. We identified 6 new compounds that exhibited differential enhancing activity between WT and W236L mutant channels. Whole cell patch-clamp electrophysiology studies were conducted to identify Kv7.2. Kv7.2/3, Kv7.4, and Kv7.5 selectivity. Our results validate the SyncroPatch platform and establish new structure activity relationships (SAR). Specifically, in addition to selective Kv7.2, Kv7.2/3, Kv7.4. and Kv7.5 agonists, we identified a novel chemotype, ZK-21, a 4-aminotetrahydroquinoline that is distinct from any of the previously described Kv7 channel modifiers. Using flexible receptor docking, ZK-21 was predicted to be stabilized by W236 and bind perpendicular to retigabine, burying the benzyl carbamate group into a tunnel reaching the core of the pore domain.
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Affiliation(s)
- Ciria C Hernandez
- Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, United States
| | - Rahilla A Tarfa
- Department of Otolaryngology, Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Jose Miguel I Limcaoco
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Ruiting Liu
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Pravat Mondal
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Clare Hill
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, United States; Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - R Keith Duncan
- Department of Otolaryngology-Head and Neck Surgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Thanos Tzounopoulos
- Department of Otolaryngology, Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Corey R J Stephenson
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, United States
| | - Matthew J O'Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Peter Wipf
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, United States; Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA 15213, United States; School of Pharmacy, University of Eastern Finland, 70210 Kuopio, Finland
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153
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Kallert E, Fischer TR, Schneider S, Grimm M, Helm M, Kersten C. Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ 1-Riboswitch. J Chem Inf Model 2022; 62:4134-4148. [PMID: 35994617 DOI: 10.1021/acs.jcim.2c00751] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ1-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.
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Affiliation(s)
- Elisabeth Kallert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Tim R Fischer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Simon Schneider
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Maike Grimm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Mark Helm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
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154
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Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies. Int J Mol Sci 2022; 23:ijms23169374. [PMID: 36012627 PMCID: PMC9409045 DOI: 10.3390/ijms23169374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/17/2022] Open
Abstract
Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.
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155
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Pandey M, Radaeva M, Mslati H, Garland O, Fernandez M, Ester M, Cherkasov A. Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks. Molecules 2022; 27:molecules27165114. [PMID: 36014351 PMCID: PMC9416537 DOI: 10.3390/molecules27165114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/03/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.
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Affiliation(s)
- Mohit Pandey
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Mariia Radaeva
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Hazem Mslati
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Olivia Garland
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Michael Fernandez
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
- Correspondence:
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156
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García-Ortegón M, Simm GNC, Tripp AJ, Hernández-Lobato JM, Bender A, Bacallado S. DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. J Chem Inf Model 2022; 62:3486-3502. [PMID: 35849793 PMCID: PMC9364321 DOI: 10.1021/acs.jcim.1c01334] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Indexed: 01/05/2023]
Abstract
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.
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Affiliation(s)
- Miguel García-Ortegón
- Statistical
Laboratory, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd., Cambridge CB3 0WB, United Kingdom
| | - Gregor N. C. Simm
- Department
of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, United Kingdom
| | - Austin J. Tripp
- Department
of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, United Kingdom
| | | | - Andreas Bender
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Rd., Cambridge CB2 1EW, United Kingdom
| | - Sergio Bacallado
- Statistical
Laboratory, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd., Cambridge CB3 0WB, United Kingdom
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157
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El Azab EF, Saleh AM, Yousif SO, Mazhari BBZ, Abu Alrub H, Elfaki EM, Hamza A, Abdulmalek S. New insights into geraniol's antihemolytic, anti-inflammatory, antioxidant, and anticoagulant potentials using a combined biological and in silico screening strategy. Inflammopharmacology 2022; 30:1811-1833. [PMID: 35932440 DOI: 10.1007/s10787-022-01039-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/15/2022] [Indexed: 11/05/2022]
Abstract
The study aims to assess the antihemolytic and antioxidant activities of geraniol versus 2, 2'-azobis, 2-amidinopropane dihydro-chloride- (AAPH-) induced oxidative damage and hemolysis to erythrocytes and its anti-inflammatory potential against lipopolysaccharide- (LPS-) induced inflammation in white blood cells (WBCs) with a focus on its integrated computational strategies against different targeted receptors participating in inflammation and coagulation. The rats' erythrocyte suspension was incubated with different geraniol concentrations. Molecular docking and simulation were used to explore the possible interaction patterns of geraniol against the potential targeted proteins for therapeutic screening. The results displayed that geraniol had a prolonged noteworthy effect on activated partial thromboplastin time and thromboplastin time. Geraniol displayed strong antioxidant effects via reduced malondialdehyde (MDA) formation and increased GSH level and SOD activity. We observed dose-dependent prevention of K+ ion leakage along with a remarkable decline of hemolysis in erythrocytes pretreated with geraniol. Geraniol 100 µg/mL and diclofenac 100 µM were nontoxic to WBCs. Geraniol significantly reduces the expression and release of cellular pro-inflammatory factors TNF-α, IL-1β, IL-8, and nitric oxide, accompanied by a significant upregulation of gene expression of anti-inflammatory cytokine IL-10 in LPS-induced WBCs compared to nontreated cells. It demonstrates a much stronger inhibition potential than diclofenac in terms of inflammation inhibition. When comparing molecular docking and simulation data, current work showed that geraniol has a good affinity toward apoptosis signal-regulating kinase 1 (ASK1) and human P2Y12 receptors and could be developed as an antioxidant, anti-inflammatory, and anticoagulant medication in the future. Consequently, geraniol is recommended to have a defensive influence against oxidative stress, and hemolysis also could be developed as a promising anti-inflammatory, antioxidant, and anticoagulant medication.
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Affiliation(s)
- Eman Fawzy El Azab
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at Al-Qurayyat, Jouf University, Al-Qurayyat, 77454, Saudi Arabia. .,Biochemistry Department, Faculty of Science, Alexandria University, Alexandria, 21511, Egypt.
| | - Abdulrahman M Saleh
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, 11884, Egypt
| | - Sara Osman Yousif
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at Al-Qurayyat, Jouf University, Al-Qurayyat, 77454, Saudi Arabia.,Department of Clinical Chemistry, Faculty of Medical Laboratory Sciences, Sudan University of Science and Technology, Khartoum, Sudan
| | - Bi Bi Zainab Mazhari
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at Al-Qurayyat, Jouf University, Al-Qurayyat, 77454, Saudi Arabia
| | - Heba Abu Alrub
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at Al-Qurayyat, Jouf University, Al-Qurayyat, 77454, Saudi Arabia
| | - Elyasa Mustafa Elfaki
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at Al-Qurayyat, Jouf University, Al-Qurayyat, 77454, Saudi Arabia
| | - Alneil Hamza
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences at Al-Qurayyat, Jouf University, Al-Qurayyat, 77454, Saudi Arabia
| | - Shaymaa Abdulmalek
- Biochemistry Department, Faculty of Science, Alexandria University, Alexandria, 21511, Egypt
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158
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Gorgulla C, Jayaraj A, Fackeldey K, Arthanari H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr Opin Chem Biol 2022; 69:102156. [PMID: 35576813 PMCID: PMC9990419 DOI: 10.1016/j.cbpa.2022.102156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/16/2022] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.
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Affiliation(s)
- Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | | | - Konstantin Fackeldey
- Institute of Mathematics, Technical University Berlin, Berlin, Germany; Zuse Institute Berlin, Berlin, Germany
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA.
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159
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Dithiocarbazate Ligand-Based Cu(II), Ni(II), and Zn(II) Complexes: Synthesis, Structural Investigations, Cytotoxicity, DNA Binding, and Molecular Docking Studies. Bioinorg Chem Appl 2022; 2022:2004052. [PMID: 35959229 PMCID: PMC9357781 DOI: 10.1155/2022/2004052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/16/2022] [Accepted: 06/25/2022] [Indexed: 11/29/2022] Open
Abstract
S-4-methylbenzyl-β-N-(2-methoxybenzylmethylene)dithiocarbazate ligand, 1, prepared from S-(4-methylbenzyl)dithiocarbazate, was used to produce a novel series of transition metal complexes of the type, [M (L)2] [M = Cu(II) (2), Ni(II) (3), and Zn(II) (4), L = 1]. The ligand and its complexes were investigated by elemental analysis, FTIR, 1H and 13C-NMR, MS spectrometry, and molar conductivity. In addition, single X-ray crystallography was also performed for ligand, 1, and complex 3. The Hirshfeld surface analyses were also performed to know about various bonding interactions in the ligand, 1, and complex 3. The investigated compounds were also tested to evaluate their cytotoxic behaviour. However, complex 2 showed promising results against MCF-7 and MDA-MB-213 cancer cell lines. Furthermore, the interaction of CT-DNA with ligand, 1, and complex 2 was also studied using the electronic absorption method, revealing that the compounds have potential DNA-binding ability via hydrogen bonding and hydrophobic and van der Waals interactions. A molecular docking study of complex 2 was also carried out, which revealed that free binding free energy value was −7.39 kcal mol−1.
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160
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Gahbauer S, Correy GJ, Schuller M, Ferla MP, Doruk YU, Rachman M, Wu T, Diolaiti M, Wang S, Neitz RJ, Fearon D, Radchenko D, Moroz Y, Irwin JJ, Renslo AR, Taylor JC, Gestwicki JE, von Delft F, Ashworth A, Ahel I, Shoichet BK, Fraser JS. Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 Macrodomain of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.06.27.497816. [PMID: 35794891 PMCID: PMC9258288 DOI: 10.1101/2022.06.27.497816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 152 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated protein dynamics within the active site, and key inhibitor motifs that will template future drug development against Mac1.
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Affiliation(s)
- Stefan Gahbauer
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
| | - Galen J. Correy
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Marion Schuller
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, UK
| | - Matteo P. Ferla
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, OX4 2PG, UK
| | - Yagmur Umay Doruk
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158, USA
| | - Moira Rachman
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
| | - Taiasean Wu
- Institute for Neurodegenerative Disease, University of California San Francisco, San Francisco, CA 94158, USA
- Chemistry and Chemical Biology Graduate Program, University of California San Francisco, San Francisco, CA 94158, USA
| | - Morgan Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158, USA
| | - Siyi Wang
- Chemistry and Chemical Biology Graduate Program, University of California San Francisco, San Francisco, CA 94158, USA
| | - R. Jeffrey Neitz
- Department of Pharmaceutical Chemistry and Small Molecule Discovery Center, University of California, San Francisco, California 94158, USA
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Dmytro Radchenko
- Enamine Ltd., Chervonotkatska Street 78, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine
| | - Yurii Moroz
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine
- Chemspace, Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adam R. Renslo
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry and Small Molecule Discovery Center, University of California, San Francisco, California 94158, USA
| | - Jenny C. Taylor
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, OX4 2PG, UK
| | - Jason E. Gestwicki
- Institute for Neurodegenerative Disease, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry and Small Molecule Discovery Center, University of California, San Francisco, California 94158, USA
| | - Frank von Delft
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
- Centre for Medicines Discovery, University of Oxford, South Parks Road, Headington, OX3 7DQ, UK
- Structural Genomics Consortium, University of Oxford, Old Road Campus, Roosevelt Drive, Headington OX3 7DQ, UK
- Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158, USA
| | - Ivan Ahel
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, UK
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
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161
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Kumar V, Parate S, Danishuddin, Zeb A, Singh P, Lee G, Jung TS, Lee KW, Ha MW. 3D-QSAR-Based Pharmacophore Modeling, Virtual Screening, and Molecular Dynamics Simulations for the Identification of Spleen Tyrosine Kinase Inhibitors. Front Cell Infect Microbiol 2022; 12:909111. [PMID: 35846777 PMCID: PMC9280624 DOI: 10.3389/fcimb.2022.909111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Spleen tyrosine kinase (SYK) is an essential mediator of immune cell signaling and has been anticipated as a therapeutic target for autoimmune diseases, notably rheumatoid arthritis, allergic rhinitis, asthma, and cancers. Significant attempts have been undertaken in recent years to develop SYK inhibitors; however, limited success has been achieved due to poor pharmacokinetics and adverse effects of inhibitors. The primary goal of this research was to identify potential inhibitors having high affinity, selectivity based on key molecular interactions, and good drug-like properties than the available inhibitor, fostamatinib. In this study, a 3D-QSAR model was built for SYK based on known inhibitor IC50 values. The best pharmacophore model was then used as a 3D query to screen a drug-like database to retrieve hits with novel chemical scaffolds. The obtained compounds were subjected to binding affinity prediction using the molecular docking approach, and the results were subsequently validated using molecular dynamics (MD) simulations. The simulated compounds were ranked according to binding free energy (ΔG), and the binding affinity was compared with fostamatinib. The binding mode analysis of selected compounds revealed that the hit compounds form hydrogen bond interactions with hinge region residue Ala451, glycine-rich loop residue Lys375, Ser379, and DFG motif Asp512. Identified hits were also observed to form a desirable interaction with Pro455 and Asn457, the rare feature observed in SYK inhibitors. Therefore, we argue that identified hit compounds ZINC98363745, ZINC98365358, ZINC98364133, and ZINC08789982 may help in drug design against SYK.
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162
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Hasan MR, Alsaiari AA, Fakhurji BZ, Molla MHR, Asseri AH, Sumon MAA, Park MN, Ahammad F, Kim B. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules 2022; 27:4169. [PMID: 35807415 PMCID: PMC9268380 DOI: 10.3390/molecules27134169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 01/18/2023] Open
Abstract
The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.
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Affiliation(s)
- Md Rifat Hasan
- Department of Mathematics, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Department of Applied Mathematics, Faculty of Science, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Ahad Amer Alsaiari
- College of Applied Medical Science, Clinical Laboratories Science Department, Taif University, Taif 21944, Saudi Arabia;
| | - Burhan Zain Fakhurji
- iGene Medical Training and Molecular Research Center, Jeddah 21589, Saudi Arabia;
| | | | - Amer H. Asseri
- Biochemistry Department, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Centre for Artificial Intelligence in Precision Medicines, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia
| | - Md Afsar Ahmed Sumon
- Department of Marine Biology, Faculty of Marine Sciences, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Moon Nyeo Park
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
| | - Foysal Ahammad
- Department of Biological Sciences, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Bonglee Kim
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
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163
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Bajusz D, Keserű GM. Maximizing the integration of virtual and experimental screening in hit discovery. Expert Opin Drug Discov 2022; 17:629-640. [PMID: 35671403 DOI: 10.1080/17460441.2022.2085685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches. AREAS COVERED After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies. EXPERT OPINION Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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164
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Taldaev A, Terekhov R, Nikitin I, Zhevlakova A, Selivanova I. Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling. Int J Mol Sci 2022; 23:6023. [PMID: 35682702 PMCID: PMC9181432 DOI: 10.3390/ijms23116023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 12/13/2022] Open
Abstract
Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.
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Affiliation(s)
- Amir Taldaev
- Laboratoty of Nanobiotechnology, Institute of Biomedical Chemistry, Pogodinskaya Str. 10/8, 119121 Moscow, Russia
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Roman Terekhov
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Ilya Nikitin
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Anastasiya Zhevlakova
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
| | - Irina Selivanova
- Department of Chemistry, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia; (R.T.); (I.N.); (A.Z.); (I.S.)
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165
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Design, Semisynthesis, and Estrogenic Activity of Lignan Derivatives from Natural Dibenzylbutyrolactones. Pharmaceuticals (Basel) 2022; 15:ph15050585. [PMID: 35631411 PMCID: PMC9145393 DOI: 10.3390/ph15050585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 12/25/2022] Open
Abstract
Based on molecular docking studies on the ERα, a series of lignan derivatives (3–16) were designed and semisynthesized from the natural dibenzylbutyrolactones bursehernin (1) and matairesinol dimethyl ether (2). To examine their estrogenic and antiestrogenic potencies, the effects of these compounds on estrogen receptor element (ERE)-driven reporter gene expression and viability in human ER+ breast cancer cells were evaluated. Lignan compounds induced ERE-driven reporter gene expression with very low potency as compared with the pure agonist E2. However, coincubation of 5 μM of lignan derivatives 1, 3, 4, 7, 8, 9, 11, 13, and 14 with increasing concentrations of E2 (from 0.01 pM to 1 nM) reduced both the potency and efficacy of pure agonists. The binding to the rhERα-LBD was validated by TR-FRET competitive binding assay and lignans bound to the rhERα with IC50 values from 0.16 μM (compound 14) to 6 μM (compound 4). Induced fit docking (IFD) and molecular dynamics (MD) simulations for compound 14 were carried out to further investigate the binding mode interactions. Finally, the in silico ADME predictions indicated that the most potent lignan derivatives exhibited good drug-likeness.
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166
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Vulpetti A, Lingel A, Dalvit C, Schiering N, Oberer L, Henry C, Lu Y. Efficient Screening of Target-Specific Selected Compounds in Mixtures by 19F NMR Binding Assay with Predicted 19F NMR Chemical Shifts. ChemMedChem 2022; 17:e202200163. [PMID: 35475323 DOI: 10.1002/cmdc.202200163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/26/2022] [Indexed: 11/06/2022]
Abstract
Ligand-based 19 F NMR screening is a highly effective and well-established hit-finding approach. The high sensitivity to protein binding makes it particularly suitable for fragment screening. Different criteria can be considered for generating fluorinated fragment libraries. One common strategy is to assemble a large, diverse, well-designed and characterized fragment library which is screened in mixtures, generated based on experimental 19 F NMR chemical shifts. Here, we introduce a complementary knowledge-based 19 F NMR screening approach, named 19 Focused screening, enabling the efficient screening of putative active molecules selected by computational hit finding methodologies, in mixtures assembled and on-the-fly deconvoluted based on predicted 19 F NMR chemical shifts. In this study, we developed a novel approach, named LEFshift , for 19 F NMR chemical shift prediction using rooted topological fluorine torsion fingerprints in combination with a random forest machine learning method. A demonstration of this approach to a real test case is reported.
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Affiliation(s)
- Anna Vulpetti
- Novartis Pharma AG, Global Discovery Chemistry, Novartis Campus, 4002, Basel, SWITZERLAND
| | - Andreas Lingel
- Novartis Institutes for BioMedical Research Basel, Global Discovery Chemistry, SWITZERLAND
| | - Claudio Dalvit
- Novartis Institutes for BioMedical Research Basel, Protease Platform, SWITZERLAND
| | - Nikolaus Schiering
- Novartis Institutes for BioMedical Research Basel, Protease Platform, SWITZERLAND
| | - Lukas Oberer
- Novartis Institutes for BioMedical Research Basel, Global Discovery Chemistry, SWITZERLAND
| | - Chrystelle Henry
- Novartis Institutes for BioMedical Research Basel, Protein Science, SWITZERLAND
| | - Yipin Lu
- Novartis Institutes for BioMedical Research Basel, Global Discovery Chemistry, SWITZERLAND
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167
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Venkatraman V, Colligan TH, Lesica GT, Olson DR, Gaiser J, Copeland CJ, Wheeler TJ, Roy A. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. Front Pharmacol 2022; 13:874746. [PMID: 35559261 PMCID: PMC9086895 DOI: 10.3389/fphar.2022.874746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas H. Colligan
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - George T. Lesica
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Daniel R. Olson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Jeremiah Gaiser
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Conner J. Copeland
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Travis J. Wheeler
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Amitava Roy
- Department of Computer Science, University of Montana, Missoula, MT, United States
- Rocky Mountain Laboratories, Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
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168
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Lu C, Liu S, Shi W, Yu J, Zhou Z, Zhang X, Lu X, Cai F, Xia N, Wang Y. Systemic evolutionary chemical space exploration for drug discovery. J Cheminform 2022; 14:19. [PMID: 35365231 PMCID: PMC8973791 DOI: 10.1186/s13321-022-00598-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/11/2022] [Indexed: 11/29/2022] Open
Abstract
Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.
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Affiliation(s)
- Chong Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Shien Liu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Weihua Shi
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Jun Yu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Zhou Zhou
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Xiaoli Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Faji Cai
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Yikai Wang
- Keen Therapeutics Co., Ltd., Shanghai, China.
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169
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Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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170
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Xue Q, Liu X, Russell P, Li J, Pan W, Fu J, Zhang A. Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 233:113323. [PMID: 35183811 DOI: 10.1016/j.ecoenv.2022.113323] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/12/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.
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Affiliation(s)
- Qiao Xue
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Paul Russell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Jin Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK
| | - Wenxiao Pan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Jianjie Fu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, PR China.
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, PR China
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171
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Kobauri P, Galenkamp NS, Schulte AM, de Vries J, Simeth NA, Maglia G, Thallmair S, Kolarski D, Szymanski W, Feringa BL. Hypothesis-Driven, Structure-Based Design in Photopharmacology: The Case of eDHFR Inhibitors. J Med Chem 2022; 65:4798-4817. [PMID: 35258959 PMCID: PMC8958501 DOI: 10.1021/acs.jmedchem.1c01962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Photopharmacology
uses light to regulate the biological activity
of drugs. This precise control is obtained through the incorporation
of molecular photoswitches into bioactive molecules. A major challenge
for photopharmacology is the rational design of photoswitchable drugs
that show light-induced activation. Computer-aided drug design is
an attractive approach toward more effective, targeted design. Herein,
we critically evaluated different structure-based approaches for photopharmacology
with Escherichia coli dihydrofolate reductase (eDHFR)
as a case study. Through the iterative examination of our hypotheses,
we progressively tuned the design of azobenzene-based, photoswitchable
eDHFR inhibitors in five design–make–switch–test–analyze
cycles. Targeting a hydrophobic subpocket of the enzyme and a specific
salt bridge only with the thermally metastable cis-isomer emerged as the most promising design strategy. We identified
three inhibitors that could be activated upon irradiation and reached
potencies in the low-nanomolar range. Above all, this systematic study
provided valuable insights for future endeavors toward rational photopharmacology.
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Affiliation(s)
- Piermichele Kobauri
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Nicole S Galenkamp
- Groningen Biomolecular Science and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Albert M Schulte
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Jisk de Vries
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Nadja A Simeth
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Institute for Organic and Biomolecular Chemistry, University of Goettingen, Tammannstr. 2, 37077 Göttingen, Germany
| | - Giovanni Maglia
- Groningen Biomolecular Science and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Sebastian Thallmair
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands.,Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
| | - Dušan Kolarski
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,DWI-Leibniz Institut für interaktive Materialien e.V., RWTH Aachen University, Forckenbeckstraße 50, 52074 Aachen, Germany
| | - Wiktor Szymanski
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Department of Radiology, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Ben L Feringa
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
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172
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Kenny SE, Antaw F, Locke WJ, Howard CB, Korbie D, Trau M. Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis. Life (Basel) 2022; 12:life12030363. [PMID: 35330114 PMCID: PMC8950575 DOI: 10.3390/life12030363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 02/23/2022] [Indexed: 12/02/2022] Open
Abstract
Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered “bottom-up” approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of “big data” and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure—when accompanied by high-quality experimental data for model training—has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.
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Affiliation(s)
- Sophie E. Kenny
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; (S.E.K.); (F.A.); (C.B.H.)
| | - Fiach Antaw
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; (S.E.K.); (F.A.); (C.B.H.)
| | - Warwick J. Locke
- Molecular Diagnostic Solutions, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Building 101, Clunies Ross Street, Canberra, ACT 2601, Australia;
| | - Christopher B. Howard
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; (S.E.K.); (F.A.); (C.B.H.)
| | - Darren Korbie
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; (S.E.K.); (F.A.); (C.B.H.)
- Correspondence: (D.K.); (M.T.)
| | - Matt Trau
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner of College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia; (S.E.K.); (F.A.); (C.B.H.)
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia
- Correspondence: (D.K.); (M.T.)
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173
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In Silico study for acyclovir, ganciclovir and its derivatives to fight the COVID-19: Molecular docking, DFT calculations, ADME and td-Molecular dynamics simulations. J INDIAN CHEM SOC 2022. [PMCID: PMC8931996 DOI: 10.1016/j.jics.2022.100433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH2OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV.
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174
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Gan JL, Kumar D, Chen C, Taylor BC, Jagger BR, Amaro RE, Lee CT. Benchmarking ensemble docking methods in D3R Grand Challenge 4. J Comput Aided Mol Des 2022; 36:87-99. [PMID: 35199221 PMCID: PMC8907095 DOI: 10.1007/s10822-021-00433-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022]
Abstract
The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.
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Affiliation(s)
- Jessie Low Gan
- San Diego Jewish Academy, San Diego, 92130, CA, USA.,California Institute of Technology, Pasadena, CA, 91125, USA
| | - Dhruv Kumar
- Rancho Bernardo High School, San Diego, CA, 92128, USA.,University of California Berkeley, Berkeley, CA, USA
| | - Cynthia Chen
- California Institute of Technology, Pasadena, CA, 91125, USA.,Canyon Crest Academy, San Diego, CA, 92130, USA
| | - Bryn C Taylor
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.,Discovery Sciences, Janssen Research and Development, San Diego, CA, 92121, USA
| | - Benjamin R Jagger
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Christopher T Lee
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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175
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Begnini F, Geschwindner S, Johansson P, Wissler L, Lewis RJ, Danelius E, Luttens A, Matricon P, Carlsson J, Lenders S, König B, Friedel A, Sjö P, Schiesser S, Kihlberg J. Importance of Binding Site Hydration and Flexibility Revealed When Optimizing a Macrocyclic Inhibitor of the Keap1-Nrf2 Protein-Protein Interaction. J Med Chem 2022; 65:3473-3517. [PMID: 35108001 PMCID: PMC8883477 DOI: 10.1021/acs.jmedchem.1c01975] [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] [Indexed: 02/07/2023]
Abstract
Upregulation of the transcription factor Nrf2 by inhibition of the interaction with its negative regulator Keap1 constitutes an opportunity for the treatment of disease caused by oxidative stress. We report a structurally unique series of nanomolar Keap1 inhibitors obtained from a natural product-derived macrocyclic lead. Initial exploration of the structure-activity relationship of the lead, followed by structure-guided optimization, resulted in a 100-fold improvement in inhibitory potency. The macrocyclic core of the nanomolar inhibitors positions three pharmacophore units for productive interactions with key residues of Keap1, including R415, R483, and Y572. Ligand optimization resulted in the displacement of a coordinated water molecule from the Keap1 binding site and a significantly altered thermodynamic profile. In addition, minor reorganizations of R415 and R483 were accompanied by major differences in affinity between ligands. This study therefore indicates the importance of accounting both for the hydration and flexibility of the Keap1 binding site when designing high-affinity ligands.
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Affiliation(s)
- Fabio Begnini
- Department
of Chemistry—BMC, Uppsala University, Box 576, 75123 Uppsala, Sweden
| | - Stefan Geschwindner
- Mechanistic
and Structural Biology, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
| | - Patrik Johansson
- Mechanistic
and Structural Biology, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
| | - Lisa Wissler
- Mechanistic
and Structural Biology, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
| | - Richard J. Lewis
- Department
of Medicinal Chemistry, Research and Early Development, Respiratory
and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183 Mölndal, Sweden
| | - Emma Danelius
- Department
of Chemistry—BMC, Uppsala University, Box 576, 75123 Uppsala, Sweden
| | - Andreas Luttens
- Science
for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box
596, 75124 Uppsala, Sweden
| | - Pierre Matricon
- Science
for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box
596, 75124 Uppsala, Sweden
| | - Jens Carlsson
- Science
for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box
596, 75124 Uppsala, Sweden
| | - Stijn Lenders
- Department
of Chemistry—BMC, Uppsala University, Box 576, 75123 Uppsala, Sweden
| | - Beate König
- Department
of Chemistry—BMC, Uppsala University, Box 576, 75123 Uppsala, Sweden
| | - Anna Friedel
- Department
of Chemistry—BMC, Uppsala University, Box 576, 75123 Uppsala, Sweden
| | - Peter Sjö
- Drugs
for Neglected Diseases Initiative (DNDi), 15 Chemin Camille-Vidart, 1202 Geneva, Switzerland
| | - Stefan Schiesser
- Department
of Medicinal Chemistry, Research and Early Development, Respiratory
and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183 Mölndal, Sweden,
| | - Jan Kihlberg
- Department
of Chemistry—BMC, Uppsala University, Box 576, 75123 Uppsala, Sweden,
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176
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Design, Synthesis, and Biological Evaluation of 4,4’-Difluorobenzhydrol Carbamates as Selective M1 Antagonists. Pharmaceuticals (Basel) 2022; 15:ph15020248. [PMID: 35215360 PMCID: PMC8879200 DOI: 10.3390/ph15020248] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/05/2022] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
Due to their important role in mediating a broad range of physiological functions, muscarinic acetylcholine receptors (mAChRs) have been a promising target for therapeutic and diagnostic applications alike; however, the list of truly subtype-selective ligands is scarce. Within this work, we have identified a series of twelve 4,4’-difluorobenzhydrol carbamates through a rigorous docking campaign leveraging commercially available amine databases. After synthesis, these compounds have been evaluated for their physico–chemical property profiles, including characteristics such as HPLC-logD, tPSA, logBB, and logPS. For all the synthesized carbamates, these characteristics indicate the potential for BBB permeation. In competitive radioligand binding experiments using Chinese hamster ovary cell membranes expressing the individual human mAChR subtype hM1-hM5, the most promising compound 2 displayed a high binding affinitiy towards hM1R (1.2 nM) while exhibiting modest-to-excellent selectivity versus the hM2-5R (4–189-fold). All 12 compounds were shown to act in an antagonistic fashion towards hM1R using a dose-dependent calcium mobilization assay. The structural eligibility for radiolabeling and their pharmacological and physico–chemical property profiles render compounds 2, 5, and 7 promising candidates for future position emission tomography (PET) tracer development.
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177
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Luttens A, Gullberg H, Abdurakhmanov E, Vo DD, Akaberi D, Talibov VO, Nekhotiaeva N, Vangeel L, De Jonghe S, Jochmans D, Krambrich J, Tas A, Lundgren B, Gravenfors Y, Craig AJ, Atilaw Y, Sandström A, Moodie LWK, Lundkvist Å, van Hemert MJ, Neyts J, Lennerstrand J, Kihlberg J, Sandberg K, Danielson UH, Carlsson J. Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses. J Am Chem Soc 2022; 144:2905-2920. [PMID: 35142215 PMCID: PMC8848513 DOI: 10.1021/jacs.1c08402] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Drugs targeting SARS-CoV-2 could have saved millions of lives during the COVID-19 pandemic, and it is now crucial to develop inhibitors of coronavirus replication in preparation for future outbreaks. We explored two virtual screening strategies to find inhibitors of the SARS-CoV-2 main protease in ultralarge chemical libraries. First, structure-based docking was used to screen a diverse library of 235 million virtual compounds against the active site. One hundred top-ranked compounds were tested in binding and enzymatic assays. Second, a fragment discovered by crystallographic screening was optimized guided by docking of millions of elaborated molecules and experimental testing of 93 compounds. Three inhibitors were identified in the first library screen, and five of the selected fragment elaborations showed inhibitory effects. Crystal structures of target-inhibitor complexes confirmed docking predictions and guided hit-to-lead optimization, resulting in a noncovalent main protease inhibitor with nanomolar affinity, a promising in vitro pharmacokinetic profile, and broad-spectrum antiviral effect in infected cells.
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Affiliation(s)
- Andreas Luttens
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
| | - Hjalmar Gullberg
- Science for Life Laboratory, Biochemical and Cellular Assay Facility, Drug Discovery and Development Platform, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Eldar Abdurakhmanov
- Science for Life Laboratory, Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Duy Duc Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
| | - Dario Akaberi
- Department of Medical Biochemistry and Microbiology, Zoonosis Science Center, Uppsala University, SE-75123 Uppsala, Sweden
| | | | - Natalia Nekhotiaeva
- Science for Life Laboratory, Biochemical and Cellular Assay Facility, Drug Discovery and Development Platform, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Laura Vangeel
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Steven De Jonghe
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Dirk Jochmans
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Janina Krambrich
- Department of Medical Biochemistry and Microbiology, Zoonosis Science Center, Uppsala University, SE-75123 Uppsala, Sweden
| | - Ali Tas
- Department of Medical Microbiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands
| | - Bo Lundgren
- Science for Life Laboratory, Biochemical and Cellular Assay Facility, Drug Discovery and Development Platform, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Ylva Gravenfors
- Science for Life Laboratory, Drug Discovery & Development Platform, Department of Organic Chemistry, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Alexander J Craig
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden
| | - Yoseph Atilaw
- Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Anja Sandström
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden
| | - Lindon W K Moodie
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden.,Uppsala Antibiotic Centre, Uppsala University, SE-75123 Uppsala, Sweden
| | - Åke Lundkvist
- Department of Medical Biochemistry and Microbiology, Zoonosis Science Center, Uppsala University, SE-75123 Uppsala, Sweden
| | - Martijn J van Hemert
- Department of Medical Microbiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands
| | - Johan Neyts
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Johan Lennerstrand
- Department of Medical Sciences, Section of Clinical Microbiology, Uppsala University, SE-75185 Uppsala, Sweden
| | - Jan Kihlberg
- Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Kristian Sandberg
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden.,Department of Physiology and Pharmacology, Karolinska Institutet, SE-17177 Stockholm, Sweden.,Science for Life Laboratory, Drug Discovery & Development Platform, Uppsala Biomedical Center, Uppsala University, SE-75123 Uppsala, Sweden
| | - U Helena Danielson
- Science for Life Laboratory, Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
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178
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Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F, Stern A, Cherkasov A. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 2022; 17:672-697. [PMID: 35121854 DOI: 10.1038/s41596-021-00659-2] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/08/2021] [Indexed: 12/14/2022]
Abstract
With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.
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Affiliation(s)
- Francesco Gentile
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Jean Charle Yaacoub
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - James Gleave
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Michael Fernandez
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Anh-Tien Ton
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada.
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179
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Steadman D, Atkinson BN, Zhao Y, Willis NJ, Frew S, Monaghan A, Patel C, Armstrong E, Costelloe K, Magno L, Bictash M, Jones EY, Fish PV, Svensson F. Virtual Screening Directly Identifies New Fragment-Sized Inhibitors of Carboxylesterase Notum with Nanomolar Activity. J Med Chem 2022; 65:562-578. [PMID: 34939789 DOI: 10.1021/acs.jmedchem.1c01735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Notum is a negative regulator of Wnt signaling acting through the hydrolysis of a palmitoleoylate ester, which is required for Wnt activity. Inhibitors of Notum could be of use in diseases where dysfunctional Notum activity is an underlying cause. A docking-based virtual screen (VS) of a large commercial library was used to shortlist 952 compounds for experimental validation as inhibitors of Notum. The VS was successful with 31 compounds having an IC50 < 500 nM. A critical selection process was then applied with two clusters and two singletons (1-4d) selected for hit validation. Optimization of 4d guided by structural biology identified potent inhibitors of Notum activity that restored Wnt/β-catenin signaling in cell-based models. The [1,2,4]triazolo[4,3-b]pyradizin-3(2H)-one series 4 represent a new chemical class of Notum inhibitors and the first to be discovered by a VS campaign. These results demonstrate the value of VS with well-designed docking models based on X-ray structures.
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Affiliation(s)
- David Steadman
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Benjamin N Atkinson
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Yuguang Zhao
- Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, The Henry Wellcome Building for Genomic Medicine, Roosevelt Drive, OxfordOX3 7BN, U.K
| | - Nicky J Willis
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Sarah Frew
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Amy Monaghan
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Chandni Patel
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Emma Armstrong
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Kathryn Costelloe
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Lorenza Magno
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Magda Bictash
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - E Yvonne Jones
- Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, The Henry Wellcome Building for Genomic Medicine, Roosevelt Drive, OxfordOX3 7BN, U.K
| | - Paul V Fish
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Fredrik Svensson
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
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180
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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181
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Kristanti AN, Aminah NS, Siswanto I, Manuhara YSW, Abdjan MI, Wardana AP, Aung EE, Takaya Y. Anticancer potential of β-sitosterol and oleanolic acid as through inhibition of human estrogenic 17beta-hydroxysteroid dehydrogenase type-1 based on an in silico approach. RSC Adv 2022; 12:20319-20329. [PMID: 35919602 PMCID: PMC9278416 DOI: 10.1039/d2ra03092f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/27/2022] [Indexed: 11/21/2022] Open
Abstract
We presented pharmacokinetic study, molecular docking, and MD simulation to study β-sitosterol and oleanolic acid compounds and potential HSD17B1 inhibitors.
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Affiliation(s)
- Alfinda Novi Kristanti
- Departement of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
- Biotechnology of Tropical Medicinal Plants Research Group, Universitas Airlangga, Indonesia
| | - Nanik Siti Aminah
- Departement of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
- Biotechnology of Tropical Medicinal Plants Research Group, Universitas Airlangga, Indonesia
| | - Imam Siswanto
- Departement of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
- Bioinformatic Laboratory, UCoE Research Center for Bio-Molecule Engineering, Universitas Airlangga, Surabaya, Indonesia
| | - Yosephine Sri Wulan Manuhara
- Biotechnology of Tropical Medicinal Plants Research Group, Universitas Airlangga, Indonesia
- Department of Biology, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
| | - Muhammad Ikhlas Abdjan
- Departement of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
- PhD Student of Mathematics and Natural Sciences, Faculty of Science and Technology, Universitas Airlangga, Komplek Kampus C UNAIR, Jl. Mulyorejo, Surabaya, 60115, Indonesia
| | - Andika Pramudya Wardana
- Departement of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
- PhD Student of Mathematics and Natural Sciences, Faculty of Science and Technology, Universitas Airlangga, Komplek Kampus C UNAIR, Jl. Mulyorejo, Surabaya, 60115, Indonesia
| | - Ei Ei Aung
- Departement of Chemistry, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia
- Departement of Chemistry, Yadanarbon University, Amarapura Township, Mandalay, Myanmar
| | - Yoshiaki Takaya
- Faculty of Pharmacy, Meijo University, 150 Yagotoyama, Tempaku, Nagoya, 468-8503 Japan
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182
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Power H, Wu J, Turville S, Aggarwal A, Valtchev P, Schindeler A, Dehghani F. Virtual screening and in vitro validation of natural compound inhibitors against SARS-CoV-2 spike protein. Bioorg Chem 2021; 119:105574. [PMID: 34971947 PMCID: PMC8693770 DOI: 10.1016/j.bioorg.2021.105574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/24/2021] [Accepted: 12/16/2021] [Indexed: 12/16/2022]
Abstract
The COVID-19 pandemic caused by the SARS-CoV-2 virus has led to a major public health burden and has resulted in millions of deaths worldwide. As effective treatments are limited, there is a significant requirement for high-throughput, low resource methods for the discovery of novel antivirals. The SARS-CoV-2 spike protein plays a key role in viral entry and has been identified as a therapeutic target. Using the available spike crystal structure, we performed a virtual screen with a library of 527 209 natural compounds against the receptor binding domain of this protein. Top hits from this screen were subjected to a second, more comprehensive molecular docking experiment and filtered for favourable ADMET properties. The in vitro activity of 10 highly ranked compounds was assessed using a virus neutralisation assay designed to facilitate viral entry in a physiologically relevant manner via the plasma membrane route. Subsequently, four compounds ZINC02111387, ZINC02122196, SN00074072 and ZINC04090608 were identified to possess antiviral activity in the µM range. These findings validate the virtual screening method as a tool for identifying novel antivirals and provide a basis for future drug development against SARS-CoV-2.
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Affiliation(s)
- Helen Power
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia,Centre for Advanced Food Engineering, The University of Sydney, Sydney, NSW, 2006, Australia,Bioengineering and Molecular Medicine Laboratory, The Children’s Hospital at Westmead and Westmead Institute for Medical Research, Westmead, NSW 2145, Australia
| | - Jiadai Wu
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia,Centre for Advanced Food Engineering, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Stuart Turville
- The Kirby Institute, University of NSW, Kensington, NSW 2052, Australia
| | - Anupriya Aggarwal
- The Kirby Institute, University of NSW, Kensington, NSW 2052, Australia
| | - Peter Valtchev
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia,Centre for Advanced Food Engineering, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Aaron Schindeler
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia,Centre for Advanced Food Engineering, The University of Sydney, Sydney, NSW, 2006, Australia,Bioengineering and Molecular Medicine Laboratory, The Children’s Hospital at Westmead and Westmead Institute for Medical Research, Westmead, NSW 2145, Australia
| | - Fariba Dehghani
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia,Centre for Advanced Food Engineering, The University of Sydney, Sydney, NSW, 2006, Australia,Corresponding author
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183
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New Inhibitors of Laccase and Tyrosinase by Examination of Cross-Inhibition between Copper-Containing Enzymes. Int J Mol Sci 2021; 22:ijms222413661. [PMID: 34948458 PMCID: PMC8707586 DOI: 10.3390/ijms222413661] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/17/2021] [Accepted: 12/17/2021] [Indexed: 11/23/2022] Open
Abstract
Coppers play crucial roles in the maintenance homeostasis in living species. Approximately 20 enzyme families of eukaryotes and prokaryotes are known to utilize copper atoms for catalytic activities. However, small-molecule inhibitors directly targeting catalytic centers are rare, except for those that act against tyrosinase and dopamine-β-hydroxylase (DBH). This study tested whether known tyrosinase inhibitors can inhibit the copper-containing enzymes, ceruloplasmin, DBH, and laccase. While most small molecules minimally reduced the activities of ceruloplasmin and DBH, aside from known inhibitors, 5 of 28 tested molecules significantly inhibited the function of laccase, with the Ki values in the range of 15 to 48 µM. Enzyme inhibitory kinetics classified the molecules as competitive inhibitors, whereas differential scanning fluorimetry and fluorescence quenching supported direct bindings. To the best of our knowledge, this is the first report on organic small-molecule inhibitors for laccase. Comparison of tyrosinase and DBH inhibitors using cheminformatics predicted that the presence of thione moiety would suffice to inhibit tyrosinase. Enzyme assays confirmed this prediction, leading to the discovery of two new dual tyrosinase and DBH inhibitors.
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184
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Virtual Screening in Search for a Chemical Probe for Angiotensin-Converting Enzyme 2 (ACE2). Molecules 2021; 26:molecules26247584. [PMID: 34946667 PMCID: PMC8707431 DOI: 10.3390/molecules26247584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 01/09/2023] Open
Abstract
We elaborate new models for ACE and ACE2 receptors with an excellent prediction power compared to previous models. We propose promising workflows for working with huge compound collections, thereby enabling us to discover optimized protocols for virtual screening management. The efficacy of elaborated roadmaps is demonstrated through the cost-effective molecular docking of 1.4 billion compounds. Savings of up to 10-fold in CPU time are demonstrated. These developments allowed us to evaluate ACE2/ACE selectivity in silico, which is a crucial checkpoint for developing chemical probes for ACE2.
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