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Shorkey SA, Zhang Y, Sharp J, Clingman S, Nguyen L, Chen J, Chen M. Tuning single-molecule ClyA nanopore tweezers for real-time tracking of the conformational dynamics of West Nile viral NS2B/NS3 protease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594247. [PMID: 38798384 PMCID: PMC11118314 DOI: 10.1101/2024.05.14.594247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The flaviviral NS2B/NS3 protease is a conserved enzyme required for flavivirus replication. Its highly dynamic conformation poses major challenges but also offers opportunities for antiviral inhibition. Here, we established a nanopore tweezers-based platform to monitor NS2B/NS3 conformational dynamics in real-time. Molecular simulations coupled with electrophysiology revealed that the protease could be captured in the middle of the ClyA nanopore lumen, stabilized mainly by dynamic electrostatic interactions. We designed a new Salmonella typhi ClyA nanopore with enhanced nanopore/protease interaction that can resolve the open and closed states at the single-molecule level for the first time. We demonstrated that the tailored ClyA could track the conformational transitions of the West Nile NS2B/NS3 protease and unravel the conformational energy landscape of various protease constructs through population and kinetic analysis. The new ClyA-protease platform paves a way to high-throughput screening strategies for discovering new allosteric inhibitors that target the NS2B and NS3 interface.
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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Ali S, Ali U, Qamar A, Zafar I, Yaqoob M, Ain QU, Rashid S, Sharma R, Nafidi HA, Bin Jardan YA, Bourhia M. Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function. Front Chem 2023; 11:1173624. [PMID: 37153521 PMCID: PMC10160440 DOI: 10.3389/fchem.2023.1173624] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/10/2023] [Indexed: 05/09/2023] Open
Abstract
The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.
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Affiliation(s)
- Sadaqat Ali
- Medical Department, DHQ Hospital Bhawalnagr, Punjab, Pakistan
| | | | - Adeem Qamar
- Department of Pathology, Sahiwal Medical College Sahiwal, Punjab, Pakistan
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Punjab, Pakistan
| | - Muhammad Yaqoob
- Department of Life Sciences, ARID University-Barani Institute of Sciences Burewala Campus, Punjab, Pakistan
| | - Qurat ul Ain
- Department of Chemistry, Government College Women University, Faisalabad, Pakistan
| | - Summya Rashid
- Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Punjab, Pakistan
| | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
- *Correspondence: Mohammed Bourhia, ; Rohit Sharma,
| | - Hiba-Allah Nafidi
- Department of Food Science, Faculty of Agricultural and Food Sciences, Laval University, Quebec City, QC, Canada
| | - Yousef A. Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
- *Correspondence: Mohammed Bourhia, ; Rohit Sharma,
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McKay K, Hamilton NB, Remington JM, Schneebeli ST, Li J. Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery. Front Mol Biosci 2022; 9:879212. [PMID: 35847975 PMCID: PMC9277106 DOI: 10.3389/fmolb.2022.879212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 05/18/2022] [Indexed: 11/23/2022] Open
Abstract
The lack of biologically relevant protein structures can hinder rational design of small molecules to target G protein-coupled receptors (GPCRs). While ensemble docking using multiple models of the protein target is a promising technique for structure-based drug discovery, model clustering and selection still need further investigations to achieve both high accuracy and efficiency. In this work, we have developed an original ensemble docking approach, which identifies the most relevant conformations based on the essential dynamics of the protein pocket. This approach is applied to the study of small-molecule antagonists for the PAC1 receptor, a class B GPCR and a regulator of stress. As few as four representative PAC1 models are selected from simulations of a homology model and then used to screen three million compounds from the ZINC database and 23 experimentally validated compounds for PAC1 targeting. Our essential dynamics ensemble docking (EDED) approach can effectively reduce the number of false negatives in virtual screening and improve the accuracy to seek potent compounds. Given the cost and difficulties to determine membrane protein structures for all the relevant states, our methodology can be useful for future discovery of small molecules to target more other GPCRs, either with or without experimental structures.
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Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies. Biomolecules 2022; 12:biom12070878. [PMID: 35883434 PMCID: PMC9312989 DOI: 10.3390/biom12070878] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/05/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.
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Adelusi TI, Oyedele AQK, Boyenle ID, Ogunlana AT, Adeyemi RO, Ukachi CD, Idris MO, Olaoba OT, Adedotun IO, Kolawole OE, Xiaoxing Y, Abdul-Hammed M. Molecular modeling in drug discovery. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100880] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
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A Structure Based Study of Selective Inhibition of Factor IXa over Factor Xa. Molecules 2021; 26:molecules26175372. [PMID: 34500804 PMCID: PMC8434132 DOI: 10.3390/molecules26175372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/25/2022] Open
Abstract
Blood coagulation is an essential physiological process for hemostasis; however, abnormal coagulation can lead to various potentially fatal disorders, generally known as thromboembolic disorders, which are a major cause of mortality in the modern world. Recently, the FDA has approved several anticoagulant drugs for Factor Xa (FXa) which work via the common pathway of the coagulation cascade. A main side effect of these drugs is the potential risk for bleeding in patients. Coagulation Factor IXa (FIXa) has recently emerged as the strategic target to ease these risks as it selectively regulates the intrinsic pathway. These aforementioned coagulation factors are highly similar in structure, functional architecture, and inhibitor binding mode. Therefore, it remains a challenge to design a selective inhibitor which may affect only FIXa. With the availability of a number of X-ray co-crystal structures of these two coagulation factors as protein–ligand complexes, structural alignment, molecular docking, and pharmacophore modeling were employed to derive the relevant criteria for selective inhibition of FIXa over FXa. In this study, six ligands (three potent, two selective, and one inactive) were selected for FIXa inhibition and six potent ligands (four FDA approved drugs) were considered for FXa. The pharmacophore hypotheses provide the distribution patterns for the principal interactions that take place in the binding site. None of the pharmacophoric patterns of the FXa inhibitors matched with any of the patterns of FIXa inhibitors. Based on pharmacophore analysis, a selectivity of a ligand for FIXa over FXa may be defined quantitatively as a docking score of lower than −8.0 kcal/mol in the FIXa-grids and higher than −7.5 kcal/mol in the FXa-grids.
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Titov IY, Stroylov VS, Rusina P, Svitanko IV. Preliminary modelling as the first stage of targeted organic synthesis. RUSSIAN CHEMICAL REVIEWS 2021. [DOI: 10.1070/rcr5012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.
The bibliography includes 353 references.
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Kleandrova VV, Speck-Planche A. The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling. Mini Rev Med Chem 2021; 20:1357-1374. [PMID: 32013845 DOI: 10.2174/1389557520666200204123156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022]
Abstract
Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe Shosse 11, 125080, Moscow, Russian Federation
| | - Alejandro Speck-Planche
- Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya Str., 8, b. 2, 119992, Moscow, Russian Federation
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10
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Shi M, Zhao M, Wang L, Liu K, Li P, Liu J, Cai X, Chen L, Xu D. Exploring the stability of inhibitor binding to SIK2 using molecular dynamics simulation and binding free energy calculation. Phys Chem Chem Phys 2021; 23:13216-13227. [PMID: 34086021 DOI: 10.1039/d1cp00717c] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Salt inducible kinase 2 (SIK2) is a calcium/calmodulin-dependent protein kinase-like kinase that is implicated in a variety of biological phenomena, including cellular metabolism, growth, and apoptosis. SIK2 is the key target for various cancers, including ovarian, breast, prostate, and lung cancers. Although potent inhibitors of SIK2 are being developed, their binding stability and functional role are not presently known. In this work, we studied the detailed interactions between SIK2 and four of its inhibitors, HG-9-91-01, KIN112, MRT67307, and MRT199665, using molecular docking, molecular dynamics simulation, binding free energy calculation, and interaction fingerprint analysis. Intermolecular interactions revealed that HG-9-91-01 and KIN112 have stronger interactions with SIK2 than those of MRT199665 and MRT67307. The key residues involved in binding with SIK2 are conserved among all four inhibitors. Our results explain the detailed interaction of SIK2 with its inhibitors at the molecular level, thus paving the way for the development of targeted efficient anti-cancer drugs.
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Affiliation(s)
- Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Min Zhao
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lun Wang
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Kongjun Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Penghui Li
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China.
| | - Jiang Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Xiaoying Cai
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lijuan Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China. and Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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11
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Wu Z, Gu L, Zhang S, Liu T, Lukka PB, Meibohm B, Bollinger JC, Zhou M, Li W. Discovery of N-(3,4-Dimethylphenyl)-4-(4-isobutyrylphenyl)-2,3,3a,4,5,9b-hexahydrofuro[3,2- c]quinoline-8-sulfonamide as a Potent Dual MDM2/XIAP Inhibitor. J Med Chem 2021; 64:1930-1950. [PMID: 33556244 PMCID: PMC9128806 DOI: 10.1021/acs.jmedchem.0c00932] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Murine double minute 2 (MDM2) and X-linked inhibitor of apoptosis protein (XIAP) are important cell survival proteins in tumor cells. As a dual MDM2/XIAP inhibitor reported previously, compound MX69 has low potency with an IC50 value of 7.5 μM against an acute lymphoblastic leukemia cell line EU-1. Herein, we report the structural optimization based on the MX69 scaffold, leading to the discovery of a 25-fold more potent analogue 14 (IC50 = 0.3 μM against EU-1). We demonstrate that 14 maintains its mode of action by dual targeting of MDM2 and XIAP through inducing MDM2 protein degradation and inhibiting XIAP mRNA translation, respectively, which resulted in cancer cell growth inhibition and cell death. The results strongly suggest that the scaffold based on 14 is promising for further optimization to develop a new therapeutic agent for leukemia and possibly other cancers where MDM2 and XIAP are dysregulated.
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Affiliation(s)
- Zhongzhi Wu
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Lubing Gu
- Department of Pediatrics and Aflac Cancer and Blood Disorders Center, Emory University School of Medicine, Atlanta, Georgia 30322, United States
| | - Sicheng Zhang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Tao Liu
- Department of Pediatrics and Aflac Cancer and Blood Disorders Center, Emory University School of Medicine, Atlanta, Georgia 30322, United States
| | - Pradeep B Lukka
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
| | - John C Bollinger
- Department of Structural Biology, Biomolecular X-Ray Crystallography Center, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, United States
| | - Muxiang Zhou
- Department of Pediatrics and Aflac Cancer and Blood Disorders Center, Emory University School of Medicine, Atlanta, Georgia 30322, United States
| | - Wei Li
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee 38163, United States
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12
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Downs RP, Xiao Z, Ikedionwu MO, Cleveland JW, Lin Chin A, Cafferty AE, Darryl Quarles L, Carrick JD. Design and development of FGF-23 antagonists: Definition of the pharmacophore and initial structure-activity relationships probed by synthetic analogues. Bioorg Med Chem 2021; 29:115877. [PMID: 33232874 PMCID: PMC8007132 DOI: 10.1016/j.bmc.2020.115877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Abstract
Hereditary hypophosphatemic disorders, TIO, and CKD conditions are believed to be influenced by an excess of Fibroblast Growth Factor-23 (FGF-23) which activates a binary renal FGFRs / α-Klotho complex to regulate homeostatic metabolism of phosphate and vitamin D. Adaptive FGF-23 responses from CKD patients with excess FGF-23 frequently lead to increased mortality from cardiovascular disease. A reversibly binding small molecule therapeutic has yet to emerge from research and development in this area. Current outcomes described in this work highlight efforts related to lead identification and modification using organic synthesis of strategic analogues to probe structure-activity relationships and preliminarily define the pharmacophore of a computationally derived hit obtained from virtual high-throughput screening. Synthetic strategies for the initial hit and analogue preparation, as well as preliminary cellular in vitro assay results highlighting sub micromolar inhibition of the FGF-23 signaling sequence at a concentration well below cytotoxicity are reported herein.
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Affiliation(s)
- Ryan P Downs
- Department of Chemistry, Tennessee Technological University, Cookeville, TN 38505-0001, USA
| | - Zhousheng Xiao
- Department of Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38165, USA
| | - Munachi O Ikedionwu
- Department of Chemistry, Tennessee Technological University, Cookeville, TN 38505-0001, USA
| | - Jacob W Cleveland
- Department of Chemistry, Tennessee Technological University, Cookeville, TN 38505-0001, USA
| | - Ai Lin Chin
- Department of Chemistry, Tennessee Technological University, Cookeville, TN 38505-0001, USA
| | - Abigail E Cafferty
- Department of Chemistry, Tennessee Technological University, Cookeville, TN 38505-0001, USA
| | - L Darryl Quarles
- Department of Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38165, USA
| | - Jesse D Carrick
- Department of Chemistry, Tennessee Technological University, Cookeville, TN 38505-0001, USA.
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Acharya A, Agarwal R, Baker M, Baudry J, Bhowmik D, Boehm S, Byler KG, Chen S, Coates L, Cooper C, Demerdash O, Daidone I, Eblen J, Ellingson S, Forli S, Glaser J, Gumbart JC, Gunnels J, Hernandez O, Irle S, Kneller D, Kovalevsky A, Larkin J, Lawrence T, LeGrand S, Liu SH, Mitchell J, Park G, Parks J, Pavlova A, Petridis L, Poole D, Pouchard L, Ramanathan A, Rogers D, Santos-Martins D, Scheinberg A, Sedova A, Shen Y, Smith J, Smith M, Soto C, Tsaris A, Thavappiragasam M, Tillack A, Vermaas J, Vuong V, Yin J, Yoo S, Zahran M, Zanetti-Polzi L. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. J Chem Inf Model 2020; 60:5832-5852. [PMID: 33326239 PMCID: PMC7754786 DOI: 10.1021/acs.jcim.0c01010] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Indexed: 01/18/2023]
Abstract
We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
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Affiliation(s)
- A. Acharya
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - R. Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - M. Baker
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - J. Baudry
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899, USA
| | - D. Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - S. Boehm
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - K. G. Byler
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899, USA
| | - S.Y. Chen
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - L. Coates
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C.J. Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - O. Demerdash
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - I. Daidone
- Department of Physical and Chemical Sciences, University of L’Aquila, I-67010 L’Aquila, Italy
| | - J.D. Eblen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - S. Ellingson
- University of Kentucky, Division of Biomedical Informatics, College of Medicine, UK Medical Center MN 150, Lexington KY, 40536, USA
| | - S. Forli
- Scripps Research, La Jolla, CA, 92037, USA
| | - J. Glaser
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - J. C. Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - J. Gunnels
- HPC Engineering, Amazon Web Services, Seattle, WA 98121, USA
| | - O. Hernandez
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - S. Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
| | - D.W. Kneller
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - A. Kovalevsky
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - J. Larkin
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - T.J. Lawrence
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - S. LeGrand
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - S.-H. Liu
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - J.C. Mitchell
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - G. Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - J.M. Parks
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - A. Pavlova
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - L. Petridis
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - D. Poole
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - L. Pouchard
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - A. Ramanathan
- Data Science and Learning Division, Argonne National Lab, Lemont, IL 60439, USA
| | - D. Rogers
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | | | | | - A. Sedova
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - Y. Shen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - J.C. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - M.D. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - C. Soto
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - A. Tsaris
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | | | | | - J.V. Vermaas
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - V.Q. Vuong
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
| | - J. Yin
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - S. Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - M. Zahran
- Department of Biological Sciences, New York City College of Technology, The City University of New York (CUNY), Brooklyn, NY 11201, USA
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14
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Acharya A, Agarwal R, Baker M, Baudry J, Bhowmik D, Boehm S, Byler KG, Coates L, Chen SY, Cooper CJ, Demerdash O, Daidone I, Eblen JD, Ellingson S, Forli S, Glaser J, Gumbart JC, Gunnels J, Hernandez O, Irle S, Larkin J, Lawrence TJ, LeGrand S, Liu SH, Mitchell JC, Park G, Parks JM, Pavlova A, Petridis L, Poole D, Pouchard L, Ramanathan A, Rogers D, Santos-Martins D, Scheinberg A, Sedova A, Shen S, Smith JC, Smith MD, Soto C, Tsaris A, Thavappiragasam M, Tillack AF, Vermaas JV, Vuong VQ, Yin J, Yoo S, Zahran M, Zanetti-Polzi L. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12725465. [PMID: 33200117 PMCID: PMC7668744 DOI: 10.26434/chemrxiv.12725465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 07/29/2020] [Indexed: 01/18/2023]
Abstract
We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.
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Affiliation(s)
- A Acharya
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
| | - R Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - M Baker
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - J Baudry
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899
| | - D Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
| | - S Boehm
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - K G Byler
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899
| | - L Coates
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
| | - S Y Chen
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - C J Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - O Demerdash
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - I Daidone
- Department of Physical and Chemical Sciences, University of L'Aquila, I-67010 L'Aquila, Italy
| | - J D Eblen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - S Ellingson
- University of Kentucky, Division of Biomedical Informatics, College of Medicine, UK Medical Center MN 150, Lexington KY, 40536
| | - S Forli
- Scripps Research, La Jolla, CA, 92037
| | - J Glaser
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - J C Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
| | - J Gunnels
- HPC Engineering, Amazon Web Services, Seattle, WA 98121
| | - O Hernandez
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - S Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996
| | - J Larkin
- NVIDIA Corporation, Santa Clara, CA 95051
| | - T J Lawrence
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - S LeGrand
- NVIDIA Corporation, Santa Clara, CA 95051
| | - S-H Liu
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - J C Mitchell
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - G Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - J M Parks
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - A Pavlova
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
| | - L Petridis
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - D Poole
- NVIDIA Corporation, Santa Clara, CA 95051
| | - L Pouchard
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - A Ramanathan
- Data Science and Learning Division, Argonne National Lab, Lemont, IL 60439
| | - D Rogers
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | | | | | - A Sedova
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - S Shen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - J C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - M D Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - C Soto
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - A Tsaris
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | | | | | - J V Vermaas
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - V Q Vuong
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996
| | - J Yin
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - S Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - M Zahran
- Department of Biological Sciences, New York City College of Technology, The City University of New York (CUNY), Brooklyn, NY 11201
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15
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Gazgalis D, Zaka M, Abbasi BH, Logothetis DE, Mezei M, Cui M. Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery. ACS OMEGA 2020; 5:14297-14307. [PMID: 32596567 PMCID: PMC7315428 DOI: 10.1021/acsomega.0c00522] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/28/2020] [Indexed: 06/11/2023]
Abstract
The virtual high-throughput screening (vHTS) approach has been widely used for large database screening to identify potential lead compounds for drug discovery. Due to its high computational demands, docking that allows receptor flexibility has been a challenging problem for virtual screening. Therefore, the selection of protein target conformations is crucial to produce useful vHTS results. Since only a single protein structure is used to screen large databases in most vHTS studies, the main challenge is to reduce false negative rates in selecting compounds for in vitro tests. False negatives are most likely to occur when using apo structures or homology models of protein targets due to the small volume of the binding pocket formed by incorrect side-chain conformations. Even holo protein structures can exhibit high false negative rates due to ligand-induced fit effects, since the shape of the binding pocket highly depends on its bound ligand. To reduce false negative rates and improve success rates for vHTS in drug discovery, we have developed a new Monte Carlo-based approach that optimizes the binding pocket of protein targets. This newly developed Monte Carlo pocket optimization (MCPO) approach was assessed on several datasets showing promising results. The binding pocket optimization approach could be a useful tool for vHTS-based drug discovery, especially in cases when only apo structures or homology models are available.
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Affiliation(s)
- Dimitris Gazgalis
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
| | - Mehreen Zaka
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
- Department
of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Bilal Haider Abbasi
- Department
of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Diomedes E. Logothetis
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
| | - Mihaly Mezei
- Department
of Pharmacological Sciences, Icahn School
of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Meng Cui
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
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16
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Abstract
Molecular Docking is used to positioning the computer-generated 3D structure of small
ligands into a receptor structure in a variety of orientations, conformations and positions. This
method is useful in drug discovery and medicinal chemistry providing insights into molecular
recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery
(CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment
of targets and ligand. Over the last decade, advances in the field of computational, proteomics and
genomics have also led to the development of different docking methods which incorporate
protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts
for more accurate binding pose predictions and a more rational depiction of protein binding
interactions with the ligand. Protein flexibility has been included by generating protein ensembles
or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also
fully explores the drug-receptor binding and recognition from both energetic and mechanistic point
of view. Though in the fast-paced drug discovery program, dynamic docking is computationally
expensive but is being progressively used for screening of large compound libraries to identify the
potential drugs. In this review, a quick introduction is presented to the available docking methods
and their application and limitations in drug discovery.
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Affiliation(s)
- Ritu Jakhar
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Mehak Dangi
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Alka Khichi
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
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17
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Bhattarai A, Wang J, Miao Y. Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor. Biochim Biophys Acta Gen Subj 2020; 1864:129615. [PMID: 32298791 DOI: 10.1016/j.bbagen.2020.129615] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/26/2020] [Accepted: 04/08/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR. METHODS Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR. RESULTS Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased. CONCLUSIONS Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking. GENERAL SIGNIFICANCE Ensemble docking is a promising approach for drug discovery targeting flexible receptors.
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Affiliation(s)
- Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA.
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18
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Lee YV, Choi SB, Wahab HA, Lim TS, Choong YS. Applications of Ensemble Docking in Potential Inhibitor Screening for Mycobacterium tuberculosis Isocitrate Lyase Using a Local Plant Database. J Chem Inf Model 2019; 59:2487-2495. [PMID: 30840452 DOI: 10.1021/acs.jcim.8b00963] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Isocitrate lyase (ICL) is a persistent factor for the survival of dormant stage Mycobacterium tuberculosis (MTB), thus a potential drug target for tuberculosis treatment. In this work, ensemble docking approach was used to screen for potential inhibitors of ICL. The ensemble conformations of ICL active site were obtained from molecular dynamics simulation on three dimer form systems, namely the apo ICL, ICL in complex with metabolites (glyoxylate and succinate), and ICL in complex with substrate (isocitrate). Together with the ensemble conformations and the X-ray crystal structures, 22 structures were used for the screening against Malaysian Natural Compound Database (NADI). The top 10 compounds for each ensemble conformation were selected. The number of compounds was then further narrowed down to 22 compounds that were within the Lipinski's Rule of Five for drug-likeliness and were also docked into more than one ensemble conformation. Theses 22 compounds were furthered evaluate using whole cell assay. Some compounds were not commercially available; therefore, plant crude extracts were used for the whole cell assay. Compared to itaconate (the known inhibitor of ICL), crude extracts from Manilkara zapota, Morinda citrifolia, Vitex negundo, and Momordica charantia showed some inhibition activity. The MIC/MBC value were 12.5/25, 12.5/25, 0.78/1.6, and 0.39/1.6 mg/mL, respectively. This work could serve as a preliminary study in order to narrow the scope for high throughput screening in the future.
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Affiliation(s)
- Yie-Vern Lee
- Institute for Research in Molecular Medicine (INFORMM) , Universiti Sains Malaysia , 11800 Minden , Penang , Malaysia
| | - Sy Bing Choi
- School of Data Science , Perdana University , 43400 Sri Kembangan , Selangor , Malaysia
| | - Habibah A Wahab
- Pharmaceutical Design and Simulation Laboratory, School of Pharmaceutical Sciences , Universiti Sains Malaysia , 11800 Minden , Penang , Malaysia
| | - Theam Soon Lim
- Institute for Research in Molecular Medicine (INFORMM) , Universiti Sains Malaysia , 11800 Minden , Penang , Malaysia.,Analytical Biochemistry Research Centre , Universiti Sains Malaysia , 11800 Minden , Penang , Malaysia
| | - Yee Siew Choong
- Institute for Research in Molecular Medicine (INFORMM) , Universiti Sains Malaysia , 11800 Minden , Penang , Malaysia
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19
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Evangelista Falcon W, Ellingson SR, Smith JC, Baudry J. Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding? J Phys Chem B 2019; 123:5189-5195. [DOI: 10.1021/acs.jpcb.8b11491] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Wilfredo Evangelista Falcon
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, United States
- UT/ORNL Center for Molecular Biophysics, Oak Ridge, Tennessee 37830, United States
- College of Medicine, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Sally R. Ellingson
- UT/ORNL Center for Molecular Biophysics, Oak Ridge, Tennessee 37830, United States
| | - Jeremy C. Smith
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, United States
- UT/ORNL Center for Molecular Biophysics, Oak Ridge, Tennessee 37830, United States
| | - Jerome Baudry
- Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, Alabama 35899, United States
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