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Aghahasani R, Shiri F, Kamaladiny H, Haddadi F, Pirhadi S. Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery. BMC Chem 2024; 18:73. [PMID: 38615023 PMCID: PMC11016228 DOI: 10.1186/s13065-024-01175-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/28/2024] [Indexed: 04/15/2024] Open
Abstract
Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.
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Affiliation(s)
| | | | | | | | - Somayeh Pirhadi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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2
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Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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3
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Hashemi A, Bougueroua S, Gaigeot MP, Pidko EA. HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts. J Chem Inf Model 2023; 63:6081-6094. [PMID: 37738303 PMCID: PMC10565810 DOI: 10.1021/acs.jcim.3c00660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Indexed: 09/24/2023]
Abstract
A method is introduced for the automated analysis of reactivity exploration for extended in silico databases of transition-metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical data sets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst data sets opens new opportunities for the design of high-performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration.
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Affiliation(s)
- Ali Hashemi
- Inorganic
Systems Engineering, Department of Chemical Engineering, Faculty of
Applied Sciences, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
| | - Sana Bougueroua
- Laboratoire
Analyse et Modélisation pour la Biologie et l’Environnement
(LAMBE) UMR8587, Paris-Saclay, Univ Evry,
CY Cergy Paris Université, CNRS, LAMBE UMR8587, Evry-Courcouronnes 91025, France
| | - Marie-Pierre Gaigeot
- Laboratoire
Analyse et Modélisation pour la Biologie et l’Environnement
(LAMBE) UMR8587, Paris-Saclay, Univ Evry,
CY Cergy Paris Université, CNRS, LAMBE UMR8587, Evry-Courcouronnes 91025, France
| | - Evgeny A. Pidko
- Inorganic
Systems Engineering, Department of Chemical Engineering, Faculty of
Applied Sciences, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
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4
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Kanev GK, Zhang Y, Kooistra AJ, Bender A, Leurs R, Bailey D, Würdinger T, de Graaf C, de Esch IJP, Westerman BA. Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks. PLoS Comput Biol 2023; 19:e1011301. [PMID: 37669273 PMCID: PMC10508635 DOI: 10.1371/journal.pcbi.1011301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/19/2023] [Accepted: 06/25/2023] [Indexed: 09/07/2023] Open
Abstract
Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model's root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.
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Affiliation(s)
- Georgi K. Kanev
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Neurosurgery, Amsterdam University Medical Centers, Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Yaran Zhang
- Department of Neurosurgery, Amsterdam University Medical Centers, Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Albert J. Kooistra
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Bender
- Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Rob Leurs
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - David Bailey
- The WINDOW consortium, www.window-consortium.org
- IOTA Pharmaceuticals Ltd, St Johns Innovation Centre, Cambridge, United Kingdom
| | - Thomas Würdinger
- Department of Neurosurgery, Amsterdam University Medical Centers, Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
- The WINDOW consortium, www.window-consortium.org
| | - Chris de Graaf
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart A. Westerman
- Department of Neurosurgery, Amsterdam University Medical Centers, Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
- The WINDOW consortium, www.window-consortium.org
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5
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Potlitz F, Link A, Schulig L. Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opin Drug Discov 2023; 18:303-313. [PMID: 36714919 DOI: 10.1080/17460441.2023.2171984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (Mpro). AREAS COVERED This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel Mpro inhibitors targeting the SARS-CoV-2 virus. EXPERT OPINION With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.
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Affiliation(s)
- Felix Potlitz
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Andreas Link
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Lukas Schulig
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
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6
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Islam MA, Barshetty MM, Srinivasan S, Dudekula DB, Rallabandi VPS, Mohammed S, Natarajan S, Park J. Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study. Biomolecules 2022; 12:biom12091279. [PMID: 36139117 PMCID: PMC9496582 DOI: 10.3390/biom12091279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/05/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022] Open
Abstract
Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC. A set of comprehensive pharmacoinformatics approaches was implemented and, finally, seventeen potential molecules were found to be effective for the modulation of hRRM1 activity. Molecular docking, negative image-based ShaEP scoring, absolute binding free energy, in silico pharmacokinetics, and toxicity assessments corroborated the potentiality of the selected molecules. Almost all molecules showed higher affinity in comparison to gemcitabine and naphthyl salicylic acyl hydrazone (NSAH). On binding interaction analysis, a number of critical amino acids was found to hold the molecules at the active site cavity. The molecular dynamics (MD) simulation study also indicated the stability between protein and ligands. High negative MM-GBSA (molecular mechanics generalized Born and surface area) binding free energy indicated the potentiality of the molecules. Therefore, the proposed molecules might have the potential to be effective therapeutics for the management of BTC.
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Affiliation(s)
- Md Ataul Islam
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | | | - Sridhar Srinivasan
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | - Dawood Babu Dudekula
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | | | - Sameer Mohammed
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | | | - Junhyung Park
- 3BIGS Co., Ltd., B-831, Geumgang Penterium IX Tower, Hwaseong 18469, Korea
- Correspondence:
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7
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Sepehri B, Ghavami R, Mahmoudi F, Irani M, Ahmadi R, Moradi D. Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:341-356. [PMID: 35502579 DOI: 10.1080/1062936x.2022.2050424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/02/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.
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Affiliation(s)
- B Sepehri
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - R Ghavami
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - F Mahmoudi
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - M Irani
- Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - R Ahmadi
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - D Moradi
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
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8
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Pai P, Kumar A, Shetty MG, Kini SG, Krishna MB, Satyamoorthy K, Babitha KS. Identification of potent HDAC 2 inhibitors using E-pharmacophore modelling, structure-based virtual screening and molecular dynamic simulation. J Mol Model 2022; 28:119. [PMID: 35419753 PMCID: PMC9007783 DOI: 10.1007/s00894-022-05103-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 03/28/2022] [Indexed: 11/26/2022]
Abstract
Histone deacetylase 2 (HDAC 2) of class I HDACs plays a major role in embryonic and neural developments. However, HDAC 2 overexpression triggers cell proliferation by diverse mechanisms in cancer. Over the decades, many pan and class-specific inhibitors of HDAC were discovered. Limitations such as toxicity and differential cell localization of each isoform led researchers to hypothesize that isoform selective inhibitors may be relevant to bring about desired effects. In this study, we have employed the PHASE module to develop an e-pharmacophore model and virtually screened four focused libraries of around 300,000 compounds to identify isoform selective HDAC 2 inhibitors. The compounds with phase fitness score greater than or equal to 2.4 were subjected to structure-based virtual screening with HDAC 2. Ten molecules with docking score greater than -12 kcal/mol were chosen for selectivity study, QikProp module (ADME prediction) and dG/bind energy identification. Compound 1A with the best dock score of -13.3 kcal/mol and compound 1I with highest free binding energy, -70.93 kcal/mol, were selected for molecular dynamic simulation studies (40 ns simulation). The results indicated that compound 1I may be a potent and selective HDAC 2 inhibitor. Further, in vitro and in vivo studies are necessary to validate the potency of selected lead molecule and its derivatives.
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Affiliation(s)
- Padmini Pai
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Avinash Kumar
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Manasa Gangadhar Shetty
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Suvarna Ganesh Kini
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Manoj Bhat Krishna
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Kapaettu Satyamoorthy
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Kampa Sundara Babitha
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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9
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Kalikadien AV, Pidko EA, Sinha V. ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold. DIGITAL DISCOVERY 2022; 1:8-25. [PMID: 35340336 PMCID: PMC8887922 DOI: 10.1039/d1dd00017a] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that ChemSpaX generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO–LUMO gaps for transition metal complexes. ChemSpaX is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery. This work introduces ChemSpaX, an open-source Python-based tool for automated exploration of chemical space of molecular scaffolds with a special focus on transition-metal complexes.![]()
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Affiliation(s)
- Adarsh V Kalikadien
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands
| | - Evgeny A Pidko
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands
| | - Vivek Sinha
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands
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Murugan NA, Podobas A, Gadioli D, Vitali E, Palermo G, Markidis S. A Review on Parallel Virtual Screening Softwares for High-Performance Computers. Pharmaceuticals (Basel) 2022; 15:63. [PMID: 35056120 PMCID: PMC8780228 DOI: 10.3390/ph15010063] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/19/2021] [Accepted: 12/28/2021] [Indexed: 02/01/2023] Open
Abstract
Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.
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Affiliation(s)
- Natarajan Arul Murugan
- Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden;
| | - Artur Podobas
- Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden;
| | - Davide Gadioli
- Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (D.G.); (E.V.); (G.P.)
| | - Emanuele Vitali
- Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (D.G.); (E.V.); (G.P.)
| | - Gianluca Palermo
- Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (D.G.); (E.V.); (G.P.)
| | - Stefano Markidis
- Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden;
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11
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Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking. DATA SCIENCE FOR COVID-19 2022. [PMCID: PMC8988906 DOI: 10.1016/b978-0-323-90769-9.00021-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was responsible for over 4 million confirmed cases of severe acute respiratory syndrome, of which more than 300,000 cases were confirmed to be dead as of May 2020. The virulent endocytotic activities of SARS-CoV-2 have been associated with angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2). Previous studies on the viral activation of TMPRSS2 focused most often than not on the isoform 2 of TMPRSS2, but the isoform 1 (529 residues) has also been shown to be expressed in target cells and contribute to viral activation in host. The inhibition of TMPRSS2 has been reported to grossly reduce the pathogenic effects of SARS-CoV-2 endocytotic activities. In this study therefore, we developed two machine learning models using random forest classifier (RFC) and neural networks (NNs) based on 2251 serine protease inhibitors to screen a database of 21,000,000 virtual compounds. We screened the hit compounds using absorption, distribution, metabolism, and excretion (ADME) properties and finally docked the filtered compounds into the predicted binding site of TMPRSS2 isoform 1 homology model to determine their corresponding binding affinity and plausible molecular interactions. One (ASONN) and four (ASOIRFC1–4) lead compounds were obtained from the ADME-NN and RFC filtered hits, respectively, having better binding affinity and lead-likeness properties than those of camostat; this could be due to extensive hydrogen and hydrophobic interactions.
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12
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Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach. Int J Mol Sci 2021; 22:ijms222413259. [PMID: 34948055 PMCID: PMC8703488 DOI: 10.3390/ijms222413259] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/09/2021] [Accepted: 11/14/2021] [Indexed: 12/12/2022] Open
Abstract
Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.
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13
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Idris MO, Yekeen AA, Alakanse OS, Durojaye OA. Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches. J Biomol Struct Dyn 2021; 39:5638-5656. [PMID: 32672528 PMCID: PMC7441808 DOI: 10.1080/07391102.2020.1792346] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 06/24/2020] [Indexed: 12/13/2022]
Abstract
Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski's and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Abeeb Abiodun Yekeen
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China
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14
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Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224:113705. [PMID: 34303871 DOI: 10.1016/j.ejmech.2021.113705] [Citation(s) in RCA: 165] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022]
Abstract
Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.
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Affiliation(s)
- Victor T Sabe
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Thandokuhle Ntombela
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Lindiwe A Jhamba
- HIV Pathogenesis Program, School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Glenn E M Maguire
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa; School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Thavendran Govender
- Faculty of Science and Agriculture, Department of Chemistry, University of Zululand, KwaDlangezwa, 3886, South Africa
| | - Tricia Naicker
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Hendrik G Kruger
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
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15
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Nayarisseri A. Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery. Curr Top Med Chem 2021; 20:1651-1660. [PMID: 32614747 DOI: 10.2174/156802662019200701164759] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
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16
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Chukwuemeka PO, Umar HI, Iwaloye O, Oretade OM, Olowosoke CB, Elabiyi MO, Igbe FO, Oretade OJ, Eigbe JO, Adeojo FJ. Targeting p53-MDM2 interactions to identify small molecule inhibitors for cancer therapy: beyond "Failure to rescue". J Biomol Struct Dyn 2021; 40:9158-9176. [PMID: 33988074 DOI: 10.1080/07391102.2021.1924267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
At present, disrupting p53-MDM2 interactions through small molecule ligands is a promising approach to safe treatment and management of human cancer. Tumor cells unlike the normal cells, are rapidly evolving affecting the efficacy of many approved anti-cancer agents due to drug resistance. Therefore, identifying a potential anticancer compound is crucial. Pharmacophore based virtual screening, followed by molecular docking, ADMET evaluation, and molecular dynamics studies against MDM2 protein was investigated to identify potential ligands that may act as inhibitors. The model (AHRR_1) with survival score (4.176) was selected among the top ranked generated Pharmacophore hypothesis. Validation of the model hypothesis by an external dataset of actives and inactive compounds produced significant validation attributes including; AUC = 0.85, BEDROC = 0.56 at α = 20.0, RIE = 8.18, AUAC = 0.88, and EF of 6.2 at the top 2% of the dataset. The model was use for screening the ZINC database, and the top 1375 hits satisfying the model hypothesis were subjected to molecular docking studies to understand the molecular and structural basis of selectivity of compounds for MDM2 protein. A sub-set of 25 compounds with binding energy lower than the reference inhibitors were evaluated for pharmacokinetic properties. Four compounds (ZINC02639178, ZINC06752762, ZINC38933175, and ZINC77969611) showed the most desired pharmacokinetic profile. Lastly, investigation of the dynamic behaviour of leads-protein complexes through MD simulation showed similar RMSD, RMSF, and H-bond occupancy profile compared to a reference inhibitor, suggesting stability throughout the simulation time. However, ZINC02639178 was found to satisfy the molecular enumeration the most compared to the other three leads. It may emerge as potential treatment option after extensive experimental studies. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Prosper Obed Chukwuemeka
- Department of Biotechnology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Haruna Isiyaku Umar
- Department of Biochemistry, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Opeyemi Iwaloye
- Bioinformatics and Molecular biology unit, Department of Biochemistry, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Oluwaseyi Matthew Oretade
- Department of Biotechnology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | | | - Michael Omoniyi Elabiyi
- Department of Microbiology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Festus Omotere Igbe
- Department of Biochemistry, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
| | - Oyeyemi Janet Oretade
- Department of Physiology, College of Health Science (CHS), Osun State University, Osogbo, Nigeria
| | - Joy Oseme Eigbe
- Department of Biomedical Technology, School of Health and Health Technology (SHHT), Federal University of Technology Akure, Akure, Nigeria
| | - Funmilayo Janet Adeojo
- Department of Biotechnology, School of Sciences (SOS), Federal University of Technology Akure, Akure, Nigeria
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17
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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18
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Saruengkhanphasit R, Butkinaree C, Ornnork N, Lirdprapamongkol K, Niwetmarin W, Svasti J, Ruchirawat S, Eurtivong C. Identification of new 3-phenyl-1H-indole-2-carbohydrazide derivatives and their structure-activity relationships as potent tubulin inhibitors and anticancer agents: A combined in silico, in vitro and synthetic study. Bioorg Chem 2021; 110:104795. [PMID: 33730670 DOI: 10.1016/j.bioorg.2021.104795] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/22/2021] [Accepted: 03/02/2021] [Indexed: 01/09/2023]
Abstract
Virtual screening of commercially available molecular entities by using CDRUG, structure-based virtual screening, and similarity identified eight new derivatives of 3-phenyl-1H-indole-2-carbohydrazide with anti-proliferative activities. The molecules were tested experimentally for inhibition of tubulin polymerisation, which revealed furan-3-ylmethylene-3-phenyl-1H-indole-2-carbohydrazide (27a) as the most potent candidate. Molecule 27a was able to induce G2/M phase arrest in A549 cell line, similar to other tubulin inhibitors. Synthetic modifications of 27a were focussed on small substitutions on the furan ring, halogenation at R1 position and alteration of furyl connectivity. Derivatives 27b, 27d and 27i exhibited the strongest tubulin inhibition activities and were comparable to 27a. Bromine substitution at R1 position showed most prominent anticancer activities; derivatives 27b-27d displayed the strongest activities against HuCCA-1 cell line and were more potent than doxorubicin and the parent molecule 27a with IC50 values <0.5 μM. Notably, 27b with a 5-methoxy substitution on furan displayed the strongest activity against HepG2 cell line (IC50 = 0.34 µM), while 27d displayed stronger activity against A549 cell line (IC50 = 0.43 µM) compared to doxorubicin and 27a. Fluorine substitutions at the R1 position tended to show more modest anti-tubulin and anticancer activities, and change of 2-furyl to 3-furyl was tolerable. The new derivatives, thiophenyl 26, displayed the strongest activity against A549 cell line (IC50 = 0.19 µM), while 1-phenylethylidene 21b and 21c exhibited more modest anticancer activities with unclear mechanisms of action; 26 and 21c demonstrated G2/M phase arrest, but showed weak tubulin inhibitory properties. Molecular docking suggests the series inhibit tubulin at the colchicine site, in agreement with the experimental findings. The calculated molecular descriptors indicated that the molecules obey Lipinski's rule which suggests the molecules are drug-like structures.
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Affiliation(s)
- Rungroj Saruengkhanphasit
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Chutikarn Butkinaree
- Program in Applied Biological Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand; National Omics Center, National Science and Technology Development Agency, Pathum Thani 12120, Thailand
| | - Narittira Ornnork
- Laboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, Thailand
| | | | - Worawat Niwetmarin
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Jisnuson Svasti
- Laboratory of Biochemistry, Chulabhorn Research Institute, Bangkok 10210, Thailand
| | - Somsak Ruchirawat
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand; Laboratory of Medicinal Chemistry, Chulabhorn Research Institute, Bangkok 10210, Thailand; Center of Excellence on Environmental Health and Toxicology (EHT), Commission on Higher Education (CHE), Ministry of Education, Bangkok 10400, Thailand
| | - Chatchakorn Eurtivong
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Bangkok 10210, Thailand; Center of Excellence on Environmental Health and Toxicology (EHT), Commission on Higher Education (CHE), Ministry of Education, Bangkok 10400, Thailand.
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19
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Kumar HSS, Kumar SR, Kumar NN, Ajith S. Molecular docking studies of gyrase inhibitors: weighing earlier screening bedrock. In Silico Pharmacol 2021; 9:2. [PMID: 33442529 DOI: 10.1007/s40203-020-00064-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/26/2020] [Indexed: 12/14/2022] Open
Abstract
For any antimicrobial assay, a standard drug is used to compare the bactericidal efficiency of the bioactive compound under screening. The standard drugs have different targets that may be intracellular or membrane located. The location of the target is believed to be determining the bioactivity of the drug depending on the drug's access to its target. Therefore, different drugs must have a different magnitude in exhibiting the biological effect. However, in most of the published literature about the screening of bioactive compounds on antimicrobial activity, generally, the standard drug is randomly chosen while comparing against the bioactive compound of interest. Further, the antimicrobial activity is inferred by comparing the randomly chosen standard drugs without knowing the physicochemical parameters of the standard drug and the test molecule. It is just like an unfair comparison of the impact of a bullet with the impact of an explosive in a combat scene. Computer-based strategies for structure-based drug discovery presents a valuable alternative to the costly and time-consuming process of random screening. The docking studies provide better insights into the binding mechanism of substrate and inhibitor at the molecular level. The evaluation of such a comparison of bioactive compounds against randomly selected standard drugs through a customized virtual screening pipeline showed 57% false positives, 18% true positive, 17% true negative, 8% false-negative results. This study directs for mandatory cheminformatics-based assessment of the bioactive compounds before choosing the standard drug to compare with. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-020-00064-9.
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Affiliation(s)
- H S Santosh Kumar
- Department of Biotechnology and Bioinformatics, Bioscience Complex, Kuvempu University, Jnana Sahyadri, Shankaraghatta, 577451 India
| | - S Ravi Kumar
- Department of Biotechnology and Bioinformatics, Bioscience Complex, Kuvempu University, Jnana Sahyadri, Shankaraghatta, 577451 India
| | - N Naveen Kumar
- Department of Biotechnology and Bioinformatics, Bioscience Complex, Kuvempu University, Jnana Sahyadri, Shankaraghatta, 577451 India
| | - S Ajith
- Department of Biotechnology and Bioinformatics, Bioscience Complex, Kuvempu University, Jnana Sahyadri, Shankaraghatta, 577451 India
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20
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Franco LS, Maia RC, Barreiro EJ. Identification of LASSBio-1945 as an inhibitor of SARS-CoV-2 main protease (M PRO) through in silico screening supported by molecular docking and a fragment-based pharmacophore model. RSC Med Chem 2021; 12:110-119. [PMID: 34046603 PMCID: PMC8130624 DOI: 10.1039/d0md00282h] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 10/11/2020] [Indexed: 12/19/2022] Open
Abstract
In December 2019, an infectious disease was detected in Wuhan, China, caused by a new pathogenic coronavirus, named SARS-CoV-2. It spread very rapidly, and on March 11th of 2020, the outbreak was declared a pandemic by the World Health Organization. Currently, effective treatment options remain limited. SARS-CoV-2 enzyme main protease (MPRO) plays a pivotal role in the viral life cycle, making it a putative drug target. In order to identify suitable hits to develop inhibitors with adequate antiviral properties, we explored the LASSBio Chemical Library employing multiple strategies of virtual screening. A fragment-based pharmacophore model enabled the identification of key interactions involved in the molecular recognition at the catalytic site of MPRO, namely, with amino acid residues His41, His163 and Glu166. Docking-based virtual screening was performed, leading to the identification of LASSBio-1945 (9), a new hit of MPRO, presenting an IC50 = 15.97 μM. This compound, an 1,3-benzodioxolyl sulfonamide, represents an interesting starting point for subsequent hit-to-lead optimization steps and, to the best of our knowledge, a new distinct chemotype for MPRO inhibition.
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Affiliation(s)
- Lucas S Franco
- Programa de Pós-Graduação em Farmacologia e Química Medicinal, Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro Avenida Carlos Chagas Filho, 373, Ilha do Fundão 21941-912 Rio de Janeiro RJ Brazil
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio®, http://www.lassbio.icb.ufrj.br), Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, Cidade Universitária Rio de Janeiro RJ Brazil
| | - Rodolfo C Maia
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio®, http://www.lassbio.icb.ufrj.br), Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, Cidade Universitária Rio de Janeiro RJ Brazil
- Instituto Nacional de Ciência e Tecnologia de Fármacos e Medicamentos (INCT-INOFAR; http://www.inct-inofar.ccs.ufrj.br/), CCS, Universidade Federal do Rio de Janeiro, Cidade Universitária Rio de Janeiro RJ Brazil
| | - Eliezer J Barreiro
- Programa de Pós-Graduação em Farmacologia e Química Medicinal, Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro Avenida Carlos Chagas Filho, 373, Ilha do Fundão 21941-912 Rio de Janeiro RJ Brazil
- Laboratório de Avaliação e Síntese de Substâncias Bioativas (LASSBio®, http://www.lassbio.icb.ufrj.br), Instituto de Ciências Biomédicas, CCS, Universidade Federal do Rio de Janeiro, Cidade Universitária Rio de Janeiro RJ Brazil
- Instituto Nacional de Ciência e Tecnologia de Fármacos e Medicamentos (INCT-INOFAR; http://www.inct-inofar.ccs.ufrj.br/), CCS, Universidade Federal do Rio de Janeiro, Cidade Universitária Rio de Janeiro RJ Brazil
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21
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Pal A, Curtin JF, Kinsella GK. In silico and in vitro screening for potential anticancer candidates targeting GPR120. Bioorg Med Chem Lett 2020; 31:127672. [PMID: 33161126 DOI: 10.1016/j.bmcl.2020.127672] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 10/30/2020] [Accepted: 11/01/2020] [Indexed: 01/02/2023]
Abstract
The G-protein coupled receptor - GPR120 has recently been implicated as a novel target for colorectal cancer (CRC) and other cancer managements. In this study, a homology model of GPR120S (short isoform) was generated to identify potential anti-cancer compounds targeting the GPR120 receptor using a combined in silico docking-based virtual screening (DBVS), structure-activity relationships (SAR) and in vitro screening approach. SPECS database of synthetic chemical compounds (~350,000) was screened using the developed GPR120S model to identify molecules binding to the orthosteric binding pocket followed by an AutoDock SMINA rigid-flexible docking protocol. The best 13 hit molecules were then tested in vitro to evaluate their cytotoxic activity against SW480 - human CRC cell line expressing GPR120. The test compound 1 (3-(4-methylphenyl)-2-[(2-oxo-2-phenylethyl)sulfanyl]-5,6-dihydrospiro(benzo[h]quinazoline-5,1'-cyclopentane)-4(3H)-one) showed ~ 90% inhibitory effects on cell growth with micromolar affinities (IC50 = 23.21-26.69 µM). Finally, SAR analysis of compound 1 led to the identification of a more active compound from the SPECS database showing better efficacy during cell-based cytotoxicity assay -5 (IC50 = 5.89-6.715 µM), while a significant reduction in cytotoxic effects of 5 was observed in GPR120-siRNA pre-treated SW480 cells. The GPR120S homology model generated, and SAR analysis conducted by this work discovered a potential chemical scaffold, dihydrospiro(benzo[h]quinazoline-5,1'-cyclopentane)-4(3H)-one, which will aid future research on anti-cancer drug development for CRC management.
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Affiliation(s)
- Ajay Pal
- School of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, Ireland; Environmental Sustainability and Health Institute (ESHI), Grangegorman, Technological University Dublin, Dublin D07 H6K8, Ireland
| | - James F Curtin
- School of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, Ireland
| | - Gemma K Kinsella
- School of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, Ireland.
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22
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Abstract
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The pandemic caused
by SARS-CoV-2 is currently representing a major
health and economic threat to humanity. So far, no specific treatment
to this viral infection has been developed and the emergency still
requires an efficient intervention. In this work, we used virtual
screening to facilitate drug repurposing against SARS-CoV-2, targeting
viral main proteinase and spike protein with 3000 existing drugs.
We used a protocol based on a docking step followed by a short molecular
dynamic simulation and rescoring by the Nwat-MMGBSA approach. Our
results provide suggestions for prioritizing in vitro and/or in vivo tests of already available compounds.
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Affiliation(s)
- Irene Maffucci
- Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319 - 60 203 Compiègne Cedex, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica "A. Marchesini", Via Venezian, 21 20133 Milano, Italy
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23
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Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 2020; 25:1624-1638. [PMID: 32663517 PMCID: PMC7572559 DOI: 10.1016/j.drudis.2020.07.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/26/2020] [Accepted: 07/06/2020] [Indexed: 02/06/2023]
Abstract
Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.
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Affiliation(s)
- Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Heather L Ciallella
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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Onawole AT, Sulaiman KO, Kolapo TU, Akinde FO, Adegoke RO. COVID-19: CADD to the rescue. Virus Res 2020; 285:198022. [PMID: 32417181 PMCID: PMC7228740 DOI: 10.1016/j.virusres.2020.198022] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/30/2020] [Accepted: 05/11/2020] [Indexed: 12/13/2022]
Abstract
The recent outbreak of the deadly COVID-19 disease, being caused by the novel coronavirus (SARS-CoV-2), has put the world on red alert as it keeps spreading and recording more fatalities. Research efforts are being carried out to curtail the disease from spreading as it has been declared as of global health emergency. Hence, there is an exigent need to identify and design drugs that are capable of curing the infection and hinder its continual spread across the globe. Herein, a computer-aided drug design tool known as the virtual screening method was used to screen a database of 44 million compounds to find compounds that have the potential to inhibit the surface glycoprotein responsible for virus entry and binding. The consensus scoring approach selected three compounds with promising physicochemical properties and favorable molecular interactions with the target protein. These selected compounds can undergo lead optimization to be further developed as drugs that can be used in treating the COVID-19 disease.
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Affiliation(s)
- Abdulmujeeb T Onawole
- Department of Chemistry, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia
| | - Kazeem O Sulaiman
- Department of Chemistry, University of Saskatchewan, 110 Science Place, Saskatoon, Saskatchewan S7N 5C9, Canada.
| | - Temitope U Kolapo
- Department of Veterinary Parasitology and Entomology, University of Ilorin,P.M.B. 1515, Ilorin, Nigeria; Department of Veterinary Microbiology, University of Saskatchewan, 52 Campus Drive, Saskatoon, Saskatchewan S7N 5B4, Canada
| | - Fatimo O Akinde
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, Liaoning, China
| | - Rukayat O Adegoke
- Department of Pure and Applied Biology, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria
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25
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Mahmud S, Rahman E, Nain Z, Billah M, Karmakar S, Mohanto SC, Paul GK, Amin A, Acharjee UK, Saleh MA. Computational discovery of plant-based inhibitors against human carbonic anhydrase IX and molecular dynamics simulation. J Biomol Struct Dyn 2020; 39:2754-2770. [DOI: 10.1080/07391102.2020.1753579] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Shafi Mahmud
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Ekhtiar Rahman
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Zulkar Nain
- Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University, Khustia, Bangladesh
| | - Mutasim Billah
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Sumon Karmakar
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | | | - Gobindo Kumar Paul
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Al Amin
- Institute of Biological Sciences, University of Rajshahi, Rajshahi, Bangladesh
| | - Uzzal Kumar Acharjee
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Md. Abu Saleh
- Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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27
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Farhadi Z, Farhadi T, Hashemian SM. Virtual screening for potential inhibitors of β(1,3)-D-glucan synthase as drug candidates against fungal cell wall. J Drug Assess 2020; 9:52-59. [PMID: 32284908 PMCID: PMC7144292 DOI: 10.1080/21556660.2020.1734010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/07/2020] [Indexed: 01/17/2023] Open
Abstract
Background To enhance the outcome in patients with invasive candidiasis, initiation of an efficient antifungal treatment in a suitable dosage is necessary. Echinocandins (e.g. caspofungin) inhibit the enzyme β(1,3)-D-glucan synthase of the fungal cell wall. Compared to azoles and other antifungal agents, echinocandins have lower adverse effects and toxicity in humans. Echinocandins are available in injectable (intravenous) form. Methods In this study, to identify the novel oral drug-like compounds that affect the fungal cell wall, downloaded oral drug-like compounds from the ZINC database were processed with a virtual screening procedure. The docking free energies were calculated and compared with the known inhibitor caspofungin. Four molecules were selected as the most potent ligands and subjected to hydrogen bonds analysis. Results Considering the hydrogen bond analysis, two compounds (ZINC71336662 and ZINC40910772) were predicted to better interact with the active site of β(1,3)-D-glucan synthase compared with caspofungin. Conclusion The introduced compound in this study may be valuable to analyze experimentally as a novel oral drug candidate targeting fungal cell walls.
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Affiliation(s)
- Zinat Farhadi
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Behavioral Disease Counseling Center, Marvdasht Health Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Department of Microbiology, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Tayebeh Farhadi
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed MohammadReza Hashemian
- Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Critical Care Department, Farhikhtegan Hospital, Tehran Medical Branch, Islamic Azad University, Tehran, Iran
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28
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Rifai EA, Ferrario V, Pleiss J, Geerke DP. Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation. J Chem Theory Comput 2020; 16:1300-1310. [PMID: 31894691 PMCID: PMC7017367 DOI: 10.1021/acs.jctc.9b00890] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Calculating free energies of binding (ΔGbind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔGbind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔGbind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein-ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.
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Affiliation(s)
- Eko Aditya Rifai
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences , Vrije Universiteit Amsterdam , De Boelelaan 1108 , 1081 HZ Amsterdam , The Netherlands
| | - Valerio Ferrario
- Institute of Biochemistry and Technical Biochemistry , Universität Stuttgart , Allmandring 31 , 70569 Stuttgart , Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry , Universität Stuttgart , Allmandring 31 , 70569 Stuttgart , Germany
| | - Daan P Geerke
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences , Vrije Universiteit Amsterdam , De Boelelaan 1108 , 1081 HZ Amsterdam , The Netherlands
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29
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Macalino SJY, Billones JB, Organo VG, Carrillo MCO. In Silico Strategies in Tuberculosis Drug Discovery. Molecules 2020; 25:E665. [PMID: 32033144 PMCID: PMC7037728 DOI: 10.3390/molecules25030665] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/15/2019] [Accepted: 12/17/2019] [Indexed: 12/16/2022] Open
Abstract
Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.
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Affiliation(s)
- Stephani Joy Y. Macalino
- Chemistry Department, De La Salle University, 2401 Taft Avenue, Manila 0992, Philippines;
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
| | - Junie B. Billones
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
| | - Voltaire G. Organo
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
| | - Maria Constancia O. Carrillo
- OVPAA-EIDR Program, “Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis in the Philippines”, Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Manila 1000, Philippines; (V.G.O.); (M.C.O.C.)
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30
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Identification of antimalarial leads with dual falcipain-2 and falcipain-3 inhibitory activity. Bioorg Med Chem 2020; 28:115155. [DOI: 10.1016/j.bmc.2019.115155] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 09/26/2019] [Accepted: 10/03/2019] [Indexed: 12/17/2022]
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31
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Pinto GP, Vavra O, Filipovic J, Stourac J, Bednar D, Damborsky J. Fast Screening of Inhibitor Binding/Unbinding Using Novel Software Tool CaverDock. Front Chem 2019; 7:709. [PMID: 31737596 PMCID: PMC6828983 DOI: 10.3389/fchem.2019.00709] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 10/09/2019] [Indexed: 11/20/2022] Open
Abstract
Protein tunnels and channels are attractive targets for drug design. Drug molecules that block the access of substrates or release of products can be efficient modulators of biological activity. Here, we demonstrate the applicability of a newly developed software tool CaverDock for screening databases of drugs against pharmacologically relevant targets. First, we evaluated the effect of rigid and flexible side chains on sets of substrates and inhibitors of seven different proteins. In order to assess the accuracy of our software, we compared the results obtained from CaverDock calculation with experimental data previously collected with heat shock protein 90α. Finally, we tested the virtual screening capabilities of CaverDock with a set of oncological and anti-inflammatory FDA-approved drugs with two molecular targets—cytochrome P450 17A1 and leukotriene A4 hydrolase/aminopeptidase. Calculation of rigid trajectories using four processors took on average 53 min per molecule with 90% successfully calculated cases. The screening identified functional tunnels based on the profile of potential energies of binding and unbinding trajectories. We concluded that CaverDock is a sufficiently fast, robust, and accurate tool for screening binding/unbinding processes of pharmacologically important targets with buried functional sites. The standalone version of CaverDock is available freely at https://loschmidt.chemi.muni.cz/caverdock/ and the web version at https://loschmidt.chemi.muni.cz/caverweb/.
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Affiliation(s)
- Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czechia.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czechia
| | - Ondrej Vavra
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czechia.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czechia
| | - Jiri Filipovic
- Institute of Computer Science, Masaryk University, Brno, Czechia
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czechia.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czechia
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czechia.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czechia
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czechia.,International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czechia
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Parveen N, Ali SA, Ali AS. Insights Into the Explication of Potent Tyrosinase Inhibitors with Reference to Computational Studies. LETT DRUG DES DISCOV 2019. [DOI: 10.2174/1570180815666180803111021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background:
Pigment melanin has primarily a photo defensive role in human skin, its
unnecessary production and irregular distribution can cause uneven skin tone ultimately results in
hyper pigmentation. Melanin biosynthesis is initiated by tyrosine oxidation through tyrosinase, the
key enzyme for melanogenesis. Not only in humans, tyrosinase is also widely distributed in plants
and liable for browning of vegetables and fruits. Search for the inhibitors of tyrosinase have been
an important target to facilitate development of therapies for the prevention of hyperpigmentary
disorders and an undesired browning of vegetables and fruits.
Methods:
Different natural and synthetic chemical compounds have been tested as potential tyrosinase
inhibitors, but the mechanism of inhibition is not known, and the quest for information regarding
interaction between tyrosinase and its inhibitors is one of the recent areas of research. Computer
based methods hence are useful to overcome such issues. Successful utilization of in silico tools
like molecular docking simulations make it possible to interpret the tyrosinase and its inhibitor’s
intermolecular interactions and helps in identification and development of new and potent tyrosinase
inhibitors.
Results:
The present review has pointed out the prominent role of computer aided approaches for
the explication of promising tyrosinase inhibitors with a focus on molecular docking approach.
Highlighting certain examples of natural compounds whose antityrosinase effects has been evaluated
using computational simulations.
Conclusion:
The investigation of new and potent inhibitors of tyrosinase using computational
chemistry and bioinformatics will ultimately help millions of peoples to get rid of hyperpigmentary
disorders as well as browning of fruits and vegetables.
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Affiliation(s)
- Naima Parveen
- Department of Biotechnology and Zoology, Saifia College of Science, Bhopal 462001, India
| | - Sharique Akhtar Ali
- Department of Biotechnology and Zoology, Saifia College of Science, Bhopal 462001, India
| | - Ayesha Sharique Ali
- Department of Biotechnology and Zoology, Saifia College of Science, Bhopal 462001, India
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Shen C, Ding J, Wang Z, Cao D, Ding X, Hou T. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1429] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
| | - Junjie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University Changsha P. R. China
| | - Xiaoqin Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
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Integrated Chemoinformatics Approaches Toward Epigenetic Drug Discovery. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2019. [DOI: 10.1007/978-3-030-05282-9_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Gahtori A, Singh A. Ligand-based Pharmacophore Model for Generation of Active Antidepressant- like Agents from Substituted 1,3,5 Triazine Class. Curr Comput Aided Drug Des 2018; 16:167-175. [PMID: 30569874 DOI: 10.2174/1573409915666181219125415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 11/06/2018] [Accepted: 12/16/2018] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Although the transition of a lead candidate into a drug is currently structured by well-defined milestone, it is still most challenging and offers no guarantee in success to the end. In fact, ligand-based pharmacophore modeling has become a key motive force for retrieving potential leads across several therapeutic areas. METHODS An urgent need towards the development of novel antidepressant agents led us to generate a pharmacophore model from an existing 44 compounds dataset. The best model with one hydrophobic, two ring aromatic, and one positive ionization features was chosen on behalf of the correlation coefficient, sensitivity, specificity, yield of actives and accuracy measures using HypoGen module of Discovery Studio. In house library consisting of 10,000 substituted 1,3,5 triazine derivatives were shortlisted to select four insilico hits. All shortlisted compounds were synthesized and characterized by FTIR, 1H-& 13C-NMR spectroscopy and finally tested for antidepressant-like activity using behavioral models on rats viz. Forced Swim Test (FST) and Elevated Plus Maze (EPM). RESULTS Two shortlisted compounds with optimal fit values showed a significant decrease in the duration of immobility as compared to standard drug Imipramine in FST while time spent in open arm in enhanced in case of EPM.
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Affiliation(s)
- Archana Gahtori
- Shri Guru Ram Rai Institute of Technology & Science, SGRR University, Patel Nagar Dehradun - 248001, Uttarakhand, India
| | - Abhishek Singh
- Shri Guru Ram Rai Institute of Technology & Science, SGRR University, Patel Nagar Dehradun - 248001, Uttarakhand, India
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Saldívar-González FI, Gómez-García A, Chávez-Ponce de León DE, Sánchez-Cruz N, Ruiz-Rios J, Pilón-Jiménez BA, Medina-Franco JL. Inhibitors of DNA Methyltransferases From Natural Sources: A Computational Perspective. Front Pharmacol 2018; 9:1144. [PMID: 30364171 PMCID: PMC6191485 DOI: 10.3389/fphar.2018.01144] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 09/21/2018] [Indexed: 12/15/2022] Open
Abstract
Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling, and other computational approaches that have been used to uncover natural products as inhibitors of DNA methyltransferases, a major family of epigenetic targets with therapeutic interest. Examples of computational approaches surveyed in this work are docking, similarity-based virtual screening, and pharmacophore modeling. It is also discussed the chemoinformatic-guided exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.
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Affiliation(s)
| | - Alejandro Gómez-García
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | | | - Norberto Sánchez-Cruz
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - Javier Ruiz-Rios
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - B Angélica Pilón-Jiménez
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
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Lu W, Zhang R, Jiang H, Zhang H, Luo C. Computer-Aided Drug Design in Epigenetics. Front Chem 2018; 6:57. [PMID: 29594101 PMCID: PMC5857607 DOI: 10.3389/fchem.2018.00057] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Accepted: 02/23/2018] [Indexed: 12/31/2022] Open
Abstract
Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.
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Affiliation(s)
- Wenchao Lu
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Rukang Zhang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Hao Jiang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Huimin Zhang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Cheng Luo
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
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Kumar R, Jade D, Gupta D. A novel identification approach for discovery of 5-HydroxyTriptamine 2A antagonists: combination of 2D/3D similarity screening, molecular docking and molecular dynamics. J Biomol Struct Dyn 2018; 37:931-943. [PMID: 29468945 DOI: 10.1080/07391102.2018.1444509] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
5-HydroxyTriptamine 2A antagonists are potential targets for treatment of various cerebrovascular and cardiovascular disorders. In this study, we have developed and performed a unique screening pipeline for filtering ZINC database compounds on the basis of similarities to known antagonists to determine novel small molecule antagonists of 5-HydroxyTriptamine 2A. The screening pipeline is based on 2D similarity, 3D dissimilarity and a combination of 2D/3D similarity. The shortlisted compounds were docked to a 5-HydroxyTriptamine 2A homology-based model, and complexes with low binding energies (287 complexes) were selected for molecular dynamics (MD) simulations in a lipid bilayer. The MD simulations of the shortlisted compounds in complex with 5-HydroxyTriptamine 2A confirmed the stability of the complexes and revealed novel interaction insights. The receptor residues S239, N343, S242, S159, Y370 and D155 predominantly participate in hydrogen bonding. π-π stacking is observed in F339, F340, F234, W151 and W336, whereas hydrophobic interactions are observed amongst V156, F339, F234, V362, V366, F340, V235, I152 and W151. The known and potential antagonists shortlisted by us have similar overlapping molecular interaction patterns. The 287 potential 5-HydroxyTriptamine 2A antagonists may be experimentally verified.
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Key Words
- , tanimoto coefficient
- 2D similarity
- 2D, two-dimensional space
- 2D/3D screening
- 3D similarity
- 3D, three-dimensional space
- 5HT
- 5HT, 5-HydroxyTryptamine
- ADHD, attention deficit hyperactivity disorders
- BLAST, basic local alignment search tool
- CNS, central nervous system
- Cl ions, chloride ions
- DOPE, discrete optimized protein energy
- G-protein coupled receptor
- GPCRs, G protein-coupled receptors
- HB, hydrogen bond
- HBA, hydrogen bond acceptors
- HBD, hydrogen bond donors
- JC virus, John Cunningham virus
- Ki, equilibrium dissociation constant for the ligand
- LBVS, ligand-based virtual screening
- MD, molecular dynamic
- MSD, mean square displacement
- MW, molecular weight
- NHB, number of hydrogen bonds
- OCD, obsessive compulsive disorder
- P5/P95, percentile calculation
- PAINS, Pan assay interference compounds
- PDB, protein data bank
- PLIP, protein–ligand interaction profiler
- PME, Particle Mesh Ewald
- PNS, peripheral nervous system
- POPC, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine
- RMSD, root mean square deviation
- RMSF, root mean square fluctuations
- Rg, radius of gyration
- SASA, solvent accessible surface area
- SCA, stochastic clustering algorithm
- SD, steepest descent
- SDF, structure data file
- SPC, single point charge
- SPD, simple point charge
- SSE, secondary structure elements
- Sn-1/sn-2, Stereospecific number
- TM, Transmembrane
- TPSA, topological polar surface area
- drug discovery
- fs, femtosecond
- kJ/mol, kilo Joule per mol
- kcal/mol, kilocalorie per mole sn-1
- ligand-based virtual screening
- nm, nanomolar
- ns, nanosecond
- Å Ångström
- β2-AR, β2 adrenergic receptor
- μM, micromolar
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Affiliation(s)
- Rakesh Kumar
- a Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB) , Aruna Asaf Ali Marg, New Delhi 110067 , India
| | - Dhananjay Jade
- a Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB) , Aruna Asaf Ali Marg, New Delhi 110067 , India
| | - Dinesh Gupta
- a Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB) , Aruna Asaf Ali Marg, New Delhi 110067 , India
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Abstract
Molecular modeling frequently constructs classification models for the prediction of two‐class entities, such as compound bio(in)activity, chemical property (non)existence, protein (non)interaction, and so forth. The models are evaluated using well known metrics such as accuracy or true positive rates. However, these frequently used metrics applied to retrospective and/or artificially generated prediction datasets can potentially overestimate true performance in actual prospective experiments. Here, we systematically consider metric value surface generation as a consequence of data balance, and propose the computation of an inverse cumulative distribution function taken over a metric surface. The proposed distribution analysis can aid in the selection of metrics when formulating study design. In addition to theoretical analyses, a practical example in chemogenomic virtual screening highlights the care required in metric selection and interpretation.
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Affiliation(s)
- J B Brown
- Kyoto University Graduate School of Medicine, Laboratory of Molecular Biosciences, 606-8501, E-109 Konoemachi, Sakyo, Kyoto, Japan
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Gancia E, De Groot M, Burton B, Clark DE. Discovery of LRRK2 inhibitors by using an ensemble of virtual screening methods. Bioorg Med Chem Lett 2017; 27:2520-2527. [DOI: 10.1016/j.bmcl.2017.03.098] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 03/29/2017] [Accepted: 03/31/2017] [Indexed: 11/29/2022]
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41
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Thirumal Kumar D, Lavanya P, George Priya Doss C, Tayubi IA, Naveen Kumar DR, Francis Yesurajan I, Siva R, Balaji V. A Molecular Docking and Dynamics Approach to Screen Potent Inhibitors Against Fosfomycin Resistant Enzyme in Clinical Klebsiella pneumoniae. J Cell Biochem 2017; 118:4088-4094. [PMID: 28409871 DOI: 10.1002/jcb.26064] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 04/13/2017] [Indexed: 01/13/2023]
Abstract
Klebsiella pneumoniae, BA6753 was cultured from a patient in the Clinical Microbiology Laboratory of Christian Medical College. K. pneumoniae, BA6753 has a multidrug resistance plasmid encoding novel FosA variant-7, fosfomycin resistance enzyme. Minimal side effects and a wide range of bactericidal activity of fosfomycin have resulted in its expanded clinical use that prompts the rise of fosfomycin-resistant strains. At present, there are no effective inhibitors available to conflict the FosA-medicated fosfomycin resistance. To develop effective FosA inhibitors, it is crucial to understand the structural and dynamic properties of resistance enzymes. Hence, the present study focuses on the identification of potent inhibitors that can effectively bind to the fosfomycin resistance enzyme, thus predispose the target to inactivate by the second antibiotic. Initially, a series of active compounds were screened against the resistant enzyme, and the binding affinities were confirmed using docking simulation analysis. For efficient activity, the binding affinity of the resistance enzyme ought to be high with the inhibitor than the fosfomycin drug. Consequently, the enzyme-ligand complex which showed higher binding affinity than the fosfomycin was employed for subsequent analysis. The stability of the top scoring enzyme-ligand complex was further validated using molecular dynamics simulation studies. On the whole, we presume that the compound 19583672 demonstrates a higher binding affinity for the resistance enzyme comparing to other compounds and fosfomycin. We believe that further enhancement of the lead compound can serve as a potential inhibitor against resistance enzyme in drug discovery process. J. Cell. Biochem. 118: 4088-4094, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- D Thirumal Kumar
- School of Biosciences and Technology, VIT University, Vellore, 632014, India
| | - P Lavanya
- Department of Clinical Microbiology, Christian Medical College, Vellore, 632004, India
| | - C George Priya Doss
- School of Biosciences and Technology, VIT University, Vellore, 632014, India
| | - Iftikhar Aslam Tayubi
- Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia
| | - D R Naveen Kumar
- Department of Clinical Microbiology, Christian Medical College, Vellore, 632004, India
| | - I Francis Yesurajan
- Department of Clinical Microbiology, Christian Medical College, Vellore, 632004, India
| | - R Siva
- School of Biosciences and Technology, VIT University, Vellore, 632014, India
| | - V Balaji
- Department of Clinical Microbiology, Christian Medical College, Vellore, 632004, India
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42
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Joshi P, McCann GJP, Sonawane VR, Vishwakarma RA, Chaudhuri B, Bharate SB. Identification of Potent and Selective CYP1A1 Inhibitors via Combined Ligand and Structure-Based Virtual Screening and Their in Vitro Validation in Sacchrosomes and Live Human Cells. J Chem Inf Model 2017; 57:1309-1320. [PMID: 28489395 DOI: 10.1021/acs.jcim.7b00095] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Target structure-guided virtual screening (VS) is a versatile, powerful, and inexpensive alternative to experimental high-throughput screening (HTS). To discover potent CYP1A1 enzyme inhibitors for cancer chemoprevention, a commercial library of 50 000 small molecules was utilized for VS guided by both ligand and structure-based strategies. For experimental validation, 300 ligands were proposed based on combined analysis of fitness scores from ligand based e-pharmacophore screening and docking score, prime MMGB/SA binding affinity and interaction pattern analysis from structure-based VS. These 300 compounds were screened, at 10 μM concentration, for in vitro inhibition of CYP1A1-Sacchrosomes (yeast-derived microsomal enzyme) in the ethoxyresorufin-O-de-ethylase assay. Thirty-two compounds displayed >50% inhibition of CYP1A1 enzyme activity at 10 μM. 2-Phenylimidazo-[1,2-a]quinoline (5121780, 119) was found to be the most potent with 97% inhibition. It also inhibited ∼95% activity of CYP1B1 and CYP1A2, the other two CYP1 enzymes. The compound 5121780 (119) showed high selectivity toward inhibition of CYP1 enzymes with respect to CYP2 and CYP3 enzymes (i.e., there was no detectable inhibition of CYP2D6/CYP2C9/CYP2C19 and CYP3A4 at 10 μM). It was further investigated in live CYP-expressing human cell system, which confirmed that compound 5121780 (119) potently inhibited CYP1A1, CYP1A2, CYP1B1 enzymes with IC50 values of 269, 30, and 56 nM, respectively. Like in Sacchrosomes, inhibition of CYP2D6/CYP2C9/CYP2C19 and CYP3A4 enzymes, expressed within live human cells, could hardly be detected at 10 μM. The compound 119 rescued CYP1A1 overexpressing HEK293 cells from CYP1A1 mediated benzo[a]pyrene (B[a]P) toxicity and also overcame cisplatin resistance in CYP1B1 overexpressing HEK293 cells. Molecular dynamics simulations of 5121780 (119) with CYP1 enzymes was performed to understand the interaction pattern to CYP isoforms. Results indicate that VS can successfully be used to identify promising CYP1A1 inhibitors, which may have potential in the development of novel cancer chemo-preventive agents.
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Affiliation(s)
- Prashant Joshi
- Medicinal Chemistry Division, Indian Institute of Integrative Medicine (CSIR) , Canal Road, Jammu-180001, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-Indian Institute of Integrative Medicine , Canal Road, Jammu-180001, India
| | - Glen J P McCann
- Leicester School of Pharmacy, De Montfort University , Leicester LE1 9BH, United Kingdom
| | - Vinay R Sonawane
- Leicester School of Pharmacy, De Montfort University , Leicester LE1 9BH, United Kingdom
| | - Ram A Vishwakarma
- Medicinal Chemistry Division, Indian Institute of Integrative Medicine (CSIR) , Canal Road, Jammu-180001, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-Indian Institute of Integrative Medicine , Canal Road, Jammu-180001, India
| | - Bhabatosh Chaudhuri
- Leicester School of Pharmacy, De Montfort University , Leicester LE1 9BH, United Kingdom.,CYP Design Limited, Innovation Centre, 49 Oxford Street, Leicester LE1 5XY, United Kingdom
| | - Sandip B Bharate
- Medicinal Chemistry Division, Indian Institute of Integrative Medicine (CSIR) , Canal Road, Jammu-180001, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-Indian Institute of Integrative Medicine , Canal Road, Jammu-180001, India
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43
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Virtual Screening for Potential Inhibitors of CTX-M-15 Protein of Klebsiella pneumoniae. Interdiscip Sci 2017; 10:694-703. [PMID: 28374117 DOI: 10.1007/s12539-017-0222-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 02/28/2017] [Accepted: 03/09/2017] [Indexed: 12/31/2022]
Abstract
The Gram-negative bacterium Klebsiella pneumoniae, responsible for a wide variety of nosocomial infections in immuno-deficient patients, involves the respiratory, urinary and gastrointestinal tract infections and septicemia. Extended spectrum β-lactamases (ESBL) belong to β-lactamases capable of conferring antibiotic resistance in Gram-negative bacteria. CTX-M-15, a prevalent ESBL reported from Enterobacteriaceae including K. pneumoniae, was selected as a potent anti-bacterial target. To identify the novel drug-like compounds, structure-based screening procedure was employed against downloaded drug-like compounds from ZINC database. An acronym for "ZINC" is not commercial. The docking free energy values were investigated and compared to the known inhibitor Avibactam. Six best novel drug-like compounds were selected and their hydrogen bindings with the receptor were determined. Based on the binding efficiency mode, three among these six identified most potential inhibitors, ZINC21811621, ZINC93091917 and ZINC19488569, were predicted as potential competitive inhibitors against CTX-M-15 compared to Avibactam. These three inhibitors may provide a framework for the experimental studies to develop anti-Klebsiella novel drug candidates targeting CTX-M-15.
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44
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Davari K, Nowroozi J, Hosseini F, Sepahy AA, Mirzaie S. Structure-based virtual screening to identify the beta-lactamase CTX-M-9 inhibitors: An in silico effort to overcome antibiotic resistance in E. coli. Comput Biol Chem 2017; 67:174-181. [DOI: 10.1016/j.compbiolchem.2017.01.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 01/05/2017] [Accepted: 01/18/2017] [Indexed: 11/26/2022]
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45
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Structure-based design and evaluation of novel N-phenyl-1H-indol-2-amine derivatives for fat mass and obesity-associated (FTO) protein inhibition. Comput Biol Chem 2016; 64:414-425. [DOI: 10.1016/j.compbiolchem.2016.09.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 09/08/2016] [Indexed: 01/01/2023]
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46
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Moretti L, Sartori L. A Simple and Resource-efficient Setup for the Computer-aided Drug Design Laboratory. Mol Inform 2016; 35:489-494. [PMID: 27647771 DOI: 10.1002/minf.201600025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 08/27/2016] [Indexed: 11/11/2022]
Abstract
Undertaking modelling investigations for Computer-Aided Drug Design (CADD) requires a proper environment. In principle, this could be done on a single computer, but the reality of a drug discovery program requires robustness and high-throughput computing (HTC) to efficiently support the research. Therefore, a more capable alternative is needed but its implementation has no widespread solution. Here, the realization of such a computing facility is discussed, from general layout to technical details all aspects are covered.
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Affiliation(s)
- Loris Moretti
- Drug Discovery Program, Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139, Milan, Italy. .,Nuevolution A/S, Rønnegade 8, DK-2100, Copenhagen, Denmark.
| | - Luca Sartori
- Drug Discovery Program, Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139, Milan, Italy.,Experimental Therapeutics Unit, IFOM -, The FIRC institute of Molecular Oncology, Via Adamello 16, 20139, Milan, Italy
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47
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Mechanism of artemisinin resistance for malaria PfATP6 L263 mutations and discovering potential antimalarials: An integrated computational approach. Sci Rep 2016; 6:30106. [PMID: 27471101 PMCID: PMC4965867 DOI: 10.1038/srep30106] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 06/27/2016] [Indexed: 11/08/2022] Open
Abstract
Artemisinin resistance in Plasmodium falciparum threatens global efforts in the elimination or eradication of malaria. Several studies have associated mutations in the PfATP6 gene in conjunction with artemisinin resistance, but the underlying molecular mechanism of the resistance remains unexplored. Associated mutations act as a biomarker to measure the artemisinin efficacy. In the proposed work, we have analyzed the binding affinity and efficacy between PfATP6 and artemisinin in the presence of L263D, L263E and L263K mutations. Furthermore, we performed virtual screening to identify potential compounds to inhibit the PfATP6 mutant proteins. In this study, we observed that artemisinin binding affinity with PfATP6 gets affected by L263D, L263E and L263K mutations. This in silico elucidation of artemisinin resistance enhanced the identification of novel compounds (CID: 10595058 and 10625452) which showed good binding affinity and efficacy with L263D, L263E and L263K mutant proteins in molecular docking and molecular dynamics simulations studies. Owing to the high propensity of the parasite to drug resistance the need for new antimalarial drugs will persist until the malarial parasites are eventually eradicated. The two compounds identified in this study can be tested in in vitro and in vivo experiments as possible candidates for the designing of new potential antimalarial drugs.
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48
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Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J Chem Inf Model 2016; 56:1399-404. [PMID: 27391578 DOI: 10.1021/acs.jcim.6b00174] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
SwissSimilarity is a new web tool for rapid ligand-based virtual screening of small to unprecedented ultralarge libraries of small molecules. Screenable compounds include drugs, bioactive and commercial molecules, as well as 205 million of virtual compounds readily synthesizable from commercially available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D molecular fingerprints as well as superpositional and fast nonsuperpositional 3D similarity methodologies. SwissSimilarity is part of a large initiative of the SIB Swiss Institute of Bioinformatics to provide online tools for computer-aided drug design, such as SwissDock, SwissBioisostere or SwissTargetPrediction with which it can interoperate, and is linked to other well-established online tools and databases. User interface and backend have been designed for simplicity and ease of use, to provide proficient virtual screening capabilities to specialists and nonexperts in the field. SwissSimilarity is accessible free of charge or login at http://www.swisssimilarity.ch .
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Affiliation(s)
- Vincent Zoete
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Antoine Daina
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Christophe Bovigny
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Olivier Michielin
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland.,Ludwig Institute for Cancer Research, Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland.,Department of Oncology, University of Lausanne and Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland
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49
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Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing. PLoS One 2016; 11:e0158898. [PMID: 27420300 PMCID: PMC4946785 DOI: 10.1371/journal.pone.0158898] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 06/23/2016] [Indexed: 12/19/2022] Open
Abstract
Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249.
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Affiliation(s)
- Ye Fang
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Yun Ding
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Wei P. Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - David M. Koppelman
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Juana Moreno
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Mark Jarrell
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - J. Ramanujam
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
- * E-mail:
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50
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Villoutreix B. Combining bioinformatics, chemoinformatics and experimental approaches to design chemical probes: Applications in the field of blood coagulation. ANNALES PHARMACEUTIQUES FRANÇAISES 2016; 74:253-66. [DOI: 10.1016/j.pharma.2016.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 03/21/2016] [Accepted: 03/21/2016] [Indexed: 11/08/2022]
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