1
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Javid A, Ahmed M. A computational odyssey: uncovering classical β-lactamase inhibitors in dry fruits. J Biomol Struct Dyn 2024; 42:4578-4604. [PMID: 37288775 DOI: 10.1080/07391102.2023.2220817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/29/2023] [Indexed: 06/09/2023]
Abstract
In the antibacterial arsenal, β-lactams have held a prominent position, but increasing resistance due to unauthorized use and genetic factors requires new strategies. Combining β-lactamase inhibitors with broad-spectrum β-lactams proves effective in combating this resistance. ESBL producers demand new inhibitors, leading to the exploration of plant-derived secondary metabolites for potent β-lactam antibiotics or alternative inhibitors. Using virtual screening, molecular docking, ADMET analysis, and molecular dynamic simulation, this study actively analyzed the inhibitory activity of figs, cashews, walnuts, and peanuts against SHV-1, NDM-1, KPC-2, and OXA-48 β-lactamases. Using AutoDock Vina, the docking affinities of various compounds for target enzymes were initially screened, revealing 12 bioactive compounds with higher affinities for the target enzymes compared to Avibactam and Tazobactam. Top-scoring metabolites, including Oleanolic acid, Protocatechuic acid, and Tannin, were subjected to MD simulation studies to further analyze the stability of the docked complexes using WebGro. The simulation coordinates, in terms of RMSD, RMSF, SASA, Rg, and hydrogen bonds formed, showed that these phytocompounds are stable enough to retain in the active sites at various orientations. The PCA and FEL analysis also showed the stability of the dynamic motion of Cα residues of phytochemical-bound enzymes. The pharmacokinetic analysis of the top phytochemicals was performed to analyze their bioavailability and toxicity. This study provides new insights into the therapeutic potential of phytochemicals of selected dry fruits and contributes to future experimental studies to identify βL inhibitors from plants.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Amina Javid
- Institute of Microbiology and Molecular Genetics, University of the Punjab, Quaid-e-Azam Campus, Lahore, Pakistan
| | - Mehboob Ahmed
- Institute of Microbiology and Molecular Genetics, University of the Punjab, Quaid-e-Azam Campus, Lahore, Pakistan
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2
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Qiao F, Binknowski TA, Broughan I, Chen W, Natarajan A, Schiltz GE, Scheidt KA, Anderson WF, Bergan R. Protein Structure Inspired Drug Discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594634. [PMID: 38826221 PMCID: PMC11142055 DOI: 10.1101/2024.05.17.594634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.
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Affiliation(s)
- Fangfang Qiao
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| | | | - Irene Broughan
- Department of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Weining Chen
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| | - Amarnath Natarajan
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| | - Gary E. Schiltz
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Karl A. Scheidt
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Wayne F. Anderson
- Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, IL 60611, USA
| | - Raymond Bergan
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
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3
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Pal S, Nare Z, Rao VA, Smith BO, Morrison I, Fitzgerald EA, Scott A, Bingham MJ, Pesnot T. Accelerating BRPF1b hit identification with BioPhysical and Active Learning Screening (BioPALS). ChemMedChem 2024; 19:e202300590. [PMID: 38372199 DOI: 10.1002/cmdc.202300590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/25/2024] [Accepted: 02/12/2024] [Indexed: 02/20/2024]
Abstract
We report the development of BioPhysical and Active Learning Screening (BioPALS); a rapid and versatile hit identification protocol combining AI-powered virtual screening with a GCI-driven biophysical confirmation workflow. Its application to the BRPF1b bromodomain afforded a range of novel micromolar binders with favorable ADMET properties. In addition to the excellent in silico/in vitro confirmation rate demonstrated with BRPF1b, binding kinetics were determined, and binding topologies predicted for all hits. BioPALS is a lean, data-rich, and standardized approach to hit identification applicable to a wide range of biological targets.
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Affiliation(s)
- Sandeep Pal
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
| | - Zandile Nare
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
| | - Vincenzo A Rao
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
| | - Brian O Smith
- University of Glasgow, School of Molecular Biosciences, College of Medical Veterinary and Life Sciences, G12 8QQ, Glasgow, UK
| | - Ian Morrison
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
| | | | - Andrew Scott
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
| | - Matilda J Bingham
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
| | - Thomas Pesnot
- Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK
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4
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Priyadarsinee L, Jamir E, Nagamani S, Mahanta HJ, Kumar N, John L, Sarma H, Kumar A, Gaur AS, Sahoo R, Vaikundamani S, Murugan NA, Priyakumar UD, Raghava GPS, Bharatam PV, Parthasarathi R, Subramanian V, Sastry GM, Sastry GN. Molecular Property Diagnostic Suite for COVID-19 (MPDS COVID-19): an open-source disease-specific drug discovery portal. GIGABYTE 2024; 2024:gigabyte114. [PMID: 38525218 PMCID: PMC10958779 DOI: 10.46471/gigabyte.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/11/2024] [Indexed: 03/26/2024] Open
Abstract
Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.
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Affiliation(s)
- Lipsa Priyadarsinee
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Esther Jamir
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Selvaraman Nagamani
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Hridoy Jyoti Mahanta
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Nandan Kumar
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Lijo John
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Himakshi Sarma
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Asheesh Kumar
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Anamika Singh Gaur
- CSIR-Indian Institute of Toxicology Research, Lucknow, 226001, Uttar Pradesh, India
| | - Rosaleen Sahoo
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - S. Vaikundamani
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - N. Arul Murugan
- Indraprastha Institute of Information Technology, Delhi, 110020, India
| | - U. Deva Priyakumar
- International Institute of Information Technology, Gachibowli, Hyderabad, 500032, India
| | - G. P. S. Raghava
- Indraprastha Institute of Information Technology, Delhi, 110020, India
| | - Prasad V. Bharatam
- National Institute of Pharmaceutical Education and Research, S.A.S. Nagar (Mohali), 160062, India
| | - Ramakrishnan Parthasarathi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- CSIR-Indian Institute of Toxicology Research, Lucknow, 226001, Uttar Pradesh, India
| | - V. Subramanian
- Department of Chemistry, Indian Institute of Technology, Chennai, 600036, India
| | - G. Madhavi Sastry
- Schrödinger Inc., Octave, Salarpuria Sattva Knowledge City, 1st Floor, Unit 3A, Hyderabad, 500081, India
| | - G. Narahari Sastry
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Indian Institute of Technology (IIT) Hyderabad, Kandi, Sangareddy, Telangana, 502284, India
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5
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Knight IS, Mailhot O, Tang KG, Irwin JJ. DockOpt: A Tool for Automatic Optimization of Docking Models. J Chem Inf Model 2024; 64:1004-1016. [PMID: 38206771 PMCID: PMC10865354 DOI: 10.1021/acs.jcim.3c01406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/17/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
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Affiliation(s)
- Ian S. Knight
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Olivier Mailhot
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Khanh G. Tang
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
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6
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Panwar U, Murali A, Khan MA, Selvaraj C, Singh SK. Virtual Screening Process: A Guide in Modern Drug Designing. Methods Mol Biol 2024; 2714:21-31. [PMID: 37676591 DOI: 10.1007/978-1-0716-3441-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.
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Affiliation(s)
- Umesh Panwar
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Aarthy Murali
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Mohammad Aqueel Khan
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Chandrabose Selvaraj
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Lucknow, Uttar Pradesh, India
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7
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Cibir Z, Hassel J, Sonneck J, Kowitz L, Beer A, Kraus A, Hallekamp G, Rosenkranz M, Raffelberg P, Olfen S, Smilowski K, Burkard R, Helfrich I, Tuz AA, Singh V, Ghosh S, Sickmann A, Klebl AK, Eickhoff JE, Klebl B, Seidl K, Chen J, Grabmaier A, Viga R, Gunzer M. ComplexEye: a multi-lens array microscope for high-throughput embedded immune cell migration analysis. Nat Commun 2023; 14:8103. [PMID: 38081825 PMCID: PMC10713721 DOI: 10.1038/s41467-023-43765-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
Autonomous migration is essential for the function of immune cells such as neutrophils and plays an important role in numerous diseases. The ability to routinely measure or target it would offer a wealth of clinical applications. Video microscopy of live cells is ideal for migration analysis, but cannot be performed at sufficiently high-throughput (HT). Here we introduce ComplexEye, an array microscope with 16 independent aberration-corrected glass lenses spaced at the pitch of a 96-well plate to produce high-resolution movies of migrating cells. With the system, we enable HT migration analysis of immune cells in 96- and 384-well plates with very energy-efficient performance. We demonstrate that the system can measure multiple clinical samples simultaneously. Furthermore, we screen 1000 compounds and identify 17 modifiers of migration in human neutrophils in just 4 days, a task that requires 60-times longer with a conventional video microscope. ComplexEye thus opens the field of phenotypic HT migration screens and enables routine migration analysis for the clinical setting.
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Affiliation(s)
- Zülal Cibir
- Institute for Experimental Immunology and Imaging, University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Jacqueline Hassel
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Justin Sonneck
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Faculty of Computer Science, Ruhr-Universität Bochum, 44801, Bochum, Germany
| | - Lennart Kowitz
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | - Alexander Beer
- Institute for Experimental Immunology and Imaging, University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Andreas Kraus
- Institute for Experimental Immunology and Imaging, University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Gabriel Hallekamp
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Martin Rosenkranz
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Pascal Raffelberg
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Sven Olfen
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Kamil Smilowski
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Roman Burkard
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Iris Helfrich
- Department of Dermatology and Allergology, Medical Faculty of the Ludwig Maximilian University of Munich, Munich, Germany
| | - Ali Ata Tuz
- Institute for Experimental Immunology and Imaging, University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Vikramjeet Singh
- Institute for Experimental Immunology and Imaging, University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Susmita Ghosh
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Medizinisches Proteom-Center, Ruhr-Universität Bochum, 44801, Bochum, Germany
- Department of Chemistry, College of Physical Sciences, University of Aberdeen, AB24 3FX, Aberdeen, UK
| | | | | | - Bert Klebl
- Lead Discovery Center GmbH, Dortmund, Germany
| | - Karsten Seidl
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | - Anton Grabmaier
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany
| | - Reinhard Viga
- Department of Electronic Components and Circuits, University of Duisburg-Essen, Duisburg, Germany.
| | - Matthias Gunzer
- Institute for Experimental Immunology and Imaging, University Hospital, University of Duisburg-Essen, Essen, Germany.
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
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8
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Alkubaisi BO, Aljobowry R, Ali SM, Sultan S, Zaraei SO, Ravi A, Al-Tel TH, El-Gamal MI. The latest perspectives of small molecules FMS kinase inhibitors. Eur J Med Chem 2023; 261:115796. [PMID: 37708796 DOI: 10.1016/j.ejmech.2023.115796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
FMS kinase is a type III tyrosine kinase receptor that plays a central role in the pathophysiology and management of several diseases, including a range of cancer types, inflammatory disorders, neurodegenerative disorders, and bone disorders among others. In this review, the pathophysiological pathways of FMS kinase in different diseases and the recent developments of its monoclonal antibodies and inhibitors during the last five years are discussed. The biological and biochemical features of these inhibitors, including binding interactions, structure-activity relationships (SAR), selectivity, and potencies are discussed. The focus of this article is on the compounds that are promising leads and undergoing advanced clinical investigations, as well as on those that received FDA approval. In this article, we attempt to classify the reviewed FMS inhibitors according to their core chemical structure including pyridine, pyrrolopyridine, pyrazolopyridine, quinoline, and pyrimidine derivatives.
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Affiliation(s)
- Bilal O Alkubaisi
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Raya Aljobowry
- College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Salma M Ali
- College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Sara Sultan
- College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Seyed-Omar Zaraei
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Anil Ravi
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Taleb H Al-Tel
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates; College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates.
| | - Mohammed I El-Gamal
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates; College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates; Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt.
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9
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Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Dev Res 2023; 84:1652-1663. [PMID: 37712494 DOI: 10.1002/ddr.22115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/14/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.
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Affiliation(s)
- Prafulla C Tiwari
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Rishi Pal
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Manju J Chaudhary
- Department of Physiology, Government Medical College, Kannauj, Uttar Pradesh, India
| | - Rajendra Nath
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
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10
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van Heerden A, Turon G, Duran-Frigola M, Pillay N, Birkholtz LM. Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds. ACS OMEGA 2023; 8:43813-43826. [PMID: 38027377 PMCID: PMC10666252 DOI: 10.1021/acsomega.3c05664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023]
Abstract
Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with inactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds' molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs.
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Affiliation(s)
- Ashleigh van Heerden
- Department
of Biochemistry, Genetics and Microbiology, Institute for Sustainable
Malaria Control, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
| | - Gemma Turon
- Ersilia
Open Source Initiative, 28 Belgrave Road, Cambridge CB1 3DE, U.K.
| | | | - Nelishia Pillay
- Department
of Computer Science, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
| | - Lyn-Marié Birkholtz
- Department
of Biochemistry, Genetics and Microbiology, Institute for Sustainable
Malaria Control, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
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11
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Binet T, Padiolleau-Lefèvre S, Octave S, Avalle B, Maffucci I. Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools. BMC Bioinformatics 2023; 24:422. [PMID: 37940855 PMCID: PMC10634105 DOI: 10.1186/s12859-023-05532-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/13/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure. RESULTS The minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with [Formula: see text] of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only [Formula: see text] of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5-10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions. CONCLUSIONS In general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots.
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Affiliation(s)
- Thomas Binet
- Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319, 60203, Compiègne Cedex, France
| | - Séverine Padiolleau-Lefèvre
- Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319, 60203, Compiègne Cedex, France
| | - Stéphane Octave
- Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319, 60203, Compiègne Cedex, France
| | - Bérangère Avalle
- Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319, 60203, Compiègne Cedex, France.
| | - Irene Maffucci
- Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319, 60203, Compiègne Cedex, France.
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12
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Zhang L, Wang CC, Zhang Y, Chen X. GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores. Comput Biol Med 2023; 166:107512. [PMID: 37788507 DOI: 10.1016/j.compbiomed.2023.107512] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/05/2023]
Abstract
Drug-target affinity prediction is a challenging task in drug discovery. The latest computational models have limitations in mining edge information in molecule graphs, accessing to knowledge in pharmacophores, integrating multimodal data of the same biomolecule and realizing effective interactions between two different biomolecules. To solve these problems, we proposed a method called Graph features and Pharmacophores augmented Cross-attention Networks based Drug-Target binding Affinity prediction (GPCNDTA). First, we utilized the GNN module, the linear projection unit and self-attention layer to correspondingly extract features of drugs and proteins. Second, we devised intramolecular and intermolecular cross-attention to respectively fuse and interact features of drugs and proteins. Finally, the linear projection unit was applied to gain final features of drugs and proteins, and the Multi-Layer Perceptron was employed to predict drug-target binding affinity. Three major innovations of GPCNDTA are as follows: (i) developing the residual CensNet and the residual EW-GCN to correspondingly extract features of drug and protein graphs, (ii) regarding pharmacophores as a new type of priors to heighten drug-target affinity prediction performance, and (iii) devising intramolecular and intermolecular cross-attention, in which the intramolecular cross-attention realizes the effective fusion of different modal data related to the same biomolecule, and the intermolecular cross-attention fulfills the information interaction between two different biomolecules in attention space. The test results on five benchmark datasets imply that GPCNDTA achieves the best performance compared with state-of-the-art computational models. Besides, relying on ablation experiments, we proved effectiveness of GNN modules, pharmacophores and two cross-attention strategies in improving the prediction accuracy, stability and reliability of GPCNDA. In case studies, we applied GPCNDTA to predict binding affinities between 3C-like proteinase and 185 drugs, and observed that most binding affinities predicted by GPCNDTA are close to corresponding experimental measurements.
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Affiliation(s)
- Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, China.
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13
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Silva-Júnior EFD. "You've got the Body I've got the Brains" - Could the current AI-based tools replace the human ingenuity for designing new drug candidates? Bioorg Med Chem 2023; 94:117475. [PMID: 37741120 DOI: 10.1016/j.bmc.2023.117475] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/12/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023]
Abstract
The emergence of artificial intelligence (AI) tools has transformed the landscape of drug discovery, providing unprecedented speed, efficiency, and cost-effectiveness in the search for new therapeutics. From target identification to drug formulation and delivery, AI-driven algorithms have revolutionized various aspects of medicinal chemistry, significantly accelerating the drug design process. Despite the transformative power of AI, this perspective article emphasizes the limitations of AI tools in drug discovery, requiring inventive skills of medicinal chemists. However, the article highlighted that there is a need for a harmonious integration of AI-based tools and human expertise in drug discovery. Such a synergistic approach promises to lead to groundbreaking therapies that address unmet medical needs and benefit humankind. As the world evolves technologically, the question remains: When will AI tools effectively design and develop drugs? The answer may lie in the seamless collaboration between AI and human researchers, unlocking transformative therapies that combat diseases effectively.
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Affiliation(s)
- Edeildo Ferreira da Silva-Júnior
- Institute of Chemistry and Biotechnology, Federal University of Alagoas, Lourival Melo Mota Avenue, AC. Simões Campus, 57072-970 Alagoas, Maceió, Brazil
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14
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John L, Nagamani S, Mahanta HJ, Vaikundamani S, Kumar N, Kumar A, Jamir E, Priyadarsinee L, Sastry GN. Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space. Mol Divers 2023:10.1007/s11030-023-10752-1. [PMID: 37902900 DOI: 10.1007/s11030-023-10752-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023]
Abstract
Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .
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Affiliation(s)
- Lijo John
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Selvaraman Nagamani
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Hridoy Jyoti Mahanta
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - S Vaikundamani
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
| | - Nandan Kumar
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Asheesh Kumar
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
| | - Esther Jamir
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Lipsa Priyadarsinee
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - G Narahari Sastry
- Advanced Computation and Data Sciences Division, CSIR - North East Institute of Science and Technology, Jorhat, 785006, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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15
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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16
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Ye J, Li A, Zheng H, Yang B, Lu Y. Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery. Adv Biol (Weinh) 2023; 7:e2200232. [PMID: 36775876 DOI: 10.1002/adbi.202200232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/30/2022] [Indexed: 02/14/2023]
Abstract
Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.
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Affiliation(s)
- Jiahao Ye
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - An Li
- Department of Critical Care Medicine, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China
- Department of Biochemical Pharmacy, School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Hao Zheng
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - Banghua Yang
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - Yiming Lu
- School of Medicine, Shanghai University, Shanghai, 200444, China
- Department of Critical Care Medicine, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China
- Department of Biochemical Pharmacy, School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
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17
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Kemisetti D, Amin R, Alam F, Gacem A, Emran TB, Alsufyani T, Alqahtani MS, Islam S, Matin MM, Jameel M. Novel Benzothiazole Derivatives Synthesis and its Analysis as Diuretic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2023; 2023:1-12. [DOI: 10.1155/2023/5460563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Benzothiazoles, an anticonvulsant, antiviral, antihypertensive, and cancer-fighting medication of the heterocyclic scaffold family, also acts as antibacterial and antiviral agents. There is much interest in this chemical’s production because of the strong and vital biological action it possesses. Substituted aromatic aldehydes were combined with 2-amino-benzothiazole-6-sulfonic acid amides, or Schiff base derivatives, to create Schiff base derivatives. Recrystallized, characterized, and tested for diuretic efficacy in vivo using online tools, m.p. (melting point), Rf, FTIR (Fourier transform infrared), 1H-NMR (proton nuclear magnetic resonance) data The molecular characteristics of all the substances created were estimated using Lipinski’s rule of 5, OSIRIS (software) molecular property explorer, Molsoft, and Autodock 4.0 docking software. Male Wistar rats were used to make all the compounds traditionally in order to test for diuretic activity. Neither the elemental nor the spectral information for the synthesized compounds disagreed. There were five different methods used to evaluate these compounds: Lipinski rule of five, Molsoft to determine molecular characteristics, PASS (prediction of activity spectra for substances) values to determine the diuretic effect, and OSIRIS software to determine toxicology. In order to investigate the diuretic effects of the selected drugs, docking analysis was used. Acetazolamide was shown to have a diuretic effect that was superior to that of compounds IIIb and IIIe, whereas 2-{(E)-[(3-hydroxyphenyl)methylidene]amino}-1,3-benzothiazole-6-sulfonamide (IIIb) was found to be the most promising potential.
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Affiliation(s)
- Durgaprasad Kemisetti
- Faculty of Pharmaceutical Science, Assam Down Town University, Panikhaiti, Guwahati, Assam, India
| | - Ruhul Amin
- Faculty of Pharmaceutical Science, Assam Down Town University, Panikhaiti, Guwahati, Assam, India
| | - Faruk Alam
- Faculty of Pharmaceutical Science, Assam Down Town University, Panikhaiti, Guwahati, Assam, India
| | - Amel Gacem
- Department of Physics, Faculty of Sciences, University 20 Août 1955, Skikda, Algeria
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
| | - Taghreed Alsufyani
- Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Mohammed S. Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, UK
- Research Center for Advanced Materials Science (RCAMS), King Khalid University, Postcode: 9004, Zip Code: 61413, Abha, Saudi Arabia
| | - Saiful Islam
- Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia
| | - Mohammed Mahbubul Matin
- Bioorganic and Medicinal Chemistry Laboratory, Faculty of Science, Department of Chemistry, University of Chittagong, Chittagong 4331, Bangladesh
| | - Mohammed Jameel
- Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia
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18
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Das B, Mathew AT, Baidya ATK, Devi B, Salmon RR, Kumar R. Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study. Mol Divers 2023:10.1007/s11030-023-10645-3. [PMID: 37022608 DOI: 10.1007/s11030-023-10645-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 03/29/2023] [Indexed: 04/07/2023]
Abstract
Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.
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Affiliation(s)
- Bhanuranjan Das
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Alen T Mathew
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Anurag T K Baidya
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Bharti Devi
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Rahul Rampa Salmon
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India
| | - Rajnish Kumar
- Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, 221005, UP, India.
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19
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Grasso D, Galderisi S, Santucci A, Bernini A. Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology. Int J Mol Sci 2023; 24:ijms24065819. [PMID: 36982893 PMCID: PMC10054308 DOI: 10.3390/ijms24065819] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.
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Affiliation(s)
- Daniela Grasso
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Silvia Galderisi
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Andrea Bernini
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
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20
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Tingle B, Tang KG, Castanon M, Gutierrez JJ, Khurelbaatar M, Dandarchuluun C, Moroz YS, Irwin JJ. ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery. J Chem Inf Model 2023; 63:1166-1176. [PMID: 36790087 PMCID: PMC9976280 DOI: 10.1021/acs.jcim.2c01253] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Indexed: 02/16/2023]
Abstract
Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.
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Affiliation(s)
- Benjamin
I. Tingle
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
| | - Khanh G. Tang
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
| | - Mar Castanon
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
| | - John J. Gutierrez
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
| | - Munkhzul Khurelbaatar
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
| | - Chinzorig Dandarchuluun
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
| | - Yurii S. Moroz
- Taras
Shevchenko National University of Kyïv, 60 Volodymyrska Street, Kyïv 01601, Ukraine
- Chemspace
LLC, 85 Chervonotkatska
Street, Kyïv 02094, Ukraine
| | - John J. Irwin
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, 1700 4th St, Mailcode 2550, San Francisco, California 94158-2330, United States
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21
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Pirintsos S, Panagiotopoulos A, Bariotakis M, Daskalakis V, Lionis C, Sourvinos G, Karakasiliotis I, Kampa M, Castanas E. From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples. Molecules 2022; 27:molecules27134060. [PMID: 35807306 PMCID: PMC9268545 DOI: 10.3390/molecules27134060] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products’ drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.
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Affiliation(s)
- Stergios Pirintsos
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
- Botanical Garden, University of Crete, 74100 Rethymnon, Greece
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Correspondence: (S.P.); (E.C.)
| | - Athanasios Panagiotopoulos
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Michalis Bariotakis
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
| | - Vangelis Daskalakis
- Department of Chemical Engineering, Cyprus University of Technology, Limassol 3603, Cyprus;
| | - Christos Lionis
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Clinic of Social and Family Medicine, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - George Sourvinos
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - Ioannis Karakasiliotis
- Laboratory of Biology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Marilena Kampa
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Elias Castanas
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
- Correspondence: (S.P.); (E.C.)
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