1
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Wahl J. PheSA: An Open-Source Tool for Pharmacophore-Enhanced Shape Alignment. J Chem Inf Model 2024; 64:5944-5953. [PMID: 39092495 DOI: 10.1021/acs.jcim.4c00516] [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: 08/04/2024]
Abstract
PheSA is an open-source pharmacophore- and shape-based screening and molecular alignment tool that is fully open-source as part of OpenChemLib. Supporting standard ligand-based screening, flexible refinement of alignments, and receptor-guided shape docking, PheSA is a very flexible tool and can be used for different use cases in structure-based drug design. We present the algorithm and different benchmark studies that investigate the screening performance and also the quality of the generated alignments and the pose prediction performance of the receptor-guided PheSA algorithm. An important finding is the effect of the type of similarity metric used for measuring screening enrichment (symmetric Tanimoto versus asymmetric Tversky), whereby we could observe improved enrichment rates by using Tversky. PheSA exhibits enrichments on the DUD-E that are on par with commercial methods.
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Affiliation(s)
- Joel Wahl
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland
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2
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Moyano-Gómez P, Lehtonen JV, Pentikäinen OT, Postila PA. Building shape-focused pharmacophore models for effective docking screening. J Cheminform 2024; 16:97. [PMID: 39123240 PMCID: PMC11312248 DOI: 10.1186/s13321-024-00857-6] [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: 10/05/2023] [Accepted: 05/12/2024] [Indexed: 08/12/2024] Open
Abstract
The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.
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Affiliation(s)
- Paola Moyano-Gómez
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland
- InFLAMES Research Flagship, University of Turku, 20014, Turku, Finland
| | - Jukka V Lehtonen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, 20500, Turku, Finland
- InFLAMES Research Flagship, Åbo Akademi University, 20500, Turku, Finland
| | - Olli T Pentikäinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland
- InFLAMES Research Flagship, University of Turku, 20014, Turku, Finland
- Aurlide Ltd, Lemminkäisenkatu 14A, 20520, Turku, Finland
| | - Pekka A Postila
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland.
- InFLAMES Research Flagship, University of Turku, 20014, Turku, Finland.
- Aurlide Ltd, Lemminkäisenkatu 14A, 20520, Turku, Finland.
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3
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Monari L, Galentino K, Cecchini M. ChemFlow_py: a flexible toolkit for docking and rescoring. J Comput Aided Mol Des 2023; 37:565-572. [PMID: 37620503 DOI: 10.1007/s10822-023-00527-z] [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: 06/07/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .
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Affiliation(s)
- Luca Monari
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Katia Galentino
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.
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4
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Jokinen EM, Niemeläinen M, Kurkinen ST, Lehtonen JV, Lätti S, Postila PA, Pentikäinen OT, Niinivehmas SP. Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators. Molecules 2023; 28:molecules28083420. [PMID: 37110655 PMCID: PMC10145393 DOI: 10.3390/molecules28083420] [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: 03/15/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.
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Affiliation(s)
- Elmeri M Jokinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Miika Niemeläinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
| | - Sami T Kurkinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Jukka V Lehtonen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, FI-20500 Turku, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, FI-20500 Turku, Finland
| | - Sakari Lätti
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Pekka A Postila
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Olli T Pentikäinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Sanna P Niinivehmas
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
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5
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Bello SO, Yunusa A, Adamu AA, Imam MU, Bello MB, Shuaibu A, Igumbor EU, Habib ZG, Popoola MA, Ochu CL, Bello AY, Deeni YY, Okoye I. Innovative, rapid, high-throughput method for drug repurposing in a pandemic-A case study of SARS-CoV-2 and COVID-19. Front Pharmacol 2023; 14:1130828. [PMID: 36937851 PMCID: PMC10014809 DOI: 10.3389/fphar.2023.1130828] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/20/2023] [Indexed: 03/05/2023] Open
Abstract
Several efforts to repurpose drugs for COVID-19 treatment have largely either failed to identify a suitable agent or agents identified did not translate to clinical use. Reasons that have been suggested to explain the failures include use of inappropriate doses, that are not clinically achievable, in the screening experiments, and the use of inappropriate pre-clinical laboratory surrogates to predict efficacy. In this study, we used an innovative algorithm, that incorporates dissemination and implementation considerations, to identify potential drugs for COVID-19 using iterative computational and wet laboratory methods. The drugs were screened at doses that are known to be achievable in humans. Furthermore, inhibition of viral induced cytopathic effect (CPE) was used as the laboratory surrogate to predict efficacy. Erythromycin, pyridoxine, folic acid and retapamulin were found to inhibit SARS-CoV-2 induced CPE in Vero cells at concentrations that are clinically achievable. Additional studies may be required to further characterize the inhibitions of CPE and the possible mechanisms.
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Affiliation(s)
- Shaibu Oricha Bello
- Department of Pharmacology and Therapeutics, Faculty of Basic Clinical Sciences, College of Health Sciences, Usmanu Danfodiyo University, Sokoto, Nigeria
- Nigerian COVID-19 Research Coalition, Nigerian Institute of Medical Research Institute, Abuja, Nigeria
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
- *Correspondence: Shaibu Oricha Bello,
| | - Abdulmajeed Yunusa
- Department of Pharmacology and Therapeutics, Faculty of Basic Clinical Sciences, College of Health Sciences, Usmanu Danfodiyo University, Sokoto, Nigeria
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Adamu Ahmed Adamu
- Department of Pharmacology and Therapeutics, Faculty of Basic Clinical Sciences, College of Health Sciences, Usmanu Danfodiyo University, Sokoto, Nigeria
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Mustapha Umar Imam
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
- Department of Medical Biochemistry, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Muhammad Bashir Bello
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
- Department of veterinary Microbiology, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Abdulmalik Shuaibu
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
- Department of veterinary Microbiology, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Ehimario Uche Igumbor
- Nigerian COVID-19 Research Coalition, Nigerian Institute of Medical Research Institute, Abuja, Nigeria
- School of Public Health, University of the Western Cape, Cape Town, South Africa
| | - Zaiyad Garba Habib
- Nigerian COVID-19 Research Coalition, Nigerian Institute of Medical Research Institute, Abuja, Nigeria
- Department of Medicine, University of Abuja Teaching Hospital, Gwagwalada, Abuja, Nigeria
| | - Mustapha Ayodele Popoola
- Nigerian COVID-19 Research Coalition, Nigerian Institute of Medical Research Institute, Abuja, Nigeria
| | - Chinwe Lucia Ochu
- Nigerian COVID-19 Research Coalition, Nigerian Institute of Medical Research Institute, Abuja, Nigeria
- Nigerian Centre for Disease Control and Prevention, Abuja, Nigeria
| | - Aishatu Yahaya Bello
- Department of Clinical pharmacy and Pharmacy Practice, Faculty of Pharmaceutical sciences, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Yusuf Yahaya Deeni
- Nigerian COVID-19 Research Coalition, Nigerian Institute of Medical Research Institute, Abuja, Nigeria
- Department of Microbiology and Biotechnology, Federal University of Dutse, Dutse, Nigeria
- Centre for Environmental and Public Health Research and Development, Kano, Nigeria
| | - Ifeoma Okoye
- University of Nigeria Centre for Clinical Trials, University of Nigeria Teaching Hospital, Enugu, Ituku Ozalla, Nigeria
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6
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Ligand-Enhanced Negative Images Optimized for Docking Rescoring. Int J Mol Sci 2022; 23:ijms23147871. [PMID: 35887220 PMCID: PMC9323918 DOI: 10.3390/ijms23147871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 12/04/2022] Open
Abstract
Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.
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7
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Bhowmick S, AlFaris NA, Zaidan ALTamimi J, ALOthman ZA, Patil PC, Aldayel TS, Wabaidur SM, Saha A. Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations. Comput Biol Med 2022; 145:105474. [PMID: 35395517 PMCID: PMC8973019 DOI: 10.1016/j.compbiomed.2022.105474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/10/2022] [Accepted: 03/29/2022] [Indexed: 12/16/2022]
Abstract
Despite significant studies on the COVID-19 pandemic, scientists around the world are still battling to find a definitive therapy against the ongoing severe global health crisis. In this study, advanced computational approaches have been employed to identify bioactive food constituents as potential SARS-CoV-2 PLpro inhibitors-modulators. As a validated antiviral drug target, PLpro has gained tremendous attention for therapeutics developments. Therefore, targeting the multifunctional SARS-CoV-2 PLpro protein, ∼1039 bioactive dietary compounds have been screened extensively through novel techniques like negative image-based (NIB) screening and molecular docking approaches. In particular, the three different models of NIB screening have been generated and used to re-score the dietary compounds based on the negative image which is created by reversing the shape and electrostatics features of PLpro protein's ligand-binding cavity. Further, 100 ns molecular dynamics simulation has been performed and MM-GBSA based binding free energies have been estimated for the final proposed four dietary compounds (PC000550, PC000361, PC000558, and PC000573) as potential inhibitors/modulators of SARS-CoV-2 PLpro protein. Employed computational study outcome also has been compared with respect to the earlier experimentally investigated compound GRL0617 against SARS-CoV-2 PLpro protein, which suggests much greater interaction potential in terms of binding affinity and other energetic contributions for the proposed dietary compounds. Hence, the present study suggests that proposed dietary compounds can be suitable chemical entities for modulating the activity of PLpro protein or can be further utilized for optimizing or screening of novel chemical surrogates, however also needs experimental evaluation for entry in clinical studies for better assessment.
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Affiliation(s)
- Shovonlal Bhowmick
- Department of Chemical Technology, University of Calcutta, 92 A.P.C. Road, Kolkata, India
| | - Nora Abdullah AlFaris
- Nutrition and Food Science, Department of Physical Sport Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia,Corresponding author
| | - Jozaa Zaidan ALTamimi
- Nutrition and Food Science, Department of Physical Sport Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Zeid A. ALOthman
- Chemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Pritee Chunarkar Patil
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune-Satara Road, Pune, India
| | - Tahany Saleh Aldayel
- Nutrition and Food Science, Department of Physical Sport Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | | | - Achintya Saha
- Department of Chemical Technology, University of Calcutta, 92 A.P.C. Road, Kolkata, India,Corresponding author
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8
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Kurkinen ST, Lehtonen JV, Pentikäinen OT, Postila PA. Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening. J Chem Inf Model 2022; 62:1100-1112. [PMID: 35133138 PMCID: PMC8889583 DOI: 10.1021/acs.jcim.1c01145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein's ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.
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Affiliation(s)
- Sami T Kurkinen
- Institute of Biomedicine, Integrative Physiology and Pharmacy, University of Turku, FI-20014 Turku, Finland.,Aurlide Ltd., FI-21420 Lieto, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Jukka V Lehtonen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, FI-20500 Turku, Finland.,InFLAMES Research Flagship Center, Åbo Akademi University, FI-20500 Turku, Finland
| | - Olli T Pentikäinen
- Institute of Biomedicine, Integrative Physiology and Pharmacy, University of Turku, FI-20014 Turku, Finland.,Aurlide Ltd., FI-21420 Lieto, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Pekka A Postila
- Institute of Biomedicine, Integrative Physiology and Pharmacy, University of Turku, FI-20014 Turku, Finland.,Aurlide Ltd., FI-21420 Lieto, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
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9
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Lätti ST, Niinivehmas S, Pentikäinen OT. Sdfconf: A Novel, Flexible, and Robust Molecular Data Management Tool. J Chem Inf Model 2021; 62:9-15. [PMID: 34932340 PMCID: PMC8757437 DOI: 10.1021/acs.jcim.1c01051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Projects in chemo- and bioinformatics often consist of scattered data in various types and are difficult to access in a meaningful way for efficient data analysis. Data is usually too diverse to be even manipulated effectively. Sdfconf is data manipulation and analysis software to address this problem in a logical and robust manner. Other software commonly used for such tasks are either not designed with molecular and/or conformational data in mind or provide only a narrow set of tasks to be accomplished. Furthermore, many tools are only available within commercial software packages. Sdfconf is a flexible, robust, and free-of-charge tool for linking data from various sources for meaningful and efficient manipulation and analysis of molecule data sets. Sdfconf packages molecular structures and metadata into a complete ensemble, from which one can access both the whole data set and individual molecules and/or conformations. In this software note, we offer some practical examples of the utilization of sdfconf.
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Affiliation(s)
- Sakari T Lätti
- Institute of Biomedicine, Faculty of Medicine, University of Turku, FI-20520 Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20520 Turku, Finland.,Aurlide ltd, FI-21420 Lieto, Finland
| | - Sanna Niinivehmas
- Institute of Biomedicine, Faculty of Medicine, University of Turku, FI-20520 Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20520 Turku, Finland.,Aurlide ltd, FI-21420 Lieto, Finland
| | - Olli T Pentikäinen
- Institute of Biomedicine, Faculty of Medicine, University of Turku, FI-20520 Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20520 Turku, Finland.,Aurlide ltd, FI-21420 Lieto, Finland
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10
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Guo J, Janet JP, Bauer MR, Nittinger E, Giblin KA, Papadopoulos K, Voronov A, Patronov A, Engkvist O, Margreitter C. DockStream: a docking wrapper to enhance de novo molecular design. J Cheminform 2021; 13:89. [PMID: 34789335 PMCID: PMC8596819 DOI: 10.1186/s13321-021-00563-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/29/2021] [Indexed: 01/09/2023] Open
Abstract
Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream .
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Affiliation(s)
- Jeff Guo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Jon Paul Janet
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Matthias R Bauer
- Structure & Biophysics, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Eva Nittinger
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Kathryn A Giblin
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | | | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Atanas Patronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
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11
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Jokinen EM, Gopinath K, Kurkinen ST, Pentikäinen OT. Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1281-1289. [PMID: 33914685 PMCID: PMC8791430 DOI: 10.1109/tcbb.2021.3076259] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 04/13/2021] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein -ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.
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12
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Kundu B, Raychaudhuri D, Mukherjee A, Sinha BP, Sarkar D, Bandopadhyay P, Pal S, Das N, Dey D, Ramarao K, Nagireddy K, Ganguly D, Talukdar A. Systematic Optimization of Potent and Orally Bioavailable Purine Scaffold as a Dual Inhibitor of Toll-Like Receptors 7 and 9. J Med Chem 2021; 64:9279-9301. [PMID: 34142551 DOI: 10.1021/acs.jmedchem.1c00532] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Several toll-like receptors (TLRs) reside inside endosomes of specific immune cells-among them, aberrant activation of TLR7 and TLR9 is implicated in myriad contexts of autoimmune diseases, making them promising therapeutic targets. However, small-molecule TLR7 and TLR9 antagonists are not yet available for clinical use. We illustrate here the importance of C2, C6, and N9 substitutions in the purine scaffold for antagonism to TLR7 and TLR9 through structure-activity relationship studies using cellular reporter assays and functional studies on primary human immune cells. Further in vitro and in vivo pharmacokinetic studies identified an orally bioavailable lead compound 29, with IC50 values of 0.08 and 2.66 μM against TLR9 and TLR7, respectively. Isothermal titration calorimetry excluded direct TLR ligand-antagonist interactions. In vivo antagonism efficacy against mouse TLR9 and therapeutic efficacy in a preclinical murine model of psoriasis highlighted the potential of compound 29 as a therapeutic candidate in relevant autoimmune contexts.
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Affiliation(s)
- Biswajit Kundu
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India
| | - Deblina Raychaudhuri
- IICB-Translational Research Unit of Excellence, Department of Cancer Biology and Inflammatory Disorders, CSIR-Indian Institute of Chemical Biology, CN6, Sector V, Salt Lake, Kolkata 700091, West Bengal, India
| | - Ayan Mukherjee
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India
| | | | - Dipika Sarkar
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India
| | - Purbita Bandopadhyay
- IICB-Translational Research Unit of Excellence, Department of Cancer Biology and Inflammatory Disorders, CSIR-Indian Institute of Chemical Biology, CN6, Sector V, Salt Lake, Kolkata 700091, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Sourav Pal
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Nirmal Das
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Debdeep Dey
- Tata Medical Center, Newtown, Kolkata 700160, West Bengal, India
| | - Kantubhukta Ramarao
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India
| | - Kasireddy Nagireddy
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India
| | - Dipyaman Ganguly
- IICB-Translational Research Unit of Excellence, Department of Cancer Biology and Inflammatory Disorders, CSIR-Indian Institute of Chemical Biology, CN6, Sector V, Salt Lake, Kolkata 700091, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
| | - Arindam Talukdar
- Department of Organic and Medicinal Chemistry, CSIR-Indian Institute of Chemical Biology, 4 Raja S. C. Mullick Road, Kolkata 700032, West Bengal, India.,Academy of Scientific and Innovative Research, Ghaziabad 201002, India
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13
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Bian G, Yang J, Elango J, Wu W, Bao B, Bao C. Natural Triterpenoids Isolated from Akebia trifoliata Stem Explants Exert a Hypoglycemic Effect via α-Glucosidase Inhibition and Glucose Uptake Stimulation in Insulin-Resistant HepG2 Cells. Chem Biodivers 2021; 18:e2001030. [PMID: 33779055 DOI: 10.1002/cbdv.202001030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/25/2021] [Indexed: 12/26/2022]
Abstract
The inhibition of α-glucosidase activity is a prospective approach to attenuate postprandial hyperglycemia in the treatment of type 2 diabetes mellitus (T2DM). Herein, the inhibition of α-glucosidase by three compounds T1 -T3 of Akebia trifoliata stem, namely hederagenin (T1 ), 3-epiakebonoic acid (T2 ), and arjunolic acid (T3 ) were investigated using enzyme kinetics and molecular docking analysis. The three triterpenoids exhibited excellent inhibitory activities against α-glucosidase. T1 -T3 showed the strongest inhibition with IC50 values of 42.1±5.4, 19.6±3.2, and 11.2±2.3 μM, respectively, compared to the acarbose positive control (IC50 =106.3±8.2). Enzyme inhibition kinetics showed that triterpenoids T1 -T3 demonstrated competitive, mixed, and noncompetitive-type inhibition against α-glucosidase, respectively. The inhibition constant (Ki ) values were 21.21, 7.70, and 3.18 μM, respectively. Docking analysis determined that the interaction of ligands T1 -T3 and α-glucosidase was mainly forced by hydrogen bonds and hydrophobic interactions, which could result in improved binding to the active site of the target enzyme. The insulin resistant (IR)-HepG2 cell model used in this study (HepG2 cells exposed to 10-7 M insulin for 24 h) and glucose uptake assays showed that compounds T1 -T3 had no cytotoxicity with concentrations ranging from 6.25 to 25 μM and displayed significant stimulation of glucose uptake in IR-HepG2 cells. Thus, triterpenoids T1 -T3 showed dual therapeutic effects of α-glucosidase inhibition and glucose uptake stimulation and could be used as potential medicinal resources to investigate new antidiabetic agents for the prevention or treatment of diabetes.
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Affiliation(s)
- Guoyong Bian
- Department of Marine Bio-Pharmacology, College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, P. R. China
| | - Jinbo Yang
- Department of Marine Bio-Pharmacology, College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, P. R. China
| | - Jeevithan Elango
- Department of Marine Bio-Pharmacology, College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, P. R. China
| | - Wenhui Wu
- Department of Marine Bio-Pharmacology, College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, P. R. China.,National R&D Branch Center for Freshwater Aquatic Products Processing Technology, Shanghai, 201306, P. R. China
| | - Bin Bao
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology, Shanghai, 201306, P. R. China
| | - Chunling Bao
- Shanghai Sixth People's Hospital East Campus, Shanghai, 201306, P. R. China
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Pentikäinen OT, Postila PA. Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening. Methods Mol Biol 2021; 2266:141-154. [PMID: 33759125 DOI: 10.1007/978-1-0716-1209-5_8] [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: 01/31/2023]
Abstract
Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.
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Affiliation(s)
- Olli T Pentikäinen
- Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
- Aurlide Ltd., Turku, Finland
| | - Pekka A Postila
- Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.
- Aurlide Ltd., Turku, Finland.
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15
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Negative Image-Based Screening: Rigid Docking Using Cavity Information. Methods Mol Biol 2021; 2266:125-140. [PMID: 33759124 DOI: 10.1007/978-1-0716-1209-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.
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16
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Vermaas JV, Sedova A, Baker MB, Boehm S, Rogers DM, Larkin J, Glaser J, Smith MD, Hernandez O, Smith JC. Supercomputing Pipelines Search for Therapeutics Against COVID-19. Comput Sci Eng 2021; 23:7-16. [PMID: 35939280 PMCID: PMC9280802 DOI: 10.1109/mcse.2020.3036540] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/31/2020] [Accepted: 11/03/2020] [Indexed: 11/15/2022]
Abstract
The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high-performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases.
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Affiliation(s)
| | - Ada Sedova
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | - Swen Boehm
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | | | - Jens Glaser
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
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17
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Gopinath K, Jokinen EM, Kurkinen ST, Pentikäinen OT. Screening of Natural Products Targeting SARS-CoV-2-ACE2 Receptor Interface - A MixMD Based HTVS Pipeline. Front Chem 2020; 8:589769. [PMID: 33330376 PMCID: PMC7717977 DOI: 10.3389/fchem.2020.589769] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/20/2020] [Indexed: 12/11/2022] Open
Abstract
The COVID-19 pandemic, caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a severe global health crisis now. SARS-CoV-2 utilizes its Spike protein receptor-binding domain (S-protein) to invade human cell through binding to Angiotensin-Converting Enzyme 2 receptor (ACE2). S-protein is the key target for many therapeutics and vaccines. Potential S-protein-ACE2 fusion inhibitor is expected to block the virus entry into the host cell. In many countries, traditional practices, based on natural products (NPs) have been in use to slow down COVID-19 infection. In this study, a protocol was applied that combines mixed solvent molecular dynamics simulations (MixMD) with high-throughput virtual screening (HTVS) to search NPs to block SARS-CoV-2 entry into the human cell. MixMD simulations were employed to discover the most promising stable binding conformations of drug-like probes in the S-protein-ACE2 interface. Detected stable sites were used for HTVs of 612093 NPs to identify molecules that could interfere with the S-protein-ACE2 interaction. In total, 19 NPs were selected with rescoring model. These top-ranked NP-S-protein complexes were subjected to classical MD simulations for 300 ns (3 replicates of 100 ns) to estimate the stability and affinity of binding. Three compounds, ZINC000002128789, ZINC000002159944 and SN00059335, showed better stability in all MD runs, of which ZINC000002128789 was predicted to have the highest binding affinity, suggesting that it could be effective modulator in RBD-ACE2 interface to prevent SARS-CoV-2 infection. Our results support that NPs may provide tools to fight COVID-19.
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Affiliation(s)
| | | | | | - Olli T. Pentikäinen
- Faculty of Medicine, Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
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18
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Singh N, Chaput L, Villoutreix BO. Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces. J Chem Inf Model 2020; 60:3910-3934. [PMID: 32786511 DOI: 10.1021/acs.jcim.0c00545] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.
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Affiliation(s)
- Natesh Singh
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
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19
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Chen X, Liu H, Xie W, Yang Y, Wang Y, Fan Y, Hua Y, Zhu L, Zhao J, Lu T, Chen Y, Zhang Y. Investigation of Crystal Structures in Structure-Based Virtual Screening for Protein Kinase Inhibitors. J Chem Inf Model 2019; 59:5244-5262. [PMID: 31689093 DOI: 10.1021/acs.jcim.9b00684] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.
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Affiliation(s)
- Xingye Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Wuchen Xie
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yan Yang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yuchen Wang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yuanrong Fan
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Lu Zhu
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Junnan Zhao
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China.,State Key Laboratory of Natural Medicines , China Pharmaceutical University , 24 Tongjiaxiang , Nanjing 210009 , China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
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A Perspective: Active Role of Lipids in Neurotransmitter Dynamics. Mol Neurobiol 2019; 57:910-925. [PMID: 31595461 PMCID: PMC7031182 DOI: 10.1007/s12035-019-01775-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 09/01/2019] [Indexed: 12/30/2022]
Abstract
Synaptic neurotransmission is generally considered as a function of membrane-embedded receptors and ion channels in response to the neurotransmitter (NT) release and binding. This perspective aims to widen the protein-centric view by including another vital component—the synaptic membrane—in the discussion. A vast set of atomistic molecular dynamics simulations and biophysical experiments indicate that NTs are divided into membrane-binding and membrane-nonbinding categories. The binary choice takes place at the water-membrane interface and follows closely the positioning of the receptors’ binding sites in relation to the membrane. Accordingly, when a lipophilic NT is on route to a membrane-buried binding site, it adheres on the membrane and, then, travels along its plane towards the receptor. In contrast, lipophobic NTs, which are destined to bind into receptors with extracellular binding sites, prefer the water phase. This membrane-based sorting splits the neurotransmission into membrane-independent and membrane-dependent mechanisms and should make the NT binding into the receptors more efficient than random diffusion would allow. The potential implications and notable exceptions to the mechanisms are discussed here. Importantly, maintaining specific membrane lipid compositions (MLCs) at the synapses, especially regarding anionic lipids, affect the level of NT-membrane association. These effects provide a plausible link between the MLC imbalances and neurological diseases such as depression or Parkinson’s disease. Moreover, the membrane plays a vital role in other phases of the NT life cycle, including storage and release from the synaptic vesicles, transport from the synaptic cleft, as well as their synthesis and degradation.
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