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      Protein–ligand binding with the coarse-grained Martini model

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          Abstract

          The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

          Abstract

          Computer-aided design of protein-ligand binding is important for the development of novel drugs. Here authors present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand binding interactions of small drug-like molecules.

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          Most cited references97

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          GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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            Baricitinib as potential treatment for 2019-nCoV acute respiratory disease

            Given the scale and rapid spread of the 2019 novel coronavirus (2019-nCoV) acute respiratory disease, there is an immediate need for medicines that can help before a vaccine can be produced. Results of rapid sequencing of 2019-nCoV, coupled with molecular modelling based on the genomes of related virus proteins, 1 have suggested a few compounds that are likely to be effective, including the anti-HIV lopinavir plus ritonavir combination. BenevolentAI's knowledge graph is a large repository of structured medical information, including numerous connections extracted from scientific literature by machine learning. 2 Together with customisations bespoke to 2019-nCoV, we used BenevolentAI to search for approved drugs that could help, focusing on those that might block the viral infection process. We identified baricitinib, which is predicted to reduce the ability of the virus to infect lung cells. Most viruses enter cells through receptor-mediated endocytosis. The receptor that 2019-nCoV uses to infect lung cells might be ACE2, a cell-surface protein on cells in the kidney, blood vessels, heart, and, importantly, lung AT2 alveolar epithelial cells (figure ). These AT2 cells are particularly prone to viral infection. 3 One of the known regulators of endocytosis is the AP2-associated protein kinase 1 (AAK1). Disruption of AAK1 might, in turn, interrupt the passage of the virus into cells and also the intracellular assembly of virus particles. 4 Figure The BenevolentAI knowledge graph The BenevolentAI knowledge graph integrates biomedical data from structured and unstructured sources. It is queried by a fleet of algorithms to identify new relationships to suggest new ways of tackling disease. 2019-nCoV=2019 novel coronavirus. AAK1=AP-2 associated kinase 1. GAK=cyclin g-associated kinase. JAK1/2=janus kinase 1/2. Of 378 AAK1 inhibitors in the knowledge graph, 47 have been approved for medical use and six inhibited AAK1 with high affinity. These included a number of oncology drugs such as sunitinib and erlotinib, both of which have been shown to inhibit viral infection of cells through the inhibition of AAK1. 5 However, these compounds bring serious side-effects, and our data infer high doses to inhibit AAK1 effectively. We do not consider these drugs would be a safe therapy for a population of sick and infected people. By contrast, one of the six high-affinity AAK1-binding drugs was the janus kinase inhibitor baricitinib, which also binds the cyclin G-associated kinase, another regulator of endocytosis. Because the plasma concentration of baricitinib on therapeutic dosing (either as 2 mg or 4 mg once daily) is sufficient to inhibit AAK1, we suggest it could be trialled, using an appropriate patient population with 2019-nCoV acute respiratory disease, to reduce both the viral entry and the inflammation in patients, using endpoints such as the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. 7
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              Computational protein-ligand docking and virtual drug screening with the AutoDock suite.

              Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.
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                Author and article information

                Contributors
                paulocts@gmail.com
                vittoriolimongelli@gmail.com
                s.j.marrink@rug.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 July 2020
                24 July 2020
                2020
                : 11
                : 3714
                Affiliations
                [1 ]ISNI 0000 0004 0407 1981, GRID grid.4830.f, Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, , University of Groningen, ; Nijenborgh 7, 9747 AG Groningen, Netherlands
                [2 ]ISNI 0000 0001 2203 2861, GRID grid.29078.34, Faculty of Biomedical Sciences, Institute of Computational Science, , Università della Svizzera italiana (USI), ; via G. Buffi 13, CH-6900 Lugano, Switzerland
                [3 ]ISNI 0000 0001 0790 385X, GRID grid.4691.a, Department of Pharmacy, , University of Naples “Federico II”, ; via D. Montesano 49, I-80131 Naples, Italy
                Author information
                http://orcid.org/0000-0003-0660-1301
                http://orcid.org/0000-0002-3396-5840
                http://orcid.org/0000-0002-5440-6167
                http://orcid.org/0000-0003-2724-0760
                http://orcid.org/0000-0003-1948-5311
                http://orcid.org/0000-0003-4835-8696
                http://orcid.org/0000-0002-4861-1199
                http://orcid.org/0000-0001-8423-5277
                Article
                17437
                10.1038/s41467-020-17437-5
                7382508
                32709852
                0f2df7bd-79a3-4739-9105-823c3d934d54
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 August 2019
                : 29 June 2020
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                © The Author(s) 2020

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                proteins,structural biology,computational biophysics,molecular biophysics,drug discovery

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