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Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Published in Debmalya Barh, Kenneth Lundstrom, COVID-19, 2022
Traditional structure-based drug discovery applies the computational ligand-receptor–binding modeling and virtual screening, whereas the stability of the resulting ligand-protein complexes is confirmed by molecular dynamics simulation. All potentially druggable SARS-CoV-2 proteins were subjected to these analyses, and the number of computational studies dedicated to finding potential drugs targeting these proteins is mounting. For example, Hosseini et al. conducted molecular docking and virtual screening of 1,615 FDA-approved drugs on the binding pocket of SARS-CoV-2 Mpro, PLpro, and RdRp proteins [127]. The authors used AutoDock Vina, Glide, and rDock followed by MD simulation using GROMACS on the top inhibitors and identified six novel ligands as potential inhibitors against SARS-CoV-2, such as antiemetics rolapitant and ondansetron for Mpro; labetalol and levomefolic acid for PLpro; and leucal and antifungal natamycin for RdRp [127]. Chourasia et al. investigated in silico binding of epigallocatechin gallate (EGCG), and other catechins to SARS-CoV-2 proteins and identified papain-like protease protein (PLPro) as a binding partner [128].
Galaxy for open-source computational drug discovery solutions
Published in Expert Opinion on Drug Discovery, 2023
Anamika Singh Gaur, Selvaraman Nagamani, Lipsa Priyadarsinee, Hridoy J. Mahanta, Ramakrishnan Parthasarathi, G. Narahari Sastry
Open-source tools such as Autodock Vina and rdock have been implemented in the Galaxy toolshed and public servers for performing molecular docking analysis. The freely accessible PLIDflow is an essential workflow designed in the Galaxy platform by integrating public domain software to process protein-ligand complexes and identification of binding sides and the affinity of the ligand [53]. The core workflow comprises tools like PDB2PQR (prepares structures by reconstruction of missing residues, removing the ligand and water molecules from the PDB file), AutoDock4 (to prepare the input file format), Open Babel (to convert chemical structures file formats), AutoGrid4 (energy evaluation for docking calculation), AutoLigand (Binding sites identification), AutoDock Vina (performs molecular docking and binding affinity), eBoxSize (to calculate the optimal docking box size), and PDBbind database (gives experimental binding affinity data for biomolecular complexes).
Development and characterisation of SMURF2-targeting modifiers
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2021
Dhanoop Manikoth Ayyathan, Gal Levy-Cohen, Moran Shubely, Sandy Boutros-Suleiman, Veronica Lepechkin-Zilbermintz, Michael Shokhen, Amnon Albeck, Arie Gruzman, Michael Blank
3D structures of the separated C2 and HECT domains of SMURF2 were acquired from the solution structure of C2 2JQZ.pdb27 and the crystal structure of the HECT fragment 1ZVD.pdb28. Initial structure of the HECT–C2 complex was generated by means of the Discovery Studio 4.0 molecular modelling package29, implementing methods ZDOCK30, and RDOCK31 for rigid body protein docking. The docked poses generated by ZDOCK were filtered by a set of C2 residues experiencing the most significant NMR chemical shift in the complex with HECT27. On the next step, RDOCK protocol was used, providing optimisation of docked poses generated by the ZDOCK protocol. RDOCK consists mainly of a two-stage energy minimisation scheme that includes the evaluation of electrostatic and desolvation energies. During the two-stage energy minimisation, RDOCK takes advantage of CHARMM molecular modelling software32 to remove clashes and optimise polar and charge interactions. Finally, the best docking pose was used as input structure for the subsequent MD simulation. Pep3, Pep5, Pep7, and Pep10 designed, synthesised and experimentally estimated in this work as promising inhibitors of SMURF2, were used for the construction of 3D structures of their complexes with HECT by molecular modelling. The best poses of the HECT-peptidyl inhibitor complexes generated by VINA docking algorithm33, implemented in the YASARA structure software34, were used as initial 3D structures for the subsequent MD simulations.
Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies
Published in Expert Opinion on Drug Discovery, 2023
Virtual screening can significantly reduce costs and expand the chemical space accessible, as well as remove inactive compounds prior to experimental testing. One of the virtual screening approaches involves the docking of virtual libraries to the 3D structure of the target, followed by evaluation of the binding energies using a scoring function (Figure 3(f)). There are a few programs that were developed specifically for docking small molecules to RNA, such as MORDOR [134] and rDock (formerly, RiboDock) [135]. In addition, there are programs, originally designed for protein targets, that were adapted or reparameterized for small-molecule docking to RNA, including Dock6 [136], ICM [137], and AutoDock [138].