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      Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach

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          Abstract

          Mycobacterium tuberculosis (Mtb) is a deadly tuberculosis (TB)-causing pathogen. The proteasome is vital to the survival of Mtb and is therefore validated as a potential target for anti-TB therapy. Mtb resistance to existing antibacterial agents has enhanced drastically, becoming a worldwide health issue. Therefore, new potential therapeutic agents need to be developed that can overcome the complications of TB. With this purpose, in the present study, 224,205 natural compounds from the ZINC database have been screened against the catalytic site of Mtb proteasome by the computational approach. The best scoring hits, ZINC3875469, ZINC4076131, and ZINC1883067, demonstrated robust interaction with Mtb proteasome with binding energy values of −7.19, −7.95, and −7.21 kcal/mol for the monomer (K-chain) and −8.05, −9.10, and −7.07 kcal/mol for the dimer (both K and L chains) of the beta subunit, which is relatively higher than that of reference compound HT1171 (−5.83 kcal/mol (monomer) and −5.97 kcal/mol (dimer)). In-depth molecular docking of top-scoring compounds with Mtb proteasome reveals that amino acid residues Thr1, Arg19, Ser20, Thr21, Gln22, Gly23, Asn24, Lys33, Gly47, Asp124, Ala126, Trp129, and Ala180 are crucial in binding. Furthermore, a molecular dynamics study showed steady-state interaction of hit compounds with Mtb proteasome. Computational prediction of physicochemical property assessment showed that these hits are non-toxic and possess good drug-likeness properties. This study proposed that these compounds could be utilized as potential inhibitors of Mtb proteasome to combat TB infection. However, there is a need for further bench work experiments for their validation as inhibitors of Mtb proteasome.

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          AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

          We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. (c) 2009 Wiley Periodicals, Inc.
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            SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

            To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.
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              pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

              Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Molecules
                Molecules
                molecules
                Molecules
                MDPI
                1420-3049
                16 April 2021
                April 2021
                : 26
                : 8
                : 2326
                Affiliations
                [1 ]Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 61421, Saudi Arabia; talmelby@ 123456kku.edu.sa
                [2 ]Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia; mesferm@ 123456kku.edu.sa (M.A.S.); moyahya@ 123456kku.edu.sa (M.Y.A.); irfancsmmu@ 123456gmail.com (I.A.)
                [3 ]Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61421, Saudi Arabia
                [4 ]Department of Microbiology and Clinical Parasitology, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia; abdalqahtani@ 123456kku.edu.sa
                [5 ]Department of Biology, College of Sciences, University of Hail, Hail 2240, Saudi Arabia; j.alam@ 123456uoh.edu.sa (M.J.A.); mo.saeed@ 123456uoh.edu.sa (M.S.)
                [6 ]Department of Biochemistry, College of Medicine, University of Hail, Hail 2240, Saudi Arabia; ma.kausar@ 123456uoh.edu.sa
                [7 ]Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 2240, Saudi Arabia; am.saeed@ 123456uoh.edu.sa
                [8 ]Faculty of Medical Laboratory Sciences, Department of Medical Microbiology, University of Medical Sciences & Technology, Khartoum 12810, Sudan
                [9 ]Department of Medical Biotechnology, Yeungnam University, Gyeongsan 38541, Korea
                [10 ]Nanomedicine & Nanobiotechnology Laboratory, Department of Biosciences, Integral University, Lucknow 226026, India
                Author notes
                Author information
                https://orcid.org/0000-0002-7096-0221
                https://orcid.org/0000-0002-9500-4623
                https://orcid.org/0000-0003-0144-6517
                https://orcid.org/0000-0002-8931-9290
                Article
                molecules-26-02326
                10.3390/molecules26082326
                8074214
                33923734
                c9c7f764-4984-41b4-84b9-cfe138ce1370
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 08 March 2021
                : 13 April 2021
                Categories
                Article

                mycobacterium tuberculosis,tuberculosis,proteasome,natural compounds

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