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      New Hybrid Compounds Combining Fragments of Usnic Acid and Monoterpenoids for Effective Tyrosyl-DNA Phosphodiesterase 1 Inhibition

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          Abstract

          Usnic acid (UA) is a secondary metabolite of lichens that exhibits a wide range of biological activities. Previously, we found that UA derivatives are effective inhibitors of tyrosyl-DNA phosphodiesterase 1 (TDP1). It can remove covalent complex DNA-topoisomerase 1 (TOP1) stabilized by the TOP1 inhibitor topotecan, neutralizing the effect of the drugs. TDP1 removes damage at the 3′ end of DNA caused by other anticancer agents. Thus, TDP1 is a promising therapeutic target for the development of drug combinations with topotecan, as well as other drugs for cancer treatment. Ten new UA enamino derivatives with variation in the terpene fragment and substituent of the UA backbone were synthesized and tested as TDP1 inhibitors. Four compounds, 11a- d, had IC 50 values in the 0.23–0.40 μM range. Molecular modelling showed that 11a- d, with relatively short aliphatic chains, fit to the important binding domains. The intrinsic cytotoxicity of 11a- d was tested on two human cell lines. The compounds had low cytotoxicity with CC 50 ≥ 60 μM for both cell lines. 11a and 11c had high inhibition efficacy and low cytotoxicity, and they enhanced topotecan’s cytotoxicity in cancerous HeLa cells but reduced it in the non-cancerous HEK293A cells. This “protective” effect from topotecan on non-cancerous cells requires further investigation.

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          The Protein Data Bank.

          The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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            Improved protein-ligand docking using GOLD.

            The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol. Copyright 2003 Wiley-Liss, Inc.
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              Development and validation of a genetic algorithm for flexible docking.

              Prediction of small molecule binding modes to macromolecules of known three-dimensional structure is a problem of paramount importance in rational drug design (the "docking" problem). We report the development and validation of the program GOLD (Genetic Optimisation for Ligand Docking). GOLD is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Numerous enhancements and modifications have been applied to the original technique resulting in a substantial increase in the reliability and the applicability of the algorithm. The advanced algorithm has been tested on a dataset of 100 complexes extracted from the Brookhaven Protein DataBank. When used to dock the ligand back into the binding site, GOLD achieved a 71% success rate in identifying the experimental binding mode.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Biomolecules
                Biomolecules
                biomolecules
                Biomolecules
                MDPI
                2218-273X
                01 July 2021
                July 2021
                : 11
                : 7
                : 973
                Affiliations
                [1 ]Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; dyrkheeva.n.s@ 123456gmail.com (N.S.D.); a.zakharenko73@ 123456gmail.com (A.L.Z.); katya.plekhanova@ 123456gmail.com (E.S.I.); amal@ 123456bionet.nsc.ru (A.A.M.); medvedev@ 123456bionet.nsc.ru (S.P.M.); zakian@ 123456bionet.nsc.ru (S.M.Z.)
                [2 ]N.N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; alfil@ 123456nioch.nsc.ru (A.S.F.); luzina@ 123456nioch.nsc.ru (O.A.L.); anvar@ 123456nioch.nsc.ru (N.F.S.)
                [3 ]Federal Research Centre Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
                [4 ]School of Pharmacy and Bioengineering, Keele University, Hornbeam Building, Staffordshire ST5 5BG, UK; j.reynisson@ 123456keele.ac.uk
                [5 ]Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
                Author notes
                [* ]Correspondence: volcho@ 123456nioch.nsc.ru (K.P.V.); lavrik@ 123456niboch.nsc.ru (O.I.L.)
                Author information
                https://orcid.org/0000-0003-2798-2844
                https://orcid.org/0000-0001-8627-3453
                https://orcid.org/0000-0003-1916-1333
                https://orcid.org/0000-0002-1520-5549
                https://orcid.org/0000-0003-4174-9512
                https://orcid.org/0000-0002-4083-9324
                https://orcid.org/0000-0001-5980-8889
                Article
                biomolecules-11-00973
                10.3390/biom11070973
                8301776
                34356597
                4ca64cf0-25d8-4d03-b4d7-1d7afb19a20d
                © 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
                : 28 May 2021
                : 26 June 2021
                Categories
                Article

                usnic acid,tyrosyl-dna phosphodiesterase 1,tdp1 inhibitor,inhibiting activity,terpene,topotecan,synergy

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