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      Structural Investigation and Molecular Modeling Studies of Strobilurin-Based Fungicides Active against the Rice Blast Pathogen Pyricularia oryzae

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          Abstract

          The increasing emergence of fungicide-resistant pathogens requires urgent solutions for crop disease management. Here, we describe a structural investigation of new fungicides obtained by combining strobilurin and succinate dehydrogenase inhibitor pharmacophores. We identified compounds endowed with very good activity against wild-type Pyricularia oryzae, combined in some cases with promising activity against strobilurin-resistant strains. The first three-dimensional model of P. oryzae cytochrome bc1 complex containing azoxystrobin as a ligand was developed. The model was validated with a set of commercially available strobilurins, and it well explains both the resistance mechanism to strobilurins mediated by the mutation G143A and the activity of metyltetraprole against strobilurin-resistant strains. The obtained results shed light on the key recognition determinants of strobilurin-like derivatives in the cytochrome bc1 active site and will guide the further rational design of new fungicides able to overcome resistance caused by G143A mutation in the rice blast pathogen.

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          Most cited references 42

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          Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

          Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand. In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses. The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose. Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms. Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose. Errors in geometry for the top-ranked pose are less than 1 A in nearly half of the cases and are greater than 2 A in only about one-third of them. Comparisons to published data on rms deviations show that Glide is nearly twice as accurate as GOLD and more than twice as accurate as FlexX for ligands having up to 20 rotatable bonds. Glide is also found to be more accurate than the recently described Surflex method.
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            Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

            A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
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              Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.

              Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of physical chemistry, particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                02 April 2021
                April 2021
                : 22
                : 7
                Affiliations
                [1 ]Department of Food, Environmental and Nutritional Science (DeFENS), University of Milan, 20133 Milan, Italy; andrea.kunova@ 123456unimi.it (A.K.); fabio.forlani@ 123456unimi.it (F.F.); giorgia.catinella@ 123456unimi.it (G.C.); loana.musso@ 123456unimi.it (L.M.); paolo.cortesi@ 123456unimi.it (P.C.); sabrina.dallavalle@ 123456unimi.it (S.D.)
                [2 ]Department of Pharmacological and Biomolecular Sciences (DiSFeB), University of Milan, 20133 Milan, Italy; luca.palazzolo@ 123456unimi.it
                [3 ]Data Science Research Center (DSRC), University of Milan, 20133 Milan, Italy
                Author notes
                Article
                ijms-22-03731
                10.3390/ijms22073731
                8038316
                c5811cf4-cca9-4d94-ba15-b594878ab478
                © 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/).

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