Chaetomugilins and Chaetoviridins—Promising Natural Metabolites: Structures, Separation, Characterization, Biosynthesis, Bioactivities, Molecular Docking, and Molecular Dynamics

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Abstract

Fungi are recognized as luxuriant metabolic artists that generate propitious biometabolites. Historically, fungal metabolites have largely been investigated as leads for various therapeutic agents. Chaetomugilins and the closely related chaetoviridins are fungal metabolites, and each has an oxygenated bicyclic pyranoquinone core. They are mainly produced by various Chaetomaceae species. These metabolites display unique chemical features and diversified bioactivities. The current review gives an overview of research about fungal chaetomugilins and chaetoviridins regarding their structures, separation, characterization, biosynthesis, and bioactivities. Additionally, their antiviral potential towards the SARS-CoV-2 protease was evaluated using docking studies and molecular dynamics (MD) simulations. We report on the docking and predictive binding energy estimations using reported crystal structures of the main protease (PDB ID: 6M2N, 6W81, and 7K0f) at variable resolutions—i.e., 2.20, 1.55, and 1.65 Å, respectively. Chaetovirdin D (43) exhibited highly negative docking scores of −7.944, −8.141, and −6.615 kcal/mol, when complexed with 6M2N, 6W81, and 7K0f, respectively. The reference inhibitors exhibited the following scores: −5.377, −6.995, and −8.159 kcal/mol, when complexed with 6M2N, 6W81, and 7K0f, respectively. By using molecular dynamics simulations, chaetovirdin D’s stability in complexes with the viral protease was analyzed, and it was found to be stable over the course of 100 ns.

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

Richard Friesner, Jay L. Banks, Robert B Murphy … (2004)

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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Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments.

G. Madhavi Sastry, Matvey Adzhigirey, Tyler Day … (2013)

Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.

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PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions.

Mats H. M. Olsson, Chresten Søndergaard, Michal Rostkowski … (2011)

In this study, we have revised the rules and parameters for one of the most commonly used empirical pKa predictors, PROPKA, based on better physical description of the desolvation and dielectric response for the protein. We have introduced a new and consistent approach to interpolate the description between the previously distinct classifications into internal and surface residues, which otherwise is found to give rise to an erratic and discontinuous behavior. Since the goal of this study is to lay out the framework and validate the concept, it focuses on Asp and Glu residues where the protein pKa values and structures are assumed to be more reliable. The new and improved implementation is evaluated and discussed; it is found to agree better with experiment than the previous implementation (in parentheses): rmsd = 0.79 (0.91) for Asp and Glu, 0.75 (0.97) for Tyr, 0.65 (0.72) for Lys, and 1.00 (1.37) for His residues. The most significant advance, however, is in reducing the number of outliers and removing unreasonable sensitivity to small structural changes that arise from classifying residues as either internal or surface.