Substrate stacking interactions in aryl-alcohol oxidase

This is the peer reviewed version of the following article: [Ferreira, P., Hernández-Ortega, A., Lucas, F., Carro, J., Herguedas, B., Borrelli, K. W., Guallar, V., Martínez, A. T. and Medina, M. (2015), Aromatic stacking interactions govern catalysis in aryl-alcohol oxidase. FEBS J, 282: 3091–3106. doi:10.1111/febs.13221], which has been published in final form at [10.1111/febs.13221]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." http://olabout.wiley.com/WileyCDA/Section/id-820227.html The version posted may not be updated or replaced with the final published version (the Version of Record).Aryl-alcohol oxidase (AAO, EC 1.1.3.7) generates H2O2 for lignin degradation at the expense of benzylic and other π system-containing primary alcohols, which are oxidized to the corresponding aldehydes. Ligand diffusion studies on Pleurotus eryngii AAO showed a T-shaped stacking interaction between the Tyr92 side chain and the alcohol substrate at the catalytically competent position for concerted hydride and proton transfers. Bi-substrate kinetics analysis revealed that reactions with 3-chloro- or 3-fluorobenzyl alcohols (halogen substituents) proceed via a ping–pong mechanism. However, mono- and dimethoxylated substituents (in 4-methoxybenzyl and 3,4-dimethoxybenzyl alcohols) altered the mechanism and a ternary complex was formed. Electron-withdrawing substituents resulted in lower quantum mechanics stacking energies between aldehyde and the tyrosine side chain, contributing to product release, in agreement with the ping–pong mechanism observed in 3-chloro- and 3-fluorobenzyl alcohol kinetics analysis. In contrast, the higher stacking energies when electron donor substituents are present result in reaction of O2 with the flavin through a ternary complex, in agreement with the kinetics of methoxylated alcohols. The contribution of Tyr92 to the AAO reaction mechanism was investigated by calculation of stacking interaction energies and site-directed mutagenesis. Replacement of Tyr92 by phenylalanine does not alter the AAO kinetic constants (on 4-methoxybenzyl alcohol), most probably because the stacking interaction is still possible. However, introduction of a tryptophan residue at this position strongly reduced the affinity for the substrate (i.e. the pre-steady state Kd and steady-state Km increase by 150-fold and 75-fold, respectively), and therefore the steady-state catalytic efficiency, suggesting that proper stacking is impossible with this bulky residue. The above results confirm the role of Tyr92 in substrate binding, thus governing the kinetic mechanism in AAO.This work was supported by the BIO2013-42978-P (to MM), BIO2011-26694 (to ATM), “Juan de la Cierva” (to FL) and CTQ2010-18123 (to VG) Grants of the Spanish Ministry of Economy and Competitiveness (MINECO) and by the INDOX (KBBE-2013-7-613549, to ATM) and PELE (ERC-2009-Adg 25027, to VG) European projects.Peer ReviewedPostprint (author's final draft

Camouflaging in a Complex Environment—Octopuses Use Specific Features of Their Surroundings for Background Matching

Living under intense predation pressure, octopuses evolved an effective and impressive camouflaging ability that exploits features of their surroundings to enable them to “blend in.” To achieve such background matching, an animal may use general resemblance and reproduce characteristics of its entire surroundings, or it may imitate a specific object in its immediate environment. Using image analysis algorithms, we examined correlations between octopuses and their backgrounds. Field experiments show that when camouflaging, Octopus cyanea and O. vulgaris base their body patterns on selected features of nearby objects rather than attempting to match a large field of view. Such an approach enables the octopus to camouflage in partly occluded environments and to solve the problem of differences in appearance as a function of the viewing inclination of the observer

Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality.

With the advent of the resolution revolution in cryoelectron microscopy (cryo-EM), low-resolution refinement is common, and likewise increases the need for a reliable force field. Here, we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the structure determination package Phenix. Our results show significantly improved structure quality and reduced ligand strain at lower resolution for X-ray refinement. For refinement of cryo-EM-based structures, we find comparable quality structures, goodness-of-fit, and reduced ligand strain. We also show how structure quality and ligand strain are related to the map-model cross-correlation as a function of data weight, and how that can detect overfitting. Signs of overfitting are found in over half of our cryo-EM dataset, which can be remedied by a re-refinement at a lower data weight. Finally, a start-to-end script for refining structures with Phenix/OPLS3e is available in the Schrödinger 2020-3 distribution

Structure-Based Virtual Screening Approach for Discovery of Covalently Bound Ligands

We present a fast and effective covalent docking approach suitable for large-scale virtual screening (VS). We applied this method to four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with known crystal structures and known covalent inhibitors. We implemented a customized “VS mode” of the Schrödinger Covalent Docking algorithm (CovDock), which we refer to as CovDock-VS. Known actives and target-specific sets of decoys were docked to selected X-ray structures, and poses were filtered based on noncovalent protein–ligand interactions known to be important for activity. We were able to retrieve 71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease, and EGFR within 5% of the decoy library, respectively. With the more challenging XPO1 target, where no specific interactions with the protein could be used for postprocessing of the docking results, we were able to retrieve 95% of the actives within 30% of the decoy library and achieved an early enrichment factor (EF1%) of 33. The poses of the known actives bound to existing crystal structures of 4 targets were predicted with an average RMSD of 1.9 Å. To the best of our knowledge, CovDock-VS is the first fully automated tool for efficient virtual screening of covalent inhibitors. Importantly, CovDock-VS can handle multiple chemical reactions within the same library, only requiring a generic SMARTS-based predefinition of the reaction. CovDock-VS provides a fast and accurate way of differentiating actives from decoys without significantly deteriorating the accuracy of the predicted poses for covalent protein–ligand complexes. Therefore, we propose CovDock-VS as an efficient structure-based virtual screening method for discovery of novel and diverse covalent ligands

Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a “best” single receptor identified from known compounds within the congeneric series