Substrates associate and products dissociate from enzyme catalytic sites rapidly, which hampers investigations of their trajectories. The high-resolution structure of the native Hordeum exo-hydrolase HvExoI isolated from seedlings reveals that non-covalently trapped glucose forms a stable enzyme-product complex. Here, we report that the alkyl β- d -glucoside and methyl 6-thio-β-gentiobioside substrate analogues perfused in crystalline HvExoI bind across the catalytic site after they displace glucose, while methyl 2-thio-β-sophoroside attaches nearby. Structural analyses and multi-scale molecular modelling of nanoscale reactant movements in HvExoI reveal that upon productive binding of incoming substrates, the glucose product modifies its binding patterns and evokes the formation of a transient lateral cavity, which serves as a conduit for glucose departure to allow for the next catalytic round. This path enables substrate-product assisted processive catalysis through multiple hydrolytic events without HvExoI losing contact with oligo- or polymeric substrates. We anticipate that such enzyme plasticity could be prevalent among exo-hydrolases.

Artificial metalloenzymes (ArMs) result from anchoring an organometallic catalyst within an evolvable protein scaffold. Thanks to its dimer of dimers quaternary structure, streptavidin allows the precise positioning of two metal cofactors to activate a single substrate, thus expanding the reaction scope accessible to ArMs. To validate this concept, we report herein on our efforts to engineer and evolve an artificial hydroaminase based on dual-gold activation of alkynes. Guided by modelling, we designed a chimeric streptavidin equipped with a hydrophobic lid shielding its active site which enforces the advantageous positioning of two synergistic biotinylated gold cofactors. Three rounds of directed evolution using E. coli cellfree extracts led to the identification of mutants favouring either the anti-Markovnikov product (an indole carboxamide with 96% regioselectivity, 51 TONs) resulting from a dual gold activation of an ethynylphenylurea substrate or the Markovnikov product (a phenyldihydroquinazolinone with 99% regioselectivity, 333 TONs) resulting from the -activation of the alkyne by gold. genetic means. Thus far, more than 40 reactions can be catalysed by ArMs. 25 Current challenges in the field include; protein-accelerated catalysis, whereby a pre-catalyst is activated upon incorporation within the host protein, 26 dual catalysis 27,28 and compatibility of the ArM with a cytosolic environment. 29 Privileged scaffolds for ArMs include: carbonic anhydrase, 30 hemoproteins, 31,32 prolyl oligopeptidase, 33 lactococcal multiresistance regulator, 23 four helix-bundles, 34,35 nitrobindin, 36 human serum albumin, 37 and (strept)avidin. 20,[38][39][40] The work presented herein capitalizes on the unique topology of Sav enabling the localization of two close-lying biotinylated probes within a hydrophobic environment. This enabled the engineering and evolution of a biocompatible artificial hydroaminase (HAMase hereafter) based on either single-or dual-gold activation of an alkyne, Figure 1. Results Design of the artificial hydroaminaseAs reported by Asensio 5,41 and van der Vlugt 42 , the regioselectivity for the hydroamination of ethynylurea 1 is by-and-large governed by the mode of activation of the alkyne by gold: the canonical -activation favours the quinazolinone 3 (Markovnikov, 6-exo-dig addition product), while the dual -gold activation affords preferentially the indole 2 (anti-Markovnikov, 5endo-dig addition product) 5,42,43 Upon -coordination of the alkyne to gold, the pKa of the terminal C-H bond is lowered, thus favouring its deprotonation and coordination by a second gold to afford the -activation mode. 41 Accordingly, the spatial arrangement of the two gold species is critical in determining the regioselectivity of the reaction. We thus selected the gold-catalyzed cyclization of the ethynylurea 1 to engineer and evolve a dual-gold catalysed hydroaminase (HAMase) based on the biotin-streptavidin technology.Thanks to its dimer of dimers quaternary structure, which places the valeric acid side ch...

COVID-19 is a disease caused by severe acute respiratory syndrome coronavirus 2. Presently, there is no effective treatment for COVID-19. As part of the worldwide efforts to find efficient therapies and preventions, it has been reported the crystalline structure of the SARS-CoV-2 main protease Mpro (also called 3CLpro) bound to a synthetic inhibitor, which represents a major druggable target. The druggability of Mpro could be used for discovering drugs to treat COVID-19. A multilevel computational study was carried out to evaluate the potential antiviral properties of the components of the medicinal herb Uncaria tomentosa (Cat’s claw), focusing on the inhibition of Mpro. The in silico approach starts with protein-ligand docking of 26 Cat’s claw key components, followed by ligand pathway calculations, molecular dynamics simulations, and MM-GBSA calculation of the free energy of binding for the best docked candidates. The structural bioinformatics approaches led to identification of three bioactive compounds of Uncaria tomentosa (speciophylline, cadambine, and proanthocyanidin B2) with potential therapeutic effects by strong interaction with 3CLpro. Additionally, in silico drug-likeness indices for these components were calculated and showed good predicted therapeutic profiles of these phytochemicals. Our findings suggest the potential effectiveness of Cat’s claw as complementary and/or alternative medicine for COVID-19 treatment.

With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates.These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis.Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. File list (2)download file view on ChemRxiv BioMetAll.pdf (20.92 MiB) download file view on ChemRxiv BioMetAll_SI.pdf (5.62 MiB)

<div><div><div><p>With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding.</p></div></div></div>