Cellulose degradation by polysaccharide monooxygenases.

Polysaccharide monooxygenases (PMOs), also known as lytic PMOs (LPMOs), enhance the depolymerization of recalcitrant polysaccharides by hydrolytic enzymes and are found in the majority of cellulolytic fungi and actinomycete bacteria. For more than a decade, PMOs were incorrectly annotated as family 61 glycoside hydrolases (GH61s) or family 33 carbohydrate-binding modules (CBM33s). PMOs have an unusual surface-exposed active site with a tightly bound Cu(II) ion that catalyzes the regioselective hydroxylation of crystalline cellulose, leading to glycosidic bond cleavage. The genomes of some cellulolytic fungi contain more than 20 genes encoding cellulose-active PMOs, suggesting a diversity of biological activities. PMOs show great promise in reducing the cost of conversion of lignocellulosic biomass to fermentable sugars; however, many questions remain about their reaction mechanism and biological function. This review addresses, in depth, the structural and mechanistic aspects of oxidative depolymerization of cellulose by PMOs and considers their biological function and phylogenetic diversity.

Morphology of a Transmembrane Aß42 Tetramer via REMD Simulations.

The folding/misfolding of membrane-permiable Amyloid beta (Aß) peptides is likely associated with the advancing stage of Alzheimer's disease (AD) by disrupting Ca2+ homeostasis. In this context, the aggregation of four transmembrane Aß17-42 peptides was investigated using temperature replica-exchange molecular dynamics (REMD) simulations. The obtained results indicated that the secondary structure of transmembrane Aß peptides tends to have different propensities compared to those in solution. Interestingly, the residues favorably forming ß-structure were interleaved by residues rigidly adopting turn-structure. A combination of ß and turn regions likely forms a pore structure. Six morphologies of 4Aß were found over the free energy landscape and clustering analyses. Among these, the morphologies include (1) Aß binding onto the membrane surface and three transmembrane Aß; (2) three helical and coil transmembrane Aß; (3) four helical transmembrane Aß; (4) three helical and one ß-hairpin transmembrane Aß; (5) two helical and two ß-strand transmembrane Aß; and (6) three ß-strand and one helical transmembrane Aß. Although the formation of the ß-barrel structure was not observed during the 0.28 ms─long MD simulation, the structure is likely to form when the simulation time is further extended.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches.

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Substrate selectivity in starch polysaccharide monooxygenases.

Degradation of polysaccharides is central to numerous biological and industrial processes. Starch-active polysaccharide monooxygenases (AA13 PMOs) oxidatively degrade starch and can potentially be used with industrial amylases to convert starch into a fermentable carbohydrate. The oxidative activities of the starch-active PMOs from the fungi Neurospora crassa and Myceliophthora thermophila, NcAA13 and MtAA13, respectively, on three different starch substrates are reported here. Using high-performance anion-exchange chromatography coupled with pulsed amperometry detection, we observed that both enzymes have significantly higher oxidative activity on amylose than on amylopectin and cornstarch. Analysis of the product distribution revealed that NcAA13 and MtAA13 more frequently oxidize glycosidic linkages separated by multiples of a helical turn consisting of six glucose units on the same amylose helix. Docking studies identified important residues that are involved in amylose binding and suggest that the shallow groove that spans the active-site surface of AA13 PMOs favors the binding of helical amylose substrates over nonhelical substrates. Truncations of NcAA13 that removed its native carbohydrate-binding module resulted in diminished binding to amylose, but truncated NcAA13 still favored amylose oxidation over other starch substrates. These findings establish that AA13 PMOs preferentially bind and oxidize the helical starch substrate amylose. Moreover, the product distributions of these two enzymes suggest a unique interaction with starch substrates.

Oversampling Free Energy Perturbation Simulation in Determination of the Ligand-Binding Free Energy.

Determination of the ligand-binding affinity is an extremely interesting problem. Normally, the free energy perturbation (FEP) method provides an appropriate result. However, it is of great interest to improve the accuracy and precision of this method. In this context, temperature replica exchange molecular dynamics implementation of the FEP computational approach, which we call replica exchange free energy perturbation (REP) was proposed. In particular, during REP simulations, the system can easily escape from being trapped in local minima by exchanging configurations with high temperatures, resulting in significant improvement in the accuracy and precision of protein-ligand binding affinity calculations. The distribution of the decoupling free energy was enlarged, and its mean values were decreased. This results in changes in the magnitude of the calculated binding free energies as well as in alteration in the binding mechanism. Moreover, the REP correlation coefficient with respect to experiment ( RREP = 0.85 ± 0.15) is significantly boosted in comparison with the FEP one ( RFEP = 0.64 ± 0.30). Furthermore, the root-mean-square error (RMSE) of REP is also smaller than FEP, RMSEREP = 4.28 ± 0.69 versus RMSEFEP = 5.80 ± 1.11 kcal/mol, respectively. © 2019 Wiley Periodicals, Inc.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease.

The novel coronavirus (SARS-CoV-2) has infected several million people and caused thousands of deaths worldwide since December 2019. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. Therefore, in this context, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro, and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a database of ∼4600 natural compounds, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including three natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit a large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals interaction. We also found that Glu166 forms H-bonds to all of the inhibitors. Replacing Glu166 by an alanine residue leads to ∼2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potential drugs inhibiting SARS-CoV-2.

Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity.

The binding pose and affinity between a ligand and enzyme are very important pieces of information for computer-aided drug design. In the initial stage of a drug discovery project, this information is often obtained by using molecular docking methods. Autodock4 and Autodock Vina are two commonly used open-source and free software tools to perform this task, and each has been cited more than 6000 times in the last ten years. It is of great interest to compare the success rate of the two docking software programs for a large and diverse set of protein-ligand complexes. In this study, we selected 800 protein-ligand complexes for which both PDB structures and experimental binding affinity are available. Docking calculations were performed for these complexes using both Autodock4 and Autodock Vina with different docking options related to computing resource consumption and accuracy. Our calculation results are in good agreement with a previous study that the Vina approach converges much faster than AD4 one. However, interestingly, AD4 shows a better performance than Vina over 21 considered targets, whereas the Vina protocol is better than the AD4 package for 10 other targets. There are 16 complexes for which both the AD4 and Vina protocols fail to produce a reasonable correlation with respected experiments so both are not suitable to use to estimate binding free energies for these cases. In addition, the best docking option for performing the AD4 approach is the long option. However, the short option is the best solution for carrying out Vina docking. The obtained results probably will be useful for future docking studies in deciding which program to use.

Impact of the Astaxanthin, Betanin, and EGCG Compounds on Small Oligomers of Amyloid Aß40 Peptide.

There is experimental evidence that the astaxanthin, betanin, and epigallocatechin-3-gallate (EGCG) compounds slow down the aggregation kinetics and the toxicity of the amyloid-ß (Aß) peptide. How these inhibitors affect the self-assembly at the atomic level remains elusive. To address this issue, we have performed for each ligand atomistic replica exchange molecular dynamic (REMD) simulations in an explicit solvent of the Aß11-40 trimer from the U-shape conformation and MD simulations starting from Aß1-40 dimer and tetramer structures characterized by different intra- and interpeptide conformations. We find that the three ligands have similar binding free energies on small Aß40 oligomers but very distinct transient binding sites that will affect the aggregation of larger assemblies and fibril elongation of the Aß40 peptide.

Starch-degrading polysaccharide monooxygenases.

Polysaccharide degradation by hydrolytic enzymes glycoside hydrolases (GHs) is well known. More recently, polysaccharide monooxygenases (PMOs, also known as lytic PMOs or LPMOs) were found to oxidatively degrade various polysaccharides via a copper-dependent hydroxylation. PMOs were previously thought to be either GHs or carbohydrate binding modules (CBMs), and have been re-classified in carbohydrate active enzymes (CAZY) database as auxiliary activity (AA) families. These enzymes include cellulose-active fungal PMOs (AA9, formerly GH61), chitin- and cellulose-active bacterial PMOs (AA10, formerly CBM33), and chitin-active fungal PMOs (AA11). These PMOs significantly boost the activity of GHs under industrially relevant conditions, and thus have great potential in the biomass-based biofuel industry. PMOs that act on starch are the latest PMOs discovered (AA13), which has expanded our perspectives in PMOs studies and starch degradation. Starch-active PMOs have many common structural features and biochemical properties of the PMO superfamily, yet differ from other PMO families in several important aspects. These differences likely correlate, at least in part, to the differences in primary and higher order structures of starch and cellulose, and chitin. In this review we will discuss the discovery, structural features, biochemical and biophysical properties, and possible biological functions of starch-active PMOs, as well as their potential application in the biofuel, food, and other starch-based industries. Important questions regarding various aspects of starch-active PMOs and possible economical driving force for their future studies will also be highlighted.

A family of starch-active polysaccharide monooxygenases.

The recently discovered fungal and bacterial polysaccharide monooxygenases (PMOs) are capable of oxidatively cleaving chitin, cellulose, and hemicelluloses that contain ß(1→4) linkages between glucose or substituted glucose units. They are also known collectively as lytic PMOs, or LPMOs, and individually as AA9 (formerly GH61), AA10 (formerly CBM33), and AA11 enzymes. PMOs share several conserved features, including a monocopper center coordinated by a bidentate N-terminal histidine residue and another histidine ligand. A bioinformatic analysis using these conserved features suggested several potential new PMO families in the fungus Neurospora crassa that are likely to be active on novel substrates. Herein, we report on NCU08746 that contains a C-terminal starch-binding domain and an N-terminal domain of previously unknown function. Biochemical studies showed that NCU08746 requires copper, oxygen, and a source of electrons to oxidize the C1 position of glycosidic bonds in starch substrates, but not in cellulose or chitin. Starch contains α(1→4) and α(1→6) linkages and exhibits higher order structures compared with chitin and cellulose. Cellobiose dehydrogenase, the biological redox partner of cellulose-active PMOs, can serve as the electron donor for NCU08746. NCU08746 contains one copper atom per protein molecule, which is likely coordinated by two histidine ligands as shown by X-ray absorption spectroscopy and sequence analysis. Results indicate that NCU08746 and homologs are starch-active PMOs, supporting the existence of a PMO superfamily with a much broader range of substrates. Starch-active PMOs provide an expanded perspective on studies of starch metabolism and may have potential in the food and starch-based biofuel industries.

X-ray absorption spectroscopic characterization of the diferric-peroxo intermediate of human deoxyhypusine hydroxylase in the presence of its substrate eIF5a.

Human deoxyhypusine hydroxylase (hDOHH) is an enzyme that is involved in the critical post-translational modification of the eukaryotic translation initiation factor 5A (eIF5A). Following the conversion of a lysine residue on eIF5A to deoxyhypusine (Dhp) by deoxyhypusine synthase, hDOHH hydroxylates Dhp to yield the unusual amino acid residue hypusine (Hpu), a modification that is essential for eIF5A to promote peptide synthesis at the ribosome, among other functions. Purification of hDOHH overexpressed in E. coli affords enzyme that is blue in color, a feature that has been associated with the presence of a peroxo-bridged diiron(III) active site. To gain further insight into the nature of the diiron site and how it may change as hDOHH goes through the catalytic cycle, we have conducted X-ray absorption spectroscopic studies of hDOHH on five samples that represent different species along its reaction pathway. Structural analysis of each species has been carried out, starting with the reduced diferrous state, proceeding through its O2 adduct, and ending with a diferric decay product. Our results show that the Fe⋯Fe distances found for the five samples fall within a narrow range of 3.4-3.5 Å, suggesting that hDOHH has a fairly constrained active site. This pattern differs significantly from what has been associated with canonical dioxygen activating nonheme diiron enzymes, such as soluble methane monooxygenase and Class 1A ribonucleotide reductases, for which the Fe⋯Fe distance can change by as much as 1 Å during the redox cycle. These results suggest that the O2 activation mechanism for hDOHH deviates somewhat from that associated with the canonical nonheme diiron enzymes, opening the door to new mechanistic possibilities for this intriguing family of enzymes.

An unusual peroxo intermediate of the arylamine oxygenase of the chloramphenicol biosynthetic pathway.

Streptomyces venezuelae CmlI catalyzes the six-electron oxygenation of the arylamine precursor of chloramphenicol in a nonribosomal peptide synthetase (NRPS)-based pathway to yield the nitroaryl group of the antibiotic. Optical, EPR, and Mössbauer studies show that the enzyme contains a nonheme dinuclear iron cluster. Addition of O(2) to the diferrous state of the cluster results in an exceptionally long-lived intermediate (t(1/2) = 3 h at 4 °C) that is assigned as a peroxodiferric species (CmlI-peroxo) based upon the observation of an (18)O(2)-sensitive resonance Raman (rR) vibration. CmlI-peroxo is spectroscopically distinct from the well characterized and commonly observed cis-µ-1,2-peroxo (µ-η(1):η(1)) intermediates of nonheme diiron enzymes. Specifically, it exhibits a blue-shifted broad absorption band around 500 nm and a rR spectrum with a ν(O-O) that is at least 60 cm(-1) lower in energy. Mössbauer studies of the peroxo state reveal a diferric cluster having iron sites with small quadrupole splittings and distinct isomer shifts (0.54 and 0.62 mm/s). Taken together, the spectroscopic comparisons clearly indicate that CmlI-peroxo does not have a µ-η(1):η(1)-peroxo ligand; we propose that a µ-η(1):η(2)-peroxo ligand accounts for its distinct spectroscopic properties. CmlI-peroxo reacts with a range of arylamine substrates by an apparent second-order process, indicating that CmlI-peroxo is the reactive species of the catalytic cycle. Efficient production of chloramphenicol from the free arylamine precursor suggests that CmlI catalyzes the ultimate step in the biosynthetic pathway and that the precursor is not bound to the NRPS during this step.

Determinants of regioselective hydroxylation in the fungal polysaccharide monooxygenases.

The ubiquitous fungal polysaccharide monooxygenases (PMOs) (also known as GH61 proteins, LPMOs, and AA9 proteins) are structurally related but have significant variation in sequence. A heterologous expression method in Neurospora crassa was developed as a step toward connecting regioselectivity of the chemistry to PMO phylogeny. Activity assays, as well as sequence and phylogenetic analyses, showed that the majority of fungal PMOs fall into three major groups with distinctive active site surface features. PMO1s and PMO2s hydroxylate glycosidic positions C1 and C4, respectively. PMO3s hydroxylate both C1 and C4. A subgroup of PMO3s (PMO3*) hydroxylate C1. Mutagenesis studies showed that an extra subdomain of about 12 amino acids contribute to C4 oxidation in the PMO3 family.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations.

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Human deoxyhypusine hydroxylase, an enzyme involved in regulating cell growth, activates O2 with a nonheme diiron center.

Deoxyhypusine hydroxylase is the key enzyme in the biosynthesis of hypusine containing eukaryotic translation initiation factor 5A (eIF5A), which plays an essential role in the regulation of cell proliferation. Recombinant human deoxyhypusine hydroxylase (hDOHH) has been reported to have oxygen- and iron-dependent activity, an estimated iron/holoprotein stoichiometry of 2, and a visible band at 630 nm responsible for the blue color of the as-isolated protein. EPR, Mössbauer, and XAS spectroscopic results presented herein provide direct spectroscopic evidence that hDOHH has an antiferromagnetically coupled diiron center with histidines and carboxylates as likely ligands, as suggested by mutagenesis experiments. Resonance Raman experiments show that its blue chromophore arises from a (mu-1,2-peroxo)diiron(III) center that forms in the reaction of the reduced enzyme with O2, so the peroxo form of hDOHH is unusually stable. Nevertheless we demonstrate that it can carry out the hydroxylation of the deoxyhypusine residue present in the elF5A substrate. Despite a lack of sequence similarity, hDOHH has a nonheme diiron active site that resembles both in structure and function those found in methane and toluene monooxygenases, bacterial and mammalian ribonucleotide reductases, and stearoyl acyl carrier protein Delta9-desaturase from plants, suggesting that the oxygen-activating diiron motif is a solution arrived at by convergent evolution. Notably, hDOHH is the only example thus far of a human hydroxylase with such a diiron active site.

Correction: Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

[This corrects the article DOI: 10.1039/D0RA06212J.].

Distal Hydrophobic Loop Modulates the Copper Active Site and Reaction of AA13 Polysaccharide Monooxygenases.

Polysaccharide monooxygenases (PMOs) use a type-2 copper center to activate O2 for the selective hydroxylation of one of the two C-H bonds of glycosidic linkages. Our electron paramagnetic resonance (EPR) analysis and molecular dynamics (MD) simulations suggest the unprecedented dynamic roles of the loop containing the residue G89 (G89 loop) on the active site structure and reaction cycle of starch-active PMOs (AA13 PMOs). In the Cu(II) state, the G89 loop could switch between an "open" and "closed" conformation, which is associated with the binding and dissociation of an aqueous ligand in the distal site, respectively. The conformation of the G89 loop influences the positioning of the copper center on the preferred substrate of AA13 PMOs. The dissociation of the distal ligand results in the bending of the T-shaped core of the Cu(II) active site, which could help facilitate its reduction to the active Cu(I) state. In the Cu(I) state, the G89 loop is in the "closed" conformation with a confined copper center, which could allow for efficient O2 binding. In addition, the G89 loop remains in the "closed" conformation in the Cu(II)-superoxo intermediate, which could prevent off-pathway superoxide release via exchange with the distal aqueous ligand. Finally, at the end of the reaction cycle, aqueous ligand binding to the distal site could switch the G89 loop to the "open" conformation and facilitate product release.

Active-site structure of a ß-hydroxylase in antibiotic biosynthesis.

X-ray absorption and resonance Raman spectroscopies show that CmlA, the ß-hydroxylase of the chloramphenicol biosynthetic pathway, contains a (µ-oxo)-(µ-1,3-carboxylato)diiron(III) cluster with 6-coordinate iron centers and 3 - 4 His ligands. This active site is found within a unique ß-lactamase fold and is distinct from those of soluble methane monooxygenase and related enzymes that utilize a highly conserved diiron cluster with a 2-His-4-carboxylate ligand set within a 4-helix bundle motif. These structural differences may have an impact on the nature of the activated oxygen species of the reaction cycle.

Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro.

SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 24 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one.