Glycosylation and charge distribution orchestrates the conformational ensembles of a biotechnologically promissory phytase in different pHs - a computational study.

Phytases [myo-inositol(1,2,3,4,5,6) hexakisphosphate phosphohydrolases] are phytate-specific phosphatases not present in monogastric animals. Nevertheless, they are an essential supplement to feeding such animals and for human special diets. It is crucial, hence, the biotechnological use of phytases with intrinsic stability and activity at the acid pHs from gastric environments. Here we use Metadynamics (METADY) simulations to probe the conformational space of the Aspergillus nidulans phytase and the differential effects of pH and glycosylation in this same space. The results suggest that strategic combinations of pH and glycosylation affect the stability of native-like conformations and alternate these structures from a metastable to a stable profile. Furthermore, the protein segments previously reported as more thermosensitive in phytases from this family present a pivotal role in the conformational changes at different conditions, especially H2, H5-7, L8, L10, L12, and L17. Also, the glycosylations and the pH-dependent charge balance modulate the mobility and interactions at these same regions, with consequences for the surface solvation and active site exposition. Finally, although the glycosylations have stabilized the native structure and improved the substrate docking at all the studied pHs, the data suggest a higher phytate receptivity at catalytic poses for the unglycosylated structure at pH 6.5 and the glycosylated one at pH 4.5. This behavior agrees with the exact change in optimum pH reported for this enzyme, expressed on low or high glycosylating systems. We hope the results and insights presented here will be helpful in future approaches for rational engineering of technologically promising phytases and intelligent planning of their heterologous expression systems and conditions for use.Communicated by Ramaswamy H. Sarma.

Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics.

The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds. To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase (PDB 4B3P), 5-HT2A serotonin receptor (PDB 6A94), and H1 histamine receptor (PDB 3RZE). The Enrichment Factor for each target was, respectively, 102.5, 12.4, 10.6, which are considered significant values. Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-2 Mpro. The best scored poses were the input for MM-PBSA calculations and metadynamics using CHARMM and AMBER force fields to predict the binding energy for each complex. Our work points out six molecules, highlighting the strong interaction obtained for Mpro-mirabegron complex. Among these six, to the best of our knowledge, ambenonium has not yet been described in the literature as a candidate inhibitor for the SARS-COV-2 main protease in its active pocket.

Conformational flexibility correlates with glucose tolerance for point mutations in ß-glucosidases - a computational study.

ß-glucosidases (EC 3.2.1.21) have been described as essential to second-generation biofuel production. They act in the last step of the lignocellulosic saccharification, cleaving the ß - 1,4 glycosidic bonds in cellobiose to produce two molecules of glucose. However, ß-glucosidases have been described as strongly inhibited by glucose, causing an increment of cellobiose concentration. Also, cellobiose is an inhibitor of other enzymes used in this process, such as exoglucanases and endoglucanases. Hence, the engineering of thermostable and glucose-tolerant ß-glucosidases has been targeted by many studies. In this study, we performed high sampling accelerated molecular dynamics for a wild glucose-tolerant GH1 ß-glucosidase (Bgl1A), a wild non-tolerant (Bgl1B), and a set of glucose-tolerant Bgl1B's mutants: V302F, N301Q/V302F, F172I, V227M, G246S, T299S, and H228T. Our results suggest that point mutations promissory to induce glucose tolerance trend to enhance the mobility of the flexible loops around the active site. Mutations affected B and C loops regions, and an αß-hairpin motif between them. Conformational clusters and free energy landscape profiles suggest that the mobility acquired by mutants allows a higher closure of the substrate channel. This closure is compatible with a higher impedance for glucose entrance and stimulus of its withdrawal. Based on mutants' structural analyses, we inferred that both the direct stereochemical effect on the glucose path and the changes in the mobility affect glucose tolerance. We hope these results be useful for the rational design of glucose-tolerant and industrially promising enzymes.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics Gives New Insights into the Glucose Tolerance and Inhibition Mechanisms on ß-Glucosidases.

ß-Glucosidases are enzymes with high importance for many industrial processes, catalyzing the last and limiting step of the conversion of lignocellulosic material into fermentable sugars for biofuel production. However, ß-glucosidases are inhibited by high concentrations of the product (glucose), which limits the biofuel production on an industrial scale. For this reason, the structural mechanisms of tolerance to product inhibition have been the target of several studies. In this study, we performed in silico experiments, such as molecular dynamics (MD) simulations, free energy landscape (FEL) estimate, Poisson-Boltzmann surface area (PBSA), and grid inhomogeneous solvation theory (GIST) seeking a better understanding of the glucose tolerance and inhibition mechanisms of a representative GH1 ß-glucosidase and a GH3 one. Our results suggest that the hydrophobic residues Y180, W350, and F349, as well the polar one D238 act in a mechanism for glucose releasing, herein called "slingshot mechanism", dependent also on an allosteric channel (AC). In addition, water activity modulation and the protein loop motions suggest that GH1 ß-Glucosidases present an active site more adapted to glucose withdrawal than GH3, in consonance with the GH1s lower product inhibition. The results presented here provide directions on the understanding of the molecular mechanisms governing inhibition and tolerance to the product in ß-glucosidases and can be useful for the rational design of optimized enzymes for industrial interests.