β-glucosidases are enzymes that catalyze the hydrolysis of oligosaccharides and disaccharides, such as cellobiose. These enzymes play a key role in cellulose degrading, such as alleviating product inhibition of cellulases. Consequently, they have been considered essential for the biofuel industry. However, the majority of the characterized β-glucosidases is inhibited by glucose. Hence, glucose-tolerant β-glucosidases have been targeted to improve the production of second-generation biofuels. In this paper, we proceeded a systematic literature review (SLR), collected protein structures and constructed a database of glucose-tolerant β-glucosidases, called betagdb. SLR was performed at PubMed, ScienceDirect and Scopus Library databases and conducted according to PRISMA framework. It was conducted in five steps: i) analysis of duplications, ii) title reading, iii) abstract reading, iv) diagonal reading, and v) full-text reading. The second, third, fourth, and fifth steps were performed independently by two researchers. Besides, we performed bioinformatics analysis on the collected data, such as structural and multiple alignments to detect the most conserved residues in the catalytic pocket, and molecular docking to characterize essential residues for substrate recognizing, glucose tolerance, and the β-glucosidase activity. We selected 27 papers, 23 sequences, and 5 PDB files of glucose-tolerant β-glucosidases. We characterized 11 highly conserved residues: H121, W122, N166, E167, N297, Y299, E355, W402, E409, W410, and F418. The presence of these residues may be essential for β-glucosidases. We also discussed the importance of residues W169, C170, L174, H181, and T226. Furthermore, we proposed that the number of contacts for each residue in the catalytic pocket might be a metric that could be used to suggest mutations. We believe that the herein propositions, together with the sequence and structural data collection, might be helpful for effective engineering of β-glucosidases for biofuel production and may help to shed some light on the degradation of cellulosic biomass.

β-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, β-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 β-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 β-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 β-glucosidases and can be useful for the rational design of optimized enzymes for industrial interests.

Background Protein–peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein–peptide databases and tools that allow the retrieval, characterization and understanding of protein–peptide recognition and consequently support peptide design. Results We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein–peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein–peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation. Conclusions Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein–peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia

With the use of genetic engineering, modified and sometimes more efficient enzymes can be created for different purposes, including industrial applications. However, building modified enzymes depends on several in vitro experiments, which may result in the process being expensive and time-consuming. Therefore, computational approaches could reduce costs and accelerate the discovery of new technological products. In this study, we present a method, called structural signature variation (SSV), to propose mutations for improving enzymes’ activity. SSV uses the structural signature variation between target enzymes and template enzymes (obtained from the literature) to determine if randomly suggested mutations may provide some benefit for an enzyme, such as improvement of catalytic activity, half-life, and thermostability, or resistance to inhibition. To evaluate SSV, we carried out a case study that suggested mutations in β-glucosidases: Essential enzymes used in biofuel production that suffer inhibition by their product. We collected 27 mutations described in the literature, and manually classified them as beneficial or not. SSV was able to classify the mutations with values of 0.89 and 0.92 for precision and specificity, respectively. Then, we used SSV to propose mutations for Bgl1B, a low-performance β-glucosidase. We detected 15 mutations that could be beneficial. Three of these mutations (H228C, H228T, and H228V) have been related in the literature to the mechanism of glucose tolerance and stimulation in GH1 β-glucosidase. Hence, SSV was capable of detecting promising mutations, already validated by in vitro experiments, that improved the inhibition resistance of a β-glucosidase and, consequently, its catalytic activity. SSV might be useful for the engineering of enzymes used in biofuel production or other industrial applications.

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