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.

We wish to evaluate the algorithm Milk-Way, using a known dataset deposited in a public repository. The new algorithm, which converges various techniques from different areas of knowledge, can classify ligands and select potential new drugs. It was used a dataset of ligands, organized by 15 Bioassays and described by different fingerprints. Full details of the dataset architecture were already published in a public repository. Through the stratified feature selection, using the Milk-Way algorithm, the True Positive and False Positive Rates reached a higher performance compared to the published paper. Using all the features available for each Bioassay, we reached the lowest metrics in all of them. We demonstrated that adding more features have not made a significant impact on the performance. In all the Bioassays, the True Positives and False Positives reached 100% and 0%, respectively, only using 50% and 75% of the features available. The Milk-Way algorithm suggests a holistic approach, which will contribute to the machine-learning area, namely to classified ligands in the virtual screening.

No abstract