Interaction of DisBa01 peptide from Bothrops alternatus venom with BRAF melanoma receptors: Modeling and molecular docking.

Metastatic melanoma is highly aggressive and challenging, often leading to a grim prognosis. Its progression is swift, especially when mutations like BRAFV600E continuously activate pathways vital for cell growth and survival. Although several treatments target this mutation, resistance typically emerges over time. In recent decades, research has underscored the potential of snake venoms and peptides as bioactive substances for innovative drugs, including anti-coagulants, anti-microbial, and anti-cancer agents. Leveraging this knowledge, we propose employing a bioinformatics simulation approach to: a) Predict how well a peptide (DisBa01) from Bothrops alternatus snake venom binds to the melanoma receptor BRAFV600E via Molecular Docking. b) Identify the specific peptide binding sites on receptors and analyze their proximity to active receptor sites. c) Evaluate the behavior of resulting complexes through molecular dynamics simulations. d) Assess whether this peptide qualifies as a candidate for anti-melanoma therapy. Our findings reveal that DisBa01 enhances stability in the BRAFV600E melanoma receptor structure by binding to its RGD motif, an interaction absent in the BRAF WT model. Consequently, both docking and molecular dynamics simulations suggest that DisBa01 shows promise as a BRAFV600E inhibitor.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein.

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

Know-how of holding a Bioinformatics competition: Structure, model, overview, and perspectives.

The article presents a framework for a Bioinformatics competition that focuses on 4 key aspects: structure, model, overview, and perspectives. Structure represents the organizational framework employed to coordinate the main tasks involved in the competition. Model showcases the competition design, which encompasses 3 phases. Overview presents our case study, the League of Brazilian Bioinformatics (LBB) 2nd Edition. Finally, the section on perspectives provides a brief discussion of the LBB 2nd Edition, along with insights and feedback from participants. LBB is a biannual team competition launched in 2019 to promote the ongoing training of human resources in Bioinformatics and Computational Biology in Brazil. LBB aims to stimulate ongoing training in Bioinformatics by encouraging participation in competitions, promoting the organization of future Bioinformatics competitions, and fostering the integration of the Bioinformatics and Computational Biology community in the country, as well as collaboration among participants. The LBB 2nd Edition was launched in 2021 and featured 251 competitors forming 91 teams. Knowledge competitions promote learning, collaboration, and innovation, which are crucial for advancing scientific knowledge and solving real-world problems. In summary, this article serves as a valuable resource for individuals and organizations interested in developing knowledge competitions, offering a model based on our experience with LBB to benefit all levels of Bioinformatics trainees.

Elucidating the molecular mechanisms of essential oils' insecticidal action using a novel cheminformatics protocol.

Essential oils (EOs) are a promising source for novel environmentally safe insecticides. However, the structural diversity of their compounds poses challenges to accurately elucidate their biological mechanisms of action. We present a new chemoinformatics methodology aimed at predicting the impact of essential oil (EO) compounds on the molecular targets of commercial insecticides. Our approach merges virtual screening, chemoinformatics, and machine learning to identify custom signatures and reference molecule clusters. By assigning a molecule to a cluster, we can determine its most likely interaction targets. Our findings reveal that the main targets of EOs are juvenile hormone-specific proteins (JHBP and MET) and octopamine receptor agonists (OctpRago). Three of the twenty clusters show strong similarities to the juvenile hormone, steroids, and biogenic amines. For instance, the methodology successfully identified E-Nerolidol, for which literature points indications of disrupting insect metamorphosis and neurochemistry, as a potential insecticide in these pathways. We validated the predictions through experimental bioassays, observing symptoms in blowflies that were consistent with the computational results. This new approach sheds a higher light on the ways of action of EO compounds in nature and biotechnology. It also opens new possibilities for understanding how molecules can interfere with biological systems and has broad implications for areas such as drug design.

Thermostabilizing mechanisms of canonical single amino acid substitutions at a GH1 ß-glucosidase probed by multiple MD and computational approaches.

ß-glucosidases play a pivotal role in second-generation biofuel (2G-biofuel) production. For this application, thermostable enzymes are essential due to the denaturing conditions on the bioreactors. Random amino acid substitutions have originated new thermostable ß-glucosidases, but without a clear understanding of their molecular mechanisms. Here, we probe by different molecular dynamics simulation approaches with distinct force fields and submitting the results to various computational analyses, the molecular bases of the thermostabilization of the Paenibacillus polymyxa GH1 ß-glucosidase by two-point mutations E96K (TR1) and M416I (TR2). Equilibrium molecular dynamic simulations (eMD) at different temperatures, principal component analysis (PCA), virtual docking, metadynamics (MetaDy), accelerated molecular dynamics (aMD), Poisson-Boltzmann surface analysis, grid inhomogeneous solvation theory and colony method estimation of conformational entropy allow to converge to the idea that the stabilization carried by both substitutions depend on different contributions of three classic mechanisms: (i) electrostatic surface stabilization; (ii) efficient isolation of the hydrophobic core from the solvent, with energetic advantages at the solvation cap; (iii) higher distribution of the protein dynamics at the mobile active site loops than at the protein core, with functional and entropic advantages. Mechanisms i and ii predominate for TR1, while in TR2, mechanism iii is dominant. Loop A integrity and loops A, C, D, and E dynamics play critical roles in such mechanisms. Comparison of the dynamic and topological changes observed between the thermostable mutants and the wildtype protein with amino acid co-evolutive networks and thermostabilizing hotspots from the literature allow inferring that the mechanisms here recovered can be related to the thermostability obtained by different substitutions along the whole family GH1. We hope the results and insights discussed here can be helpful for future rational approaches to the engineering of optimized ß-glucosidases for 2G-biofuel production for industry, biotechnology, and science.

Prioritizing Virtual Screening with Interpretable Interaction Fingerprints.

Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity FingerPrint (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP). LUNA also provides visual strategies to make the fingerprints interpretable. We performed three major experiments exploring the fingerprints' use. First, we trained machine learning models to reproduce DOCK3.7 scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61) superior to related molecular and interaction fingerprints. Second, we used LUNA to support interpretable machine learning models. Finally, we demonstrate that interaction fingerprints can accurately identify similarities across molecular complexes that other fingerprints overlook. Hence, we envision LUNA and its interface fingerprints as promising methods for machine learning-based virtual screening campaigns. LUNA is freely available at https://github.com/keiserlab/LUNA.

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.

E-Volve: understanding the impact of mutations in SARS-CoV-2 variants spike protein on antibodies and ACE2 affinity through patterns of chemical interactions at protein interfaces.

Background: The SARS-CoV-2 pandemic reverberated, posing health and social hygiene obstacles throughout the globe. Mutant lineages of the virus have concerned scientists because of convergent amino acid alterations, mainly on the viral spike protein. Studies have shown that mutants have diminished activity of neutralizing antibodies and enhanced affinity with its human cell receptor, the ACE2 protein. Methods: Hence, for real-time measuring of the impacts caused by variant strains in such complexes, we implemented E-Volve, a tool designed to model a structure with a list of mutations requested by users and return analyses of the variant protein. As a proof of concept, we scrutinized the spike-antibody and spike-ACE2 complexes formed in the variants of concern, B.1.1.7 (Alpha), B.1.351 (Beta), and P.1 (Gamma), by using contact maps depicting the interactions made amid them, along with heat maps to quantify these major interactions. Results: The results found in this study depict the highly frequent interface changes made by the entire set of mutations, mainly conducted by N501Y and E484K. In the spike-Antibody complex, we have noticed alterations concerning electrostatic surface complementarity, breaching essential sites in the P17 and BD-368-2 antibodies. Alongside, the spike-ACE2 complex has presented new hydrophobic bonds. Discussion: Molecular dynamics simulations followed by Poisson-Boltzmann calculations corroborate the higher complementarity to the receptor and lower to the antibodies for the K417T/E484K/N501Y (Gamma) mutant compared to the wild-type strain, as pointed by E-Volve, as well as an intensification of this effect by changes at the protein conformational equilibrium in solution. A local disorder of the loop α1'/ß1', as well its possible effects on the affinity to the BD-368-2 antibody were also incorporated to the final conclusions after this analysis. Moreover, E-Volve can depict the main alterations in important biological structures, as shown in the SARS-CoV-2 complexes, marking a major step in the real-time tracking of the virus mutant lineages. E-Volve is available at http://bioinfo.dcc.ufmg.br/evolve.

Propedia: a database for protein-peptide identification based on a hybrid clustering algorithm.

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.

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.

From In-Person to the Online World: Insights Into Organizing Events in Bioinformatics.

Bioinformatics is a fast-evolving research field, requiring effective educational initiatives to bring computational knowledge to Life Sciences. Since 2017, an organizing committee composed of graduate students and postdoctoral researchers from the Universidade Federal de Minas Gerais (Brazil) promotes a week-long event named Summer Course in Bioinformatics (CVBioinfo). This event aims to diffuse bioinformatic principles, news, and methods mainly focused on audiences of undergraduate students. Furthermore, as the advent of the COVID-19 global pandemic has precluded in-person events, we offered the event in online mode, using free video transmission platforms. Herein, we present and discuss the insights obtained from promoting the Online Workshop in Bioinformatics (WOB) organized in November 2020, comparing it to our experience in previous in-person editions of the same event.

VTR: A Web Tool for Identifying Analogous Contacts on Protein Structures and Their Complexes.

Evolutionarily related proteins can present similar structures but very dissimilar sequences. Hence, understanding the role of the inter-residues contacts for the protein structure has been the target of many studies. Contacts comprise non-covalent interactions, which are essential to stabilize macromolecular structures such as proteins. Here we show VTR, a new method for the detection of analogous contacts in protein pairs. The VTR web tool performs structural alignment between proteins and detects interactions that occur in similar regions. To evaluate our tool, we proposed three case studies: we 1) compared vertebrate myoglobin and truncated invertebrate hemoglobin; 2) analyzed interactions between the spike protein RBD of SARS-CoV-2 and the cell receptor ACE2; and 3) compared a glucose-tolerant and a non-tolerant ß-glucosidase enzyme used for biofuel production. The case studies demonstrate the potential of VTR for the understanding of functional similarities between distantly sequence-related proteins, as well as the exploration of important drug targets and rational design of enzymes for industrial applications. We envision VTR as a promising tool for understanding differences and similarities between homologous proteins with similar 3D structures but different sequences. VTR is available at http://bioinfo.dcc.ufmg.br/vtr.

GRaSP: a graph-based residue neighborhood strategy to predict binding sites.

MOTIVATION: The discovery of protein-ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein-ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. RESULTS: We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10-20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2-5 h on average. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/charles-abreu/GRaSP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Glutantßase: a database for improving the rational design of glucose-tolerant ß-glucosidases.

Β-glucosidases are key enzymes used in second-generation biofuel production. They act in the last step of the lignocellulose saccharification, converting cellobiose in glucose. However, most of the ß-glucosidases are inhibited by high glucose concentrations, which turns it a limiting step for industrial production. Thus, ß-glucosidases have been targeted by several studies aiming to understand the mechanism of glucose tolerance, pH and thermal resistance for constructing more efficient enzymes. In this paper, we present a database of ß-glucosidase structures, called Glutantßase. Our database includes 3842 GH1 ß-glucosidase sequences collected from UniProt. We modeled the sequences by comparison and predicted important features in the 3D-structure of each enzyme. Glutantßase provides information about catalytic and conserved amino acids, residues of the coevolution network, protein secondary structure, and residues located in the channel that guides to the active site. We also analyzed the impact of beneficial mutations reported in the literature, predicted in analogous positions, for similar enzymes. We suggested these mutations based on six previously described mutants that showed high catalytic activity, glucose tolerance, or thermostability (A404V, E96K, H184F, H228T, L441F, and V174C). Then, we used molecular docking to verify the impact of the suggested mutations in the affinity of protein and ligands (substrate and product). Our results suggest that only mutations based on the H228T mutant can reduce the affinity for glucose (product) and increase affinity for cellobiose (substrate), which indicates an increment in the resistance to product inhibition and agrees with computational and experimental results previously reported in the literature. More resistant ß-glucosidases are essential to saccharification in industrial applications. However, thermostable and glucose-tolerant ß-glucosidases are rare, and their glucose tolerance mechanisms appear to be related to multiple and complex factors. We gather here, a set of information, and made predictions aiming to provide a tool for supporting the rational design of more efficient ß-glucosidases. We hope that Glutantßase can help improve second-generation biofuel production. Glutantßase is available at http://bioinfo.dcc.ufmg.br/glutantbase .

Proteus: An algorithm for proposing stabilizing mutation pairs based on interactions observed in known protein 3D structures.

BACKGROUND: Protein engineering has many applications for industry, such as the development of new drugs, vaccines, treatment therapies, food, and biofuel production. A common way to engineer a protein is to perform mutations in functionally essential residues to optimize their function. However, the discovery of beneficial mutations for proteins is a complex task, with a time-consuming and high cost for experimental validation. Hence, computational approaches have been used to propose new insights for experiments narrowing the search space and reducing the costs. RESULTS: In this study, we developed Proteus (an acronym for Protein Engineering Supporter), a new algorithm for proposing mutation pairs in a target 3D structure. These suggestions are based on contacts observed in other known structures from Protein Data Bank (PDB). Proteus' basic assumption is that if a non-interacting pair of amino acid residues in the target structure is exchanged to an interacting pair, this could enhance protein stability. This trade is only allowed if the main-chain conformation of the residues involved in the contact is conserved. Furthermore, no steric impediment is expected between the proposed mutations and the surrounding protein atoms. To evaluate Proteus, we performed two case studies with proteins of industrial interests. In the first case study, we evaluated if the mutations suggested by Proteus for four protein structures enhance the number of inter-residue contacts. Our results suggest that most mutations proposed by Proteus increase the number of interactions into the protein. In the second case study, we used Proteus to suggest mutations for a lysozyme protein. Then, we compared Proteus' outcomes to mutations with available experimental evidence reported in the ProTherm database. Four mutations, in which our results agree with the experimental data, were found. This could be initial evidence that changes in the side-chain of some residues do not cause disturbances that harm protein structure stability. CONCLUSION: We believe that Proteus could be used combined with other methods to give new insights into the rational development of engineered proteins. Proteus user-friendly web-based tool is available at < http://proteus.dcc.ufmg.br >.

visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level.

BACKGROUND: Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces. RESULTS: To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets. CONCLUSIONS: We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.

nAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale.

Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions. Nonetheless, there is not an all-in-one tool that couples large-scale statistical, visual, and interactive analysis of conserved protein-ligand interactions. Therefore, we propose nAPOLI (Analysis of PrOtein-Ligand Interactions), a web server that combines large-scale analysis of conserved interactions in protein-ligand complexes at the atomic-level, interactive visual representations, and comprehensive reports of the interacting residues/atoms to detect and explore conserved non-covalent interactions. We demonstrate the potential of nAPOLI in detecting important conserved interacting residues through four case studies: two involving a human cyclin-dependent kinase 2 (CDK2), one related to ricin, and other to the human nuclear receptor subfamily 3 (hNR3). nAPOLI proved to be suitable to identify conserved interactions according to literature, as well as highlight additional interactions. Finally, we illustrate, with a virtual screening ligand selection, how nAPOLI can be widely applied in structural biology and drug design. nAPOLI is freely available at bioinfo.dcc.ufmg.br/napoli/.

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.